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Valdivieso-Castro MP, Vázquez-Gómez L, Olabarri M, Presno-López I, Espinosa-Góngora R, Orejuela-Ribera A, Cámara-Otegui A, Rodríguez-Fernández S, López-Rojo M, Marfil-Godoy L, Medina-Esquitino C, Soriano-Arola M, Aquino E, Faci E, Pérez-Sáez MA, Henares-Rodríguez A, Navarro-Lopez IJ, Romero-Castillo E, Garralda-Torres I, Lobato-Salinas Z, Lopez-Oreja A, Hinojosa-Mateo CM, Mintegi S. Clinical Prediction Rules for Identifying Children With Testicular Torsion: A Multicenter Prospective Study. Pediatr Emerg Care 2025:00006565-990000000-00632. [PMID: 40231580 DOI: 10.1097/pec.0000000000003394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2024] [Accepted: 03/13/2025] [Indexed: 04/16/2025]
Abstract
OBJECTIVES To validate clinical scores [Testicular Workup for Ischemia and Suspected Torsion (TWIST), testicular torsion (TT) score, Artificial Intelligence-based Score (AIS), Boettcher Alert Score (BALS)] when evaluating children under 18 with non-traumatic testicular pain in the emergency department. Our secondary objective was to create and compare a new TT score [Testicular Emergency Score for Torsion (TEST) score]. METHODS This was a multicenter prospective study in 21 Spanish pediatric emergency departments between 2020 and 2022, including 903 children 3 months to 18 years old with non-traumatic unilateral testicular pain, of them 93 TT (10.3%). To create a new score, the sample was randomly divided into derivation and validation set. RESULTS The performance of the TWIST, TT score, AIS, and BALS was good, and the proportion of patients correctly classified as low risk was 37.9%, 52.7%, 30.3%, and 28%, respectively. The TEST score included the following predictors of TT identified by multivariable logistic regression analysis: age, duration of pain, nausea/vomiting, testicular volume increase, testicular elevation, induration, and absence of cremasteric reflex. TEST score had a higher area under the receiver operating curve (area under the curve) and correctly classified in the low-risk group of 63.6% of the patients. CONCLUSIONS Although TWIST, TT score, BALS, and AIS scores showed a good performance, the TEST score identifies a larger group of low-risk patients suitable for safe management without Doppler ultrasound.
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Affiliation(s)
| | | | - Mikel Olabarri
- Department of Pediatric Emergency, Cruces University Hospital, University of the Basque Country, Barakaldo, Basque Country
| | | | | | | | - Amaia Cámara-Otegui
- Department of Pediatric Emergency, Donostia University Hospital, San Sebastián, Spain
| | | | - Myriam López-Rojo
- Department of Pediatrics, Hospital Universitario Rio Hortega, Valladolid
| | | | | | - Marta Soriano-Arola
- Pediatric Emergency, Department. Son Espases Hospital. Palma de Mallorca, Spain
| | | | - Elena Faci
- Department of Pediatrics, Hospital de Barbastro
| | | | | | | | | | | | | | - Amaia Lopez-Oreja
- Osakidetza Basque Health Service, Mendaro Hospital, Pediatric Service, Mendaro, España
| | | | - Santiago Mintegi
- Department of Pediatric Emergency, Cruces University Hospital, University of the Basque Country, Barakaldo, Basque Country
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Essel NOM, Couperthwaite S, Yang EH, Fisher S, Rowe BH. Patients Presenting to the Emergency Department with Bleeding in Early Pregnancy: Comparing Different Models to Predict Pregnancy Success. JOURNAL OF OBSTETRICS AND GYNAECOLOGY CANADA 2025; 47:102789. [PMID: 39956164 DOI: 10.1016/j.jogc.2025.102789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2024] [Revised: 01/23/2025] [Accepted: 01/24/2025] [Indexed: 02/18/2025]
Abstract
OBJECTIVES Bleeding in early pregnancy is a common obstetric presentation in the emergency department (ED), and the outcome is difficult to predict. We developed and compared random forest machine learning (Live Birth Risk Score [LiBRisk]) and nomogram models for predicting the likelihood of a live birth among women presenting at 3 Canadian EDs with bleeding in early pregnancy. METHODS Data were prospectively collected on 200 patients with bleeding in early pregnancy using a structured questionnaire, medical record review, and administrative databases. We developed the nomogram with variables selected via multivariable logistic regression analysis. LiBRisk was built using the Shapley variable importance cloud (ShapleyVIC) to derive a simple point-based clinical risk scoring system. RESULTS Overall, 115 (55%) patients experienced a miscarriage. We excluded duration of vaginal bleeding and pain score, which did not enhance model performance, and constructed LiBRisk with the 8 most important variables (β-human chorionic gonadotrophin level, age, gestational age, gravidity, parity, proportionality of uterine size to gestational age, abdominal cramping, and number of prior spontaneous abortions). All 10 variables were included in the nomogram. The area under the receiver operating characteristic curve of LiBRisk in the test and validation sets were 0.913 (95% CI 0.907-0.919) and 0.900 (95% CI 0.887-0.913), respectively. The C-index of the nomogram was 0.720 (95% CI 0.714-0.726) and 0.860 (95% CI 0.853-0.867) in the test and validation sets, respectively. LiBRisk outperformed the nomogram in all metrics. CONCLUSIONS We developed and compared LiBRisk and nomogram models for determining the probability of eventual pregnancy success/failure in women presenting to the ED with bleeding in early pregnancy. LiBRisk was more parsimonious, incorporating only 8 variables, and outperformed the nomogram in all metrics. Given these promising results, further testing seems warranted.
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Affiliation(s)
- Nana Owusu M Essel
- Department of Emergency Medicine, Faculty of Medicine & Dentistry, College of Health Sciences, University of Alberta, Edmonton, AB
| | - Stephanie Couperthwaite
- Department of Emergency Medicine, Faculty of Medicine & Dentistry, College of Health Sciences, University of Alberta, Edmonton, AB
| | - Esther H Yang
- SPOR SUPPORT Unit, Alberta Health Services (AHS), Edmonton, AB
| | - Steven Fisher
- Department of Emergency Medicine, Faculty of Medicine & Dentistry, College of Health Sciences, University of Alberta, Edmonton, AB
| | - Brian H Rowe
- Department of Emergency Medicine, Faculty of Medicine & Dentistry, College of Health Sciences, University of Alberta, Edmonton, AB; School of Public Health, College of Health Sciences, University of Alberta, Edmonton, AB.
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3
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Kang C, Zhu AS, Waldman O, Kashner TM, Greenberg PB. Cataract surgery risk stratification models: a systematic review. Graefes Arch Clin Exp Ophthalmol 2025:10.1007/s00417-025-06761-5. [PMID: 39900805 DOI: 10.1007/s00417-025-06761-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2024] [Revised: 01/14/2025] [Accepted: 01/22/2025] [Indexed: 02/05/2025] Open
Abstract
PURPOSE Risk stratification models can assist cataract surgeons in clinical decision-making by categorizing patients into distinct groups based on their likelihood of complications. In this systematic review, we assess the characteristics of cataract surgery risk stratification models. METHODS Using the Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines, we searched six databases (PubMed, OVID, Embase, CINAHL, Cochrane Trials, and Web of Science) in January 2024. We included peer-reviewed, full-text, English-language studies describing models used preoperatively to assess the likelihood of complications in cataract surgery. We excluded letters, editorials, and non-peer-reviewed publications, such as conference abstracts and studies describing predictive models that did not group the patients into distinct risk categories. We constructed a checklist from three frameworks to critically appraise the participants, predictors, and risk of bias in the models. RESULTS Of 4192 articles, eight met the inclusion criteria. Most models were designed for attending surgeons only and for phacoemulsification to predict zonular complications and posterior capsule rupture. The most common risk factors identified in the models were poor patient positioning, advanced age, small pupils, and pseudoexfoliation syndrome. Methodological limitations included the lack of multivariable modeling, standardized outcome measures, and external validation. CONCLUSION Cataract surgeons should understand the limitations of cataract surgery risk stratification models. Existing models can be improved with more robust methods, the use of standardized metrics, and external validation.
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Affiliation(s)
- Chaerim Kang
- Division of Ophthalmology, Alpert Medical School, Brown University, Coro Center West, 1 Hoppin St, Suite 200, Providence, Rhode Island, 02903, USA
| | - Angela S Zhu
- Division of Ophthalmology, Alpert Medical School, Brown University, Coro Center West, 1 Hoppin St, Suite 200, Providence, Rhode Island, 02903, USA
| | - Olivia Waldman
- School of Public Health, Brown University, Providence, Rhode Island, USA
| | - T Michael Kashner
- Department of Medicine, Loma Linda University School of Medicine, Loma Linda, CA, USA
- Office of Academic Affiliations, Department of Veterans Affairs, Washington, District of Columbia, USA
| | - Paul B Greenberg
- Division of Ophthalmology, Alpert Medical School, Brown University, Coro Center West, 1 Hoppin St, Suite 200, Providence, Rhode Island, 02903, USA.
- Section of Ophthalmology, VA Providence Healthcare System, Providence, Rhode Island, USA.
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Huang YC, Liu PC, Lin HH, Wang ST, Su YP, Chou PH, Yao YC. Risk prediction model of pedicle screw loosening within 2 years after decompression and instrumented fusion surgery for degenerative lumbar disease. Spine J 2025:S1529-9430(25)00061-0. [PMID: 39894275 DOI: 10.1016/j.spinee.2025.01.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/13/2024] [Revised: 12/17/2024] [Accepted: 01/20/2025] [Indexed: 02/04/2025]
Abstract
BACKGROUND CONTEXT Pedicle screw loosening (PSL) after spinal fusion surgery is one of the most frequently reported complications and leads to poor clinical outcomes. PURPOSE This study aimed to develop and validate a risk prediction model for PSL within two years in patients undergoing lumbar instrumented fusion surgery based on their risk profiles. STUDY DESIGN/SETTING Retrospective, observational study. PATIENT SAMPLE Patients who underwent lumbar instrumented fusion surgery at a single academic institution between May 2015 and February 2019. OUTCOME MEASURES Risk assessment of PSL and development of a rating score based on patient characteristics. METHODS The demographic profiles and radiographic parameters using computed tomography were obtained. These factors were analyzed to determine possible risk factors related to postoperative PSL after 2 years. A scoring system was developed using these independent risk factors and validated using prospectively collected data from another center between May 2019 and December 2021. RESULTS The occurrence of PSL within 2 years postoperation was 12.7% (40/315). PSL was significantly predicted by smoking, low Hounsfield units (HU) of the pedicle tract at the index level (P), and a low psoas-lumbar vertebral index (PLVI). The risk of PSL according to the categories of the risk score was 1.1% for those with a score of 0-1, 15.1% for a score of 2-3, and 61.5% for a score of 4-6. In validation, this model demonstrated both good discrimination and calibration results. The area under the curve was 0.887 (95% CI 0.830-0.938) for the derivation cohort and 0.835 (95% CI 0.738-0.918) for the external validation cohort. CONCLUSIONS This PSL risk score, including smoking, Index P HU, and PLVI, is a novel approach to predict PSL 2 years post-surgery. This approach highlights the role of factors associated with osteoporosis and sarcopenia in the development of PSL and could aid in preoperative decision-making and surgical planning.
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Affiliation(s)
- Yen-Chun Huang
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Orthopedics and Traumatology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Po-Chun Liu
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Hsi-Hsien Lin
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Orthopedics and Traumatology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Shih-Tien Wang
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Orthopedics and Traumatology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Yu-Ping Su
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Orthopedics and Traumatology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Po-Hsin Chou
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Orthopedics and Traumatology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Yu-Cheng Yao
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Orthopedics and Traumatology, Taipei Veterans General Hospital, Taipei, Taiwan.
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Larsen A, Pintye J, Abuna F, Dettinger JC, Gomez L, Marwa MM, Ngumbau N, Odhiambo B, Richardson BA, Watoyi S, Stern J, Kinuthia J, John-Stewart G. Identifying psychosocial predictors and developing a risk score for preterm birth among Kenyan pregnant women. BMC Pregnancy Childbirth 2025; 25:2. [PMID: 39748327 PMCID: PMC11697889 DOI: 10.1186/s12884-024-07058-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 12/10/2024] [Indexed: 01/04/2025] Open
Abstract
BACKGROUND Preterm birth (PTB) is a leading cause of neonatal mortality, particularly in sub-Saharan Africa where 40% of global neonatal deaths occur. We identified and combined demographic, clinical, and psychosocial correlates of PTB among Kenyan women to develop a risk score. METHODS We used data from a prospective study enrolling HIV-negative women from 20 antenatal clinics in Western Kenya (NCT03070600). Depressive symptoms were assessed by study nurses using the Center for Epidemiologic Studies Depression Scale (CESD-10), intimate partner violence (IPV) with the Hurt, Insult, Threaten, Scream scale (HITS), and social support using the Medical Outcomes Survey scale (MOS-SSS). Predictors of PTB (birth < 37 weeks gestation) were identified using multivariable Cox proportional hazards models, clustered by facility. We used stratified k-fold cross-validation methods for risk score derivation and validation. Area under the receiver operating characteristic curve (AUROC) was used to evaluate discrimination of the risk score and Brier score for calibration. RESULTS Among 4084 women, 19% had PTB (incidence rate: 70.9 PTB per 100 fetus-years (f-yrs)). Predictors of PTB included being unmarried (HR:1.29, 95% CI:1.08-1.54), lower education (years) (HR:0.97, 95% CI:0.94-0.99), IPV (HITS score ≥ 5, HR:1.28, 95% CI:0.98-1.68), higher CESD-10 score (HR:1.02, 95% CI:0.99-1.04), lower social support score (HR:0.99, 95% CI:0.97-1.01), and mild-to-severe depressive symptoms (CESD-10 score ≥ 5, HR:1.46, 95% CI:1.07-1.99). The final risk score included being unmarried, social support score, IPV, and MSD. The risk score had modest discrimination between PTB and term deliveries (AUROC:0.56, 95% CI:0.54-0.58), and Brier Score was 0.4672. Women considered "high risk" for PTB (optimal risk score cut-point) had 40% higher risk of PTB (83.6 cases per 100 f-yrs) than "low risk" women (59.6 cases per 100 f-ys; HR:1.6, 95% CI:1.2-1.7, p < 0.001). CONCLUSION A fifth of pregnancies were PTB in this large multi-site cohort; PTB was associated with several social factors amenable to intervention. Combining these factors in a risk score did not predict PTB, reflecting the multifactorial nature of PTB and need to include other unmeasured factors. However, our findings suggest PTB risk could be better understood by integrating mental health and support services into routine antenatal care.
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Affiliation(s)
- Anna Larsen
- Department of Epidemiology, University of Washington, 3980 15th Ave NE, Box 351619, Seattle, WA, 98195, USA.
