|
1
|
Bakkes THGF, De Bie Dekker AJR, van der Stam JA, Kaymak U, Mischi M, Bouwman RA, Turco S. Validation of the advanced alert monitor in a Dutch hospital using local optimization and refinement of the outcome definition. Int J Med Inform 2025; 201:105930. [PMID: 40273595 DOI: 10.1016/j.ijmedinf.2025.105930] [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: 08/06/2024] [Revised: 02/19/2025] [Accepted: 04/11/2025] [Indexed: 04/26/2025]
Abstract
BACKGROUND The Advanced Alert Monitor (AAM) is a data-driven Early Warning Score (EWS) system designed to predict ICU admission or mortality within 12 hours. It has demonstrated superior performance compared to the National Early Warning Score (NEWS) and contributed to a consortium-wide reduction in mortality, showcasing its potential for clinical use. OBJECTIVE The study evaluates the generalizability of the AAM in a Dutch hospital setting, comparing its performance against the NEWS and a locally optimized AAM (LO-AAM). Additionally, it investigates how different outcome definitions influence system performance. METHOD Electronic medical record (EMR) data from Catharina Hospital in Eindhoven, Netherlands, were used to reproduce the AAM and train the LO-AAM. Both were evaluated against the NEWS using two outcome definitions: the original study definition and an adapted definition that includes mortality regardless of care order status. Feature importance analysis was conducted to assess the impact of these outcome definitions on model performance. RESULTS The AAM achieved an AUROC of 79.9% against NEWS's 74.2%. However, under the modified outcome definition, the NEWS outperformed the AAM. LO-AAM outperformed both AAM and NEWS in all outcomes. Feature importance analysis showed a greater emphasis on physiological features for the LO-AAM trained on the adapted outcome. CONCLUSION The AAM demonstrates generalizability beyond its original population, but local optimization significantly enhances its performance. Outcome definitions critically affect the performance of the NEWS, AAM, and LO-AAM. The adapted outcome includes a wider scope of mortality as an adverse event, leading to an increase in performance for all EWSs.
Collapse
Affiliation(s)
- Tom H G F Bakkes
- Eindhoven University of Technology, Department of Electrical Engineering, De Groene Loper 19, Eindhoven, 5612AP, Noord-Brabant, the Netherlands.
| | | | - Jonna A van der Stam
- Catharina Hospital, Michelangelolaan 2, Eindhoven, 5623 EJ, Noord-Brabant, the Netherlands
| | - Uzay Kaymak
- Eindhoven University of Technology, Department of Electrical Engineering, De Groene Loper 19, Eindhoven, 5612AP, Noord-Brabant, the Netherlands
| | - Massimo Mischi
- Eindhoven University of Technology, Department of Electrical Engineering, De Groene Loper 19, Eindhoven, 5612AP, Noord-Brabant, the Netherlands
| | - R Arthur Bouwman
- Eindhoven University of Technology, Department of Electrical Engineering, De Groene Loper 19, Eindhoven, 5612AP, Noord-Brabant, the Netherlands; Catharina Hospital, Michelangelolaan 2, Eindhoven, 5623 EJ, Noord-Brabant, the Netherlands
| | - Simona Turco
- Eindhoven University of Technology, Department of Electrical Engineering, De Groene Loper 19, Eindhoven, 5612AP, Noord-Brabant, the Netherlands
| |
Collapse
|
|
2
|
Krishnamoorthy S, Thiruvengadam G, Sekar H, Palaniyandi V, Ramadurai S, Narayanasamy S. Modified National Early Warning Score 2, a reliable early warning system for predicting treatment outcomes in patients with emphysematous pyelonephritis. World J Nephrol 2025; 14:103035. [DOI: 10.5527/wjn.v14.i2.103035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/05/2024] [Revised: 02/21/2025] [Accepted: 03/04/2025] [Indexed: 04/09/2025] Open
Abstract
BACKGROUND Emphysematous pyelonephritis (EPN) is a life-threatening necrotizing renal parenchyma infection characterized by gas formation due to severe bacterial infection, predominantly affecting diabetic and immunocompromised patients. It carries high morbidity and mortality, requiring early diagnosis and timely intervention. Various prognostic scoring systems help in triaging critically ill patients. The National Early Warning Score 2 (NEWS 2) scoring system is a widely used physiological assessment tool that evaluates clinical deterioration based on vital parameters, but its standard form lacks specificity for risk stratification in EPN, necessitating modifications to improve treatment decision-making and prognostic accuracy in this critical condition.
AIM To highlight the need to modify the NEWS 2 score to enable more intense monitoring and better treatment outcomes.
METHODS This prospective study was done on all EPN patients admitted to our hospital over the past 12 years. A weighted average risk-stratification index was calculated for each of the three groups, mortality risk was calculated for each of the NEWS 2 scores, and the need for intervention for each of the three groups was calculated. The NEWS 2 score was subsequently modified with 0-6, 7-14 and 15-20 scores included in groups 1, 2 and 3, respectively.
RESULTS A total of 171 patients with EPN were included in the study, with a predominant association with diabetes (90.6%) and a female-to-male ratio of 1.5:1. The combined prognostic scoring of the three groups was 10.7, 13.0, and 21.9, respectively (P < 0.01). All patients managed conservatively belonged to group 1 (P < 0.01). Eight patients underwent early nephrectomy, with six from group 3 (P < 0.01). Overall mortality was 8 (4.7%), with seven from group 3 (87.5%). The cutoff NEWS 2 score for mortality was identified to be 15, with a sensitivity of 87.5%, specificity of 96.9%, and an overall accuracy rate of 96.5%. The area under the curve to predict mortality based on the NEWS 2 score was 0.98, with a confidence interval of (0.97, 1.0) and P < 0.001.
CONCLUSION Modified NEWS 2 (mNEWS 2) score dramatically aids in the appropriate assessment of treatment-related outcomes. MNEWS 2 scores should become the practice standard to reduce the morbidity and mortality associated with this dreaded illness.
Collapse
Affiliation(s)
- Sriram Krishnamoorthy
- Department of Urology and Renal Transplantation, Sri Ramachandra Institute of Higher Education and Research, Chennai 600116, Tamil Nādu, India
| | - Gayathri Thiruvengadam
- Faculty of Allied Health Sciences, Sri Ramachandra Institute of Higher Education and Research, Chennai 600116, Tamil Nādu, India
| | - Hariharasudhan Sekar
- Department of Urology and Renal Transplantation, Sri Ramachandra Institute of Higher Education and Research, Chennai 600116, Tamil Nādu, India
| | - Velmurugan Palaniyandi
- Department of Urology and Renal Transplantation, Sri Ramachandra Institute of Higher Education and Research, Chennai 600116, Tamil Nādu, India
| | - Srinivasan Ramadurai
- Department of General Medicine, Sri Ramachandra Institute of Higher Education and Research, Chennai 600116, Tamil Nādu, India
| | - Senthil Narayanasamy
- Department of General Medicine, Sri Ramachandra Institute of Higher Education and Research, Chennai 600116, Tamil Nādu, India
| |
Collapse
|
|
3
|
Barrington J, Subbe C, Kellett J, Frischknecht Christensen E, Brabrand M, Nanayakkara P, Alsma J. Feasibility of Using Resting Heart Rate and Step Counts From Patient-Held Sensors During Clinical Assessment of Medical Emergencies (FUSE): Protocol for Prospective Observational Study in European Hospitals. JMIR Res Protoc 2025; 14:e55975. [PMID: 40293791 PMCID: PMC12070009 DOI: 10.2196/55975] [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] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 06/01/2024] [Accepted: 01/10/2025] [Indexed: 04/30/2025] Open
Abstract
BACKGROUND Abnormalities of vital signs are quantified by comparison with normal ranges, which are those observed in resting healthy populations. It might be more appropriate to compare the vital sign values of an individual in distress with their own usual values recorded when they were stable and well. Sensors from smartwatches or smartphones might make this possible at scale, but the proportion of patients using them is not known. OBJECTIVE This study aimed to assess the feasibility of using heart rate and mobility data from patients' own wearable sensors as part of clinical assessments at the time of presentation to hospitals with medical emergencies, and to quantify the difference between heart rate and the change in daily steps taken by the patient on admission to acute care compared with the previously recorded values at home. METHODS This is an international, multicenter observational study using the flashmob research design. The study will recruit patients aged 18 years and older who present to emergency departments, acute medical departments, or ambulatory emergency care with an acute medical complaint. Main end points of the study include the proportion of patients assessed for an acute complaint who use wearable devices to record vital signs. The study will describe the population that uses devices that collect vital signs in terms of sex, age group, digital literacy, and the severity of illness on presentation (as measured by a standard set of vital signs and frailty). Trends in heart rate and step counts measured in the month before presentation to acute care services will be reported according to discharge or admission status. Data will be collected during a pilot phase and during a single week in centers across Europe. RESULTS The study has been registered and passed the required approvals in the Netherlands Medical Ethics Committee (MEC-2022-0795) and the United Kingdom Integrated Research Application System (IRAS 321129). Based on the results of a pilot study performed at a single site in the United Kingdom, a flashmob study has been concluded in hospitals throughout Europe in May 2024 and reported in 2025. CONCLUSIONS With the increasing availability of consumer held devices able to record medically relevant information this study will provide information about the availability of these data for clinical use in a number of European settings. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/55975.
Collapse
Affiliation(s)
- Jack Barrington
- Department of Acute Medicine, Ysbyty Gwynedd, Bangor, United Kingdom
| | - Christian Subbe
- Department of Acute Medicine, Ysbyty Gwynedd, Bangor, United Kingdom
- North Wales Medical School, Bangor University, Bangor, United Kingdom
| | - John Kellett
- School of Health and Social Care, University of Bolton, Bolton, United Kingdom
| | | | - Mikkel Brabrand
- Research Unit for Emergency Medicine, Odense University Hospital, Odense, Denmark
| | - Prabath Nanayakkara
- Amsterdam University Medical Centre, Vree University Medical Centre, Amsterdam, The Netherlands
| | - Jelmer Alsma
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
| |
Collapse
|
|
4
|
Khedr RA, Ali E, Ahmed El-Mahallawy H, Eldeen NE. Successful Management of Pediatric Patients with Low-Risk Febrile Neutropenia Using a Clinical Care Pathway in Egypt. Infect Chemother 2025; 57:57.e24. [PMID: 40343420 DOI: 10.3947/ic.2025.0009] [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: 02/03/2025] [Accepted: 02/25/2025] [Indexed: 05/11/2025] Open
Abstract
BACKGROUND Criteria for home management of low-risk febrile neutropenia remain challenging in supportive care. Careful selection of low-risk febrile neutropenic pediatric patients can improve outcomes and decrease complications. In the current study, we implemented a clinical pathway for pediatric patients presenting to the emergency room department with low-risk febrile neutropenia by using strict inclusion criteria. MATERIALS AND METHODS This is a prospective study from December 2021 to September 2022; all patients presented to the emergency room department were screened for pathway evaluation, and risk stratification was performed using a strict checklist. Patients were included if they met the low-risk criteria. Thorough clinical and laboratory assessments were performed on these patients. All patients started oral antibiotics and were instructed about alarming signs. Patients were followed up at the outpatient clinic on days 3 and 7. RESULTS Two hundred and three patients with 200 episodes of low-risk febrile neutropenia were enrolled; one hundred and ten were males, and 90 were females; underlying hematological malignancies accounted for 54.0%. On day three, 181 patients out of 200 were afebrile for 24 hours (90.0%), and 47.5% were still neutropenic. At day seven, all study patients were afebrile, had recovering counts, and stopped antibiotics regardless of the count. Absolute neutrophil count recovery on day seven was achieved in 95.5% of patients. CONCLUSION Our inclusion criteria for patients with low-risk febrile neutropenia proved to be safe without deaths or intensive care unit admission and successful with the lowest admission rate, so it can be used for a stewardship program to avoid unnecessary patient admissions and help healthcare givers to optimize patient allocation and follow-up safely.
Collapse
Affiliation(s)
- Reham Abdelaziz Khedr
- Department of Pediatric Oncology, National Cancer Institute, Cairo University, Cairo, Egypt
- Department of Hematology/Oncology, Children Cancer Hospital of Egypt, Egypt.
| | - Ebtehal Ali
- Department of Pediatric Oncology, National Cancer Institute, Cairo University, Cairo, Egypt
| | | | - Nashwa Ezz Eldeen
- Department of Pediatric Oncology, National Cancer Institute, Cairo University, Cairo, Egypt
- Department of Hematology/Oncology, Children Cancer Hospital of Egypt, Egypt. ,
| |
Collapse
|
|
5
|
Lim L, Kim M, Cho K, Yoo D, Sim D, Ryu HG, Lee HC. Multicenter validation of a machine learning model to predict intensive care unit readmission within 48 hours after discharge. EClinicalMedicine 2025; 81:103112. [PMID: 40034564 PMCID: PMC11872568 DOI: 10.1016/j.eclinm.2025.103112] [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: 09/16/2024] [Revised: 01/16/2025] [Accepted: 01/29/2025] [Indexed: 03/05/2025] Open
Abstract
Background Intensive care unit (ICU) readmission is a crucial indicator of patient safety. However, discharge decisions often rely on subjective assessment due to a lack of standardized guidelines. We aimed to develop a machine-learning model to predict ICU readmission within 48 h and compare its performance to traditional scoring systems. Methods We developed an ensemble model, iREAD, that generates a probability score at ICU discharge, representing the likelihood of the patient being readmitted to the ICU within 48 h, using data from Seoul National University Hospital (SNUH) and validated it using the MIMIC-III and eICU-CRD datasets. From September 2007 to August 2021, a total of 70,842 patients were included from SNUH. The MIMIC-III datasets comprised 43,237 patients admitted to ICUs between 2001 and 2012 at Beth Israel Deaconess Medical Center, and the eICU-CRD datasets included 90,271 ICU admissions across 208 hospitals between 2014 and 2015. Patients younger than 18, those who died in ICUs, or who refused life-sustaining treatment were excluded from the final analysis. The model's performance was evaluated using the area under the receiver operating characteristic curve (AUROC) and compared to the traditional scores and conventional machine learning models. Kaplan-Meier analysis was performed to compare the outcome between the high-risk and low-risk groups. Findings We developed the iREAD, that utilized 30 input features, encompassing demographics, length of stay, vital signs, GCS, and laboratory values. iREAD demonstrated superior performance compared with other models across all cohorts (all P < 0.001). In the internal validation, iREAD achieved AUROCs of 0.771 (95% CI 0.743-0.798), 0.834 (0.821-0.846), and 0.820 (0.808-0.832) for early (≤48 h), late (>48 h), and overall ICU readmissions, respectively. External validations with MIMIC-III and eICU-CRD also showed modest performance with AUROCs of 0.768 (0.748-0.787) and 0.725 (0.712-0.739) for overall readmission in MIMIC-III and eICU-CRD respectively, demonstrating superior performance compared to other models (All P < 0.001; higher than other models). Kaplan-Meier analysis revealed that over 40% of high-risk patients predicted by iREAD were readmitted within 48 h, representing a more than four-fold increase in predictive performance compared to the traditional scores. Interpretation iREAD demonstrates superior performance in predicting ICU readmission within 48 h after discharge compared to traditional scoring systems or conventional machine learning models in both internal and external validations. While the performance degradation observed in the external validations suggests the need for further prospective validation on diverse patient populations, the robust performance and ability to identify high-risk patients have the potential to guide clinical decision-making. Funding This work was supported by the Korea Health Technology Research & Development Project through the Korea Health Industry Development Institute, funded by the Ministry of Health and Welfare, Republic of Korea (grant number RS-2021-KH114109).
Collapse
Affiliation(s)
- Leerang Lim
- Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
| | - Mincheol Kim
- VUNO, 479 Gangnam-daero, Seocho-gu, Seoul, 06541, Republic of Korea
- Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, 115 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Kyungjae Cho
- VUNO, 479 Gangnam-daero, Seocho-gu, Seoul, 06541, Republic of Korea
| | - Dongjoon Yoo
- VUNO, 479 Gangnam-daero, Seocho-gu, Seoul, 06541, Republic of Korea
- Department of Critical Care Medicine and Emergency Medicine, Inha University College of Medicine, 100 Inha-ro, Michuhol-gu, Incheon, 22212, Republic of Korea
| | - Dayeon Sim
- Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
| | - Ho Geol Ryu
- Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
- Department of Critical Care Medicine, Seoul National University College of Medicine, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
| | - Hyung-Chul Lee
- Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
| |
Collapse
|
|
6
|
Weaver JMJ, Nagy B, Wilson C, Lewis A, Armstrong A, Cooksley T. Low-risk febrile neutropenia: does combined chemotherapy/immune checkpoint inhibitor necessitate a change in approach? Support Care Cancer 2025; 33:112. [PMID: 39825158 DOI: 10.1007/s00520-025-09168-4] [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: 06/23/2024] [Accepted: 01/10/2025] [Indexed: 01/20/2025]
Abstract
PURPOSE Management of patients with low-risk febrile neutropenia in an outpatient setting guided by the MASCC score is proven to be safe and effective. Most patients on ambulatory low-risk febrile neutropenia pathways are undergoing treatment for breast cancer. Recent data has shown benefit of the addition of immune checkpoint inhibitor therapy to cytotoxic chemotherapy in the neoadjuvant setting for patients with early triple-negative breast cancer. We examined whether the addition of ICI therapy altered the clinical severity of febrile neutropenia in this cohort and the ability to manage these patients in an ambulatory setting. METHODS An observational analysis was performed at a specialist oncology hospital in the North West of England. We compared patients with triple negative breast cancer presenting with febrile neutropenia following treatment with PC-EC/pembrolizumab to those treated with PC-EC in the neoadjuvant setting. RESULTS In the study periods, 152 patients received PC-EC and 151 PC-EC/Pembro. Twenty-five patients presented with FN in the PC-EC/Pembro group compared to 16 in those receiving PC-EC (16% vs 11%, p > 0.05). Patients with febrile neutropenia treated with PC-EC/Pembro had more severe clinical presentations as assessed by the MASCC score (18 vs 24; p = 0.01), had worse physiological parameters (NEWS2 at presentation 3 vs 2; p = 0.023) and had a longer length of hospital stay (median 5 days vs 0 days; p = 0.044). There were no deaths at 30 or 90 days in either cohort. CONCLUSION Triple-negative breast cancer patients receiving neoadjuvant pembrolizumab in addition to PC-EC appear to have more severe presentations with febrile neutropenia. This may necessitate greater caution in pathways for ambulatory management for this cohort.
Collapse
Affiliation(s)
- Jamie M J Weaver
- Department of Acute Medicine, The Christie NHS Foundation Trust, Wilmslow Road, Manchester, UK
- University of Manchester, Manchester, UK
- Department of Medical Oncology, The Christie NHS Foundation Trust, Wilmslow Road, Manchester, UK
| | - Bence Nagy
- Department of Acute Medicine, The Christie NHS Foundation Trust, Wilmslow Road, Manchester, UK
- Department of Medical Oncology, The Christie NHS Foundation Trust, Wilmslow Road, Manchester, UK
| | - Caroline Wilson
- Department of Medical Oncology, The Christie NHS Foundation Trust, Wilmslow Road, Manchester, UK
| | - Alexandra Lewis
- Department of Medical Oncology, The Christie NHS Foundation Trust, Wilmslow Road, Manchester, UK
| | - Anne Armstrong
- University of Manchester, Manchester, UK
- Department of Medical Oncology, The Christie NHS Foundation Trust, Wilmslow Road, Manchester, UK
| | - Tim Cooksley
- Department of Acute Medicine, The Christie NHS Foundation Trust, Wilmslow Road, Manchester, UK.
- University of Manchester, Manchester, UK.
| |
Collapse
|
|
7
|
Sivayoham N, O'Mara H, Trenchard Turner N, Sysum K, Wicks G, Mason O. Validation of the REDS score in hospitalised patients who deteriorated and were admitted to the intensive care unit-a retrospective cohort study. BMJ Open Qual 2025; 14:e003054. [PMID: 39762056 PMCID: PMC11784166 DOI: 10.1136/bmjoq-2024-003054] [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: 08/06/2024] [Accepted: 12/20/2024] [Indexed: 02/02/2025] Open
Abstract
BACKGROUND Hospitalised patients are at risk of deterioration and death. Delayed identification and transfer to the intensive care unit (ICU) are known to be associated with increased mortality rates. The Risk-stratification of Emergency Department suspected Sepsis (REDS) score was derived and validated in emergency department patients with suspected sepsis. It is unknown if the REDS score would risk-stratify undifferentiated hospitalised patients who deteriorate. OBJECTIVES To validate the REDS score in hospitalised patients who deteriorate. METHODS This retrospective cohort single-centre study involved hospitalised adult patients who deteriorated and were transferred to the ICU between 1 April 2022 and 31 March 2023. The first admission to the ICU was studied. The National Early Warning Score2 (NEWS2), REDS, Sequential Organ Failure Assessment (SOFA) and change-in-SOFA (ΔSOFA) scores were calculated at the time of referral to the Critical Care Outreach Team (CCOT). The primary outcome measure was in-hospital all-cause mortality. The area under the receiver operator characteristic (AUROC) curves for the scores were compared. Test characteristics at the cut-off points individually and in combination were noted. RESULTS Of the 289 patients studied, 91 died. The REDS score had the largest AUROC curve at 0.70 (95% CI 0.65 to 0.75), greater than the NEWS2 score at 0.62 (95% CI 0.56 to 0.68), p=0.03, and similar to the SOFA score 0.67 (95% CI 0.61 to 0.72), p=0.3. The cut-off points for the NEWS2, REDS, SOFA and ΔSOFA scores were >9, >3, >6 and >4, respectively. The sensitivity and specificity for a ΔSOFA≥2 was 91.2% (95% CI 83.4 to 96.1) and 15.7% (95% CI 10.9 to 21.5), respectively. REDS≥4 or NEWS2≥7 had a sensitivity of 87.9% (95% CI 79.4 to 93.8) and specificity of 29.3% (95% CI 23.1 to 36.2). CONCLUSION The prognostic performance of the REDS score was similar to the SOFA score, but greater than the NEWS2 score. The REDS score could be used in addition to the established NEWS2 score to risk-stratify hospitalised patients for mortality.
Collapse
Affiliation(s)
- Narani Sivayoham
- Department of Emergency Medicine, St George's University Hospitals NHS Foundation Trust, London, UK
| | - Harriet O'Mara
- Department of Intensive Care Medicine, St George's University Hospitals NHS Foundation Trust, London, UK
| | - Natasha Trenchard Turner
- Department of Intensive Care Medicine, St George's University Hospitals NHS Foundation Trust, London, UK
| | - Katie Sysum
- Department of Intensive Care Medicine, St George's University Hospitals NHS Foundation Trust, London, UK
| | - Georgina Wicks
- Department of Intensive Care Medicine, St George's University Hospitals NHS Foundation Trust, London, UK
| | - Oliver Mason
- Department of Emergency Medicine, St George's University Hospitals NHS Foundation Trust, London, UK
| |
Collapse
|
|
8
|
Cylwik B, Gan K, Kazberuk M, Gruszewska E, Panasiuk A, Chrostek L. Diagnostic Usefulness of Serum Hyaluronic Acid in Patients with SARS-CoV-2 Infection. J Clin Med 2024; 13:7471. [PMID: 39685929 DOI: 10.3390/jcm13237471] [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: 10/21/2024] [Revised: 11/06/2024] [Accepted: 12/05/2024] [Indexed: 12/18/2024] Open
Abstract
Background/Objective: The aim of our study is to comprehensively assess the diagnostic usefulness of serum hyaluronic acid (HA) determination in COVID-19 patients. Methods: The study group included 87 patients with COVID-19 disease and 45 healthy subjects. The HA concentration was measured using the immunochemical method. Results: The serum HA concentration was significantly higher in the COVID-19 patients before admission to hospital than that in the controls (p < 0.001). Differences were found in HA levels between the groups categorized according to disease severity (p = 002), being significantly higher in patients with critical as compared to moderate disease severity (p < 0.001). The HA concentration varied depending on the type of oxygen therapy (p = 0.004). It was significantly higher in patients on a ventilator than in those without oxygen therapy (p = 0.002). In patients who qualified for the steroid treatment and immunotherapy, the HA levels were significantly higher compared to those who did not qualify for such therapies (p = 0.043, p = 0.049, respectively). The HA levels were significantly higher in patients with cytokine storm compared to those without it (p < 0.001) and were significantly more elevated in non-survivors than in survivors (p < 0.001). HA had an excellent diagnostic power (AUC = 0.994) with sensitivity (83.3%) and specificity (97.8%) in identifying patients with critical disease severity and an excellent diagnostic power (AUC = 0.932) with sensitivity (88.2%) and specificity (95.6%) in identifying non-surviving patients. Conclusions: In summary, the results of our study indicate that HA is closely associated with severe SARS-CoV-2 infection and could be used as a novel serum biomarker to predict the risk of disease progression and as a predictor of COVID-19 mortality.
Collapse
Affiliation(s)
- Bogdan Cylwik
- Department of Paediatric Laboratory Diagnostics, Medical University of Bialystok, 15-274 Bialystok, Poland
| | - Kacper Gan
- Department of Gastroenterology, Hepatology and Internal Diseases, Provincial Welded Hospital, 15-278 Bialystok, Poland
| | - Marcin Kazberuk
- Department of Gastroenterology, Hepatology and Internal Diseases, Provincial Welded Hospital, 15-278 Bialystok, Poland
| | - Ewa Gruszewska
- Department of Biochemical Diagnostics, Medical University of Bialystok, 15-269 Bialystok, Poland
| | - Anatol Panasiuk
- Department of Gastroenterology, Hepatology and Internal Diseases, Provincial Welded Hospital, 15-278 Bialystok, Poland
- Department of Clinical Medicine, Medical University of Bialystok, 15-269 Bialystok, Poland
| | - Lech Chrostek
- Department of Biochemical Diagnostics, Medical University of Bialystok, 15-269 Bialystok, Poland
| |
Collapse
|
|
9
|
Yousefi MR, Karajizadeh M, Ghasemian M, Paydar S. Comparing NEWS2, TRISS, RTS, SI, GAP, and MGAP in predicting early and total mortality rates in trauma patients based on emergency department data set: A diagnostic study. Curr Probl Surg 2024; 61:101636. [PMID: 39647965 DOI: 10.1016/j.cpsurg.2024.101636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2024] [Revised: 07/14/2024] [Accepted: 09/22/2024] [Indexed: 12/10/2024]
Affiliation(s)
- Mohammad Reza Yousefi
- Trauma Research Center, Shahid Rajaee (Emtiaz) Trauma Hospital, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Mehrdad Karajizadeh
- Trauma Research Center, Shahid Rajaee (Emtiaz) Trauma Hospital, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Mehdi Ghasemian
- Trauma Research Center, Shahid Rajaee (Emtiaz) Trauma Hospital, Shiraz University of Medical Sciences, Shiraz, Iran.
| | - Shahram Paydar
- Trauma Research Center, Shahid Rajaee (Emtiaz) Trauma Hospital, Shiraz University of Medical Sciences, Shiraz, Iran; Department of surgery, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
| |
Collapse
|
|
10
|
Yousefi MR, Karajizadeh M, Ghasemian M, Paydar S. Comparing NEWS2, TRISS, and RTS in predicting mortality rate in trauma patients based on prehospital data set: a diagnostic study. BMC Emerg Med 2024; 24:163. [PMID: 39251893 PMCID: PMC11382384 DOI: 10.1186/s12873-024-01084-w] [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: 06/25/2024] [Accepted: 09/02/2024] [Indexed: 09/11/2024] Open
Abstract
BACKGROUND In the recent years, National Early Warning Score2 (NEWS2) is utilized to predict early on, the worsening of clinical status in patients. To this date the predictive accuracy of National Early Warning Score (NEWS2), Revised Trauma Score (RTS), and Trauma and injury severity score (TRISS) regarding the trauma patients' mortality rate have not been compared. Therefore, the objective of this study is comparing NEWS2, TRISS, and RTS in predicting mortality rate in trauma patients based on prehospital data set. METHODS This cross-sectional retrospective diagnostic study performed on 6905 trauma patients, of which 4191 were found eligible, referred to the largest trauma center in southern Iran, Shiraz, during 2022-2023 based on their prehospital data set in order to compare the prognostic power of NEWS2, RTS, and TRISS in predicting in-hospital mortality rate. Patients are divided into deceased and survived groups. Demographic data, vital signs, and GCS were obtained from the patients and scoring systems were calculated and compared between the two groups. TRISS and ISS are calculated with in-hospital data set; others are based on prehospital data set. RESULTS A total of 129 patients have deceased. Age, cause of injury, length of hospital stay, SBP, RR, HR, temperature, SpO2, and GCS were associated with mortality (p-value < 0.001). TRISS and RTS had the highest sensitivity and specificity respectively (77.52, CI 95% [69.3-84.4] and 93.99, CI 95% [93.2-94.7]). TRISS had the highest area under the ROC curve (0.934) followed by NEWS2 (0.879), GCS (0.815), RTS (0.812), and ISS (0.774). TRISS and NEWS were superior to RTS, GCS, and ISS (p-value < 0.0001). CONCLUSION This novel study compares the accuracy of NEWS2, TRISS, and RTS scoring systems in predicting mortality rate based on prehospital data. The findings suggest that all the scoring systems can predict mortality, with TRISS being the most accurate of them, followed by NEWS2. Considering the time consumption and ease of use, NEWS2 seems to be accurate and quick in predicting mortality based on prehospital data set.