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, USA.
| | - Jillian Pintye
- Department of Global Health, University of Washington, Seattle, WA, USA
- Department of Biobehavioral Nursing and Health Informatics, University of Washington, Seattle, WA, USA
| | | | - Julia C Dettinger
- Department of Global Health, University of Washington, Seattle, WA, USA
| | - Laurén Gomez
- Department of Global Health, University of Washington, Seattle, WA, USA
| | | | - Nancy Ngumbau
- Department of Research and Programs, Kenyatta National Hospital, Nairobi, Kenya
| | | | - Barbra A Richardson
- Department of Global Health, University of Washington, Seattle, WA, USA
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | | | - Joshua Stern
- Department of Global Health, University of Washington, Seattle, WA, USA
| | - John Kinuthia
- Department of Global Health, University of Washington, Seattle, WA, USA
- University of Nairobi, Nairobi, Kenya
- Department of Research and Programs, Kenyatta National Hospital, Nairobi, Kenya
| | - Grace John-Stewart
- Department of Epidemiology, University of Washington, 3980 15th Ave NE, Box 351619, Seattle, WA, 98195, USA
- Department of Global Health, University of Washington, Seattle, WA, USA
- School of Medicine, Department of Pediatrics, University of Washington, Seattle, WA, USA
- School of Medicine, Department of Allergy and Infectious Disease, University of Washington, Seattle, WA, USA
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Schiariti MP, Mazzapicchi E, Gemma M, Pasquale E, Restelli F, Ciceri EFM, Falco J, Broggi M, DiMeco F, Ferroli P, Acerbi F. Proposal of a predictive score for the occurrence of postoperative cerebral vasospasm: analysis of a large single institution retrospective series and literature review. Neurosurg Rev 2024; 47:896. [PMID: 39652235 DOI: 10.1007/s10143-024-03142-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2024] [Revised: 11/25/2024] [Accepted: 11/30/2024] [Indexed: 12/17/2024]
Abstract
Despite being uncommon, postoperative vasospasm (PoVS) present notably high morbidity and mortality rates. Our aim was to identify prognostic factors associated with this condition and introduce a scoring system to improve subsequent clinical and radiological surveillance strategies. We conducted a retrospective analysis of our institutional database covering patients aged over 18 who underwent craniotomic or transsphenoidal surgery for elective tumor removal at the Neurosurgical Unit of our institution between January 2016 and August 2023. A comprehensive search was conducted using the Cochrane Database of Systematic Reviews and PubMed database to identify the most correlated risk factors. Literature review included a final group of 32 studies (52 patients) and identified SAH, vessel encasement or vessel manipulation, hypothalamic disfunction, meningitis, younger age, tumor size > 3 cm, and long operative time as predictive factors for PoVS. Our cohort included 2132 patients, with only 13 individuals (0.61%) presenting PoVS. To predict the occurrence of PoVS, we developed a logistic multivariate regression model that identified thick (defined as Fisher grade ≥ 3) subarachnoid hemorrhage (coeff. 6.7, p < 0.001), intraparenchymal hemorrhage (coeff. 3.44, p < 0.001), lesion located in the parasellar region (coeff. 2.1, p = 0.064), and lesion size ≥ 4 cm (coeff. 2.0, p = 0.069) as potential independent predictors of PoVS. Based on statistical model for these variables was assigned a score: thick SAH 7 points, intraparenchymal hemorrhage 3 points, parasellar lesion site 2 points, and lesion size ≥ 4 cm 2 points. The cumulative scores ranged from 0 to 14. PoVS is a rare complication but its association with significant morbidity and mortality underscores the importance of early identification and treatment. In our study we proposed a stratified risk score to identify high risk patients. However, due to rarity of this condition, our score proposal should be considered as a training set a to be validated in future studies with a multicenter setting.
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Affiliation(s)
- Marco Paolo Schiariti
- Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico C. Besta, University of Milan, Via Giovanni Celoria 11, 20133, Milan, Italy
| | - Elio Mazzapicchi
- Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico C. Besta, University of Milan, Via Giovanni Celoria 11, 20133, Milan, Italy.
| | - Marco Gemma
- Department of Neuroanesthesia and Intensive Care, Fondazione IRCCS Istituto Neurologico C. Besta, University of Milan, Milan, Italy
| | - Erica Pasquale
- Department of Neurosurgery, Azienda Ospedaliero Universitaria Pisana (AOUP), Pisa, Italy
| | - Francesco Restelli
- Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico C. Besta, University of Milan, Via Giovanni Celoria 11, 20133, Milan, Italy
| | - Elisa Francesca Maria Ciceri
- Department of Diagnostic and Interventional Neuroradiology, Fondazione IRCCS Istituto Neurologico C. Besta, University of Milan, Milan, Italy
| | - Jacopo Falco
- Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico C. Besta, University of Milan, Via Giovanni Celoria 11, 20133, Milan, Italy
| | - Morgan Broggi
- Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico C. Besta, University of Milan, Via Giovanni Celoria 11, 20133, Milan, Italy
| | - Francesco DiMeco
- Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico C. Besta, University of Milan, Via Giovanni Celoria 11, 20133, Milan, Italy
| | - Paolo Ferroli
- Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico C. Besta, University of Milan, Via Giovanni Celoria 11, 20133, Milan, Italy
| | - Francesco Acerbi
- Department of Neurosurgery, Azienda Ospedaliero Universitaria Pisana (AOUP), Pisa, Italy
- Department of Translational Research and New Technologies in Medicine and Surgery, Scientific Disciplinary Sector, Università Di Pisa, Pisa, Italy
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Lee NJ, Lenke LG, Arvind V, Shi T, Dionne AC, Nnake C, Yeary M, Fields M, Simhon M, Ferraro A, Cooney M, Lewerenz E, Reyes JL, Roth SG, Hung CW, Scheer JK, Zervos T, Thuet ED, Lombardi JM, Sardar ZM, Lehman RA, Roye BD, Vitale MG, Hassan FM. A Novel Preoperative Scoring System to Accurately Predict Cord-Level Intraoperative Neuromonitoring Data Loss During Spinal Deformity Surgery: A Machine-Learning Approach. J Bone Joint Surg Am 2024:00004623-990000000-01269. [PMID: 39813599 DOI: 10.2106/jbjs.24.00386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/18/2025]
Abstract
BACKGROUND An accurate knowledge of a patient's risk of cord-level intraoperative neuromonitoring (IONM) data loss is important for an informed decision-making process prior to deformity correction, but no prediction tool currently exists. METHODS A total of 1,106 patients with spinal deformity and 205 perioperative variables were included. A stepwise machine-learning (ML) approach using random forest (RF) analysis and multivariable logistic regression was performed. Patients were randomly allocated to training (75% of patients) and testing (25% of patients) groups. Feature score weights were derived by rounding up the regression coefficients from the multivariable logistic regression model. Variables in the final scoring calculator were automatically selected through the ML process to optimize predictive performance. RESULTS Eight features were included in the scoring system: sagittal deformity angular ratio (sDAR) of ≥15 (score = 2), type-3 spinal cord shape (score = 2), conus level below L2 (score = 2), cervical upper instrumented vertebra (score = 2), preoperative upright largest thoracic Cobb angle of ≥75° (score = 2), preoperative lower-extremity motor deficit (score = 2), preoperative upright largest thoracic kyphosis of ≥80° (score = 1), and total deformity angular ratio (tDAR) of ≥25 (score = 1). Higher cumulative scores were associated with increased rates of cord-level IONM data loss: patients with a cumulative score of ≤2 had a cord-level IONM data loss rate of 0.9%, whereas those with a score of ≥7 had a loss rate of 86%. When evaluated in the testing group, the scoring system achieved an accuracy of 93%, a sensitivity of 75%, a specificity of 94%, and an AUC (area under the receiver operating characteristic curve) of 0.898. CONCLUSIONS This is the first study to provide an ML-derived preoperative scoring system that predicts cord-level IONM data loss during pediatric and adult spinal deformity surgery with >90% accuracy. LEVEL OF EVIDENCE Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.
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Affiliation(s)
- Nathan J Lee
- Department of Orthopedic Surgery, Columbia University Irving Medical Center, New York, NY
- The Och Spine Hospital at New York-Presbyterian, New York, NY
| | - Lawrence G Lenke
- Department of Orthopedic Surgery, Columbia University Irving Medical Center, New York, NY
- The Och Spine Hospital at New York-Presbyterian, New York, NY
| | - Varun Arvind
- Department of Orthopedic Surgery, Columbia University Irving Medical Center, New York, NY
| | - Ted Shi
- Vagelos College of Physicians and Surgeons, Columbia University, New York, NY
| | - Alexandra C Dionne
- Department of Orthopedic Surgery, Columbia University Irving Medical Center, New York, NY
- Vagelos College of Physicians and Surgeons, Columbia University, New York, NY
| | - Chidebelum Nnake
- Vagelos College of Physicians and Surgeons, Columbia University, New York, NY
| | - Mitchell Yeary
- Vagelos College of Physicians and Surgeons, Columbia University, New York, NY
| | - Michael Fields
- Department of Orthopedic Surgery, Columbia University Irving Medical Center, New York, NY
| | - Matt Simhon
- Department of Orthopedic Surgery, Columbia University Irving Medical Center, New York, NY
| | - Anastasia Ferraro
- Department of Orthopedic Surgery, Columbia University Irving Medical Center, New York, NY
| | - Matthew Cooney
- Department of Orthopedic Surgery, Columbia University Irving Medical Center, New York, NY
| | - Erik Lewerenz
- Department of Orthopedic Surgery, Columbia University Irving Medical Center, New York, NY
| | - Justin L Reyes
- Department of Orthopedic Surgery, Columbia University Irving Medical Center, New York, NY
| | - Steven G Roth
- Department of Orthopedic Surgery, Columbia University Irving Medical Center, New York, NY
- The Och Spine Hospital at New York-Presbyterian, New York, NY
| | - Chun Wai Hung
- Department of Orthopedic Surgery, Columbia University Irving Medical Center, New York, NY
- The Och Spine Hospital at New York-Presbyterian, New York, NY
| | - Justin K Scheer
- Department of Orthopedic Surgery, Columbia University Irving Medical Center, New York, NY
- The Och Spine Hospital at New York-Presbyterian, New York, NY
| | - Thomas Zervos
- Department of Orthopedic Surgery, Columbia University Irving Medical Center, New York, NY
- Morgan Stanley Children's Hospital, New-York Presbyterian, New York, NY
| | - Earl D Thuet
- The Och Spine Hospital at New York-Presbyterian, New York, NY
| | - Joseph M Lombardi
- Department of Orthopedic Surgery, Columbia University Irving Medical Center, New York, NY
- The Och Spine Hospital at New York-Presbyterian, New York, NY
| | - Zeeshan M Sardar
- Department of Orthopedic Surgery, Columbia University Irving Medical Center, New York, NY
- The Och Spine Hospital at New York-Presbyterian, New York, NY
| | - Ronald A Lehman
- Department of Orthopedic Surgery, Columbia University Irving Medical Center, New York, NY
- The Och Spine Hospital at New York-Presbyterian, New York, NY
| | - Benjamin D Roye
- Department of Orthopedic Surgery, Columbia University Irving Medical Center, New York, NY
- Morgan Stanley Children's Hospital, New-York Presbyterian, New York, NY
| | - Michael G Vitale
- Department of Orthopedic Surgery, Columbia University Irving Medical Center, New York, NY
- Morgan Stanley Children's Hospital, New-York Presbyterian, New York, NY
| | - Fthimnir M Hassan
- Department of Orthopedic Surgery, Columbia University Irving Medical Center, New York, NY
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Seki T, Takiguchi T, Akagi Y, Ito H, Kubota K, Miyake K, Okada M, Kawazoe Y. Iterative random forest-based identification of a novel population with high risk of complications post non-cardiac surgery. Sci Rep 2024; 14:26741. [PMID: 39500963 PMCID: PMC11538396 DOI: 10.1038/s41598-024-78482-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2024] [Accepted: 10/31/2024] [Indexed: 11/08/2024] Open
Abstract
Assessing the risk of postoperative cardiovascular events before performing non-cardiac surgery is clinically important. The current risk score systems for preoperative evaluation may not adequately represent a small subset of high-risk populations. Accordingly, this study aimed at applying iterative random forest to analyze combinations of factors that could potentially be clinically valuable in identifying these high-risk populations. To this end, we used the Japan Medical Data Center database, which includes claims data from Japan between January 2005 and April 2021, and employed iterative random forests to extract factor combinations that influence outcomes. The analysis demonstrated that a combination of a prior history of stroke and extremely low LDL-C levels was associated with a high non-cardiac postoperative risk. The incidence of major adverse cardiovascular events in the population characterized by the incidence of previous stroke and extremely low LDL-C levels was 15.43 events per 100 person-30 days [95% confidence interval, 6.66-30.41] in the test data. At this stage, the results only show correlation rather than causation; however, these findings may offer valuable insights for preoperative risk assessment in non-cardiac surgery.
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Affiliation(s)
- Tomohisa Seki
- Department of Healthcare Information Management, The University of Tokyo Hospital, Tokyo, Japan.
| | - Toru Takiguchi
- Department of Healthcare Information Management, The University of Tokyo Hospital, Tokyo, Japan
| | - Yu Akagi
- Department of Biomedical Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Hiromasa Ito
- Department of Healthcare Information Management, The University of Tokyo Hospital, Tokyo, Japan
| | - Kazumi Kubota
- Department of Healthcare Information Management, The University of Tokyo Hospital, Tokyo, Japan
| | - Kana Miyake
- Department of Healthcare Information Management, The University of Tokyo Hospital, Tokyo, Japan
| | - Masafumi Okada
- Department of Healthcare Information Management, The University of Tokyo Hospital, Tokyo, Japan
| | - Yoshimasa Kawazoe
- Department of Healthcare Information Management, The University of Tokyo Hospital, Tokyo, Japan
- Artificial Intelligence and Digital Twin in Healthcare, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
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9
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Barchick SR, Masada KM, Fryhofer GW, Alqazzaz A, Donegan DJ, Mehta S. The hip fracture assessment tool: A scoring system to assess high risk geriatric hip fracture patients for post-operative critical care monitoring. Injury 2024; 55:111584. [PMID: 38762944 DOI: 10.1016/j.injury.2024.111584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Revised: 04/16/2024] [Accepted: 04/17/2024] [Indexed: 05/21/2024]
Abstract
INTRODUCTION Intensive care unit risk stratification models have been utilized in elective joint arthroplasty; however, hip fracture patients are fundamentally different in their clinical course. Having a critical care risk calculator utilizing pre-operative risk factors can improve resourcing for hip fracture patients in the peri‑operative period. METHODS A cohort of geriatric hip fracture patients at a single institution were reviewed over a three-year period. Non-operative patients, peri‑implant fractures, additional procedures performed under the same anesthesia period, and patients admitted to the intensive care unit (ICU) prior to surgery were excluded. Pre-operative laboratory values, Revised Cardiac Risk Index (RCRI), and American Society of Anesthesiologists (ASA) scores were calculated. Pre-operative ambulatory status was determined. The primary outcome measure was ICU admission in the post-operative period. Outcomes were assessed with Fisher's exact test, Kruskal-Wallis test, logistic regression, and ROC curve. RESULTS 315 patient charts were analyzed with 262 patients meeting inclusion criteria. Age ≥ 80 years, ASA ≥ 4, pre-operative hemoglobin < 10 g/dL, and a history of CVA/TIA were found to be significant factors and utilized within a "training" data set to create a 4-point scoring system after reverse stepwise elimination. The 4-point scoring system was then assessed within a separate "validation" data set to yield an ROC area under the curve (AUC) of 0.747. Score ≥ 3 was associated with 96.8 % specificity and 14.2 % sensitivity for predicting post-op ICU admission. Score ≥ 3 was associated with a 50 % positive predictive value and 83 % negative predictive value. CONCLUSION A hip fracture risk stratification scoring system utilizing pre-operative patient specific values to stratify geriatric hip patients to the ICU post-operatively can improve the pre-operative decision-making of surgical and critical care teams. This has important implications for triaging vital hospital resources. LEVEL OF EVIDENCE III (retrospective study).
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Affiliation(s)
- Stephen R Barchick
- Department of Orthopaedic Surgery, Hospital of the University of Pennsylvania, Philadelphia, PA, USA.
| | - Kendall M Masada
- Department of Orthopaedic Surgery, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - George W Fryhofer
- Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA
| | - Aymen Alqazzaz
- Department of Orthopaedic Surgery, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Derek J Donegan
- Department of Orthopaedic Surgery, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Samir Mehta
- Department of Orthopaedic Surgery, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
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10
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Liu S, Lin Z, Kang Y, Liu S, Bao R, Xie M, Wang Z, Li J, Zhang Z. Fibular free flap necrosis after mandibular reconstruction surgery with osteoradionecrosis: Establishment and verification of an early warning model. JOURNAL OF STOMATOLOGY, ORAL AND MAXILLOFACIAL SURGERY 2024; 125:101730. [PMID: 38072232 DOI: 10.1016/j.jormas.2023.101730] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 11/22/2023] [Accepted: 12/06/2023] [Indexed: 05/01/2024]
Abstract
OBJECTIVE Fibular free flap necrosis (FFFN) is the most common complication in patients with osteoradionecrosis (ORN) after mandibular reconstruction surgery. However, there are no effective forecasting tools at present. This research is aimed to establish and verify a nomogram model to predict the risk of FFFN after mandibular reconstruction surgery in ORN patients. METHODS A total of 193 ORN patients with mandibular reconstruction using fibular free flap (150 cases in the model group and 43 cases in the validation group) were enrolled in this study. In the model group, the variables were optimized by lasso regression. Then the prediction model was established by binary logistic regression analysis, and the nomogram was drawn. The bootstrap self-sampling method was used for internal verification. Moreover, 43 cases in the validation group were used for external validation. RESULTS The results of lasso regression and binary logistic regression analysis showed that the radiotherapy interval (≤2 years), trismus, diabetes, without deep venous anastomoses, and American society of anesthesiologists (ASA) III were the independent risk factors for FFFN after mandibular reconstruction surgery in ORNJ patients (P<0.05). Based on the above-mentioned risk factors, the nomogram model was established. The AUC values of the model group and the validation group were 0.936 and 0.964, respectively. The curve analysis showed that when the probability thresholds of the model group and the validation group were 5.699%∼98.229% and 0.413%∼99.721%, respectively. So the patient's clinical net profit rate was the highest. CONCLUSION A nomogram combining the factors of radiotherapy interval (≤2 years), trismus, diabetes, without deep venous anastomoses, and ASA III provided a comparatively effective way to predict the risk of FFFN after mandibular reconstruction surgery in ORN patients, which has distinct applied clinical value.