Collapse
Affiliation(s)
| | - Mehrdad Karajizadeh
- Trauma Research Center, Shiraz University of Medical Sciences, Shiraz, Iran.
| | - Mehdi Ghasemian
- Trauma Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Shahram Paydar
- Trauma Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| |
Collapse
|
|
11
|
Bassetti M, Giacobbe DR, Magnasco L, Fantin A, Vena A, Castaldo N. Antibiotic Strategies for Severe Community-Acquired Pneumonia. Semin Respir Crit Care Med 2024; 45:187-199. [PMID: 38301712 DOI: 10.1055/s-0043-1778641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2024]
Abstract
Despite advancements in health systems and intensive care unit (ICU) care, along with the introduction of novel antibiotics and microbiologic techniques, mortality rates in severe community-acquired pneumonia (sCAP) patients have not shown significant improvement. Delayed admission to the ICU is a major risk factor for higher mortality. Apart from choosing the appropriate site of care, prompt and appropriate antibiotic therapy significantly affects the prognosis of sCAP. Treatment regimens involving ceftaroline or ceftobiprole are currently considered the best options for managing patients with sCAP. Additionally, several other molecules, such as delafloxacin, lefamulin, and omadacycline, hold promise as therapeutic strategies for sCAP. This review aims to provide a comprehensive summary of the key challenges in managing adults with severe CAP, focusing on essential aspects related to antibiotic treatment and investigating potential strategies to enhance clinical outcomes in sCAP patients.
Collapse
Affiliation(s)
- Matteo Bassetti
- Infectious Diseases Unit, Policlinico San Martino Hospital, IRCCS, Genoa, Italy
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
| | - Daniele R Giacobbe
- Infectious Diseases Unit, Policlinico San Martino Hospital, IRCCS, Genoa, Italy
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
| | - Laura Magnasco
- Infectious Diseases Unit, Policlinico San Martino Hospital, IRCCS, Genoa, Italy
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
| | - Alberto Fantin
- Department of Pulmonology, Azienda Sanitaria Universitaria Integrata di Udine, Udine, Italy
| | - Antonio Vena
- Infectious Diseases Unit, Policlinico San Martino Hospital, IRCCS, Genoa, Italy
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
| | - Nadia Castaldo
- Department of Pulmonology, Azienda Sanitaria Universitaria Integrata di Udine, Udine, Italy
| |
Collapse
|
|
12
|
Feng T, Noren DP, Kulkarni C, Mariani S, Zhao C, Ghosh E, Swearingen D, Frassica J, McFarlane D, Conroy B. Machine learning-based clinical decision support for infection risk prediction. Front Med (Lausanne) 2023; 10:1213411. [PMID: 38179280 PMCID: PMC10765581 DOI: 10.3389/fmed.2023.1213411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 11/21/2023] [Indexed: 01/06/2024] Open
Abstract
Background Healthcare-associated infection (HAI) remains a significant risk for hospitalized patients and a challenging burden for the healthcare system. This study presents a clinical decision support tool that can be used in clinical workflows to proactively engage secondary assessments of pre-symptomatic and at-risk infection patients, thereby enabling earlier diagnosis and treatment. Methods This study applies machine learning, specifically ensemble-based boosted decision trees, on large retrospective hospital datasets to develop an infection risk score that predicts infection before obvious symptoms present. We extracted a stratified machine learning dataset of 36,782 healthcare-associated infection patients. The model leveraged vital signs, laboratory measurements and demographics to predict HAI before clinical suspicion, defined as the order of a microbiology test or administration of antibiotics. Results Our best performing infection risk model achieves a cross-validated AUC of 0.88 at 1 h before clinical suspicion and maintains an AUC >0.85 for 48 h before suspicion by aggregating information across demographics and a set of 163 vital signs and laboratory measurements. A second model trained on a reduced feature space comprising demographics and the 36 most frequently measured vital signs and laboratory measurements can still achieve an AUC of 0.86 at 1 h before clinical suspicion. These results compare favorably against using temperature alone and clinical rules such as the quick sequential organ failure assessment (qSOFA) score. Along with the performance results, we also provide an analysis of model interpretability via feature importance rankings. Conclusion The predictive model aggregates information from multiple physiological parameters such as vital signs and laboratory measurements to provide a continuous risk score of infection that can be deployed in hospitals to provide advance warning of patient deterioration.
Collapse
Affiliation(s)
- Ting Feng
- Philips Research North America, Cambridge, MA, United States
| | - David P. Noren
- Philips Research North America, Cambridge, MA, United States
| | | | - Sara Mariani
- Philips Research North America, Cambridge, MA, United States
| | - Claire Zhao
- Philips Research North America, Cambridge, MA, United States
| | - Erina Ghosh
- Philips Research North America, Cambridge, MA, United States
| | - Dennis Swearingen
- Department of Medical Informatics, Banner Health, Phoenix, AZ, United States
- Department of Biomedical Informatics, University of Arizona College of Medicine, Phoenix, AZ, United States
| | - Joseph Frassica
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, United States
| | | | - Bryan Conroy
- Philips Research North America, Cambridge, MA, United States
| |
Collapse
|
|
13
|
Chang PC, Liu ZY, Huang YC, Hsu YC, Chen JS, Lin CH, Tsai R, Chou CC, Wen MS, Wo HT, Lee WC, Liu HT, Wang CC, Kuo CF. Machine learning-based prediction of acute mortality in emergency department patients using twelve-lead electrocardiogram. Front Cardiovasc Med 2023; 10:1245614. [PMID: 37965090 PMCID: PMC10641780 DOI: 10.3389/fcvm.2023.1245614] [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: 06/23/2023] [Accepted: 10/13/2023] [Indexed: 11/16/2023] Open
Abstract
Background The risk of mortality is relatively high among patients who visit the emergency department (ED), and stratifying patients at high risk can help improve medical care. This study aimed to create a machine-learning model that utilizes the standard 12-lead ECG to forecast acute mortality risk in ED patients. Methods The database included patients who visited the EDs and underwent standard 12-lead ECG between October 2007 and December 2017. A convolutional neural network (CNN) ECG model was developed to classify survival and mortality using 12-lead ECG tracings acquired from 345,593 ED patients. For machine learning model development, the patients were randomly divided into training, validation and testing datasets. The performance of the mortality risk prediction in this model was evaluated for various causes of death. Results Patients who visited the ED and underwent one or more ECG examinations experienced a high incidence of 30-day mortality [18,734 (5.42%)]. The developed CNN model demonstrated high accuracy in predicting acute mortality (hazard ratio 8.50, 95% confidence interval 8.20-8.80) with areas under the receiver operating characteristic (ROC) curve of 0.84 for the 30-day mortality risk prediction models. This CNN model also demonstrated good performance in predicting one-year mortality (hazard ratio 3.34, 95% confidence interval 3.30-3.39). This model exhibited good predictive performance for 30-day mortality not only for cardiovascular diseases but also across various diseases. Conclusions The machine learning-based ECG model utilizing CNN screens the risks for 30-day mortality. This model can complement traditional early warning scoring indexes as a useful screening tool for mortality prediction.
Collapse
Affiliation(s)
- Po-Cheng Chang
- Division of Cardiology, Department of Internal Medicine, Chang Gung Memorial Hospital, Linkou and Chang Gung University Medical School, Taoyuan, Taiwan
| | - Zhi-Yong Liu
- Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Yu-Chang Huang
- Division of Cardiology, Department of Internal Medicine, Chang Gung Memorial Hospital, Linkou and Chang Gung University Medical School, Taoyuan, Taiwan
| | - Yu-Chun Hsu
- Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Jung-Sheng Chen
- Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Ching-Heng Lin
- Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Richard Tsai
- Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Chung-Chuan Chou
- Division of Cardiology, Department of Internal Medicine, Chang Gung Memorial Hospital, Linkou and Chang Gung University Medical School, Taoyuan, Taiwan
| | - Ming-Shien Wen
- Division of Cardiology, Department of Internal Medicine, Chang Gung Memorial Hospital, Linkou and Chang Gung University Medical School, Taoyuan, Taiwan
| | - Hung-Ta Wo
- Division of Cardiology, Department of Internal Medicine, Chang Gung Memorial Hospital, Linkou and Chang Gung University Medical School, Taoyuan, Taiwan
| | - Wen-Chen Lee
- Division of Cardiology, Department of Internal Medicine, Chang Gung Memorial Hospital, Linkou and Chang Gung University Medical School, Taoyuan, Taiwan
| | - Hao-Tien Liu
- Division of Cardiology, Department of Internal Medicine, Chang Gung Memorial Hospital, Linkou and Chang Gung University Medical School, Taoyuan, Taiwan
| | - Chun-Chieh Wang
- Division of Cardiology, Department of Internal Medicine, Chang Gung Memorial Hospital, Linkou and Chang Gung University Medical School, Taoyuan, Taiwan
| | - Chang-Fu Kuo
- Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan
- Division of Rheumatology, Allergy and Clinical Immunology, Chang Gung Memorial Hospital, Linkou and Chang Gung University Medical School, Taoyuan, Taiwan
| |
Collapse
|
|
14
|
Li S, Ning Y, Ong MEH, Chakraborty B, Hong C, Xie F, Yuan H, Liu M, Buckland DM, Chen Y, Liu N. FedScore: A privacy-preserving framework for federated scoring system development. J Biomed Inform 2023; 146:104485. [PMID: 37660960 DOI: 10.1016/j.jbi.2023.104485] [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: 03/29/2023] [Revised: 08/08/2023] [Accepted: 08/31/2023] [Indexed: 09/05/2023]
Abstract
OBJECTIVE We propose FedScore, a privacy-preserving federated learning framework for scoring system generation across multiple sites to facilitate cross-institutional collaborations. MATERIALS AND METHODS The FedScore framework includes five modules: federated variable ranking, federated variable transformation, federated score derivation, federated model selection and federated model evaluation. To illustrate usage and assess FedScore's performance, we built a hypothetical global scoring system for mortality prediction within 30 days after a visit to an emergency department using 10 simulated sites divided from a tertiary hospital in Singapore. We employed a pre-existing score generator to construct 10 local scoring systems independently at each site and we also developed a scoring system using centralized data for comparison. RESULTS We compared the acquired FedScore model's performance with that of other scoring models using the receiver operating characteristic (ROC) analysis. The FedScore model achieved an average area under the curve (AUC) value of 0.763 across all sites, with a standard deviation (SD) of 0.020. We also calculated the average AUC values and SDs for each local model, and the FedScore model showed promising accuracy and stability with a high average AUC value which was closest to the one of the pooled model and SD which was lower than that of most local models. CONCLUSION This study demonstrates that FedScore is a privacy-preserving scoring system generator with potentially good generalizability.
Collapse
Affiliation(s)
- Siqi Li
- Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore
| | - Yilin Ning
- 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
| | - 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, USA
| | - Chuan Hong
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA
| | - 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
| | - Han Yuan
- Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore
| | - Mingxuan Liu
- Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore
| | - Daniel M Buckland
- Department of Emergency Medicine, Duke University School of Medicine, Durham, NC, USA
| | - Yong Chen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - 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; Institute of Data Science, National University of Singapore, Singapore, Singapore.
| |
Collapse
|
|
15
|
Kuo YT, Hsiao CT, Wu PH, Wu KH, Chang CP. Comparison of National Early Warning Score with shock index in patients with necrotizing fasciitis. Medicine (Baltimore) 2023; 102:e34651. [PMID: 37682200 PMCID: PMC10489463 DOI: 10.1097/md.0000000000034651] [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: 03/29/2023] [Accepted: 07/18/2023] [Indexed: 09/09/2023] Open
Abstract
Shock index (SI) and national early warning score (NEWS) are more frequently used as assessment tools in acute illnesses, patient disposition and early identification of critical condition. Both they are consisted of common vital signs and parameters including heart rate, systolic blood pressure, respiratory rate, oxygen saturation and level of conscious, which made it easy to evaluate in medical facilities. Its ability to predict mortality in patients with necrotizing fasciitis (NF) in the emergency department remains unclear. This study was conducted to compare the predictive capability of the risk scores among NF patients. A retrospective cohort study of hospitalized patients with NF was conducted in 2 tertiary teaching hospitals in Taiwan between January 2013 and March 2015. We investigated the association of NEWS and SI with mortality in NF patients. Of the 395 NF patients, 32 (8.1%) died in the hospital. For mortality, the area under the receiver curve value of NEWS (0.81, 95% confidence interval 0.76-0.86) was significantly higher than SI (0.76, 95% confidence interval 0.73-0.79, P = .016). The sensitivities of NEWS of 3, 4, and 5 for mortality were 98.1%, 95.6%, and 92.3%. On the contrast, the sensitivities of SI of 0.5, 0.6, and 0.7 for mortality were 87.8%, 84.7%, and 81.5%. NEWS had advantage in better discriminative performance of mortality in NF patients. The NEWS may be used to identify relative low risk patients among NF patients.