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Affiliation(s)
- Shuchang Liu
- Department of Oral and Maxillofacial Surgery, Haizhu Square Branch, Stomatological Hospital, School of Stomatology, Southern Medical University, Guangzhou, PR China
| | - Zhaoyu Lin
- Department of Oral and Maxillofacial Surgery, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangdong, PR China; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangdong, PR China
| | - Yujie Kang
- Department of Oral and Maxillofacial Surgery, Stomatological Hospital, School of Stomatology, Southern Medical University, Guangzhou, PR China
| | - Shuguang Liu
- Department of Oral and Maxillofacial Surgery, Stomatological Hospital, School of Stomatology, Southern Medical University, Guangzhou, PR China
| | - Rui Bao
- Medical Record Room, Stomatological Hospital, School of Stomatology, Southern Medical University, Guangzhou, PR China
| | - Menglan Xie
- Department of Oral and Maxillofacial Surgery, Stomatological Hospital, School of Stomatology, Southern Medical University, Guangzhou, PR China
| | - Zhiping Wang
- Department of Oral and Maxillofacial Surgery, Stomatological Hospital, School of Stomatology, Southern Medical University, Guangzhou, PR China
| | - Jinsong Li
- Department of Oral and Maxillofacial Surgery, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangdong, PR China; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangdong, PR China.
| | - Zhaoqiang Zhang
- Department of Oral and Maxillofacial Surgery, Stomatological Hospital, School of Stomatology, Southern Medical University, Guangzhou, PR China.
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Marabotto E, Pasta A, Calabrese F, Bodini G, Furnari M, Giannini EG, Savarino E. Is global score better than a single histological parameter for assessing microscopic esophagitis? Neurogastroenterol Motil 2024; 36:e14685. [PMID: 37793137 DOI: 10.1111/nmo.14685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 09/19/2023] [Indexed: 10/06/2023]
Affiliation(s)
- Elisa Marabotto
- Division of Gastroenterology, Department of Internal Medicine, University of Genoa, Genoa, Italy
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Andrea Pasta
- Division of Gastroenterology, Department of Internal Medicine, University of Genoa, Genoa, Italy
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Francesco Calabrese
- Division of Gastroenterology, Department of Internal Medicine, University of Genoa, Genoa, Italy
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Giorgia Bodini
- Division of Gastroenterology, Department of Internal Medicine, University of Genoa, Genoa, Italy
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Manuele Furnari
- Division of Gastroenterology, Department of Internal Medicine, University of Genoa, Genoa, Italy
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Edoardo Giovanni Giannini
- Division of Gastroenterology, Department of Internal Medicine, University of Genoa, Genoa, Italy
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Edoardo Savarino
- Department of Surgery, Oncology and Gastroenterology, University of Padua, Padua, Italy
- Gastroenterology Unit, Azienda Ospedale Università di Padova, Padua, Italy
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12
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Zalama-Sánchez D, Martín-Rodríguez F, López-Izquierdo R, Benito JFD, Soberón IS, Vegas CDP, Sanz-García A. Prehospital Targeting of 1-Year Mortality in Acute Chest Pain by Cardiac Biomarkers. Diagnostics (Basel) 2023; 13:3681. [PMID: 38132265 PMCID: PMC10743255 DOI: 10.3390/diagnostics13243681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 12/12/2023] [Accepted: 12/14/2023] [Indexed: 12/23/2023] Open
Abstract
The identification and appropriate management of patients at risk of suffering from acute chest pain (ACP) in prehospital care are not straightforward. This task could benefit, as occurs in emergency departments (EDs), from cardiac enzyme assessment. The aim of the present work was to derive and validate a scoring system based on troponin T (cTnT), N-terminal pro B-type natriuretic peptide (NT-proBNP), and D-dimer to predict 1-year mortality in patients with ACP. This was a prospective, multicenter, ambulance-based cohort study of adult patients with a prehospital ACP diagnosis who were evacuated by ambulance to the ED between October 2019 and July 2021. The primary outcome was 365-day cumulative mortality. A total of 496 patients fulfilled the inclusion criteria. The mortality rate was 12.1% (60 patients). The scores derived from cTnT, NT-proBNP, and D-dimer presented an AUC of 0.802 (95% CI: 0718-0.886) for 365-day mortality. This AUC was superior to that of each individual cardiac enzyme. Our study provides promising evidence for the predictive value of a risk score based on cTnT, NT-proBNP, and D-dimer for the prediction of 1-year mortality in patients with ACP. The implementation of this score has the potential to benefit emergency medical service care and facilitate the on-scene decision-making process.
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Affiliation(s)
- Daniel Zalama-Sánchez
- Servicio de Urgencias, Hospital Clínico Universitario de Valladolid, Gerencia Regional de Salud de Castilla y León (SACYL), 47007 Valladolid, Spain; (D.Z.-S.); (C.d.P.V.)
| | - Francisco Martín-Rodríguez
- Facultad de Medicina, Universidad de Valladolid, Gerencia de Emergencias Sanitarias, Gerencia Regional de Salud de Castilla y León (SACYL), 47007 Valladolid, Spain
| | - Raúl López-Izquierdo
- Servicio de Urgencias, Hospital Universitario Rio Hortega de Valladolid, Gerencia Regional de Salud de Castilla y León (SACYL), 47007 Valladolid, Spain;
| | - Juan F. Delgado Benito
- Gerencia de Emergencias Sanitarias, Gerencia Regional de Salud de Castilla y León (SACYL), 47007 Valladolid, Spain; (J.F.D.B.); (I.S.S.)
| | - Irene Sánchez Soberón
- Gerencia de Emergencias Sanitarias, Gerencia Regional de Salud de Castilla y León (SACYL), 47007 Valladolid, Spain; (J.F.D.B.); (I.S.S.)
| | - Carlos del Pozo Vegas
- Servicio de Urgencias, Hospital Clínico Universitario de Valladolid, Gerencia Regional de Salud de Castilla y León (SACYL), 47007 Valladolid, Spain; (D.Z.-S.); (C.d.P.V.)
| | - Ancor Sanz-García
- Grupo de Investigación en Innovación Tecnológica Aplicada a la Salud (Grupo ITAS), Facultad de Ciencias de la Salud, Universidad de Castilla la Mancha, 13071 Talavera de la Reina, Spain;
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13
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Skurzak S, Bonini A, Cerchiara P, Laici C, De Gasperi A, Prosperi M, Perego M, Guffanti EA, Chierego G, Azan G, Balagna R, Siniscalchi A, Monti G, Tosi M, Esposito C, Cerutti E, Finazzi S. A simple machine learning-derived rule to promote ERAS pathways in Liver Transplantation. JOURNAL OF LIVER TRANSPLANTATION 2023; 12:100179. [DOI: 10.1016/j.liver.2023.100179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/03/2025] Open
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14
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Martín-Rodríguez F, Vaquerizo-Villar F, López-Izquierdo R, Castro-Villamor MA, Sanz-García A, Del Pozo-Vegas C, Hornero R. Derivation and validation of a blood biomarker score for 2-day mortality prediction from prehospital care: a multicenter, cohort, EMS-based study. Intern Emerg Med 2023; 18:1797-1806. [PMID: 37079244 PMCID: PMC10116443 DOI: 10.1007/s11739-023-03268-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 03/31/2023] [Indexed: 04/21/2023]
Abstract
Identifying potentially life-threatening diseases is a key challenge for emergency medical services. This study aims at examining the role of different prehospital biomarkers from point-of-care testing to derive and validate a score to detect 2-day in-hospital mortality. We conducted a prospective, observational, prehospital, ongoing, and derivation-validation study in three Spanish provinces, in adults evacuated by ambulance and admitted to the emergency department. A total of 23 ambulance-based biomarkers were collected from each patient. A biomarker score based on logistic regression was fitted to predict 2-day mortality from an optimum subset of variables from prehospital blood analysis, obtained through an automated feature selection stage. 2806 cases were analyzed, with a median age of 68 (interquartile range 51-81), 42.3% of women, and a 2-day mortality rate of 5.5% (154 non-survivors). The blood biomarker score was constituted by the partial pressure of carbon dioxide, lactate, and creatinine. The score fitted with logistic regression using these biomarkers reached a high performance to predict 2-day mortality, with an AUC of 0.933 (95% CI 0.841-0.973). The following risk levels for 2-day mortality were identified from the score: low risk (score < 1), where only 8.2% of non-survivors were assigned to; medium risk (1 ≤ score < 4); and high risk (score ≥ 4), where the 2-day mortality rate was 57.6%. The novel blood biomarker score provides an excellent association with 2-day in-hospital mortality, as well as real-time feedback on the metabolic-respiratory patient status. Thus, this score can help in the decision-making process at critical moments in life-threatening situations.
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Affiliation(s)
- Francisco Martín-Rodríguez
- Faculty of Medicine, Universidad de Valladolid, Valladolid, Spain
- Advanced Life Support, Emergency Medical Services (SACYL), Valladolid, Spain
- Prehospital Early Warning Scoring-System Investigation Group, Valladolid, Spain
| | - Fernando Vaquerizo-Villar
- Biomedical Engineering Group, Facultad de Medicina, Universidad de Valladolid, Av. Ramón y Cajal, 7, 47003, Valladolid, Spain.
- CIBER-BBN, Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, Valladolid, Spain.
| | - Raúl López-Izquierdo
- Faculty of Medicine, Universidad de Valladolid, Valladolid, Spain
- Prehospital Early Warning Scoring-System Investigation Group, Valladolid, Spain
- Emergency Department, Hospital Universitario Rio Hortega, Valladolid, Spain
| | - Miguel A Castro-Villamor
- Faculty of Medicine, Universidad de Valladolid, Valladolid, Spain
- Prehospital Early Warning Scoring-System Investigation Group, Valladolid, Spain
| | - Ancor Sanz-García
- Prehospital Early Warning Scoring-System Investigation Group, Valladolid, Spain
- Health Research Institute, Hospital de la Princesa, Madrid (IIS-IP), Spain
| | - Carlos Del Pozo-Vegas
- Faculty of Medicine, Universidad de Valladolid, Valladolid, Spain
- Prehospital Early Warning Scoring-System Investigation Group, Valladolid, Spain
- Emergency Department, Hospital Clínico Universitario, Valladolid, Spain
| | - Roberto Hornero
- Biomedical Engineering Group, Facultad de Medicina, Universidad de Valladolid, Av. Ramón y Cajal, 7, 47003, Valladolid, Spain
- CIBER-BBN, Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, Valladolid, Spain
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Kwak S, Lee SA, Lim J, Yang S, Hwang D, Lee HJ, Choi HM, Hwang IC, Lee S, Yoon YE, Park JB, Kim HK, Kim YJ, Song JM, Cho GY, Kang DH, Kim DH, Lee SP. Data-driven mortality risk prediction of severe degenerative mitral regurgitation patients undergoing mitral valve surgery. Eur Heart J Cardiovasc Imaging 2023; 24:1156-1165. [PMID: 37115641 DOI: 10.1093/ehjci/jead077] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Revised: 03/06/2023] [Accepted: 03/27/2023] [Indexed: 04/29/2023] Open
Abstract
AIMS The outcomes of mitral valve replacement/repair (MVR) in severe degenerative mitral regurgitation (MR) patients depend on various risk factors. We aimed to develop a risk prediction model for post-MVR mortality in severe degenerative MR patients using machine learning. METHODS AND RESULTS Consecutive severe degenerative MR patients undergoing MVR were analysed (n = 1521; 70% training/30% test sets). A random survival forest (RSF) model was constructed, with 3-year post-MVR all-cause mortality as the outcome. Partial dependency plots were used to define the thresholds of each risk factor. A simple scoring system (MVR-score) was developed to stratify post-MVR mortality risk. At 3 years following MVR, 90 patients (5.9%) died in the entire cohort (59 and 31 deaths in the training and test sets). The most important predictors of mortality in order of importance were age, haemoglobin, valve replacement, glomerular filtration rate, left atrial dimension, and left ventricular (LV) end-systolic diameter. The final RSF model with these six variables demonstrated high predictive performance in the test set (3-year C-index 0.880, 95% confidence interval 0.834-0.925), with mortality risk increased strongly with left atrial dimension >55 mm, and LV end-systolic diameter >45 mm. MVR-score demonstrated effective risk stratification and had significantly higher predictability compared to the modified Mitral Regurgitation International Database score (3-year C-index 0.803 vs. 0.750, P = 0.034). CONCLUSION A data-driven machine learning model provided accurate post-MVR mortality prediction in severe degenerative MR patients. The outcome following MVR in severe degenerative MR patients is governed by both clinical and echocardiographic factors.
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Affiliation(s)
- Soongu Kwak
- Division of Cardiology, Department of Internal Medicine, Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul 03080, South Korea
| | - Seung-Ah Lee
- Division of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul 05505, South Korea
| | - Jaehyun Lim
- Division of Cardiology, Department of Internal Medicine, Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul 03080, South Korea
| | - Seokhun Yang
- Division of Cardiology, Department of Internal Medicine, Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul 03080, South Korea
| | - Doyeon Hwang
- Division of Cardiology, Department of Internal Medicine, Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul 03080, South Korea
| | - Hyun-Jung Lee
- Division of Cardiology, Department of Internal Medicine, Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul 03080, South Korea
| | - Hong-Mi Choi
- Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, 82, Gumiro 173beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do 13620, Republic of Korea
- Department of Internal Medicine, Seoul National University College of Medicine, 103, Daehak-ro, Jongno-gu, Seoul 03080, South Korea
| | - In-Chang Hwang
- Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, 82, Gumiro 173beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do 13620, Republic of Korea
- Department of Internal Medicine, Seoul National University College of Medicine, 103, Daehak-ro, Jongno-gu, Seoul 03080, South Korea
| | - Sahmin Lee
- Division of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul 05505, South Korea
| | - Yeonyee E Yoon
- Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, 82, Gumiro 173beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do 13620, Republic of Korea
- Department of Internal Medicine, Seoul National University College of Medicine, 103, Daehak-ro, Jongno-gu, Seoul 03080, South Korea
| | - Jun-Bean Park
- Division of Cardiology, Department of Internal Medicine, Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul 03080, South Korea
- Department of Internal Medicine, Seoul National University College of Medicine, 103, Daehak-ro, Jongno-gu, Seoul 03080, South Korea
| | - Hyung-Kwan Kim
- Division of Cardiology, Department of Internal Medicine, Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul 03080, South Korea
- Department of Internal Medicine, Seoul National University College of Medicine, 103, Daehak-ro, Jongno-gu, Seoul 03080, South Korea
| | - Yong-Jin Kim
- Division of Cardiology, Department of Internal Medicine, Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul 03080, South Korea
- Department of Internal Medicine, Seoul National University College of Medicine, 103, Daehak-ro, Jongno-gu, Seoul 03080, South Korea
| | - Jong-Min Song
- Division of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul 05505, South Korea
| | - Goo-Yeong Cho
- Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, 82, Gumiro 173beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do 13620, Republic of Korea
- Department of Internal Medicine, Seoul National University College of Medicine, 103, Daehak-ro, Jongno-gu, Seoul 03080, South Korea
| | - Duk-Hyun Kang
- Division of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul 05505, South Korea
| | - Dae-Hee Kim
- Division of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul 05505, South Korea
| | - Seung-Pyo Lee
- Division of Cardiology, Department of Internal Medicine, Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul 03080, South Korea
- Department of Internal Medicine, Seoul National University College of Medicine, 103, Daehak-ro, Jongno-gu, Seoul 03080, South Korea
- Center for Precision Medicine, Seoul National University Hospital, 71, Daehak-ro, Jongno-gu, Seoul 03082, South Korea
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Zuo ZC, Fan XH, Tang Y, Zhang Y, Peng X, Zeng WH, Zeng Y. Deep learning-powered 3D segmentation derives factors associated with lymphovascular invasion and prognosis in clinical T1 stage non-small cell lung cancer. Heliyon 2023; 9:e15147. [PMID: 37095981 PMCID: PMC10121934 DOI: 10.1016/j.heliyon.2023.e15147] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 03/22/2023] [Accepted: 03/28/2023] [Indexed: 04/03/2023] Open
Abstract
Background Lymphovascular invasion (LVI) is an invasive biologic behavior that affects the treatment and prognosis of patients with early-stage lung cancer. This study aimed to identify LVI diagnostic and prognostic biomarkers using deep learning-powered 3D segmentation with artificial intelligence (AI) technology. Methods Between January 2016 and October 2021, we enrolled patients with clinical T1 stage non-small cell lung cancer (NSCLC). We used commercially available AI software (Dr. Wise system, Deep-wise Corporation, China) to extract quantitative AI features of pulmonary nodules automatically. Dimensionality reduction was achieved through least absolute shrinkage and selection operator regression; subsequently, the AI score was calculated.Then, the univariate and multivariate analysis was further performed on the AI score and patient baseline parameters. Results Among 175 enrolled patients, 22 tested positive for LVI at pathology review. Based on the multivariate logistic regression results, we incorporated the AI score, carcinoembryonic antigen, spiculation, and pleural indentation into the nomogram for predicting LVI. The nomogram showed good discrimination (C-index = 0.915 [95% confidence interval: 0.89-0.94]); moreover, calibration of the nomogram revealed good predictive ability (Brier score = 0.072). Kaplan-Meier analysis revealed that relapse-free survival and overall survival were significantly higher among patients with a low-risk AI score and without LVI than those among patients with a high-risk AI score (p = 0.008 and p = 0.002, respectively) and with LVI (p = 0.013 and p = 0.008, respectively). Conclusions Our findings indicate that a high-risk AI score is a diagnostic biomarker for LVI in patients with clinical T1 stage NSCLC; accordingly, it can serve as a prognostic biomarker for these patients.