Collapse
Affiliation(s)
- Yen-Ting Kuo
- Department of Emergency Medicine, Chang Gung Memorial Hospital, Chiayi, Taiwan
| | - Cheng-Ting Hsiao
- Department of Emergency Medicine, Chang Gung Memorial Hospital, Chiayi, Taiwan
- Department of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Po-Han Wu
- Department of Emergency Medicine, Chang Gung Memorial Hospital, Chiayi, Taiwan
| | - Kai-Hsiang Wu
- Department of Emergency Medicine, Chang Gung Memorial Hospital, Chiayi, Taiwan
| | - Chia-Peng Chang
- Department of Emergency Medicine, Chang Gung Memorial Hospital, Chiayi, Taiwan
| |
Collapse
|
|
16
|
Karabacak P, Bindal A, Turan İ, Erdemoglu E, Ceylan BG. The NEWS2 score predicts prolonged hospitalization in the intensive care unit in major surgery patients. Turk J Obstet Gynecol 2023; 20:179-183. [PMID: 37667477 PMCID: PMC10478721 DOI: 10.4274/tjod.galenos.2023.04987] [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: 04/20/2023] [Accepted: 06/01/2023] [Indexed: 09/06/2023] Open
Abstract
Objective Gynecological malignancies are significant causes of mortality and morbidity in women worldwide. Although surgery is an important treatment method, both the extent of the surgery and the factors related to the patient affect postoperative processes. The National Early Warning Score 2 (NEWS2) is a simple, inexpensive, and safe early warning score developed in 2012 and updated in 2017. Although it is not commonly used in surgical patients, its use in patients who will undergo major surgery may provide insights about the postoperative process. This study investigates the importance of NEWS2 and its relationship in patients with for major gynecologic oncology surgery. Materials and Methods Forty-four patients with gynecologic malignancies scheduled for major abdominal surgery were included in this study. Patients with a NEWS-2 score of <3 were included in group 1, and patients with a NEWS-2 score of more than 3 were included in groups 2. NEWS2 Score, Sequential Organ Failure Assessment (SOFA), and Acute Physiology and Chronic Health Evaluation 2 scores (APACHE 2) were calculated. In addition, postoperative routine clinical and laboratory parameters were evaluated. Operation time, duration of intubation in the intensive care unit (ICU), the length of the intensive care stay, and length of hospitalization were recorded. Results Duration of intubation in the ICU in group 1 with a NEWS2 <3 [8.2 (0-18) vs 16.2 (3-39), respectively; p<0.01], ICU length of stay [21.6 (4-27) vs 47.3 (4-113), respectively; p<0.01], length of hospitalization [11.6 (5-56) vs 18.6 (8-67), respectively; p<0.01]. NEWS2 >3 was significantly higher compared to group 2. The SOFA score was significantly higher in group 2 compared with group 1 [1.2±0.5 vs 4.1±1.9; respectively; p<0.01]. In the correlation analysis, the NEWS2 score level was positively correlated with the SOFA score (p<0.001, r=0.81) and hospitalization time (p<0.001, r=0.60) and neutrophil lymphocyte ratio (NLR) (p<0.001, r=0.47). Conclusion These findings suggest that the NEWS2 score may be correlated with the length of intensive care intubation, length of intensive care stay, and length of hospitalization. NEWS2 is an effective and simple scoring system that provides information about postoperative outcomes in gynecologic oncology patients scheduled for major surgery.
Collapse
Affiliation(s)
- Pınar Karabacak
- Süleyman Demirel University Faculty of Medicine, Department of Anesthesiology and Reanimation, Division of Critical Care Medicine, Isparta, Turkey
| | - Ahmet Bindal
- Süleyman Demirel University Faculty of Medicine, Department of Anesthesiology and Reanimation, Division of Critical Care Medicine, Isparta, Turkey
| | - İlyas Turan
- Süleyman Demirel University Faculty of Medicine, Department of Gyneacologic Oncology, Isparta, Turkey
| | - Evrim Erdemoglu
- Süleyman Demirel University Faculty of Medicine, Department of Gyneacologic Oncology, Isparta, Turkey
| | - Berit Gökçe Ceylan
- Süleyman Demirel University Faculty of Medicine, Department of Anesthesiology and Reanimation, Isparta, Turkey
| |
Collapse
|
|
17
|
Milivojević V, Bogdanović J, Babić I, Todorović N, Ranković I. Metabolic Associated Fatty Liver Disease (MAFLD) and COVID-19 Infection: An Independent Predictor of Poor Disease Outcome? MEDICINA (KAUNAS, LITHUANIA) 2023; 59:1438. [PMID: 37629728 PMCID: PMC10456234 DOI: 10.3390/medicina59081438] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Revised: 08/03/2023] [Accepted: 08/04/2023] [Indexed: 08/27/2023]
Abstract
Background and Objectives: Early reports on COVID-19 infection suggested that the SARS-CoV-2 virus solely attacks respiratory tract cells. As the pandemic spread, it became clear that the infection is multiorganic. Metabolic associated fatty liver disease (MAFLD) is a chronic liver disease strongly associated with insulin resistance and diabetes. The aim of this study was to assess a possible interplay between MAFLD and COVID-19 infection and its implication in COVID-19 outcome. Materials and Methods: A retrospective observational study, including 130 COVID-19 positive patients was conducted. MAFLD diagnosis was made based on the International Consensus criteria. Patients were divided into two groups, group A (MAFLD) and group B (nonMAFLD). Anthropometric and laboratory analysis were obtained. COVID-19 severity was assessed using the NEWS2 score. Disease outcome was threefold and regarded as discharged, patients who required mechanical ventilation (MV), and deceased patients. Results: MAFLD prevalence was 42%, 67% of patients were discharged, and 19% needed MV. Mortality rate was 14%. MAFLD patients were significantly younger (p < 0.001), and had higher body mass index (p < 0.05), respiratory rate (p < 0.05) and systolic blood pressure (p < 0.05) than nonMAFLD patients. Regarding metabolic syndrome and inflammatory markers: group A had significantly higher glycemia at admission (p = 0.008), lower HDL-c (p < 0.01), higher triglycerides (p < 0.01), CRP (p < 0.001), IL-6 (p < 0.05) and ferritin (p < 0.05) than group B. MAFLD was associated with more prevalent type 2 diabetes (p = 0.035) and hypertension (p < 0.05). MAFLD patients had a more severe disease course (NEWS2 score, 6.5 ± 0.5 vs. 3 ± 1.0, p < 0.05). MAFLD presence was associated with lower patient discharge (p < 0.01) and increased need for MV (p = 0.024). Multiple regression analysis showed that BMI (p = 0.045), IL-6 (p = 0.03), and MAFLD (p < 0.05) are significant independent risk factors for a poor COVID-19 outcome. Conclusions: The prevalence of MAFLD is relatively high. MAFLD patients had a more severe COVID-19 clinical course and worse disease outcome. Our results imply that early patient stratification and risk assessment are mandatory in order to avoid poor outcomes.
Collapse
Affiliation(s)
- Vladimir Milivojević
- Clinic for Gastroenterology and Hepatology University Clinical Centre of Serbia, Dr Koste Todorovica 2, 11000 Belgrade, Serbia
- Faculty of Medicine, University of Belgrade, Dr Subotica 8, 11000 Belgrade, Serbia
| | - Jelena Bogdanović
- Faculty of Medicine, University of Belgrade, Dr Subotica 8, 11000 Belgrade, Serbia
- Clinic for Endocrinology, Diabetes and Metabolic Diseases University Clinical Centre of Serbia, Dr Subotica 13, 11000 Belgrade, Serbia
| | - Ivana Babić
- Clinic for Endocrinology, Diabetes and Metabolic Diseases University Clinical Centre of Serbia, Dr Subotica 13, 11000 Belgrade, Serbia
| | - Nevena Todorović
- Clinic for Infectious and Tropical Diseases University Clinical Centre of Serbia, Bulevar Oslobođenja 16, 11000 Belgrade, Serbia
| | - Ivan Ranković
- Department of Gastroenterology, Royal Cornwall Hospitals NHS Trust, Truro TR1 3LJ, UK;
| |
Collapse
|
|
18
|
Moor M, Bennett N, Plečko D, Horn M, Rieck B, Meinshausen N, Bühlmann P, Borgwardt K. Predicting sepsis using deep learning across international sites: a retrospective development and validation study. EClinicalMedicine 2023; 62:102124. [PMID: 37588623 PMCID: PMC10425671 DOI: 10.1016/j.eclinm.2023.102124] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 06/29/2023] [Accepted: 07/17/2023] [Indexed: 08/18/2023] Open
Abstract
Background When sepsis is detected, organ damage may have progressed to irreversible stages, leading to poor prognosis. The use of machine learning for predicting sepsis early has shown promise, however international validations are missing. Methods This was a retrospective, observational, multi-centre cohort study. We developed and externally validated a deep learning system for the prediction of sepsis in the intensive care unit (ICU). Our analysis represents the first international, multi-centre in-ICU cohort study for sepsis prediction using deep learning to our knowledge. Our dataset contains 136,478 unique ICU admissions, representing a refined and harmonised subset of four large ICU databases comprising data collected from ICUs in the US, the Netherlands, and Switzerland between 2001 and 2016. Using the international consensus definition Sepsis-3, we derived hourly-resolved sepsis annotations, amounting to 25,694 (18.8%) patient stays with sepsis. We compared our approach to clinical baselines as well as machine learning baselines and performed an extensive internal and external statistical validation within and across databases, reporting area under the receiver-operating-characteristic curve (AUC). Findings Averaged over sites, our model was able to predict sepsis with an AUC of 0.846 (95% confidence interval [CI], 0.841-0.852) on a held-out validation cohort internal to each site, and an AUC of 0.761 (95% CI, 0.746-0.770) when validating externally across sites. Given access to a small fine-tuning set (10% per site), the transfer to target sites was improved to an AUC of 0.807 (95% CI, 0.801-0.813). Our model raised 1.4 false alerts per true alert and detected 80% of the septic patients 3.7 h (95% CI, 3.0-4.3) prior to the onset of sepsis, opening a vital window for intervention. Interpretation By monitoring clinical and laboratory measurements in a retrospective simulation of a real-time prediction scenario, a deep learning system for the detection of sepsis generalised to previously unseen ICU cohorts, internationally. Funding This study was funded by the Personalized Health and Related Technologies (PHRT) strategic focus area of the ETH domain.
Collapse
Affiliation(s)
- Michael Moor
- Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland
- SIB Swiss Institute of Bioinformatics, Switzerland
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Nicolas Bennett
- Seminar for Statistics, Department of Mathematics, ETH Zurich, Switzerland
| | - Drago Plečko
- Seminar for Statistics, Department of Mathematics, ETH Zurich, Switzerland
| | - Max Horn
- Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland
- SIB Swiss Institute of Bioinformatics, Switzerland
| | - Bastian Rieck
- Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland
- SIB Swiss Institute of Bioinformatics, Switzerland
| | | | - Peter Bühlmann
- Seminar for Statistics, Department of Mathematics, ETH Zurich, Switzerland
| | - Karsten Borgwardt
- Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland
- SIB Swiss Institute of Bioinformatics, Switzerland
| |
Collapse
|
|
19
|
Kakehi E, Uehira R, Ohara N, Akamatsu Y, Osaka T, Sakurai S, Hirotani A, Nozaki T, Shoji K, Adachi S, Kotani K. Utility of the New Early Warning Score (NEWS) in combination with the neutrophil-lymphocyte ratio for the prediction of prognosis in older patients with pneumonia. Fam Med Community Health 2023; 11:e002239. [PMID: 37344123 DOI: 10.1136/fmch-2023-002239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/23/2023] Open
Abstract
OBJECTIVE Predictors of prognosis are necessary for use in routine clinical practice for older patients with pneumonia, given the ageing of the population. Recently, the National Early Warning Score (NEWS), a comprehensive predictor of severity that consists solely of physiological indicators, has been proposed to predict the prognosis of pneumonia. The neutrophil/lymphocyte ratio (NLR) is a simple index of inflammation that may also be predictive of pneumonia. In the present study, we aimed to determine whether NEWS or a combination of NEWS and NLR predicts mortality in older patients with pneumonia. DESIGN A retrospective cohort study. SETTING A general hospital in Japan. PARTICIPANTS We collected data from patients aged ≥65 years with pneumonia who were admitted between 2018 and 2020 (n=282; age=85.3 (7.9)). Data regarding vital signs, demographics and the length of hospital stay, in addition to the NEWS and NLR, were extracted from the participants' electronic medical records. INTERVENTION The utility of the combination of NEWS and NLR was assessed using NEWS×NLR and NEWS+NLR. MAIN OUTCOME MEASURES Their predictive ability for 30-day mortality as the primary outcome was assessed using receiver operating characteristic (ROC) curve analysis. RESULTS According to the NEWS classification, 80 (28.3%), 64 (22.7%) and 138 (48.9%) of the participants were at low, medium and high risk of mortality, respectively. The 30-day mortality for the entire cohort was 9.2% (n=26), and the mortality rate increased with the NEWS classification: low, 1.3%; medium, 7.8%; and high, 14.5%. The NLRs were 6.0 (4.2-9.8), 6.8 (4.8-10.4) and 14.6 (9.4-22.2), respectively (p<0.001). The areas under the ROC curves for 30-day mortality were 0.73 for the NEWS score, 0.84 for NEWS×NLR and 0.83 for NEWS+NLR, indicating that the combinations represent superior predictors of mortality to the NEWS alone. NEWS×NLR and NEWS+NLR tended to have better sensitivity, accuracy, positive predictive value and negative predictive value than NEWS alone (p=0.06). CONCLUSIONS A combination of the NEWS and NLR (NEWS×NLR or NEWS+NLR) may be superior to the NEWS alone for the prediction of 30-day mortality in older patients with pneumonia. However, further validation of these combinations for use in the prediction of prognosis is required.
Collapse
Affiliation(s)
- Eiichi Kakehi
- Department of General Medicine, Tottori Municipal Hospital, Tottori, Japan
| | - Ryo Uehira
- Department of General Medicine, Tottori Municipal Hospital, Tottori, Japan
| | - Nobuaki Ohara
- Department of General Medicine, Tottori Municipal Hospital, Tottori, Japan
| | - Yukinobu Akamatsu
- Department of General Medicine, Tottori Municipal Hospital, Tottori, Japan
| | - Taeko Osaka
- Department of General Medicine, Tottori Municipal Hospital, Tottori, Japan
| | - Shigehisa Sakurai
- Department of General Medicine, Tottori Municipal Hospital, Tottori, Japan
| | - Akane Hirotani
- Department of General Medicine, Tottori Municipal Hospital, Tottori, Japan
| | - Takafumi Nozaki
- Department of General Medicine, Tottori Municipal Hospital, Tottori, Japan
| | - Keisuke Shoji
- Department of General Medicine, Tottori Municipal Hospital, Tottori, Japan
| | - Seiji Adachi
- Department of General Medicine, Tottori Municipal Hospital, Tottori, Japan
| | - Kazuhiko Kotani
- Division of Community and Family Medicine, Jichi Medical University, Shimotsuke, Japan
| |
Collapse
|
|
20
|
Placido D, Thorsen-Meyer HC, Kaas-Hansen BS, Reguant R, Brunak S. Development of a dynamic prediction model for unplanned ICU admission and mortality in hospitalized patients. PLOS DIGITAL HEALTH 2023; 2:e0000116. [PMID: 37294826 PMCID: PMC10256150 DOI: 10.1371/journal.pdig.0000116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 04/24/2023] [Indexed: 06/11/2023]
Abstract
Frequent assessment of the severity of illness for hospitalized patients is essential in clinical settings to prevent outcomes such as in-hospital mortality and unplanned admission to the intensive care unit (ICU). Classical severity scores have been developed typically using relatively few patient features. Recently, deep learning-based models demonstrated better individualized risk assessments compared to classic risk scores, thanks to the use of aggregated and more heterogeneous data sources for dynamic risk prediction. We investigated to what extent deep learning methods can capture patterns of longitudinal change in health status using time-stamped data from electronic health records. We developed a deep learning model based on embedded text from multiple data sources and recurrent neural networks to predict the risk of the composite outcome of unplanned ICU transfer and in-hospital death. The risk was assessed at regular intervals during the admission for different prediction windows. Input data included medical history, biochemical measurements, and clinical notes from a total of 852,620 patients admitted to non-intensive care units in 12 hospitals in Denmark's Capital Region and Region Zealand during 2011-2016 (with a total of 2,241,849 admissions). We subsequently explained the model using the Shapley algorithm, which provides the contribution of each feature to the model outcome. The best model used all data modalities with an assessment rate of 6 hours, a prediction window of 14 days and an area under the receiver operating characteristic curve of 0.898. The discrimination and calibration obtained with this model make it a viable clinical support tool to detect patients at higher risk of clinical deterioration, providing clinicians insights into both actionable and non-actionable patient features.