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17
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Martín-Rodríguez F, Ortega GJ, Castro Villamor MA, Del Pozo Vegas C, Delgado Benito JF, Martín-Conty JL, Sanz-García A, López-Izquierdo R. Development of a prehospital lactic acidosis score for early-mortality. A prospective, multicenter, ambulance-based, cohort study. Am J Emerg Med 2023; 65:16-23. [PMID: 36580696 DOI: 10.1016/j.ajem.2022.12.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 12/02/2022] [Accepted: 12/13/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Lactic acidosis is a clinical status related to clinical worsening. Actually, higher levels of lactate is a well-established trigger of emergency situations. The aim of this work is to build-up a prehospital early warning score to predict 2-day mortality and intensive care unit (ICU) admission, constructed with other components of the lactic acidosis besides the lactate. METHODS Prospective, multicenter, observational, derivation-validation cohort study of adults evacuated by ambulance and admitted to emergency department with acute diseases, between January 1st, 2020 and December 31st, 2021. Including six advanced life support, thirty-eight basic life support units, referring to four hospitals (Spain). The primary and secondary outcome of the study were 2-day all-cause mortality and ICU-admission. The prehospital lactic acidosis (PLA) score was derived from the analysis of prehospital blood parameters associated with the outcome using a logistic regression. The calibration, clinical utility, and discrimination of PLA were determined and compared to the performance of each component of the score alone. RESULTS A total of 3334 patients were enrolled. The final PLA score included: lactate, pCO2, and pH. For 2-day mortality, the PLA showed an AUC of 0.941 (95%CI: 0.914-0.967), a better performance in calibration, and a higher net benefit as compared to the other score components alone. For the ICU admission, the PLA only showed a better performance for AUC: 0.75 (95%CI: 0.706-0.794). CONCLUSIONS Our results showed that PLA predicts 2-day mortality better than other lactic acidosis components alone. Including PLA score in prehospital setting could improve emergency services decision-making.
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Affiliation(s)
- Francisco Martín-Rodríguez
- Faculty of Medicine, Universidad de Valladolid, Valladolid, Spain; Advanced Life Support, Emergency Medical Services (SACYL), Valladolid, Spain; Prehospital early warning scoring-system investigation group, Valladolid, Spain
| | - Guillermo J Ortega
- Health Research Institute, Hospital de la Princesa, Madrid (IIS-IP), Spain; CONICET, Argentina
| | - Miguel A Castro Villamor
- Faculty of Medicine, Universidad de Valladolid, Valladolid, Spain; Prehospital early warning scoring-system investigation group, Valladolid, Spain
| | - Carlos Del Pozo Vegas
- Faculty of Medicine, Universidad de Valladolid, Valladolid, Spain; Prehospital early warning scoring-system investigation group, Valladolid, Spain; Emergency Department, Hospital Clínico Universitario, Valladolid, Spain
| | - Juan F Delgado Benito
- Advanced Life Support, Emergency Medical Services (SACYL), Valladolid, Spain; Prehospital early warning scoring-system investigation group, Valladolid, Spain
| | - José L Martín-Conty
- Faculty of Health Sciences, Universidad de Castilla la Mancha, Talavera de la Reina, Spain
| | - Ancor Sanz-García
- Prehospital early warning scoring-system investigation group, Valladolid, Spain; Health Research Institute, Hospital de la Princesa, Madrid (IIS-IP), Spain.
| | - Raúl López-Izquierdo
- Faculty of Medicine, Universidad de Valladolid, Valladolid, Spain; Prehospital early warning scoring-system investigation group, Valladolid, Spain; Emergency Department, Hospital Universitario Rio Hortega, Valladolid, Spain
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18
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Ramírez Cervantes KL, Mora E, Campillo Morales S, Huerta Álvarez C, Marcos Neira P, Nanwani Nanwani KL, Serrano Lázaro A, Silva Obregón JA, Quintana Díaz M. A Clinical Prediction Rule for Thrombosis in Critically Ill COVID-19 Patients: Step 1 Results of the Thromcco Study. J Clin Med 2023; 12:jcm12041253. [PMID: 36835788 PMCID: PMC9966844 DOI: 10.3390/jcm12041253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Revised: 02/01/2023] [Accepted: 02/02/2023] [Indexed: 02/09/2023] Open
Abstract
The incidence of thrombosis in COVID-19 patients is exceptionally high among intensive care unit (ICU)-admitted individuals. We aimed to develop a clinical prediction rule for thrombosis in hospitalized COVID-19 patients. Data were taken from the Thromcco study (TS) database, which contains information on consecutive adults (aged ≥ 18) admitted to eight Spanish ICUs between March 2020 and October 2021. Diverse logistic regression model analysis, including demographic data, pre-existing conditions, and blood tests collected during the first 24 h of hospitalization, was performed to build a model that predicted thrombosis. Once obtained, the numeric and categorical variables considered were converted to factor variables giving them a score. Out of 2055 patients included in the TS database, 299 subjects with a median age of 62.4 years (IQR 51.5-70) (79% men) were considered in the final model (SE = 83%, SP = 62%, accuracy = 77%). Seven variables with assigned scores were delineated as age 25-40 and ≥70 = 12, age 41-70 = 13, male = 1, D-dimer ≥ 500 ng/mL = 13, leukocytes ≥ 10 × 103/µL = 1, interleukin-6 ≥ 10 pg/mL = 1, and C-reactive protein (CRP) ≥ 50 mg/L = 1. Score values ≥28 had a sensitivity of 88% and specificity of 29% for thrombosis. This score could be helpful in recognizing patients at higher risk for thrombosis, but further research is needed.
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Affiliation(s)
- Karen L. Ramírez Cervantes
- Patient Blood Management Research Group, Hospital La Paz Institute for Health Research, 28040 Madrid, Spain
- Correspondence:
| | - Elianne Mora
- Department of Statistics, Charles III University of Madrid, 28903 Getafe, Spain
| | - Salvador Campillo Morales
- Patient Blood Management Research Group, Hospital La Paz Institute for Health Research, 28040 Madrid, Spain
| | - Consuelo Huerta Álvarez
- Department of Public Health & Maternal and Child Health, Faculty of Medicine, Complutense University of Madrid, 28040 Madrid, Spain
| | - Pilar Marcos Neira
- Intensive Care Unit, Hospital Germans Trias i Pujol, 08916 Badalona, Spain
| | | | | | | | - Manuel Quintana Díaz
- Patient Blood Management Research Group, Hospital La Paz Institute for Health Research, 28040 Madrid, Spain
- Intensive Care Unit, La Paz University Hospital, 28040 Madrid, Spain
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19
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Namen AM, Saha AK, Chatterjee AB. Mitigation of biases and DOISNORE50 attributes as a sleep apnea questionnaire predictive of OSA and medical emergency team activation. J Clin Sleep Med 2023; 19:419-420. [PMID: 36448334 PMCID: PMC9892744 DOI: 10.5664/jcsm.10362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 11/10/2022] [Indexed: 12/03/2022]
Affiliation(s)
- Andrew M. Namen
- Department of Internal Medicine, Section on Pulmonary Critical Care and Allergy and Immunologic Diseases, Wake Forest University School of Medicine, Winston-Salem, North Carolina
| | - Amit K. Saha
- Department of Anesthesiology, Wake Forest University School of Medicine, Winston-Salem, North Carolina
| | - Arjun B. Chatterjee
- Department of Internal Medicine, Section on Pulmonary Critical Care and Allergy and Immunologic Diseases, Wake Forest University School of Medicine, Winston-Salem, North Carolina
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20
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Banerjee S, Kundu K, Saini LK. Utility of DOISNORE50 questionnaire as a predictive tool for obstructive sleep apnea and postoperative medical emergency team activation. J Clin Sleep Med 2023; 19:417. [PMID: 36263858 PMCID: PMC9892729 DOI: 10.5664/jcsm.10352] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 10/13/2022] [Accepted: 10/14/2022] [Indexed: 02/04/2023]
Affiliation(s)
- Saikat Banerjee
- Department of Pulmonary Medicine, All India Institute of Medical Sciences–Rishikesh, Rishikesh, India
| | - Kaustav Kundu
- Department of Psychiatry, All India Institute of Medical Sciences–Rishikesh, Rishikesh, India
| | - Lokesh Kumar Saini
- Department of Pulmonary Medicine, All India Institute of Medical Sciences–Rishikesh, Rishikesh, India
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21
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Biswas S, MacArthur J, Sarkar V, Thompson H, Saleemi M, George KJ. Development and Validation of the Chronic Subdural HematOma Referral oUtcome Prediction Using Statistics (CHORUS) Score: A Retrospective Study at a National Tertiary Center. World Neurosurg 2023; 170:e724-e736. [PMID: 36442777 DOI: 10.1016/j.wneu.2022.11.097] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 11/21/2022] [Accepted: 11/22/2022] [Indexed: 11/27/2022]
Abstract
BACKGROUND Chronic subdural hematoma (CSDH) is a common neurosurgical condition with an increasing rate of patient referrals. CSDH referral decision-making is a subjective clinical process, and our aim was to develop a simple scoring system capable of acting as a decision support tool aiding referral triage. METHODS A single tertiary center retrospective case series analysis of all CSDH patient referrals from 2015 to 2020 was conducted. Ten independent variables used in the referral process were analyzed to predict the binary outcome of either accepting or rejecting the CSDH referral. Following feature selection analysis, a multivariable scoring system was developed and evaluated. RESULTS 1500 patient referrals were included. Stepwise multivariable logistic and least absolute shrinkage and selection operator regression identified age <85 years, the presence of headaches, dementia, motor weakness, radiological midline shift, a reasonable premorbid quality of life, and a large sized hematoma to be statistically significant predictors of CSDH referral acceptance (P <0.04). These variables derived a scoring system ranging from -9 to 6 with an optimal cut-off for referral acceptance at any score >1 (P <0.0001). This scoring system demonstrated optimal calibration (brier score loss = 0.0552), with a score >1 predicting referral acceptance with an area under the curve of 0.899 (0.876-0.922), a sensitivity of 83.838% (76.587-91.089), and a specificity of 96.000% (94.080-97.920). CONCLUSIONS Certain patient specific clinical and radiological characteristics can predict the acceptance or rejection of a CSDH referral. Considering the precision of this scoring system, it has the potential for effectively triaging CSDH referrals.
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Affiliation(s)
- Sayan Biswas
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Joshua MacArthur
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Ved Sarkar
- Division of Computer Information Systems, De Anza College, Cupertino, California, USA
| | - Helena Thompson
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Mohammad Saleemi
- Department of Neurosurgery, Salford Royal Hospital, Manchester, UK
| | - K Joshi George
- Department of Neurosurgery, Salford Royal Hospital, Manchester, UK.
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22
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Beyrer J, Nelson DR, Sheffield KM, Huang YJ, Lau YK, Hincapie AL. Development and Validation of Coding Algorithms to Identify Patients with Incident Non-Small Cell Lung Cancer in United States Healthcare Claims Data. Clin Epidemiol 2023; 15:73-89. [PMID: 36659903 PMCID: PMC9842515 DOI: 10.2147/clep.s389824] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 12/23/2022] [Indexed: 01/13/2023] Open
Abstract
Purpose We sought to develop and validate an incident non-small cell lung cancer (NSCLC) algorithm for United States (US) healthcare claims data. Diagnoses and procedures, but not medications, were incorporated to support longer-term relevance and reliability. Methods Patients with newly diagnosed NSCLC per Surveillance, Epidemiology, and End Results (SEER) served as cases. Controls included newly diagnosed small-cell lung cancer and other lung cancers, and two 5% random samples for other cancer and without cancer. Algorithms derived from logistic regression and machine learning methods used the entire sample (Approach A) or started with a previous algorithm for those with lung cancer (Approach B). Sensitivity, specificity, positive predictive values (PPV), negative predictive values, and F-scores (compared for 1000 bootstrap samples) were calculated. Misclassification was evaluated by calculating the odds of selection by the algorithm among true positives and true negatives. Results The best performing algorithm utilized neural networks (Approach B). A 10-variable point-score algorithm was derived from logistic regression (Approach B); sensitivity was 77.69% and PPV = 67.61% (F-score = 72.30%). This algorithm was less sensitive for patients ≥80 years old, with Medicare follow-up time <3 months, or missing SEER data on stage, laterality, or site and less specific for patients with SEER primary site of main bronchus, SEER summary stage 2000 regional by direct extension only, or pre-index chronic pulmonary disease. Conclusion Our study developed and validated a practical, 10-variable, point-based algorithm for identifying incident NSCLC cases in a US claims database based on a previously validated incident lung cancer algorithm.
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Affiliation(s)
- Julie Beyrer
- Eli Lilly and Company, Indianapolis, IN, USA,Correspondence: Julie Beyrer, Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN, 46285, USA, Tel +1 317 651 8236, Email
| | | | | | | | | | - Ana L Hincapie
- University of Cincinnati James L. Winkle College of Pharmacy, Cincinnati, OH, USA
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23
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Farzaliyev F, Steinau HU, Halmyradov A, Malamutmann E, Sleutel A, Illg C, Podleska LE. Optimization of the preoperative requirements of blood units for the surgical treatment of extra-abdominal soft tissue sarcoma: the TRANSAR score. World J Surg Oncol 2022; 20:378. [DOI: 10.1186/s12957-022-02839-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Accepted: 11/18/2022] [Indexed: 12/05/2022] Open
Abstract
Abstract
Background and objectives
Excessive preoperative blood orders frequently occur during the preoperative planning of resections of sarcomas. We aimed to develop a prediction score model that would be able to identify a patient cohort in which the cross-matching could be safely evaded.
Patients and methods
We retrospectively analyzed data of 309 consecutive patients with extra-abdominal soft tissue sarcomas treated between September 2012 and December 2014. Scorecard scores for variables were calculated and summarized to a total score that can be used for risk stratification. The score was used in a logistic regression model. Results of the optimized model were described as a receiver operating characteristic curve.
Results
Preoperative units of red blood cells were requested for 206 (66.7%) patients, of which only 31 (10%) received them. Five parameters were identified with high predictive power. In the visualized barplot, there was an increased risk of blood transfusion with a higher score of TRANSAR.
Conclusion
A TRANSAR score is a new tool that can predict the probability of transfusion for patients with sarcoma. This may reduce the number of preoperative cross-matching and blood product ordering and associated costs without compromising patient care.
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24
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High-resolution anoscopy predictive modeling of anal canal cancer response after definitive chemoradiotherapy in COVID19 era. Transl Oncol 2022; 27:101590. [PMID: 36444781 PMCID: PMC9703035 DOI: 10.1016/j.tranon.2022.101590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 11/10/2022] [Accepted: 11/17/2022] [Indexed: 11/24/2022] Open
Abstract
PURPOSE To develop a predictive index model, integrating both clinical and high-resolution anoscopy (HRA) features to further personalize the decision making process in anal canal carcinoma in COVID19 era. METHODS AND MATERIALS We assess HRA parameters after definitive chemoradiotherapy in patients with anal canal malignant lesions. RESULTS HRA features could be important to assess the effect of CRT and a risk stratification system should be introduced in clinical practice to better allocate therapeutic interventions. CONCLUSION To our knowledge this is the first proposal for HRA findings in anal canal cancer after definitive CRT. We believe that a risk score can be useful to estimate the risk of treatment failure (in term of persistence disease and/or recurrence) and its clinical relevance should not to be underestimated.
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25
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Van Praet KM, Kofler M, Falk V, Kempfert J. Reply to Condello et al. EUROPEAN JOURNAL OF CARDIO-THORACIC SURGERY : OFFICIAL JOURNAL OF THE EUROPEAN ASSOCIATION FOR CARDIO-THORACIC SURGERY 2022; 62:6772519. [PMID: 36282545 DOI: 10.1093/ejcts/ezac511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 10/19/2022] [Indexed: 01/07/2023]
Affiliation(s)
- Karel M Van Praet
- Department of Cardiothoracic and Vascular Surgery, German Heart Center Berlin, Berlin, Germany
| | - Markus Kofler
- Department of Cardiothoracic and Vascular Surgery, German Heart Center Berlin, Berlin, Germany
| | - Volkmar Falk
- Department of Cardiothoracic and Vascular Surgery, German Heart Center Berlin, Berlin, Germany
- Department of Cardiovascular Surgery, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Berlin, Germany
- Translational Cardiovascular Technologies, Institute of Translational Medicine, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH) Zurich, Zurich, Switzerland
| | - Jörg Kempfert
- Department of Cardiothoracic and Vascular Surgery, German Heart Center Berlin, Berlin, Germany
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26
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Elijah IM, Amsalu E, Jian X, Cao M, Mibei EK, Kerosi DO, Mwatsahu FG, Wang W, Onyangore F, Wang Y. Characterization and determinant factors of critical illness and in-hospital mortality of COVID-19 patients: A retrospective cohort of 1,792 patients in Kenya. BIOSAFETY AND HEALTH 2022; 4:330-338. [PMID: 35782165 PMCID: PMC9236624 DOI: 10.1016/j.bsheal.2022.06.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 06/16/2022] [Accepted: 06/21/2022] [Indexed: 01/09/2023] Open
Abstract
Limited data is available on the coronavirus disease 2019 (COVID-19), critical illness rate, and in-hospital mortality in the African setting. This study investigates determinants of critical illness and in-hospital mortality among COVID-19 patients in Kenya. We conducted a retrospective cohort study at Kenyatta National Hospital (KNH) in Kenya. Multivariate logistic regression and Cox proportional hazard regression were employed to determine predictor factors for intensive care unit (ICU) admission and in-hospital mortality, respectively. In addition, the Kaplan-Meier model was used to compare the survival times using log-rank tests. As a result, 346 (19.3%) COVID-19 patients were admitted to ICU, and 271 (15.1%) died. The majority of those admitted to the hospital were male, 1,137 (63.4%) and asymptomatic, 1,357 (75.7%). The most prevalent clinical features were shortness of breath, fever, and dry cough. In addition, older age, male, health status, patient on oxygen (O2), oxygen saturation levels (SPO2), headache, dry cough, comorbidities, obesity, cardiovascular diseases (CVDs), diabetes, chronic lung disease (CLD), and malignancy/cancer can predicate the risk of ICU admission, with an area under the receiver operating characteristic curve (AUC-ROC) of 0.90 (95% confidence interval [CI]: 0.88-0.92). Survival analysis indicated 271 (15.1%) patients died and identified older age, male, headache, shortness of breath, health status, patient on oxygen, SPO2, headache, comorbidity, CVDs, diabetes, CLD, malignancy/cancer, and smoking as risk factors for mortality (AUC-ROC: 0.90, 95% CI: 0.89-0.91). This is the first attempt to explore predictors for ICU admission and hospital mortality among COVID-19 patients in Kenya.
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Affiliation(s)
- Isinta M Elijah
- Beijing Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, Beijing 100069, China
| | - Endawoke Amsalu
- Armauer Hansen Research Institute, Ministry of Health, Addis Ababa 1005, Ethiopia
| | - Xuening Jian
- Beijing Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, Beijing 100069, China
| | - Mingyang Cao
- Beijing Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, Beijing 100069, China
| | - Eric K Mibei
- University of Kabianga, School of Health Sciences, Kericho 2030-20200, Kenya
| | - Danvas O Kerosi
- Medical Genetics Laboratory, School of Life Science, Central South University, Changsha 410083, China
| | - Francis G Mwatsahu
- Department of Environmental Health and Disease Control, School of Public Health, Jomo Kenyatta University of Agriculture and Technology, Nairobi 62000 – 00200, Kenya
| | - Wei Wang
- Beijing Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, Beijing 100069, China,Centre for Precision Medicine, Edith Cowan University, Perth WA 6027, Australia
| | - Faith Onyangore
- University of Kabianga, School of Health Sciences, Kericho 2030-20200, Kenya,Corresponding authors: School of Public Health, Capital Medical University, 10 Youanmenwai Xitoutiao Road, Fengtai District, Beijing 100069, China (Y. Wang); University of Kabianga, Department of Public Health, P.O Box 2030-20200, Kericho-Kenya (F. Onyangore)
| | - Youxin Wang
- Beijing Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, Beijing 100069, China,Centre for Precision Medicine, Edith Cowan University, Perth WA 6027, Australia,Corresponding authors: School of Public Health, Capital Medical University, 10 Youanmenwai Xitoutiao Road, Fengtai District, Beijing 100069, China (Y. Wang); University of Kabianga, Department of Public Health, P.O Box 2030-20200, Kericho-Kenya (F. Onyangore)
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27
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Polo Friz H, Esposito V, Marano G, Primitz L, Bovio A, Delgrossi G, Bombelli M, Grignaffini G, Monza G, Boracchi P. Machine learning and LACE index for predicting 30-day readmissions after heart failure hospitalization in elderly patients. Intern Emerg Med 2022; 17:1727-1737. [PMID: 35661313 DOI: 10.1007/s11739-022-02996-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Accepted: 04/20/2022] [Indexed: 11/05/2022]
Abstract
Machine learning (ML) techniques may improve readmission prediction performance in heart failure (HF) patients. This study aimed to assess the ability of ML algorithms to predict unplanned all-cause 30-day readmissions in HF elderly patients, and to compare them with conventional LACE (Length of hospitalization, Acuity, Comorbidities, Emergency department visits) index. All patients aged ≥ 65 years discharged alive between 2010 and 2019 after a hospitalization for acute HF were included in this retrospective cohort study. We applied MICE (Multivariate Imputation via Chained Equations) method to obtain a balanced, fully valued dataset and LASSO (Least Absolute Shrinkage and Selection Operator) algorithm to get the most significant features. Training (80% of records) and test (20%) cohorts were randomly selected. Study population: 3079 patients, 394 (12.8%) presented at least one readmission within 30 days, and 2685 (87.2%) did not. In the test cohort AUCs (IC95%) of XGBoost, Ada Boost Classifier, Random forest, and Gradient Boosting, and LACE Index were: 0.803 (0.734-0.872), 0.782 (0.711-0.854), 0.776 (0.703-0.848), 0.786 (0.715-0.857), and 0.504 (0.414-0.594), respectively, for predicting readmissions. A SHAP analysis was performed to offer a breakdown of the ML variables associated with readmission. Positive and negative predicting values estimates of the different ML models and LACE index were also provided, for several values of readmission rate prevalence. Among elderly patients, the rate of all-cause unplanned 30-day readmissions after hospitalization due to an acute HF was high. ML models performed better than the conventional LACE index for predicting readmissions. ML models can be proposed as promising tools for the identification of subjects at high risk of hospitalization in this clinical setting, enabling care teams to target interventions for improving overall clinical outcomes.
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Affiliation(s)
- Hernan Polo Friz
- Internal Medicine, Medical Department, Vimercate Hospital, Azienda Socio Sanitaria Territoriale (ASST) della Brianza, Via Santi Cosma e Damiano 10, 20871, Vimercate, MB, Italy.
| | | | - Giuseppe Marano
- Department of Biomedical and Clinical Sciences "L. Sacco", University of Milan, Milan, Italy
| | - Laura Primitz
- Internal Medicine, Medical Department, Vimercate Hospital, Azienda Socio Sanitaria Territoriale (ASST) della Brianza, Via Santi Cosma e Damiano 10, 20871, Vimercate, MB, Italy
| | | | | | - Michele Bombelli
- Internal Medicine, Medical Department, Desio Hospital, ASST della Brianza, Desio, Italy
| | - Guido Grignaffini
- Director for Health and Social Care, ASST della Brianza, Vimercate, Italy
| | | | - Patrizia Boracchi
- Department of Biomedical and Clinical Sciences "L. Sacco", University of Milan, Milan, Italy
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Predictive factors of non-operative management failure in 494 blunt liver injuries: a multicenter retrospective study. Updates Surg 2022; 74:1901-1913. [DOI: 10.1007/s13304-022-01367-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 08/21/2022] [Indexed: 10/15/2022]
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29
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He F, Xie L, Sun X, Xu J, Li Y, Liu R, Sun K, Shen D, Gu J, Ji T, Guo W. A Scoring System for Predicting Neoadjuvant Chemotherapy Response in Primary High-Grade Bone Sarcomas: A Multicenter Study. Orthop Surg 2022; 14:2499-2509. [PMID: 36017768 PMCID: PMC9531107 DOI: 10.1111/os.13469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Revised: 07/10/2022] [Accepted: 07/25/2022] [Indexed: 11/27/2022] Open
Abstract
Objective Currently, there is a lack of good clinical tools for evaluating the effect of chemotherapy preoperatively on primary high‐grade bone sarcomas. Our goal was to investigate the predictive value of the clinical findings and establish a scoring system to predict chemotherapy response. Methods We conducted a retrospective multicenter cohort study and reviewed 322 patients with primary high‐grade bone sarcomas. Patients who routinely received neoadjuvant chemotherapy and underwent primary tumor resection with an assessment of tumor necrosis rate (TNR) were enrolled in this study. The medical records of patients were collected from November 1, 2011, to March 1, 2018, at Peking University People's Hospital (PKUPH) and Peking University Shougang Hospital (PKUSH). The mean age of the patients was 16.2 years (range 3–52 years), of whom 65.5% were male. The clinical data collected before and after neoadjuvant chemotherapy included the degree of pain, laboratory inspection, X‐ray, CT, contrast‐enhanced magnetic resonance (MR), and positron emission tomography‐computed tomography (PET‐CT). Several machine learning models, including logistic regression, decision trees, support vector machines, and neural networks, were used to classify the chemotherapy responses. Area under the curve (AUC) of the scoring system to predict chemotherapy response is the primary outcome measure. Results For patients without events, a minimum follow‐up of 24 months was achieved. The median follow‐up time was 43.3 months, and it ranged from 24 to 84 months. The 5 years progression‐free survival (PFS) of the included patients was 54.1%. The 5 years PFS rate was 39.7% for poor responders and 74.9% for good responders. Features such as longest diameter reduction ratio (up to three points), clear bone boundary formation (up to two points), tumor necrosis measured by magnetic resonance (up to two points), maximum standard uptake value (SUVmax) decrease (up to three points), and significant alkaline phosphatase decrease (up to 1 point) were identified as significant predictors of good histological response and constituted the scoring system. A score ≥4 predicts a good response to chemotherapy. The scoring system based on the above factors performed well, achieving an AUC of 0.893. For nonmeasurable lesions (classified by the revised Response Evaluation Criteria in Solid Tumors [RECIST 1.1]), the AUC was 0.901. Conclusion We first devised a well‐performing comprehensive scoring system to predict the response to neoadjuvant chemotherapy in primary high‐grade bone sarcomas.
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Affiliation(s)
- Fangzhou He
- Musculoskeletal Tumor Center, Peking University People's Hospital, Beijing, China
| | - Lu Xie
- Musculoskeletal Tumor Center, Peking University People's Hospital, Beijing, China
| | - Xin Sun
- Musculoskeletal Tumor Center, Peking University People's Hospital, Beijing, China
| | - Jie Xu
- Musculoskeletal Tumor Center, Peking University People's Hospital, Beijing, China
| | - Yuan Li
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Rong Liu
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Kunkun Sun
- Department of Pathology, Peking University People's Hospital, Beijing, China
| | - Danhua Shen
- Department of Pathology, Peking University People's Hospital, Beijing, China
| | - Jin Gu
- Department of Surgical Oncology, Peking University Shougang Hospital, Beijing, China
| | - Tao Ji
- Musculoskeletal Tumor Center, Peking University People's Hospital, Beijing, China
| | - Wei Guo
- Musculoskeletal Tumor Center, Peking University People's Hospital, Beijing, China
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Grasa CD, Fernández-Cooke E, Domínguez-Rodríguez S, Aracil-Santos J, Barrios Tascon A, Sánchez-Manubens J, Mercader B, Antón J, Nuñez E, Villalobos E, Bustillo M, Camacho M, Oltra Benavent M, Giralt G, Bello Naranjo AM, Rocandio B, Calvo C. Risk scores for Kawasaki disease, a management tool developed by the KAWA-RACE cohort. Clin Rheumatol 2022; 41:3759-3768. [PMID: 35939163 DOI: 10.1007/s10067-022-06319-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 06/13/2022] [Accepted: 07/26/2022] [Indexed: 11/25/2022]
Abstract
INTRODUCTION/OBJECTIVES Asian scores developed to predict unresponsiveness to intravenous immunoglobulin (IVIG) or development of coronary artery aneurysms (CAA) in patients with Kawasaki disease (KD) are not appropriate in Western populations. The purpose of this study is to develop 2 scores, to predict unresponsiveness to IVIG and development of CAA, appropriate for Spanish population. METHOD Data of 625 Spanish children with KD collected retrospectively (2011-2016) were used to identify variables to develop the 2 scores of interest: unresponsiveness to IVIG and development of CAA. A statistical model selected best variables to create the scores, and scores were validated with data from 98 patients collected prospectively. RESULTS From 625 patients of the retrospective cohort, final analysis was performed in 439 subjects: 37 developed CAA, and 212 were unresponsive to IVIG. For the score to predict CAA, a cutoff ≥ 8 was considered for high risk, considering a score system with a different weight for each of the eight variables. External validation showed a sensitivity of 22% and a specificity of 75%. The score to predict unresponsiveness to IVIG established a cutoff ≥ 8 for high risk, considering a score system with a different weight for each of the nine variables. External validation showed a sensitivity of 78% and a specificity of 50%. CONCLUSIONS Two risk scores for KD were developed from Spanish population, to predict development of CAA and unresponsiveness to IVIG; validation in other cohorts could help to implement these tools in the management of KD in other Western populations.
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Affiliation(s)
- Carlos D Grasa
- Department of Pediatric Infectious Diseases, La Paz Children's Hospital (IdiPaz Foundation), Madrid, Spain
- IdiPaz, Institute for Health Research from La Paz Hospital, Madrid, Spain
- CIBERINFEC, CIBER of Infectious Diseases in Spain (Instituto de Salud Carlos III - ISCIII), Seville, Spain
| | - Elisa Fernández-Cooke
- Pediatric Infectious Diseases Unit, Pediatric Research and Clinical Trial Unit (UPIC), Department of Pediatrics, Hospital Universitario 12 Octubre, Avda. Córdoba s/n., 28041, Madrid, Spain.
- imas12, Instituto de Investigación Sanitaria Hospital 12 de Octubre, Madrid, Spain.