Collapse
Affiliation(s)
- Davide Placido
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Denmark
| | - Hans-Christian Thorsen-Meyer
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Denmark
- Department of Intensive Care Medicine, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Benjamin Skov Kaas-Hansen
- Department of Intensive Care Medicine, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
- Section for Biostatistics, Department of Public Health, University of Copenhagen, Denmark
| | - Roc Reguant
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Denmark
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, New South Wales, Sydney, Australia
| | - Søren Brunak
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Denmark
- Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| |
Collapse
|
|
21
|
Prediction of Acid-Base and Potassium Imbalances in Intensive Care Patients Using Machine Learning Techniques. Diagnostics (Basel) 2023; 13:diagnostics13061171. [PMID: 36980479 PMCID: PMC10047445 DOI: 10.3390/diagnostics13061171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 03/16/2023] [Accepted: 03/17/2023] [Indexed: 03/22/2023] Open
Abstract
Acid–base disorders occur when the body’s normal pH is out of balance. They can be caused by problems with kidney or respiratory function or by an excess of acids or bases that the body cannot properly eliminate. Acid–base and potassium imbalances are mechanistically linked because acid–base imbalances can alter the transport of potassium. Both acid–base and potassium imbalances are common in critically ill patients. This study investigated machine learning models for predicting the occurrence of acid–base and potassium imbalances in intensive care patients. We used an institutional dataset of 1089 patients with 87 variables, including vital signs, general appearance, and laboratory results. Gradient boosting (GB) was able to predict nine clinical conditions related to acid–base and potassium imbalances: mortality (AUROC = 0.9822), hypocapnia (AUROC = 0.7524), hypercapnia (AUROC = 0.8228), hypokalemia (AUROC = 0.9191), hyperkalemia (AUROC = 0.9565), respiratory acidosis (AUROC = 0.8125), respiratory alkalosis (AUROC = 0.7685), metabolic acidosis (AUROC = 0.8682), and metabolic alkalosis (AUROC = 0.8284). Some predictions remained relatively robust even when the prediction window was increased. Additionally, the decision-making process was made more interpretable and transparent through the use of SHAP analysis. Overall, the results suggest that machine learning could be a useful tool to gain insight into the condition of intensive care patients and assist in the management of acid–base and potassium imbalances.
Collapse
|
|
22
|
Predictive Ability of the National Early Warning Score in Mortality Prediction of Acute Pulmonary Embolism in the Southeast Asian Population. J Cardiovasc Dev Dis 2023; 10:jcdd10020060. [PMID: 36826556 PMCID: PMC9960332 DOI: 10.3390/jcdd10020060] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 01/26/2023] [Accepted: 01/31/2023] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND The National Early Warning Scores (NEWS) easily and objectively measures acute clinical deterioration. However, the performance of NEWS to predict mortality in patients with acute pulmonary embolism (APE) is still required. Therefore, the objective of this study was to evaluate the performance of the NEWS in predicting the mortality of patients with APE. METHODS NEWS and Pulmonary Embolism Severity Index (PESI) at diagnosis time were calculated. Risk regression analysis was performed to identify the NEWS and PESI risk classification as a predictor for 30 days all-cause mortality and PE-related mortality. RESULTS NEWS was significantly higher in non-survivors compared to survivors (median (IQR) was 10 (7, 11) vs. 7 (2, 9), respectively, p < 0.001). The best cut-off point of NEWS in discriminating APE patients who non-survived from those who survived at 30 days was ≥9, with a sensitivity and specificity of 66.9% and 66.3%, respectively. The adjusted risk ratio of 30-day all-cause mortality in patients with initial NEWS ≥ 9 was 2.96 (95% CI; 2.13, 4.12, p < 0.001). CONCLUSIONS The NEWS can be used for mortality prediction in patients with APE. APE patients with NEWS ≥ 9 are associated with a high risk of mortality and should be closely monitored.
Collapse
|
|
23
|
Lin SF, Lin HA, Pan YH, Hou SK. A novel scoring system combining Modified Early Warning Score with biomarkers of monocyte distribution width, white blood cell counts, and neutrophil-to-lymphocyte ratio to improve early sepsis prediction in older adults. Clin Chem Lab Med 2023; 61:162-172. [PMID: 36103663 DOI: 10.1515/cclm-2022-0656] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 09/08/2022] [Indexed: 01/08/2023]
Abstract
OBJECTIVES This study aims to investigate whether combining scoring systems with monocyte distribution width (MDW) improves early sepsis detection in older adults in the emergency department (ED). METHODS In this prospective observational study, we enrolled older adults aged ≥60 years who presented with confirmed infectious diseases to the ED. Three scoring systems-namely quick sepsis-related organ failure assessment (qSOFA), Modified Early Warning Score (MEWS), and National Early Warning Score (NEWS), and biomarkers including MDW, neutrophil-to-lymphocyte ratio (NLR), and C-reactive protein (CRP), were assessed in the ED. Logistic regression models were used to construct sepsis prediction models. RESULTS After propensity score matching, we included 522 and 2088 patients with and without sepsis in our analysis from January 1, 2020, to September 30, 2021. NEWS ≥5 and MEWS ≥3 exhibited a moderate-to-high sensitivity and a low specificity for sepsis, whereas qSOFA score ≥2 demonstrated a low sensitivity and a high specificity. When combined with biomarkers, the NEWS-based, the MEWS-based, and the qSOFA-based models exhibited improved diagnostic accuracy for sepsis detection without CRP inclusion (c-statistics=0.842, 0.842, and 0.826, respectively). Of the three models, MEWS ≥3 with white blood cell (WBC) count ≥11 × 109/L, NLR ≥8, and MDW ≥20 demonstrated the highest diagnostic accuracy in all age subgroups (c-statistics=0.886, 0.825, and 0.822 in patients aged 60-74, 75-89, and 90-109 years, respectively). CONCLUSIONS Our novel scoring system combining MEWS with WBC, NLR, and MDW effectively detected sepsis in older adults.
Collapse
Affiliation(s)
- Sheng-Feng Lin
- Department of Public Health, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.,School of Public Health, College of Public Health, Taipei Medical University, Taipei, Taiwan.,Department of Emergency Medicine, Taipei Medical University Hospital, Taipei, Taiwan
| | - Hui-An Lin
- Department of Emergency Medicine, Taipei Medical University Hospital, Taipei, Taiwan.,Graduate Institute of Injury Prevention and Control, College of Public Health, Taipei Medical University, Taipei, Taiwan
| | - Yi-Hsiang Pan
- Department of Emergency Medicine, Taipei Medical University Hospital, Taipei, Taiwan
| | - Sen-Kuang Hou
- Department of Emergency Medicine, Taipei Medical University Hospital, Taipei, Taiwan.,Department of Emergency Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| |
Collapse
|
|
24
|
The National Early Warning Score 2(NEWS2) to Predict Early Progression to Severe Community-Acquired Pneumonia. Trop Med Infect Dis 2023; 8:tropicalmed8020068. [PMID: 36828485 PMCID: PMC9962139 DOI: 10.3390/tropicalmed8020068] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 01/13/2023] [Accepted: 01/16/2023] [Indexed: 01/20/2023] Open
Abstract
This study aimed to assess the predictive performance of the National Early Warning Score 2 (NEWS2) to identify the early progression to severe disease in patients with community-acquired pneumonia (CAP). A prospective-cohort study was conducted among patients with CAP admitted to a university hospital between October 2020 and December 2021. The endpoint of interest was the progression to severe CAP, defined as the requirement for a mechanical ventilator, a vasopressor, or death within 72 h after hospital admission. Among 260 patients, 53 (25.6%) had early progression to severe CAP. The median NEWS2 of the early progression group was higher than that of the non-progression group [8 (6-9) vs. 7 (5-8), p = 0.015, respectively]. The AUROC of NEWS2 to predict early progression to severe CAP was 0.61 (95% CI: 0.52-0.70), while IDSA/ATS minor criteria ≥ 3 had AUROC 0.56 (95% CI 0.48-0.65). The combination of NEWS2 ≥ 8, albumin level < 3 g/dL and BUN ≥ 30 mg/dL improved AUROC from 0.61 to 0.71 (p = 0.015). NEWS2 and IDSA/ATS minor criteria showed fair predictive-accuracy in predicting progression to severe CAP. The NEWS2 cut-off ≥ 8 in combination with low albumin and uremia improved predictive-accuracy, and could be easily used in general practice.
Collapse
|
|
25
|
Rostam Niakan Kalhori S, Deserno TM, Haghi M, Ganapathy N. A protocol for a systematic review of electronic early warning/track-and-trigger systems (EW/TTS) to predict clinical deterioration: Focus on automated features, technologies, and algorithms. PLoS One 2023; 18:e0283010. [PMID: 36920960 PMCID: PMC10016632 DOI: 10.1371/journal.pone.0283010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 02/28/2023] [Indexed: 03/16/2023] Open
Abstract
BACKGROUND This is a systematic review protocol to identify automated features, applied technologies, and algorithms in the electronic early warning/track and triage system (EW/TTS) developed to predict clinical deterioration (CD). METHODOLOGY This study will be conducted using PubMed, Scopus, and Web of Science databases to evaluate the features of EW/TTS in terms of their automated features, technologies, and algorithms. To this end, we will include any English articles reporting an EW/TTS without time limitation. Retrieved records will be independently screened by two authors and relevant data will be extracted from studies and abstracted for further analysis. The included articles will be evaluated independently using the JBI critical appraisal checklist by two researchers. DISCUSSION This study is an effort to address the available automated features in the electronic version of the EW/TTS to shed light on the applied technologies, automated level of systems, and utilized algorithms in order to smooth the road toward the fully automated EW/TTS as one of the potential solutions of prevention CD and its adverse consequences. TRIAL REGISTRATION Systematic review registration: PROSPERO CRD42022334988.
Collapse
Affiliation(s)
- Sharareh Rostam Niakan Kalhori
- Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Braunschweig, Germany
- Health Information Management and Medical Informatics Department, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
- * E-mail:
| | - Thomas M. Deserno
- Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Braunschweig, Germany
| | - Mostafa Haghi
- Ubiquitous Computing Laboratory, Department of Computer Science, Konstanz University of Applied Sciences, Konstanz, Germany
| | - Nagarajan Ganapathy
- Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Braunschweig, Germany
- Biomedical Informatics Laboratory, Department of Biomedical Engineering, Indian Institute of Technology, Hyderabad, India
| |
Collapse
|
|
26
|
Panahi Y, Ghanei M, Rahimi M, Samim A, Vahedian‐Azimi A, Atkin SL, Sahebkar A. Evaluation the efficacy and safety of N-acetylcysteine inhalation spray in controlling the symptoms of patients with COVID-19: An open-label randomized controlled clinical trial. J Med Virol 2023; 95:e28393. [PMID: 36495185 PMCID: PMC9878233 DOI: 10.1002/jmv.28393] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Revised: 10/07/2022] [Accepted: 12/06/2022] [Indexed: 12/14/2022]
Abstract
The aim of this study was to evaluate the effect and safety of N-acetylcysteine (NAC) inhalation spray in the treatment of patients with coronavirus disease 2019 (COVID-19). This randomized controlled clinical trial study was conducted on patients with COVID-19. Eligible patients (n = 250) were randomly allocated into the intervention group (routine treatment + NAC inhaler spray one puff per 12 h, for 7 days) or the control group who received routine treatment alone. Clinical features, hemodynamic, hematological, biochemical parameters and patient outcomes were assessed and compared before and after treatment. The mortality rate was significantly higher in the control group than in the intervention group (39.2% vs. 3.2%, p < 0.001). Significant differences were found between the two groups (intervention and control, respectively) for white blood cell count (6.2 vs. 7.8, p < 0.001), hemoglobin (12.3 vs. 13.3, p = 0.002), C-reactive protein (CRP: 6 vs. 11.5, p < 0.0001) and aspartate aminotransferase (AST: 32 vs. 25.5, p < 0.0001). No differences were seen for hospital length of stay (11.98 ± 3.61 vs. 11.81 ± 3.52, p = 0.814) or the requirement for intensive care unit (ICU) admission (7.2% vs. 11.2%, p = 0.274). NAC was beneficial in reducing the mortality rate in patients with COVID-19 and inflammatory parameters, and a reduction in the development of severe respiratory failure; however, it did not affect the length of hospital stay or the need for ICU admission. Data on the effectiveness of NAC for Severe Acute Respiratory Syndrome Coronavirus-2 is limited and further research is required.
Collapse
Affiliation(s)
- Yunes Panahi
- Pharmacotherapy Department, School of PharmacyBaqiyatallah University of Medical SciencesTehranIran
| | - Mostafa Ghanei
- Chemical Injuries Center, Systems Biology and Poisoning InstituteBaqiyatallah University of Medical SciencesTehranIran
| | - Morteza Rahimi
- Chemical Injuries Center, Systems Biology and Poisoning InstituteBaqiyatallah University of Medical SciencesTehranIran
| | - Abbas Samim
- Chemical Injuries Center, Systems Biology and Poisoning InstituteBaqiyatallah University of Medical SciencesTehranIran
| | - Amir Vahedian‐Azimi
- Trauma Research Center, Nursing FacultyBaqiyatallah University of Medical SciencesTehranIran
| | - Stephen L. Atkin
- School of Postgraduate Studies and ResearchRCSI Medical University of BahrainBusaiteenKingdom of Bahrain
| | - Amirhossein Sahebkar
- Applied Biomedical Research CenterMashhad University of Medical SciencesMashhadIran,Biotechnology Research Center, Pharmaceutical Technology InstituteMashhad University of Medical SciencesMashhadIran,Department of Biotechnology, School of PharmacyMashhad University of Medical SciencesMashhadIran
| |
Collapse
|
|
27
|
A Propensity Score-Matched Comparison of In-Hospital Mortality between Dedicated Regional Trauma Centers and Emergency Medical Centers in the Republic of Korea. Emerg Med Int 2022; 2022:5749993. [PMID: 36438862 PMCID: PMC9683976 DOI: 10.1155/2022/5749993] [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: 10/10/2022] [Revised: 11/09/2022] [Accepted: 11/10/2022] [Indexed: 11/18/2022] Open
Abstract
Background In the Republic of Korea, a trauma care system was not created until 2012, at which point regional trauma centers (RTCs) were established nationwide. In accordance with the national emergency care system and legislation, regional and local emergency medical centers (EMCs) also treat patients presenting with trauma. The aim of the present study was to assess whether treatment in RTCs is truly associated with better patient outcomes than that in EMCs by means of propensity score-matched comparisons and to identify populations that would benefit from treatment in RTCs. Methods This study analyzed the data of patients with consecutive emergency visits between January 1, 2018, and December 31, 2018, collected in the National Emergency Department Information System registry. Data from RTCs, designated regional EMCs, or local EMCs were included; data from smaller emergency departments were excluded because, in Korea, dedicated RTCs are established only in hospitals with regional or local EMCs. Propensity scores for treatment in RTCs or EMCs were estimated by logistic regression using linear terms. Mortality rates in RTCs and EMCs were compared between the matched samples. Results The in-hospital mortality rates in the matched cases treated in RTCs and EMCs were 1.4% and 1.6%, respectively. The odds ratio for in-hospital mortality in RTCs over EMCs was 0.984 (95% confidence interval: 0.813–1.191). Among the subgroups evaluated, the subgroup of patients with injuries involving the chest or lower limbs showed a significant difference in the in-hospital mortality rate. Conclusion There was no significant difference in the overall severity-adjusted mortality rate between patients treated in RTCs and EMCs. Treatment in an RTC might benefit those with injuries involving the chest or lower limbs.