- RITIP, Spanish Network for the Research in Pediatric Infectious Diseases, Madrid, Spain.
| | - Sara Domínguez-Rodríguez
- Pediatric Infectious Diseases Unit, Pediatric Research and Clinical Trial Unit (UPIC), Department of Pediatrics, Hospital Universitario 12 Octubre, Avda. Córdoba s/n., 28041, Madrid, Spain
- imas12, Instituto de Investigación Sanitaria Hospital 12 de Octubre, Madrid, Spain
| | - Javier Aracil-Santos
- Department of Pediatric Infectious Diseases, La Paz Children's Hospital (IdiPaz Foundation), Madrid, Spain
| | - Ana Barrios Tascon
- Department of Pediatrics, Hospital Universitario Infanta Sofia, San Sebastian de los Reyes, Madrid, Spain
| | - Judith Sánchez-Manubens
- Department of Pediatric Rheumatology, Hospital Sant Joan de Deu, Universitat de Barcelona, Barcelona, Spain
- Department of Pediatric Rheumatology, Hospital Parc Tauli, Sabadell, Spain
| | - Beatriz Mercader
- Department of Pediatrics, Hospital Virgen de la Arrixaca, Murcia, Spain
| | - Jordi Antón
- Department of Pediatric Rheumatology, Hospital Sant Joan de Deu, Universitat de Barcelona, Barcelona, Spain
| | - Esmeralda Nuñez
- Pediatric Rheumatology Unit, Department of Pediatrics, Hospital Regional Universitario, Malaga, Spain
| | | | - Matilde Bustillo
- Department of Pediatric Infectious Diseases, Hospital Miguel Servet, Zaragoza, Spain
| | - Marisol Camacho
- Pediatric Infectious Diseases, Rheumatology and Immunology Unit, Department of Pediatrics, Hospital Virgen del Rocio, Sevilla, Spain
| | - Manuel Oltra Benavent
- Department of Pediatrics, Hospital Universitario y Politécnico La Fe, Valencia, Spain
| | - Gemma Giralt
- Department of Pediatric Cardiology, Hospital Vall d'Hebron, Barcelona, Spain
| | - Ana Maria Bello Naranjo
- Department of Pediatrics, Hospital Universitario Materno-Infantil de Las Palmas de Gran Canaria, Canarias, Spain
| | - Beatriz Rocandio
- Department of Pediatrics, Hospital Universitario Donostia, San Sebastian, Spain
| | - Cristina Calvo
- Department of Pediatric Infectious Diseases, La Paz Children's Hospital (IdiPaz Foundation), Madrid, Spain
- IdiPaz, Institute for Health Research from La Paz Hospital, Madrid, Spain
- CIBERINFEC, CIBER of Infectious Diseases in Spain (Instituto de Salud Carlos III - ISCIII), Seville, Spain
- RITIP, Spanish Network for the Research in Pediatric Infectious Diseases, Madrid, Spain
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Saima B, Mikel T, Maria B, Yolanda S, Juan ÁC, Victor VM, Laura P, Montserrat R, Carme GM, Alan M, Joaquín S. Progressive Lacunar Atrokes: A Predictive Score. J Stroke Cerebrovasc Dis 2022; 31:106510. [DOI: 10.1016/j.jstrokecerebrovasdis.2022.106510] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Revised: 04/01/2022] [Accepted: 04/08/2022] [Indexed: 02/05/2023] Open
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The Clinical Application Value of the Prognostic Nutritional Index for the Overall Survival Prognosis of Patients with Esophageal Cancer: A Robust Real-World Observational Study in China. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:3889588. [PMID: 35872955 PMCID: PMC9300322 DOI: 10.1155/2022/3889588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 06/27/2022] [Indexed: 12/24/2022]
Abstract
Esophageal cancer is a kind of cancer with high morbidity and mortality, which is accompanied by a profound poor prognosis. A prognostic nutritional index, based on serum albumin levels and peripheral lymphocyte count, has been confirmed to be significantly associated with various cancers. This study was aimed at exploring the prognostic significance of PNI in the overall survival prognosis of patients with esophageal cancer. As a real-world study based on the big database, clinical data of 2661 patients with esophageal cancer were evaluated retrospectively, and the individuals were randomly divided into training and testing cohorts. In these two cohorts, patients are classified into a high-risk group (PNI < 49) and a low-risk group (PNI ≥ 49). Univariate and multivariate analyses were performed to analyze the independent risk factors for the prognosis of esophageal cancer patients by using the Cox proportional hazards regression model. In this study, whether in the training cohort or the testing cohort, according to the univariate analysis, gender, tumor size, tumor grade, T stage, N stage, M stage, TNM stage, and PNI were significantly correlated with overall survival. Furthermore, the multivariate analysis showed that gender, T stage, N stage, M stage, TNM stage, and PNI were independent prognostic risk factors for esophageal cancer. PNI can be regarded as an independent prognostic factor combined with gender, T stage, N stage, M stage, and TNM stage, and it might be a novel reliable biomarker for esophageal cancer.
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Perioperative Risk Stratification Model for Readmission after Panniculectomy. Plast Reconstr Surg 2022; 150:181-188. [PMID: 35583949 DOI: 10.1097/prs.0000000000009265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
BACKGROUND Readmission is an important metric for surgical quality of care. This study aimed to develop a validated risk model that reliably predicts readmission after panniculectomy using the American College of Surgeons National Surgical Quality Improvement Program database. METHODS The American College of Surgeons National Surgical Quality Improvement Program database was queried to identify all patients who had undergone panniculectomy from 2005 to 2018. The outcome of interest was 30-day readmission. The cohort was divided randomly into 70 percent development and 30 percent validation groups. Independent predictors of 30-day readmission were identified using multivariable logistic regression on the development group. The predictors were weighted according to beta coefficients to generate an integer-based clinical risk score predictive of readmission, which was validated against the validation group. RESULTS For the model selection, 22 variables were identified based on criteria of p < 0.05 percent and complete data availability. Variables included in the development model included inpatient surgery, hypertension, obesity, functional dependence, chronic obstructive pulmonary disease, wound class greater than or equal to 3, American Society of Anesthesiologists class greater than 3, and liposuction. Receiver operating characteristic curve analysis of the validation group rendered an area under the curve of 0.710, which demonstrates the accuracy of this prediction model. The predicted incidence within each risk stratum was statistically similar to the observed incidence in the validation group ( p < 0.01), further highlighting the accuracy of the model. CONCLUSIONS The authors present a validated risk stratification model for readmission following panniculectomy. Prospective studies are needed to determine whether the implementation of the authors' clinical risk score optimizes safety and reduces readmission rates. CLINICAL QUESTION/LEVEL OF EVIDENCE Risk, III.
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Ning Y, Li S, Ong MEH, Xie F, Chakraborty B, Ting DSW, Liu N. A novel interpretable machine learning system to generate clinical risk scores: An application for predicting early mortality or unplanned readmission in a retrospective cohort study. PLOS DIGITAL HEALTH 2022; 1:e0000062. [PMID: 36812536 PMCID: PMC9931273 DOI: 10.1371/journal.pdig.0000062] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 05/10/2022] [Indexed: 01/19/2023]
Abstract
Risk scores are widely used for clinical decision making and commonly generated from logistic regression models. Machine-learning-based methods may work well for identifying important predictors to create parsimonious scores, but such 'black box' variable selection limits interpretability, and variable importance evaluated from a single model can be biased. We propose a robust and interpretable variable selection approach using the recently developed Shapley variable importance cloud (ShapleyVIC) that accounts for variability in variable importance across models. Our approach evaluates and visualizes overall variable contributions for in-depth inference and transparent variable selection, and filters out non-significant contributors to simplify model building steps. We derive an ensemble variable ranking from variable contributions across models, which is easily integrated with an automated and modularized risk score generator, AutoScore, for convenient implementation. In a study of early death or unplanned readmission after hospital discharge, ShapleyVIC selected 6 variables from 41 candidates to create a well-performing risk score, which had similar performance to a 16-variable model from machine-learning-based ranking. Our work contributes to the recent emphasis on interpretability of prediction models for high-stakes decision making, providing a disciplined solution to detailed assessment of variable importance and transparent development of parsimonious clinical risk scores.
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Affiliation(s)
- Yilin Ning
- Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore
| | - Siqi Li
- Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore
| | - Marcus Eng Hock Ong
- Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore,Health Services Research Centre, Singapore Health Services, Singapore, Singapore,Department of Emergency Medicine, Singapore General Hospital, Singapore, Singapore
| | - Feng Xie
- Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore,Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore
| | - Bibhas Chakraborty
- Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore,Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore,Department of Statistics and Data Science, National University of Singapore, Singapore, Singapore,Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, United States of America
| | - Daniel Shu Wei Ting
- Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore,Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore,SingHealth AI Health Program, Singapore Health Services, Singapore, Singapore
| | - Nan Liu
- Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore,Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore,Health Services Research Centre, Singapore Health Services, Singapore, Singapore,SingHealth AI Health Program, Singapore Health Services, Singapore, Singapore,Institute of Data Science, National University of Singapore, Singapore, Singapore,* E-mail:
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Ghirardi V, De Felice F, Rosati A, Ergasti R, Alletti SG, Mascilini F, Scambia G, Fagotti A. A laparoscopic adjusted model able to predict the risk of intraoperative capsule rupture in early stage ovarian cancer: Laparoscopic Ovarian Cancer Spillage Score (LOChneSS Study). J Minim Invasive Gynecol 2022; 29:961-967. [DOI: 10.1016/j.jmig.2022.04.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Revised: 04/23/2022] [Accepted: 04/25/2022] [Indexed: 12/24/2022]
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Lim WH, Park CM. Validation for measurements of skeletal muscle areas using low-dose chest computed tomography. Sci Rep 2022; 12:463. [PMID: 35013501 PMCID: PMC8748601 DOI: 10.1038/s41598-021-04492-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 12/23/2021] [Indexed: 11/21/2022] Open
Abstract
Various methods were suggested to measure skeletal muscle areas (SMAs) using chest low-dose computed tomography (chest LDCT) as a substitute for SMA at 3rd lumbar vertebra level (L3-SMA). In this study, four SMAs (L1-SMA, T12-erector spinae muscle areas, chest wall muscle area at carina level, pectoralis muscle area at aortic arch level) were segmented semi-automatically in 780 individuals taking concurrent chest and abdomen LDCT for healthcare screening. Four SMAs were compared to L3-SMA and annual changes were calculated from individuals with multiple examinations (n = 101). Skeletal muscle index (SMI; SMA/height2) cut-off for sarcopenia was determined by lower 5th percentile of young individuals (age ≤ 40 years). L1-SMA showed the greatest correlation to L3-SMA (men, R2 = 0.7920; women, R2 = 0.7396), and the smallest annual changes (0.3300 ± 4.7365%) among four SMAs. L1-SMI cut-offs for determining sarcopenia were 39.2cm2/m2 in men, and 27.5cm2/m2 in women. Forty-six men (9.5%) and ten women (3.4%) were found to have sarcopenia using L1-SMI cut-offs. In conclusion, L1-SMA could be a reasonable substitute for L3-SMA in chest LDCT. Suggested L1-SMI cut-offs for sarcopenia were 39.2cm2/m2 for men and 27.5cm2/m2 for women in Asian.
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Affiliation(s)
- Woo Hyeon Lim
- Department of Radiology, Namwon Medical Center, Namwon-si, Jeollabuk-do, Korea.,Department of Radiology, Seoul National University College of Medicine, 101 Daehak-ro, Chongno-gu, Seoul, 03080, Korea
| | - Chang Min Park
- Department of Radiology, Seoul National University College of Medicine, 101 Daehak-ro, Chongno-gu, Seoul, 03080, Korea. .,Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea. .,Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea. .,Institute of Medical and Biological Engineering, Medical Research Center, Seoul National University, Seoul, Korea.
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Kim MJ, Ha SO, Park YS, Yi JH, Yang WS, Kim JH. Validation and modification of HEART score components for patients with chest pain in the emergency department. Clin Exp Emerg Med 2021; 8:279-288. [PMID: 35000355 PMCID: PMC8743685 DOI: 10.15441/ceem.20.106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Accepted: 09/24/2020] [Indexed: 01/11/2023] Open
Abstract
OBJECTIVE This study aimed to clarify the relative prognostic value of each History, Electrocardiography, Age, Risk Factors, and Troponin (HEART) score component for major adverse cardiac events (MACE) within 3 months and validate the modified HEART (mHEART) score. METHODS This study evaluated the HEART score components for patients with chest symptoms visiting the emergency department from November 19, 2018 to November 19, 2019. All components were evaluated using logistic regression analysis and the scores for HEART, mHEART, and Thrombolysis in Myocardial Infarction (TIMI) were determined using the receiver operating characteristics curve. RESULTS The patients were divided into a derivation (809 patients) and a validation group (298 patients). In multivariate analysis, age did not show statistical significance in the detection of MACE within 3 months and the mHEART score was calculated after omitting the age component. The areas under the receiver operating characteristics curves for HEART, mHEART and TIMI scores in the prediction of MACE within 3 months were 0.88, 0.91, and 0.83, respectively, in the derivation group; and 0.88, 0.91, and 0.81, respectively, in the validation group. When the cutoff value for each scoring system was determined for the maintenance of a negative predictive value for a MACE rate >99%, the mHEART score showed the highest sensitivity, specificity, positive predictive value, and negative predictive value (97.4%, 54.2%, 23.7%, and 99.3%, respectively). CONCLUSION Our study showed that the mHEART score better detects short-term MACE in high-risk patients and ensures the safe disposition of low-risk patients than the HEART and TIMI scores.
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Affiliation(s)
- Min Jae Kim
- Department of Emergency Medicine, Hallym University Sacred Heart Hospital, Hallym University Medical Center, Anyang, Korea
| | - Sang Ook Ha
- Department of Emergency Medicine, Hallym University Sacred Heart Hospital, Hallym University Medical Center, Anyang, Korea,Correspondence to: Sang Ook Ha Department of Emergency Medicine, Hallym University Sacred Heart Hospital, Hallym University Medical Center, 22 Gwanpyeong-ro 170 beongil, Donan-gu, Anyang 14068, Korea E-mail:
| | - Young Sun Park
- Department of Emergency Medicine, Hallym University Sacred Heart Hospital, Hallym University Medical Center, Anyang, Korea
| | - Jeong Hyeon Yi
- Department of Emergency Medicine, Hallym University Sacred Heart Hospital, Hallym University Medical Center, Anyang, Korea
| | - Won Seok Yang
- Department of Emergency Medicine, Hallym University Sacred Heart Hospital, Hallym University Medical Center, Anyang, Korea
| | - Jin Hyuck Kim
- Department of Neurology, Hallym University Sacred Heart Hospital, Hallym University Medical Center, Anyang, Korea
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Haji Aghajani M, Sistanizad M, Pourhoseingholi A, Asadpoordezaki Z, Taherpour N. Development of a scoring system for the prediction of in-hospital mortality among COVID-19 patients. CLINICAL EPIDEMIOLOGY AND GLOBAL HEALTH 2021; 12:100871. [PMID: 34632161 PMCID: PMC8492387 DOI: 10.1016/j.cegh.2021.100871] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 09/18/2021] [Accepted: 09/27/2021] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND The aim of this study is to develop and validate a scoring system as a tool for predicting the in-hospital mortality in COVID-19 patients in early stage of disease. METHODS This retrospective cohort study, conducted on 893 COVID-19 patients in Tehran from February 18 to July 20, 2020. Potential factors were chosen via stepwise selection and multivariable logistic regression model. Cross-validation method was employed to assess the predictive performance of the model as well as the scoring system such as discrimination, calibration, and validity indices. RESULTS The COVID-19 patients' median age was 63 yrs (54.98% male) and 233 (26.09%) patients expired during the study. The scoring system was developed based on 8 selected variables: age ≥55 yrs (OR = 5.67, 95% CI: 3.25-9.91), males (OR = 1.51, 95% CI: 1.007-2.29), ICU need (OR = 16.32, 95% CI 10.13-26.28), pulse rate >90 (OR = 1.89, 95% CI: 1.26-2.83), lymphocytes <17% (OR = 2.33, 95%CI: 1.54-3.50), RBC ≤4, 10 6/L (OR = 2.10, 95% CI: 1.35-3.26), LDH >700 U/L (OR = 1.68, 95%CI: 1.13-2.51) and troponin I level >0.03 ng/mL (OR = 1.75, 95%CI: 1.17-2.62). The AUC and the accuracy of scoring system after cross-validation were 79.4% and 79.89%, respectively. CONCLUSION This study showed that developed scoring system has a good performance and can use to help physicians for identifying high-risk patients in early stage of disease .
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Affiliation(s)
- Mohammad Haji Aghajani
- Prevention of Cardiovascular Disease Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mohammad Sistanizad
- Prevention of Cardiovascular Disease Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- Department of Clinical Pharmacy, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Asma Pourhoseingholi
- Prevention of Cardiovascular Disease Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ziba Asadpoordezaki
- Imam Hossein Medical and Educational Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Niloufar Taherpour
- Prevention of Cardiovascular Disease Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Correlates of Delayed Initial Contact to Emergency Services among Patients with Suspected ST-Elevation Myocardial Infarction. Cardiol Res Pract 2021; 2021:8483817. [PMID: 34567802 PMCID: PMC8457972 DOI: 10.1155/2021/8483817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 08/26/2021] [Indexed: 11/25/2022] Open
Abstract
Background Early diagnosis and treatment of a patient displaying symptoms of myocardial ischemia is paramount in preventing detrimental tissue damage, arrhythmias, and death. Patient-related hospital delay is the greatest considerable cause of total delay in treatment for acute myocardial infarction. Objective To identify patient characteristics contributing to prehospital delay and ultimately developing health interventions to prevent future delay and improve health outcomes. Methods A retrospective chart review of 287 patients diagnosed with ST-elevation myocardial infarction (STEMI) was evaluated to examine correlates of patient-related delays to care. Results Stepwise logistic regression modeling with forward selection (likelihood ratio) was performed to identify predictors of first medical contact (FMC) within 120 minutes of symptom onset and door-to-balloon (DTB) time within 90 minutes. Distance from the hospital, being unmarried, self-medicating, disability, and hemodynamic stability emerged as variables that were found to be predictive of FMC within the first 120 minutes after symptom onset. Similarly, patient characteristics of gender and disability and having an initial nondiagnostic electrocardiogram emerged as significant predictors of DTB within 90 minutes. Conclusions Individual attention to high-risk patients and public education campaigns using printed materials, public lectures, and entertainment mediums are likely needed to disseminate information to improve prevention strategies. Future research should focus on identifying the strengths of prehospital predictors and finding other variables that can be established as forecasters of delay. Interventions to enhance survival in acute STEMI should continue as to provide substantial advances in overall health outcomes.
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Risk factors for treatment failure in women with uncomplicated lower urinary tract infection. PLoS One 2021; 16:e0256464. [PMID: 34464397 PMCID: PMC8407559 DOI: 10.1371/journal.pone.0256464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 08/06/2021] [Indexed: 11/19/2022] Open
Abstract
Given rising antibiotic resistance and increasing use of delayed prescription for uncomplicated lower urinary tract infections (UTI), patients at risk for treatment failure should be identified early. We assessed risk factors for clinical and microbiological failure in women with lower UTI. This case-control study nested within a randomized clinical trial included all women in the per-protocol population (PPP), those in the PPP with microbiologically confirmed UTI, and those in the PPP with UTI due to Escherichia coli. Cases were women who experienced clinical and/or microbiologic failure; controls were those who did not. Risk factors for failure were assessed using multivariate logistic regression. In the PPP, there were 152 clinical cases for 307 controls. Among 340 women with microbiologically confirmed UTI, 126 and 102 cases with clinical and microbiological failure were considered with, respectively, 214 and 220 controls. Age ≥52 years was independently associated with clinical (adjusted OR 3.01; 95%CI 1.84-4.98) and microbiologic failure (aOR 2.55; 95%CI 1.54-4.25); treatment with fosfomycin was associated with clinical failure (aOR 2.35; 95%CI 1.47-3.80). The association with age persisted among all women, and women with E. coli-related UTI. Diabetes was not an independent risk factor, nor were other comorbidities. Postmenopausal age emerged as an independent risk factor for both clinical and microbiological treatment failure in women with lower UTI and should be considered to define women at-risk for non-spontaneous remission, and thus for delayed antibiotic therapy; diabetes mellitus was not associated with failure.
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Amoo M, O'Cearbhaill RM, McHugh P, Henry J, O'Byrne K, Ben Husien M, Javadpour M. Derivation of a Clinical Score for Prediction of Recurrence Following Evacuation of Chronic Subdural Hematoma: A Retrospective Cohort Study at a National Referral Centre. World Neurosurg 2021; 154:e743-e753. [PMID: 34343685 DOI: 10.1016/j.wneu.2021.07.126] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 07/26/2021] [Accepted: 07/27/2021] [Indexed: 11/15/2022]
Abstract
BACKGROUND Chronic subdural hematoma (cSDH) is a common pathology, and recurrence is a common complication, which may be predicted by certain patient and radiologic factors. Empiric radiologic surveillance has been shown to convey no benefit. METHODS A retrospective review of a prospectively collated database was performed. Preoperative and postoperative noncontrast computed tomography scans were reviewed. Radiologic appearance, preoperative hematoma volume, patient age, presence of bilateral hematomas, maximal hematoma thickness, and therapeutic coagulopathy were assessed as predictors. Receiver operating characteristic curve analysis, logistic regression, and LASSO regression were used to select potential predictors. A multivariate model was then fitted, and a score was derived. RESULTS A total of 142 patients were included. Maximal hematoma thickness >12 mm (P = 0.02) and age >65 years (P = 0.01) were found to correlate with the likelihood of recurrence. Bilateral hematomas and a hyperdense or mixed density appearance were also identified on LASSO regression. Bilateral hematomas (P = 0.19), hyperdense or mixed density (P = 0.66), maximum thickness >12 mm (P = 0.01), and age >65 years (P = 0.02) were included in the multivariate model. A 6-point score was derived. A score of >3 had a sensitivity of 89% (95% confidence interval [CI] 78%-97%) and specificity of 26% (95% CI, 17%-34%) for predicting recurrence, with recurrence significantly more likely in patients with a score of 4-6 versus those with a score of 0-3 (P = 0.02). CONCLUSIONS Certain radiologic findings may predict the recurrence of cSDH following evacuation. The score derived may be useful in identifying patients who might benefit from routine postoperative surveillance imaging.
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Affiliation(s)
- Michael Amoo
- National Neurosurgical Centre, Beaumont Hospital, Dublin, Ireland; Royal College of Surgeons in Ireland, Dublin, Ireland.
| | - Roisin M O'Cearbhaill
- Department of Neuroradiology, Beaumont Hospital, Dublin, Ireland; Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Paul McHugh
- National Neurosurgical Centre, Beaumont Hospital, Dublin, Ireland
| | - Jack Henry
- National Neurosurgical Centre, Beaumont Hospital, Dublin, Ireland; School of Medicine, University College Dublin, Dublin, Ireland
| | - Kevin O'Byrne
- National Neurosurgical Centre, Beaumont Hospital, Dublin, Ireland
| | - Mohammed Ben Husien
- National Neurosurgical Centre, Beaumont Hospital, Dublin, Ireland; Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Mohsen Javadpour
- National Neurosurgical Centre, Beaumont Hospital, Dublin, Ireland; Royal College of Surgeons in Ireland, Dublin, Ireland; Department of Academic Neurology, Trinity College, Dublin, Ireland
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Gaastra B, Barron P, Newitt L, Chhugani S, Turner C, Kirkpatrick P, MacArthur B, Galea I, Bulters D. CRP (C-Reactive Protein) in Outcome Prediction After Subarachnoid Hemorrhage and the Role of Machine Learning. Stroke 2021; 52:3276-3285. [PMID: 34238015 DOI: 10.1161/strokeaha.120.030950] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND AND PURPOSE Outcome prediction after aneurysmal subarachnoid hemorrhage (aSAH) is challenging. CRP (C-reactive protein) has been reported to be associated with outcome, but it is unclear if this is independent of other predictors and applies to aSAH of all grades. Therefore, the role of CRP in aSAH outcome prediction models is unknown. The purpose of this study is to assess if CRP is an independent predictor of outcome after aSAH, develop new prognostic models incorporating CRP, and test whether these can be improved by application of machine learning. METHODS This was an individual patient-level analysis of data from patients within 72 hours of aSAH from 2 prior studies. A panel of statistical learning methods including logistic regression, random forest, and support vector machines were used to assess the relationship between CRP and modified Rankin Scale. Models were compared with the full Subarachnoid Hemmorhage International Trialists' (SAHIT) prediction tool of outcome after aSAH and internally validated using cross-validation. RESULTS One thousand and seventeen patients were included for analysis. CRP on the first day after ictus was an independent predictor of outcome. The full SAHIT model achieved an area under the receiver operator characteristics curve (AUC) of 0.831. Addition of CRP to the predictors of the full SAHIT model improved model performance (AUC, 0.846, P=0.01). This improvement was not enhanced when learning was performed using a random forest (AUC, 0.807), but was with a support vector machine (AUC of 0.960, P <0.001). CONCLUSIONS CRP is an independent predictor of outcome after aSAH. Its inclusion in prognostic models improves performance, although the magnitude of improvement is probably insufficient to be relevant clinically on an individual patient level, and of more relevance in research. Greater improvements in model performance are seen with support vector machines but these models have the highest classification error rate on internal validation and require external validation and calibration.
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Affiliation(s)
- Ben Gaastra
- Department of Neurosurgery, Wessex Neurological Centre, University Hospital Southampton, United Kingdom (B.G., D.B.)
| | - Peter Barron
- University of Southampton Faculty of Medicine, United Kingdom (P.B., L.N., S.C.)
| | - Laura Newitt
- University of Southampton Faculty of Medicine, United Kingdom (P.B., L.N., S.C.)
| | - Simran Chhugani
- University of Southampton Faculty of Medicine, United Kingdom (P.B., L.N., S.C.)
| | - Carole Turner
- Department of Neurosurgery, Cambridge University Hospital, United Kingdom (C.T., P.K.)
| | - Peter Kirkpatrick
- Department of Neurosurgery, Cambridge University Hospital, United Kingdom (C.T., P.K.)
| | - Ben MacArthur
- Mathematical Sciences, University of Southampton, United Kingdom (B.M.)
| | - Ian Galea
- Clinical Neurosciences, Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, United Kingdom (I.G.)
| | - Diederik Bulters
- Department of Neurosurgery, Wessex Neurological Centre, University Hospital Southampton, United Kingdom (B.G., D.B.)
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Sanz-García A, Cecconi A, Vera A, Camarasaltas JM, Alfonso F, Ortega GJ, Jimenez-Borreguero J. Electrocardiographic biomarkers to predict atrial fibrillation in sinus rhythm electrocardiograms. Heart 2021; 107:1813-1819. [PMID: 34088763 DOI: 10.1136/heartjnl-2021-319120] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Revised: 04/19/2021] [Accepted: 04/23/2021] [Indexed: 11/03/2022] Open
Abstract
OBJECTIVE Early prediction of atrial fibrillation (AF) development would improve patient outcomes. We propose a simple and cheap ECG based score to predict AF development. METHODS A cohort of 16 316 patients was analysed. ECG measures provided by the computer-assisted ECG software were used to identify patients. A first group included patients in sinus rhythm who showed an ECG with AF at any time later (n=505). A second group included patients with all their ECGs in sinus rhythm (n=15 811). By using a training set (75% of the cohort) the initial sinus rhythm ECGs of both groups were analysed and a predictive risk score based on a multivariate logistic model was constructed. RESULTS A multivariate regression model was constructed with 32 variables showing a predictive value characterised by an area under the curve (AUC) of 0.776 (95% CI: 0.738 to 0.814). The subsequent risk score included the following variables: age, duration of P-wave in aVF, V4 and V5; duration of T-wave in V3, mean QT interval adjusted for heart rate, transverse P-wave clockwise rotation, transverse P-wave terminal angle and transverse QRS complex terminal vector magnitude. Risk score values ranged from 0 (no risk) to 5 (high risk). The predictive validity of the score reached an AUC of 0.764 (95% CI: 0.722 to 0.806) with a global specificity of 61% and a sensitivity of 55%. CONCLUSIONS The automatic assessment of ECG biomarkers from ECGs in sinus rhythm is able to predict the risk for AF providing a low-cost screening strategy for early detection of this pathology.
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Affiliation(s)
- Ancor Sanz-García
- Data Analysis Unit, Hospital Universitario de la Princesa, Madrid, Spain
| | - Alberto Cecconi
- Cardiology Department, Hospital Universitario de la Princesa, Madrid, Spain
| | - Alberto Vera
- Cardiology Department, Hospital Universitario de la Princesa, Madrid, Spain
| | | | - Fernando Alfonso
- Cardiology Department, Hospital Universitario de la Princesa, Madrid, Spain
| | - Guillermo Jose Ortega
- Data Analysis Unit, Hospital Universitario de la Princesa, Madrid, Spain .,CONICET; Consejo Nacional de Investigaciones Científicas y Técnicas, Buenos Aires, Argentina
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Prieto JM, Harting MT, Calvo RY, Carroll JM, Sykes AG, Ignacio RC, Ebanks AH, Lazar DA. Identifying risk factors for enteral access procedures in neonates with congenital diaphragmatic hernia: A novel risk-assessment score. J Pediatr Surg 2021; 56:1130-1134. [PMID: 33745741 DOI: 10.1016/j.jpedsurg.2021.02.029] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Accepted: 02/05/2021] [Indexed: 10/22/2022]
Abstract
BACKGROUND/PURPOSE The purpose of this study was to evaluate the characteristics of neonates with congenital diaphragmatic hernia (CDH) undergoing enteral access procedures (gastrostomy or jejunostomy) during their initial hospitalization, and establish a clinical scoring system based on these characteristics. METHODS Data were obtained from the multicenter, multinational CDH Study Group database (CDHSG Registry) between 2007 and 2019. Patients were randomly partitioned into model-derivation and validation subsets. Weighted scores were assigned to risk factors based on their calculated β-coefficients after logistic regression. RESULTS Of 4537 total patients, 597 (13%) underwent gastrostomy or jejunostomy tube placement. In the derivation subset, factors independently associated with an increased risk for enteral access included oxygen requirement at 30-days, chromosomal abnormalities, gastroesophageal reflux, major cardiac anomalies, ECMO requirement, liver herniation, and increased defect size. Based on the devised scoring system, patients could be stratified into very low (0-4 points; <10% risk), low (5-6 points; 10-20% risk), intermediate (7-9 points; 30-60% risk), and high risk (≥10 points; 70% risk) groups for enteral access. CONCLUSION This study identifies risk factors associated with enteral access procedures in neonates with congenital diaphragmatic hernia and establishes a novel scoring system that may be used to guide clinical decision making in those with poor oral feeding. TYPE OF STUDY Prognosis study.
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Affiliation(s)
- James M Prieto
- Division of Pediatric Surgery, Department of Surgery, University of California San Diego, Rady Children's Hospital San Diego, 3020 Children's Way, MC 5136, San Diego, CA 92123, United States
| | - Matthew T Harting
- Department of Pediatric Surgery, McGovern Medical School at the University of Texas Health Science Center at Houston, Houston, TX, United States
| | | | - Jeanne M Carroll
- Division of Neonatology, Department of Pediatrics, University of California San Diego, San Diego, CA, United States
| | - Alicia G Sykes
- Division of Pediatric Surgery, Department of Surgery, University of California San Diego, Rady Children's Hospital San Diego, 3020 Children's Way, MC 5136, San Diego, CA 92123, United States
| | - Romeo C Ignacio
- Division of Pediatric Surgery, Department of Surgery, University of California San Diego, Rady Children's Hospital San Diego, 3020 Children's Way, MC 5136, San Diego, CA 92123, United States
| | - Ashley H Ebanks
- Department of Pediatric Surgery, McGovern Medical School at the University of Texas Health Science Center at Houston, Houston, TX, United States
| | - David A Lazar
- Division of Pediatric Surgery, Department of Surgery, University of California San Diego, Rady Children's Hospital San Diego, 3020 Children's Way, MC 5136, San Diego, CA 92123, United States.
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Gasperini C, Prosperini L, Rovira À, Tintoré M, Sastre-Garriga J, Tortorella C, Haggiag S, Galgani S, Capra R, Pozzilli C, Montalban X, Río J. Scoring the 10-year risk of ambulatory disability in multiple sclerosis: the RoAD score. Eur J Neurol 2021; 28:2533-2542. [PMID: 33786942 DOI: 10.1111/ene.14845] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Revised: 03/04/2021] [Accepted: 03/25/2021] [Indexed: 11/27/2022]
Abstract
BACKGROUND AND PURPOSE Both baseline prognostic factors and short-term predictors of treatment response can influence the long-term risk of disability accumulation in patients with relapsing-remitting multiple sclerosis (RRMS). The objective was to develop and validate a scoring system combining baseline prognostic factors and 1-year variables of treatment response into a single numeric score predicting the long-term risk of disability. METHODS We analysed two independent datasets of patients with RRMS who started interferon beta or glatiramer acetate, had an Expanded Disability Status Scale (EDSS) score <4.0 at treatment start and were followed for at least 10 years. The first dataset ('training set') included patients attending three MS centres in Italy and served as a framework to create the so-called RoAD score (Risk of Ambulatory Disability). The second ('validation set') included a cohort of patients followed in Barcelona, Spain, to explore the performance of the RoAD score in predicting the risk of reaching an EDSS score ≥6.0. RESULTS The RoAD score (ranging from 0 to 8) derived from the training set (n = 1225), was based on demographic (age), clinical baseline prognostic factors (disease duration, EDSS) and 1-year predictors of treatment response (number of relapses, presence of gadolinium enhancement and new T2 lesions). The best cut-off score for discriminating patients at higher risk of reaching the disability milestone was ≥4. When applied to the validation set (n = 296), patients with a RoAD score ≥4 had an approximately 4-fold increased risk for reaching the disability milestone (p < 0.001). DISCUSSION The RoAD score is proposed as an useful tool to predict individual prognosis and optimize treatment strategy of patients with RRMS.