Collapse
|
|
28
|
Vital signs and early warning score monitoring: perceptions of clinical staff about current practices and introducing an electronic rapid response system. Heliyon 2022; 8:e11182. [PMID: 36325132 PMCID: PMC9618998 DOI: 10.1016/j.heliyon.2022.e11182] [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: 04/10/2022] [Revised: 06/08/2022] [Accepted: 10/17/2022] [Indexed: 11/06/2022] Open
Abstract
Aims and objectives This study investigated clinical staff perceptions of learning about current monitoring practices and the planned introduction of an electronic system for patient monitoring. The aim of this research was to evaluate the perceptions of clinical staff (nurses and doctors) about the perceived strengths and weaknesses of the current state of the rapid response system (RRS) and how those strengths and weakness would be affected by introducing an electronic RRS. Methods This research applied a descriptive study methodology. Two detailed sessions on demonstration on the electronic RRS for measuring and recording vital signs and the electronic Early Warning System (EWS) were followed by two structured surveys administered through an online portal (SurveyMonkey) for nurses and doctors working at Taranaki District Health Board. The study was planned and conducted between October 2020 and May 2021 at Taranaki Base Hospital, New Plymouth, New Zealand. Results We found that the perceptions of clinical staff were a combination of key practice issues with current manual monitoring, expectations of improved visibility of vital sign charts, better communication between staff and thus improved patient care with the introduction of an electronic system. A majority (24, 60%) of nurses reported that, when called to assess deteriorating patients, the responders arrive at bedside within 5–30 min and an additional 11 (27%) said the responders arrive within 5 min. That is a collective 87% responder arrival within 30 min Conclusion Staff believe that an electronic RRS could improve communication, speed up decision making and have a positive impact on patient outcomes.
Collapse
|
|
29
|
Jamil Z, Samreen S, Mukhtar B, Khaliq M, Abbasi SM, Ahmed J, Hussain T. The Clinical Implementation of NEWS, SOFA, and CALL Scores in Predicting the In-Hospital Outcome of Severe or Critical COVID-19 Patients. Eurasian J Med 2022; 54:213-218. [PMID: 35950820 PMCID: PMC9797769 DOI: 10.5152/eurasianjmed.2021.21149] [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] [Indexed: 01/05/2023] Open
Abstract
OBJECTIVE To date, there is no specific validated coronavirus disease 2019 score to assess the disease severity. This study aimed to evaluate the performance of the National Early Warning Score, Sequential Organ Failure Assessment, and Comorbidity-Age-Lymphocyte count-Lactate dehydrogenase scores in predicting the in-hospital outcome of critical or severe coronavirus disease 2019 patients. MATERIALS AND METHODS Single-centered analytical study was carried out in the coronavirus disease 2019 high dependency unit from April to August 2020. National Early Warning Score, Sequential Organ Failure Assessment, and Comorbidity-Age-Lymphocyte count-Lactate dehydrogenase scores were calculated for each critical to severely ill coronavirus disease 2019 patient. The diagnostic accuracy of these 3 scores in determining the in-hospital outcome of coronavirus disease 2019 patients was assessed by area under the receiver operating characteristic curve. The cut-off value of each score along with sensitivity, specificity, and positive and negative likelihood ratio were calculated by Youden index. Predictors of outcome in coronavirus disease 2019 patients were analyzed by Cox-regression analysis. RESULTS The area under the curve was highest for the Comorbidity-Age-Lymphocyte count-Lactate dehydrogenase score (area under the curve=0.85) while the Sequential Organ Failure Assessment score had an area under the curve of 0.72. The cut-off values for National Early Warning Score score was 8 (sensitivity=72.34%, specificity=76.10%), Sequential Organ Failure Assessment score was 3 (sensitivity=68.97%, specificity=67.42%), and Comorbidity-Age-Lymphocyte count-Lactate dehydrogenase score was 8 (sensitivity=88.89%, specificity=66.67%). The pairwise comparison showed that the difference between the area under the curve of these 3 scores was statistically insignificant (P > .05). The rate of mortality and invasive ventilation was significantly high in groups with high National Early Warning Score, Sequential Organ Failure Assessment, and Comorbidity-Age-Lymphocyte count-Lactate dehydrogenase scores (P > .0001). These 3 scores, age, low platelets, and high troponin-T levels were found to be statistically significant predictors of outcome Conclusion:Comorbidity-Age-Lymphocyte count-Lactate dehydrogenase score had a good area under the curve, the highest sensitivity of its cut-off value, required only 4 parameters, and is easy to calculate so it may be a better tool among the 3 scores in outcome prediction for coronavirus disease 2019 patients.
Collapse
Affiliation(s)
- Zubia Jamil
- Department of Medicine, Foundation University Medical College, Foundation University, DHA Phase 1 Islamabad, Pakistan,Corresponding author: Zubia Jamil E-mail:
| | - Saba Samreen
- Foundation University Medical College, Foundation University, DHA Phase 1 Islamabad, Pakistan
| | - Bisma Mukhtar
- Foundation University Medical College, Foundation University, DHA Phase 1 Islamabad, Pakistan
| | - Madiha Khaliq
- Foundation University Medical College, Foundation University, DHA Phase 1 Islamabad, Pakistan
| | | | - Jamal Ahmed
- Head of Department of Pulmonology, Fauji Foundation Hospital, Rawalpindi, Pakistan
| | - Tassawar Hussain
- Head of Department of Medicine, Foundation University Medical College, Foundation University, DHA Phase 1 Islamabad, Pakistan
| |
Collapse
|
|
30
|
Arieli M, Agmon M, Gil E, Kizony R. The contribution of functional cognition screening during acute illness hospitalization of older adults in predicting participation in daily life after discharge. BMC Geriatr 2022; 22:739. [PMID: 36089574 PMCID: PMC9464608 DOI: 10.1186/s12877-022-03398-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 08/09/2022] [Indexed: 12/02/2022] Open
Abstract
Background Cognitive assessment in acutely hospitalized older adults is mainly limited to neuropsychological screening measures of global cognition. Performance-based assessments of functional cognition better indicate functioning in real-life situations. However, their predictive validity has been less studied in acute hospital settings. The aim of this study was to explore the unique contribution of functional cognition screening during acute illness hospitalization in predicting participation of older adults one and three months after discharge beyond traditional neuropsychological measures. Methods This prospective longitudinal study included 84 older adults ≥ 65 years hospitalized in internal medicine wards due to acute illness, followed by home visits at one month and telephone interviews at three months (n = 77). Participation in instrumental activities of daily living, social and leisure activities was measured by the Activity Card Sort. In-hospital factors included cognitive status (telephone version of the Mini-Mental State Examination, Color Trails Test), functional cognition screening (medication sorting task from the alternative Executive Function Performance Test), emotional status (Hospital Anxiety and Depression scale), functional decline during hospitalization (modified Barthel index), length of hospital stay, the severity of the acute illness, symptoms severity and comorbidities. Results Functional cognition outperformed the neuropsychological measures in predicting participation declines in a sample of relatively high-functioning older adults. According to a hierarchical multiple linear regression analysis, the overall model explained 28.4% of the variance in participation after one month and 19.5% after three months. Age and gender explained 18.6% of the variance after one month and 13.5% after three months. The medication sorting task explained an additional 5.5% of the variance of participation after one month and 5.1% after three months, beyond age and gender. Length of stay and the Color Trails Test were not significant contributors to the change in participation. Conclusions By incorporating functional cognition into acute settings, healthcare professionals would be able to better detect older adults with mild executive dysfunctions who are at risk for participation declines. Early identification of executive dysfunctions can improve continuity of care and planning of tailored post-discharge rehabilitation services, especially for high-functioning older adults, a mostly overlooked population in acute settings. The results support the use of functional cognition screening measure of medication management ability in acute settings.
Collapse
|
|
31
|
Mahmoodpoor A, Sanaie S, Saghaleini SH, Ostadi Z, Hosseini MS, Sheshgelani N, Vahedian-Azimi A, Samim A, Rahimi-Bashar F. Prognostic value of National Early Warning Score and Modified Early Warning Score on intensive care unit readmission and mortality: A prospective observational study. Front Med (Lausanne) 2022; 9:938005. [PMID: 35991649 PMCID: PMC9386480 DOI: 10.3389/fmed.2022.938005] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 07/19/2022] [Indexed: 12/03/2022] Open
Abstract
BACKGROUND Modified Early Warning Score (MEWS) and National Early Warning Score (NEWS) are widely used in predicting the mortality and intensive care unit (ICU) admission of critically ill patients. This study was conducted to evaluate and compare the prognostic value of NEWS and MEWS for predicting ICU readmission, mortality, and related outcomes in critically ill patients at the time of ICU discharge. METHODS This multicenter, prospective, observational study was conducted over a year, from April 2019 to March 2020, in the general ICUs of two university-affiliated hospitals in Northwest Iran. MEWS and NEWS were compared based on the patients' outcomes (including mortality, ICU readmission, time to readmission, discharge type, mechanical ventilation (MV), MV duration, and multiple organ failure after readmission) using the univariable and multivariable binary logistic regression. The receiver operating characteristic (ROC) curve was used to determine the outcome predictability of MEWS and NEWS. RESULTS A total of 410 ICU patients were enrolled in this study. According to multivariable logistic regression analysis, both MEWS and NEWS were predictors of ICU readmission, time to readmission, MV status after readmission, MV duration, and multiple organ failure after readmission. The area under the ROC curve (AUC) for predicting mortality was 0.91 (95% CI = 0.88-0.94, P < 0.0001) for the NEWS and 0.88 (95% CI = 0.84-0.91, P < 0.0001) for the MEWS. There was no significant difference between the AUC of the NEWS and the MEWS for predicting mortality (P = 0.082). However, for ICU readmission (0.84 vs. 0.71), time to readmission (0.82 vs. 0.67), MV after readmission (0.83 vs. 0.72), MV duration (0.81 vs. 0.67), and multiple organ failure (0.833 vs. 0.710), the AUCs of MEWS were significantly greater (P < 0.001). CONCLUSION National Early Warning Score and MEWS values of >4 demonstrated high sensitivity and specificity in identifying the risk of mortality for the patients' discharge from ICU. However, we found that the MEWS showed superiority over the NEWS score in predicting other outcomes. Eventually, MEWS could be considered an efficient prediction score for morbidity and mortality of critically ill patients.
Collapse
Affiliation(s)
- Ata Mahmoodpoor
- Research Center for Integrative Medicine in Aging, Aging Research Institute, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Sarvin Sanaie
- Research Center for Integrative Medicine in Aging, Aging Research Institute, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Seied Hadi Saghaleini
- Department of Anesthesiology and Intensive Care, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Zohreh Ostadi
- Department of Anesthesiology and Intensive Care, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
| | | | - Naeeme Sheshgelani
- Student Research Committee, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Amir Vahedian-Azimi
- Trauma Research Center, Nursing Faculty, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Abbas Samim
- Chemical Injuries Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Farshid Rahimi-Bashar
- Anesthesia and Critical Care Department, Hamadan University of Medical Sciences, Hamadan, Iran
| |
Collapse
|
|
32
|
Faust O, Hong W, Loh HW, Xu S, Tan RS, Chakraborty S, Barua PD, Molinari F, Acharya UR. Heart rate variability for medical decision support systems: A review. Comput Biol Med 2022; 145:105407. [DOI: 10.1016/j.compbiomed.2022.105407] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 03/09/2022] [Accepted: 03/12/2022] [Indexed: 12/22/2022]
|
|
33
|
Arieli M, Kizony R, Gil E, Agmon M. Many Paths to Recovery: Comparing Basic Function and Participation in High-Functioning Older Adults After Acute Hospitalization. J Appl Gerontol 2022; 41:1896-1904. [PMID: 35543173 DOI: 10.1177/07334648221089481] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Function after acute hospitalization is mostly operationalized by Basic Activities of Daily Living (BADL), a limited concept that overshadows a wide range of instrumental, social, and recreational activities, otherwise referred to as participation. Participation is important for patients' health and quality of life after hospitalization. This study focuses on high-functioning older adults, examining functional recovery after hospitalization by comparing BADL assessment with assessment of participation at one and three months following discharge relative to pre-hospitalization. Quantitative data were collected from 72 participants divided into two age groups of hospitalized older adults (age 65-74, n = 38; age ≥75, n = 34), followed by home visits after 1 month and telephone interviews 3 months after discharge. Both groups experienced a significantly greater decline in participation, compared with BADL, which were mostly preserved. A comprehensive assessment of participation better captures functional changes in high-functioning older adults. Early identification of participation withdrawal is crucial for preventing disability.