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Affiliation(s)
- Claudio Gasperini
- Department of Neurosciences, San Camillo-Forlanini Hospital, Rome, Italy
| | - Luca Prosperini
- Department of Neurosciences, San Camillo-Forlanini Hospital, Rome, Italy
| | - Àlex Rovira
- Centre d'Esclerosi Multiple de Catalunya (Cemcat), Department of Neurology/Neuroimmunology, Hospital Universitari Vall d'Hebron, Universitat Autonoma de Barcelona, Barcelona, Spain
| | - Mar Tintoré
- Centre d'Esclerosi Multiple de Catalunya (Cemcat), Department of Neurology/Neuroimmunology, Hospital Universitari Vall d'Hebron, Universitat Autonoma de Barcelona, Barcelona, Spain
| | - Jaume Sastre-Garriga
- Centre d'Esclerosi Multiple de Catalunya (Cemcat), Department of Neurology/Neuroimmunology, Hospital Universitari Vall d'Hebron, Universitat Autonoma de Barcelona, Barcelona, Spain
| | - Carla Tortorella
- Department of Neurosciences, San Camillo-Forlanini Hospital, Rome, Italy
| | - Shalom Haggiag
- Department of Neurosciences, San Camillo-Forlanini Hospital, Rome, Italy
| | - Simonetta Galgani
- Department of Neurosciences, San Camillo-Forlanini Hospital, Rome, Italy
| | - Ruggero Capra
- Multiple Sclerosis Centre, ASST Spedali Civili di Brescia, P.O. Montichiari, Montichiari, Brescia, Italy
| | - Carlo Pozzilli
- Department of Human Neuroscience, Sapienza University, Rome, Italy
| | - Xavier Montalban
- Centre d'Esclerosi Multiple de Catalunya (Cemcat), Department of Neurology/Neuroimmunology, Hospital Universitari Vall d'Hebron, Universitat Autonoma de Barcelona, Barcelona, Spain
| | - Jordi Río
- Centre d'Esclerosi Multiple de Catalunya (Cemcat), Department of Neurology/Neuroimmunology, Hospital Universitari Vall d'Hebron, Universitat Autonoma de Barcelona, Barcelona, Spain
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Schöning V, Liakoni E, Baumgartner C, Exadaktylos AK, Hautz WE, Atkinson A, Hammann F. Development and validation of a prognostic COVID-19 severity assessment (COSA) score and machine learning models for patient triage at a tertiary hospital. J Transl Med 2021; 19:56. [PMID: 33546711 PMCID: PMC7862984 DOI: 10.1186/s12967-021-02720-w] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Accepted: 01/26/2021] [Indexed: 01/28/2023] Open
Abstract
Background Clinical risk scores and machine learning models based on routine laboratory values could assist in automated early identification of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) patients at risk for severe clinical outcomes. They can guide patient triage, inform allocation of health care resources, and contribute to the improvement of clinical outcomes. Methods In- and out-patients tested positive for SARS-CoV-2 at the Insel Hospital Group Bern, Switzerland, between February 1st and August 31st (‘first wave’, n = 198) and September 1st through November 16th 2020 (‘second wave’, n = 459) were used as training and prospective validation cohort, respectively. A clinical risk stratification score and machine learning (ML) models were developed using demographic data, medical history, and laboratory values taken up to 3 days before, or 1 day after, positive testing to predict severe outcomes of hospitalization (a composite endpoint of admission to intensive care, or death from any cause). Test accuracy was assessed using the area under the receiver operating characteristic curve (AUROC). Results Sex, C-reactive protein, sodium, hemoglobin, glomerular filtration rate, glucose, and leucocytes around the time of first positive testing (− 3 to + 1 days) were the most predictive parameters. AUROC of the risk stratification score on training data (AUROC = 0.94, positive predictive value (PPV) = 0.97, negative predictive value (NPV) = 0.80) were comparable to the prospective validation cohort (AUROC = 0.85, PPV = 0.91, NPV = 0.81). The most successful ML algorithm with respect to AUROC was support vector machines (median = 0.96, interquartile range = 0.85–0.99, PPV = 0.90, NPV = 0.58). Conclusion With a small set of easily obtainable parameters, both the clinical risk stratification score and the ML models were predictive for severe outcomes at our tertiary hospital center, and performed well in prospective validation.
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Affiliation(s)
- Verena Schöning
- Clinical Pharmacology and Toxicology, Department of General Internal Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Evangelia Liakoni
- Clinical Pharmacology and Toxicology, Department of General Internal Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Christine Baumgartner
- Department of General Internal Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Aristomenis K Exadaktylos
- Department of Emergency Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Wolf E Hautz
- Department of Emergency Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Andrew Atkinson
- Pediatric Pharmacology and Pharmacometrics Research Group, University Children's Hospital Basel, Basel, Switzerland.,Department of Infectious Diseases, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Felix Hammann
- Clinical Pharmacology and Toxicology, Department of General Internal Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
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Pelletier JH, Rakkar J, Au AK, Fuhrman D, Clark RSB, Horvat CM. Trends in US Pediatric Hospital Admissions in 2020 Compared With the Decade Before the COVID-19 Pandemic. JAMA Netw Open 2021; 4:e2037227. [PMID: 33576819 PMCID: PMC7881361 DOI: 10.1001/jamanetworkopen.2020.37227] [Citation(s) in RCA: 131] [Impact Index Per Article: 32.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
IMPORTANCE In early 2020, the United States declared a public health emergency in response to coronavirus disease 2019 (COVID-19) and implemented a variety of social distancing measures. The association between the COVID-19 pandemic and the number of pediatric admissions is unclear. OBJECTIVE To determine the changes in patterns of pediatric admissions in 2020 compared with the prior decade. DESIGN, SETTING, AND PARTICIPANTS This cross-sectional study included 49 US hospitals contributing to the Pediatric Health Information Systems database. Inpatient admissions were transformed into time-series data, and ensemble forecasting models were generated to analyze admissions across a range of diagnoses in 2020 compared with previous years. The setting was inpatient admissions. All patients discharged between January 1, 2010, and June 30, 2020, from an inpatient hospital encounter were included. MAIN OUTCOMES AND MEASURES Number of hospital admissions by primary diagnosis for each encounter. RESULTS Of 5 424 688 inpatient encounters among 3 372 839 patients (median [interquartile range] age, 5.1 [0.7-13.3] years; 2 823 748 [52.1%] boys; 3 171 224 [58.5%] White individuals) at 49 hospitals, 213 571 (3.9%) were between January 1, 2020, and June 30, 2020. There was a decrease in the number of admissions beginning in March 2020 compared with the period from 2010 to 2019. At the nadir, admissions in April 2020 were reduced 45.4% compared with prior years (23 798 in April 2020 compared with a median [interquartile range] of 43 550 [42 110-43 946] in April 2010-2019). Inflation-adjusted hospital charges decreased 27.7% in the second quarter of 2020 compared with prior years ($4 327 580 511 in 2020 compared with a median [interquartile range] of $5 983 142 102 [$5 762 690 022-$6 324 978 456] in 2010-2019). Seasonal patterns were evident between 2010 and 2019 for a variety of common pediatric conditions, including asthma, atrial septal defects, bronchiolitis, diabetic ketoacidosis, Kawasaki syndrome, mental health admissions, and trauma. Ensemble models were able to discern seasonal patterns in admission diagnoses and accurately predicted admission rates from July 2019 until December 2019 but not from January 2020 to June 2020. All diagnoses except for birth decreased below the model 95% CIs between January 2020 and June 2020. CONCLUSIONS AND RELEVANCE In this cross-sectional study, pediatric admissions to US hospitals decreased in 2020 across an array of pediatric conditions. Although some conditions may have decreased in incidence, others may represent unmet needs in pediatric care during the COVID-19 pandemic.
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Affiliation(s)
- Jonathan H. Pelletier
- Division of Pediatric Critical Care Medicine, Department of Critical Care Medicine, UPMC Children’s Hospital of Pittsburgh, Pittsburgh, Pennsylvania
| | - Jaskaran Rakkar
- Division of Pediatric Critical Care Medicine, Department of Critical Care Medicine, UPMC Children’s Hospital of Pittsburgh, Pittsburgh, Pennsylvania
| | - Alicia K. Au
- Division of Pediatric Critical Care Medicine, Department of Critical Care Medicine, UPMC Children’s Hospital of Pittsburgh, Pittsburgh, Pennsylvania
| | - Dana Fuhrman
- Division of Pediatric Critical Care Medicine, Department of Critical Care Medicine, UPMC Children’s Hospital of Pittsburgh, Pittsburgh, Pennsylvania
| | - Robert S. B. Clark
- Division of Pediatric Critical Care Medicine, Department of Critical Care Medicine, UPMC Children’s Hospital of Pittsburgh, Pittsburgh, Pennsylvania
| | - Christopher M. Horvat
- Division of Pediatric Critical Care Medicine, Department of Critical Care Medicine, UPMC Children’s Hospital of Pittsburgh, Pittsburgh, Pennsylvania
- Division of Health Informatics, Department of Pediatrics, UPMC Children’s Hospital of Pittsburgh, Pittsburgh, Pennsylvania
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Zhu JS, Ge P, Jiang C, Zhang Y, Li X, Zhao Z, Zhang L, Duong TQ. Deep-learning artificial intelligence analysis of clinical variables predicts mortality in COVID-19 patients. J Am Coll Emerg Physicians Open 2020; 1:1364-1373. [PMID: 32838390 PMCID: PMC7405082 DOI: 10.1002/emp2.12205] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 07/01/2020] [Accepted: 07/13/2020] [Indexed: 01/01/2023] Open
Abstract
Objective The large number of clinical variables associated with coronavirus disease 2019 (COVID-19) infection makes it challenging for frontline physicians to effectively triage COVID-19 patients during the pandemic. This study aimed to develop an efficient deep-learning artificial intelligence algorithm to identify top clinical variable predictors and derive a risk stratification score system to help clinicians triage COVID-19 patients. Methods This retrospective study consisted of 181 hospitalized patients with confirmed COVID-19 infection from January 29, 2020 to March 21, 2020 from a major hospital in Wuhan, China. The primary outcome was mortality. Demographics, comorbidities, vital signs, symptoms, and laboratory tests were collected at initial presentation, totaling 78 clinical variables. A deep-learning algorithm and a risk stratification score system were developed to predict mortality. Data were split into 85% training and 15% testing. Prediction performance was compared with those using COVID-19 severity score, CURB-65 score, and pneumonia severity index (PSI). Results Of the 181 COVID-19 patients, 39 expired and 142 survived. Five top predictors of mortality were D-dimer, O2 Index, neutrophil:lymphocyte ratio, C-reactive protein, and lactate dehydrogenase. The top 5 predictors and the resultant risk score yielded, respectively, an area under curve (AUC) of 0.968 (95% CI = 0.87-1.0) and 0.954 (95% CI = 0.80-0.99) for the testing dataset. Our models outperformed COVID-19 severity score (AUC = 0.756), CURB-65 score (AUC = 0.671), and PSI (AUC = 0.838). The mortality rates for our risk stratification scores (0-5) were 0%, 0%, 6.7%, 18.2%, 67.7%, and 83.3%, respectively. Conclusions Deep-learning prediction model and the resultant risk stratification score may prove useful in clinical decisionmaking under time-sensitive and resource-constrained environment.
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Affiliation(s)
- Jocelyn S Zhu
- Departments of Radiology, Renaissance School of MedicineStony Brook UniversityStony BrookNew YorkUSA
| | - Peilin Ge
- Departments of Radiology, Renaissance School of MedicineStony Brook UniversityStony BrookNew YorkUSA
| | - Chunguo Jiang
- Department of Respiratory and Critical Care Medicine, Beijing Institute of Respiratory Medicine, Beijing Chaoyang HospitalCapital Medical UniversityBeijingChina
| | - Yong Zhang
- Department of Hepatobiliary Surgery, Union Hospital, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina
| | - Xiaoran Li
- Departments of Radiology, Renaissance School of MedicineStony Brook UniversityStony BrookNew YorkUSA
| | - Zirun Zhao
- Departments of Radiology, Renaissance School of MedicineStony Brook UniversityStony BrookNew YorkUSA
| | - Liming Zhang
- Department of Respiratory and Critical Care Medicine, Beijing Institute of Respiratory Medicine, Beijing Chaoyang HospitalCapital Medical UniversityBeijingChina
| | - Tim Q. Duong
- Departments of Radiology, Renaissance School of MedicineStony Brook UniversityStony BrookNew YorkUSA
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Lopes MC, Amadeu TP, Ribeiro-Alves M, da Costa CH, Silva BRA, Rodrigues LS, Bessa EJC, Bruno LP, Lopes AJ, Rufino R. Defining prognosis in sarcoidosis. Medicine (Baltimore) 2020; 99:e23100. [PMID: 33235069 PMCID: PMC7710206 DOI: 10.1097/md.0000000000023100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
Sarcoidosis is a multi-systemic granulomatous disease. Affected individuals can show spontaneous healing, develop remission with drug treatment within 2 years, or become chronically ill. Our main goal was to identify features that are related to prognosis.The study consisted of 101 patients, recruited at a single center, who were already diagnosed with sarcoidosis at the start of the study or were diagnosed within 48 months. Ninety individuals were followed-up for at least 24 months and were classified according to clinical outcome status (COS 1 to 9). Those with COS 1-4 and COS 5-9 were classified as having favorable and unfavorable outcomes, respectively. Unconditional logistic regression analyses were conducted to define which variables were associated with sarcoidosis outcomes. Subsequently, we established a scoring system to help predict the likelihood of a favorable or unfavorable outcome.Of our patients, 48% developed a chronic form of the disease (COS 5-9). Three clinical features were predictive of prognosis in sarcoidosis. We built a score-based model where the absence of rheumatological markers (1 point), normal pulmonary functions (2 points), and the presence of early respiratory symptoms manifestations (2 points) were associated with a favorable prognosis. We predicted that a patient with a score of 5 had an 86% (95% confidence interval [CI] 74%-98%) probability of having a favorable prognosis, while those with scores of 4, 3, 2, 1, and 0 had probabilities of 72% (95% CI 59-85%), 52% (95% CI 40-63%), 31% (95% CI 17-44%), 15% (95% CI 2-28%), and 7% (95% CI 0-16%) of having a favorable prognosis, respectively. Thus, our easy-to-compute algorithm can help to predict prognosis of sarcoidosis patients, facilitating their management.
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Affiliation(s)
| | - Thaís Porto Amadeu
- Department of Pathology and Laboratories, State University of Rio de Janeiro
| | - Marcelo Ribeiro-Alves
- National Institute of Infectology Evandro Chagas, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil
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Indrayan A. Aleatory and epistemic uncertainties can completely derail medical research results. J Postgrad Med 2020; 66:94-98. [PMID: 32134004 PMCID: PMC7239410 DOI: 10.4103/jpgm.jpgm_585_19] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Aleatory uncertainties are generated by intrinsic factors such as studying a sample rather than the whole population and the source of epistemic uncertainties is extraneous such as limitations of knowledge. These uncertainties inflict all the findings in empirical medical research, but they are rarely appreciated. This article highlights these uncertainties and shows with the help of an example how apparently valid and reliable findings can completely derail due to these uncertainties. We conclude that aleatory and epistemic uncertainties should get due consideration while drawing conclusions and before the results are put into practice. Methods to reduce their impact on results are also presented.
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Affiliation(s)
- A Indrayan
- Department of Clinical Research, Max Healthcare Institute, Saket, New Delhi, India
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