Collapse
Affiliation(s)
- Maya Arieli
- Department of Occupational Therapy, Faculty of Social Welfare & Health Sciences, University of Haifa, Israel
| | - Rachel Kizony
- Department of Occupational Therapy, Faculty of Social Welfare & Health Sciences, University of Haifa, Israel.,Department of Occupational Therapy, Sheba Medical Center, Tel Hashomer, Israel
| | - Efrat Gil
- Geriatric Unit, Clalit Health Services, Haifa and West Galilee.,Faculty of Medicine, Technion, Haifa, Israel
| | - Maayan Agmon
- The Cheryl Spencer Department of Nursing, Faculty of Social Welfare and Health Science, University of Haifa, Israel
| |
Collapse
|
|
34
|
Cenci A, Macchia I, La Sorsa V, Sbarigia C, Di Donna V, Pietraforte D. Mechanisms of Action of Ozone Therapy in Emerging Viral Diseases: Immunomodulatory Effects and Therapeutic Advantages With Reference to SARS-CoV-2. Front Microbiol 2022; 13:871645. [PMID: 35531273 PMCID: PMC9069003 DOI: 10.3389/fmicb.2022.871645] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 03/07/2022] [Indexed: 12/24/2022] Open
Abstract
Medical oxygen-ozone (O2-O3) is a successful therapeutic approach accounting on the assessed beneficial action of ozone in the range 30–45 μg/ml (expanded range 10–80 μg/ml according to different protocols), as in this dosage range ozone is able to trigger a cellular hormetic response via the modulating activity of reactive oxygen species (ROS), as signaling molecules. The ozone-dependent ROS-mediated fatty acid oxidation leads to the formation of lipid ozonization products (LOPs), which act as signal transducers by triggering ROS signaling and therefore mitohormetic processes. These processes ultimately activate survival mechanisms at a cellular level, such as the Nrf2/Keap1/ARE system activation, the AMPK/FOXO/mTOR/Sir1 pathway and the Nrf2/NF-kB cross talk. Furthermore, indirectly, via these pathways, LOPs trigger the HIF-1α pathway, the HO-1 signaling and the NO/iNOS biochemical machinery. Ozone-driven shift of cytokine activation pathways, from pro-inflammatory to anti-inflammatory immediately afterwards, also exert direct immunoregulatory effects on regulatory T lymphocytes as well as on the intestinal microbiota, which in turn can affect immune response thus influencing the progression of the disease. In this review, we will describe the biological and biochemical mechanisms of action of ozone therapy with the aim of evaluating both positive and critical aspects of ozone use as a therapeutic adjuvant in the light of emerging viral infections, such as SARS-CoV-2 and microbiome-associated disorders related to SARS-CoV-2.
Collapse
Affiliation(s)
- Alessandra Cenci
- Core Facilities, Italian National Institute of Health, Rome, Italy
- *Correspondence: Alessandra Cenci,
| | - Iole Macchia
- Department of Oncology and Molecular Medicine, Italian National Institute of Health, Rome, Italy
| | - Valentina La Sorsa
- Research Coordination and Support Service, Italian National Institute of Health, Rome, Italy
| | | | | | | |
Collapse
|
|
35
|
Comparison of the National Early Warning Scores and Rapid Emergency Medicine Scores with the APACHE II Scores as a Prediction of Mortality in Patients with Medical Emergency Team Activation: a Single-centre Retrospective Cohort Study. J Crit Care Med (Targu Mures) 2021; 7:283-289. [PMID: 34934818 PMCID: PMC8647673 DOI: 10.2478/jccm-2021-0040] [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: 03/18/2021] [Accepted: 10/10/2021] [Indexed: 11/20/2022] Open
Abstract
Introduction The medical emergency team enables the limitation of patients’ progression to critical illness in the general ward. The early warning scoring system (EWS) is one of the criteria for medical emergency team activation; however, it is not a valid criterion to predict the prognosis of patients with MET activation. Aim In this study, the National Early Warning Score (NEWS) and Rapid Emergency Medicine Score (REMS) was compared with that of the Acute Physiology and Chronic Health Evaluation II (APACHE II) score in predicting the prognosis of patients who had been treated a medical emergency team. Material and Methods In this single-centre retrospective cohort study, patients treated by a medical emergency team between April 2013 and March 2019 and the 28-day prognosis of MET-activated patients were assessed using APACHE II, NEWS, and REMS. Results Of the 196 patients enrolled, 152 (77.5%) were men, and 44 (22.5%) were women. Their median age was 68 years (interquartile range: 57-76 years). The most common cause of medical emergency team activation was respiratory failure (43.4%). Univariate analysis showed that APACHE II score, NEWS, and REMS were associated with 28-day prognostic mortality. There was no significant difference in the area under the receiver operating characteristic curve of APACHE II (0.76), NEWS (0.67), and REMS (0.70); however, the sensitivity of NEWS (0.70) was superior to that of REMS (0.47). Conclusion NEWS is a more sensitive screening tool like APACHE II than REMS for predicting the prognosis of patients with medical emergency team activation. However, because the accuracy of NEWS was not sufficient compared with that of APACHE II score, it is necessary to develop a screening tool with higher sensitivity and accuracy that can be easily calculated at the bedside in the general ward.
Collapse
|
|
36
|
Bucknall T, Quinney R, Booth L, McKinney A, Subbe CP, Odell M. When patients (and families) raise the alarm: Patient and family activated rapid response as a safety strategy for hospitals. Future Healthc J 2021; 8:e609-e612. [PMID: 34888450 DOI: 10.7861/fhj.2021-0134] [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] [Indexed: 12/14/2022]
Abstract
Patients and those close to them often have an intimate understanding of their condition and can participate in a broad range of clinical processes. During times of deterioration, their concerns might go unheard. Advocacy of family and friends can fulfil an important safety function and can support patients and healthcare professionals looking after them. If concerns by patients are not heard by the patient's primary team in hospital, patient and family activated rapid response systems allow patients and family members to alert critical care outreach teams directly. These types of systems are stipulated by regulators in Australia and in parts of the USA, and there are examples in the UK built around the 'Call for Concern' model championed by the Royal Berkshire Hospital. Implementation is not without its problems and requires a deep understanding of barriers and enablers. Empowering patients to escalate directly might help to change safety culture and have protective effects for patients and staff. Policy makers are urged to consider standardised regulation to aid implementation.
Collapse
Affiliation(s)
- Tracey Bucknall
- Alfred Health, Melbourne Australia and director, Centre for Quality and Patient Safety Research, Burwood, Australia
| | - Rett Quinney
- Australian Catholic University, Ballarat, Australia
| | - Lisa Booth
- East Suffolk and North Essex NHS Foundation Trust, Colchester, UK
| | | | - Christian P Subbe
- Ysbyty Gwynedd, Bangor, UK, senior clinical lecturer, Bangor University, Bangor, UK and improvement science fellow, The Health Foundation, London, UK
| | - Mandy Odell
- Royal Berkshire NHS Foundation Trust, Reading, UK
| |
Collapse
|
|
37
|
Adams K, Tenforde MW, Chodisetty S, Lee B, Chow EJ, Self WH, Patel MM. A literature review of severity scores for adults with influenza or community-acquired pneumonia - implications for influenza vaccines and therapeutics. Hum Vaccin Immunother 2021; 17:5460-5474. [PMID: 34757894 PMCID: PMC8903905 DOI: 10.1080/21645515.2021.1990649] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 10/02/2021] [Indexed: 12/11/2022] Open
Abstract
Influenza vaccination and antiviral therapeutics may attenuate disease, decreasing severity of illness in vaccinated and treated persons. Standardized assessment tools, definitions of disease severity, and clinical endpoints would support characterizing the attenuating effects of influenza vaccines and antivirals. We review potential clinical parameters and endpoints that may be useful for ordinal scales evaluating attenuating effects of influenza vaccines and antivirals in hospital-based studies. In studies of influenza and community-acquired pneumonia, common physiologic parameters that predicted outcomes such as mortality, ICU admission, complications, and duration of stay included vital signs (hypotension, tachypnea, fever, hypoxia), laboratory results (blood urea nitrogen, platelets, serum sodium), and radiographic findings of infiltrates or effusions. Ordinal scales based on these parameters may be useful endpoints for evaluating attenuating effects of influenza vaccines and therapeutics. Factors such as clinical and policy relevance, reproducibility, and specificity of measurements should be considered when creating a standardized ordinal scale for assessment.
Collapse
Affiliation(s)
- Katherine Adams
- Influenza Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Mark W. Tenforde
- Influenza Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Shreya Chodisetty
- Influenza Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Benjamin Lee
- Influenza Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Eric J. Chow
- Influenza Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Wesley H. Self
- Department of Emergency Medicine and Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Manish M. Patel
- Influenza Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| |
Collapse
|
|
38
|
Li XR, Lv C, Guo H, Shen ZS, Li JG. Application of a new checklist in the differential diagnosis of abdominal pain in the department of general medicine. Asian J Surg 2021; 45:586-587. [PMID: 34840043 DOI: 10.1016/j.asjsur.2021.10.044] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Revised: 10/17/2021] [Accepted: 10/25/2021] [Indexed: 11/02/2022] Open
Affiliation(s)
- Xu-Rui Li
- Department of General Practice, Hebei General Hospital, Shijiazhuang, 050051, China.
| | - Chang Lv
- Department of Emergency, Hebei General Hospital, Shijiazhuang, 050051, China
| | - Hui Guo
- Department of Emergency, Hebei General Hospital, Shijiazhuang, 050051, China
| | - Zhang-Shun Shen
- Department of Emergency, Hebei General Hospital, Shijiazhuang, 050051, China
| | - Jian-Guo Li
- Department of Emergency, Hebei General Hospital, Shijiazhuang, 050051, China
| |
Collapse
|
|
39
|
Subbe CP, Tomos H, Jones GM, Barach P. Express check-in: developing a personal health record for patients admitted to hospital with medical emergencies: a mixed-method feasibility study. Int J Qual Health Care 2021; 33:6354901. [PMID: 34410422 DOI: 10.1093/intqhc/mzab121] [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: 04/26/2021] [Revised: 06/07/2021] [Accepted: 08/18/2021] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Patient participation is increasingly recognized as a key component in the redesign of healthcare processes and is advocated as a means to improve patient safety. OBJECTIVE To explore the usage of participatory engagement in patient-created and co-designed medical records for emergency admission to the hospital. METHODS design: prospective iterative development and feasibility testing of personal health records; setting: an acute medical unit in a university-affiliated hospital; participants: patients admitted to hospital for medical emergencies; interventions: we used a design-led development of personal health record prototypes and feasibility testing of records completed by patients during the process of emergency admission. 'Express-check-in' records contained items of social history, screening questions for sepsis and acute kidney injury in addition to the patients' ideas, concerns and expectations; main outcome measures: the outcome metrics focused on feasibility and a selection of quality domains, namely effectiveness of recording relevant history, time efficiency of the documentation process, patient-centredness of resulting records and staff and patient feedback. The incidence of sepsis and acute kidney injury were used as surrogate measures for assessing the safety impact. RESULTS The medical record prototypes were developed in an iterative fashion and tested with 100 patients, in which 39 patients were 70 or older and 25 patients were classified as clinically frail. Ninety-six per cent of the data items were completed by patients with no or minimal help from healthcare professionals. The completeness of these patient records was superior to that of the corresponding medical records in that they contained deeply held beliefs and fears, whereas concerns and expectations recorded by patients were only mirrored in a small proportion of the formal clinical records. The sepsis self-screening tool identified 68% of patients requiring treatment with antibiotics. The intervention was feasible, independent of the level of formal education and effective in frail and elderly patients with support from family and staff. The prototyped records were well received and felt to be practical by patients and staff. The staff indicated that reading the patients' documentation led to significant changes in their clinical management. CONCLUSIONS Medical record accessibility to patients during hospital care contributes to the co-management of personal healthcare and might add critical information over and above the records compiled by healthcare professionals.
Collapse
Affiliation(s)
- Christian P Subbe
- Department of Medicine, Ysbyty Gwynedd, Penrhosgarnedd, Bangor LL57 2PW, UK.,School of Medical Sciences, Bangor University, Brigantia Building, Bangor LL57 2DG, UK
| | - Hawys Tomos
- Helen Hamlyn Centre for Design, Royal College of Art, Howie Street, Battersea, London SW11 4AY, UK
| | - Gwenlli Mai Jones
- Cardiff University, Penrhosgarnedd, Bangor LL57 2PW, UK.,Ysbyty Gwynedd, Penrhosgarnedd, Bangor LL57 2PW, UK
| | - Paul Barach
- Children's Hospital of Michigan, Wayne State University School of Medicine, 3901 Beaubien Blvd, Detroit, MI 48201, USA
| |
Collapse
|
|
40
|
Changes in Emergency Department Performance during Strike of Junior Physicians in Korea. Emerg Med Int 2021; 2021:1786728. [PMID: 34306757 PMCID: PMC8285189 DOI: 10.1155/2021/1786728] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 07/03/2021] [Indexed: 11/17/2022] Open
Abstract
Objective A nationwide strike that took place from August 21 to September 7, 2020, which was led by young doctors represented by residents and interns, resulted in shortages of manpower at almost all university and training hospitals. This study aimed to identify differences in the process and outcomes of emergency department (ED) patient care by comparing the performance over about 2 weeks of the strike with that during the usual ED operations. Methods This retrospective observational study evaluated ED flow and performance during the junior doctors' strike and compared it with the usual period in a single tertiary-care academic hospital. The outcome variables were defined as ED length of stay, crude mortality, and hospital mortality and adjusted for demographic and clinical parameters. The effect of the doctors' strike on hospital mortality adjusted for demographic and clinical variables was investigated using logistic regression. Results A total of 1,121 and 1,496 patients visited the ED during the strike and control periods (both 17 days), respectively. The care usually provided by four or six physicians, including one specialist, was replaced with that by one or two specialists at any one time. During the trainee doctors' strike, EM specialists managed patients with fewer consultations. However, the proportion of patients who underwent laboratory and radiologic tests did not change significantly. The median ED length of stay significantly decreased from 359 minutes (interquartile range, IQR: 147–391) in the control period to 326 minutes (IQR: 123–318) during the strike period (P < 0.001). The doctors' strike was not found to have a significant effect on mortality after adjustments with other variables. Conclusion During the junior doctors' strike in 2020 in Korea, EM specialists efficiently managed the care of emergency patients with higher levels of acuity without compromising the survival rate, through fewer consultations and faster disposition.
Collapse
|
|
41
|
Hamza M, Alsma J, Kellett J, Brabrand M, Christensen EF, Cooksley T, Haak HR, Nanayakkara PW, Merten H, Schouten B, Weichert I, Subbe CP. Can vital signs recorded in patients' homes aid decision making in emergency care? A Scoping Review. Resusc Plus 2021; 6:100116. [PMID: 33870237 PMCID: PMC8035051 DOI: 10.1016/j.resplu.2021.100116] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2020] [Revised: 03/16/2021] [Accepted: 03/19/2021] [Indexed: 10/31/2022] Open
Abstract
AIM Use of tele-health programs and wearable sensors that allow patients to monitor their own vital signs have been expanded in response to COVID-19. We aimed to explore the utility of patient-held data during presentation as medical emergencies. METHODS We undertook a systematic scoping review of two groups of studies: studies using non-invasive vital sign monitoring in patients with chronic diseases aimed at preventing unscheduled reviews in primary care, hospitalization or emergency department visits and studies using vital sign measurements from wearable sensors for decision making by clinicians on presentation of these patients as emergencies. Only studies that described a comparator or control group were included. Studies limited to inpatient use of devices were excluded. RESULTS The initial search resulted in 896 references for screening, nine more studies were identified through searches of references. 26 studies fulfilled inclusion and exclusion criteria and were further analyzed. The majority of studies were from telehealth programs of patients with congestive heart failure or Chronic Obstructive Pulmonary Disease. There was limited evidence that patient held data is currently used to risk-stratify the admission or discharge process for medical emergencies. Studies that showed impact on mortality or hospital admission rates measured vital signs at least daily. We identified no interventional study using commercially available sensors in watches or smart phones. CONCLUSIONS Further research is needed to determine utility of patient held monitoring devices to guide management of acute medical emergencies at the patients' home, on presentation to hospital and after discharge back to the community.
Collapse
Affiliation(s)
- Muhammad Hamza
- Department of Acute Medicine, Ysbyty Gwynedd Hospital, Bangor, United Kingdom
| | - Jelmer Alsma
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - John Kellett
- Department of Emergency Medicine, Hospital of South West Jutland, Esbjerg, Denmark
| | - Mikkel Brabrand
- Department of Emergency Medicine, Odense University Hospital, Odense, Denmark
| | - Erika F. Christensen
- Center for Prehospital and Emergency Research, Clinic of Internal and Emergency Medicine, Aalborg University Hospital, Aalborg, Denmark
| | - Tim Cooksley
- Department of Acute Medicine, University Hospital of South Manchester, Manchester, United Kingdom
| | - Harm R. Haak
- Department of Internal Medicine, Division of General Internal Medicine, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Prabath W.B. Nanayakkara
- Section of Acute Medicine, Department of Internal Medicine, Amsterdam Public Health research institute, Amsterdam University Medical Center, location VU University Medical Center, Amsterdam, The Netherlands
| | - Hanneke Merten
- Department of Public and Occupational Health, Amsterdam Public Health research institute, Amsterdam University Medical Center, location VU University Medical Center, Amsterdam, The Netherlands
| | - Bo Schouten
- Department of Public and Occupational Health, Amsterdam Public Health research institute, Amsterdam University Medical Center, location VU University Medical Center, Amsterdam, The Netherlands
| | - Immo Weichert
- Department of Acute Medicine, Ipswich Hospital, East Suffolk and North Essex NHS Foundation Trust, Ipswich, United Kingdom
| | | |
Collapse
|
|
42
|
Youssef A, Kouchaki S, Shamout F, Armstrong J, El-Bouri R, Taylor T, Birrenkott D, Vasey B, Soltan A, Zhu T, Clifton DA, Eyre DW. Development and validation of early warning score systems for COVID-19 patients. Healthc Technol Lett 2021; 8:105-117. [PMID: 34221413 PMCID: PMC8239612 DOI: 10.1049/htl2.12009] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 02/22/2021] [Accepted: 03/19/2021] [Indexed: 12/15/2022] Open
Abstract
COVID‐19 is a major, urgent, and ongoing threat to global health. Globally more than 24 million have been infected and the disease has claimed more than a million lives as of November 2020. Predicting which patients will need respiratory support is important to guiding individual patient treatment and also to ensuring sufficient resources are available. The ability of six common Early Warning Scores (EWS) to identify respiratory deterioration defined as the need for advanced respiratory support (high‐flow nasal oxygen, continuous positive airways pressure, non‐invasive ventilation, intubation) within a prediction window of 24 h is evaluated. It is shown that these scores perform sub‐optimally at this specific task. Therefore, an alternative EWS based on the Gradient Boosting Trees (GBT) algorithm is developed that is able to predict deterioration within the next 24 h with high AUROC 94% and an accuracy, sensitivity, and specificity of 70%, 96%, 70%, respectively. The GBT model outperformed the best EWS (LDTEWS:NEWS), increasing the AUROC by 14%. Our GBT model makes the prediction based on the current and baseline measures of routinely available vital signs and blood tests.
Collapse
Affiliation(s)
- Alexey Youssef
- Department of Engineering Science Institute of Biomedical Engineering University of Oxford Oxford UK
| | - Samaneh Kouchaki
- Department of Engineering Science Institute of Biomedical Engineering University of Oxford Oxford UK.,Centre for Vision, Speech, and Signal Processing University of Surrey Guildford UK
| | - Farah Shamout
- Engineering Division New York University Abu Dhabi Abu Dhabi United Arab Emirates
| | - Jacob Armstrong
- Department of Engineering Science Institute of Biomedical Engineering University of Oxford Oxford UK.,Big Data Institute Nuffield Department of Population Health University of Oxford Oxford UK
| | - Rasheed El-Bouri
- Department of Engineering Science Institute of Biomedical Engineering University of Oxford Oxford UK
| | - Thomas Taylor
- Department of Engineering Science Institute of Biomedical Engineering University of Oxford Oxford UK
| | - Drew Birrenkott
- Stanford School of Medicine Stanford University Palo Alto USA
| | - Baptiste Vasey
- Department of Engineering Science Institute of Biomedical Engineering University of Oxford Oxford UK.,Nuffield Department of Surgical Sciences University of Oxford Oxford UK
| | - Andrew Soltan
- John Radcliffe Hospital Oxford University Hospitals NHS Foundation Trust Oxford UK.,Division of Cardiovascular Medicine Radcliffe Department of Medicine John Radcliffe Hospital University of Oxford Oxford UK
| | - Tingting Zhu
- Department of Engineering Science Institute of Biomedical Engineering University of Oxford Oxford UK
| | - David A Clifton
- Department of Engineering Science Institute of Biomedical Engineering University of Oxford Oxford UK.,Oxford-Suzhou Centre for Advanced Research Suzhou China
| | - David W Eyre
- Big Data Institute Nuffield Department of Population Health University of Oxford Oxford UK.,John Radcliffe Hospital Oxford University Hospitals NHS Foundation Trust Oxford UK
| |
Collapse
|
|
43
|
Oduncu AF, Kıyan GS, Yalçınlı S. Comparison of qSOFA, SIRS, and NEWS scoring systems for diagnosis, mortality, and morbidity of sepsis in emergency department. Am J Emerg Med 2021; 48:54-59. [PMID: 33839632 DOI: 10.1016/j.ajem.2021.04.006] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 03/26/2021] [Accepted: 04/02/2021] [Indexed: 11/15/2022] Open
Abstract
PURPOSE This study was aimed to compare the quick Sequential Organ Failure Assessment (qSOFA), Systemic Inflammatory Response Syndrome (SIRS), and National Early Warning Score (NEWS) scoring systems for diagnosing sepsis and predicting mortality and morbidity. PATIENTS AND METHODS A prospective study was designed. qSOFA, SIRS, and NEWS scores were calculated at the admission. The diagnosis of sepsis was made with SOFA scoring initially. The morbidity and mortality of the patients were identified during follow-up. Also, the sensitivity, specificity, negative predictive value, and positive predictive value of three scoring systems were calculated. The scoring systems were compared with ROC analysis. RESULTS A total of 463 patients were evaluated. There were 287 (62.0%) patients diagnosed with sepsis, and septic shock occurred in 64 (13.8%) of patients. Seven-day mortality rate was 8.4% (n = 39), 30-day mortality rate was 18.1% (n = 84). The sensitivity for qSOFA, SIRS, and NEWS for diagnosis of sepsis was 23%, 77%, 58%, and specificity was 99%, 35%, 81% respectively. The sensitivity of the qSOFA, SIRS and NEWS scoring systems for mortality was 39%, 82%, 77% and specificity 91%, 29%, and 64%, respectively. AUROC values for mortality detected as NEWS = 0.772, qSOFA = 0.758, SIRS = 0.542. According to the ROC analysis, the SIRS system was significantly less useful than the qSOFA and NEWS system in the diagnosis of sepsis and mortality (p < 0.0001). CONCLUSION NEWS and qSOFA scoring systems have similar prognosis in both diagnosing sepsis and predicting mortality and both are superior to SIRS.
Collapse
Affiliation(s)
- Ali Fuat Oduncu
- Ege University, Faculty of Medicine, Department of Emergency Medicine, İzmir, Turkey.
| | | | - Sercan Yalçınlı
- Ege University, Faculty of Medicine, Department of Emergency Medicine, İzmir, Turkey
| |
Collapse
|
|
44
|
Emergency Presentations of Immune Checkpoint Inhibitor-Related Endocrinopathies. J Emerg Med 2021; 61:140-146. [PMID: 33795170 DOI: 10.1016/j.jemermed.2021.02.020] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2020] [Revised: 01/07/2021] [Accepted: 02/06/2021] [Indexed: 12/25/2022]
Abstract
BACKGROUND Immune checkpoint inhibitors (ICI) are an important component of anticancer treatment, with indications across an increasing range of oncological diagnoses. ICIs are associated with a range of immune-mediated toxicities. Immune-related endocrinopathies pose a distinct challenge, given the nonspecific symptom profile and potentially life-threatening sequelae if not recognized. OBJECTIVES To determine the frequency and clinical presentations of immune-mediated endocrinopathies in patients treated with ICIs presenting as emergencies. METHODS A prospective observational cohort study was undertaken at a specialist oncology hospital in North West England from May 20, 2018 to May 19, 2020. Within the hospital, the Oncology Assessment Unit (OAU) acts as the receiving unit in which assessments are undertaken of all emergency presentations. All patients treated with ICIs presenting to the OAU were included. The primary outcome was diagnosis of an immune-mediated endocrinopathy. Length of inpatient stay, and 7- and 30-day mortality rates were examined. RESULTS During the study period, 684 patients treated with ICIs presented to the OAU. Twenty-four (3.5%) patients had an acute immune-mediated endocrinopathy, of which 17 had hypophysitis, 4 diabetes mellitus, 2 thyrotoxicosis, and 1 adrenalitis. Median length of stay for patients with hypophysitis was 1 day. No patient with an immune-mediated endocrinopathy died within 30 days of presentation. CONCLUSIONS Presentations to emergency settings with acute immune-mediated endocrinopathies are rare. Early recognition of immune-mediated toxicities is important, and particularly pertinent in ICI-related endocrinopathies, where even in life-threatening cases, the presentation can be vague and nonspecific.
Collapse
|
|
45
|
Early Detection of Septic Shock Onset Using Interpretable Machine Learners. J Clin Med 2021; 10:jcm10020301. [PMID: 33467539 PMCID: PMC7830968 DOI: 10.3390/jcm10020301] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Revised: 12/31/2020] [Accepted: 01/12/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Developing a decision support system based on advances in machine learning is one area for strategic innovation in healthcare. Predicting a patient's progression to septic shock is an active field of translational research. The goal of this study was to develop a working model of a clinical decision support system for predicting septic shock in an acute care setting for up to 6 h from the time of admission in an integrated healthcare setting. METHOD Clinical data from Electronic Health Record (EHR), at encounter level, were used to build a predictive model for progression from sepsis to septic shock up to 6 h from the time of admission; that is, T = 1, 3, and 6 h from admission. Eight different machine learning algorithms (Random Forest, XGBoost, C5.0, Decision Trees, Boosted Logistic Regression, Support Vector Machine, Logistic Regression, Regularized Logistic, and Bayes Generalized Linear Model) were used for model development. Two adaptive sampling strategies were used to address the class imbalance. Data from two sources (clinical and billing codes) were used to define the case definition (septic shock) using the Centers for Medicare & Medicaid Services (CMS) Sepsis criteria. The model assessment was performed using Area under Receiving Operator Characteristics (AUROC), sensitivity, and specificity. Model predictions for each feature window (1, 3 and 6 h from admission) were consolidated. RESULTS Retrospective data from April 2005 to September 2018 were extracted from the EHR, Insurance Claims, Billing, and Laboratory Systems to create a dataset for septic shock detection. The clinical criteria and billing information were used to label patients into two classes-septic shock patients and sepsis patients at three different time points from admission, creating two different case-control cohorts. Data from 45,425 unique in-patient visits were used to build 96 prediction models comparing clinical-based definition versus billing-based information as the gold standard. Of the 24 consolidated models (based on eight machine learning algorithms and three feature windows), four models reached an AUROC greater than 0.9. Overall, all the consolidated models reached an AUROC of at least 0.8820 or higher. Based on the AUROC of 0.9483, the best model was based on Random Forest, with a sensitivity of 83.9% and specificity of 88.1%. The sepsis detection window at 6 h outperformed the 1 and 3-h windows. The sepsis definition based on clinical variables had improved performance when compared to the sepsis definition based on only billing information. CONCLUSION This study corroborated that machine learning models can be developed to predict septic shock using clinical and administrative data. However, the use of clinical information to define septic shock outperformed models developed based on only administrative data. Intelligent decision support tools can be developed and integrated into the EHR and improve clinical outcomes and facilitate the optimization of resources in real-time.
Collapse
|
|
46
|
Safety and efficacy of ozone therapy in mild to moderate COVID-19 patients: A phase 1/11 randomized control trial (SEOT study). Int Immunopharmacol 2020; 91:107301. [PMID: 33421928 PMCID: PMC7758022 DOI: 10.1016/j.intimp.2020.107301] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2020] [Revised: 12/10/2020] [Accepted: 12/11/2020] [Indexed: 12/18/2022]
Abstract
Introduction The Corona virus disease 19 (COVID-19) has accounted for multiple deaths and economic woes.While the entire medical fraternity and scientists are putting their best feet forward to find a solution to contain this deadly pandemic, there is a growing interest in integrating other known alternative therapies in to standard care. This study is aimed at evaluating the safety and efficacy of ozone therapy (OT), as an adjuvant to the standard of care (SOC). Methods In the current randomized control trial, 60 patients with mild to moderate score NEWS score were included in two parallel groups (n = 30/group). The interventional group (OZ) received ozonized rectal insufflation and minor auto haemotherapy, daily along with SOC, while the control group (ST) received SOC alone. The main outcome measures included changes in clinical features, oxygenation index (SpO2), NEWS score, Reverse transcription polymerase chain reaction(RT-PCR), inflammatory markers, requirement of advanced care, and metabolic profiles. Results The OZ group has shown clinically significant improvement in the mean values of all the parameters tested compared to ST Group. However, statistical significance were only observed in RT-PCR negative reaction (P = 0.01), changes in clinical symptoms (P < 0.05) and requirement for Intensive care (P < 0.05). No adverse events were reported in OZ group, as against 2 deaths reported in ST group. Conclusion OT when integrated with SOC can improve the clinical status and rapidly reduce the viral load compared to SOC alone, which facilitate early recovery and check the need for advanced care and mortality as demonstrated in this study.
Collapse
|
|
47
|
Xie F, Chakraborty B, Ong MEH, Goldstein BA, Liu N. AutoScore: A Machine Learning-Based Automatic Clinical Score Generator and Its Application to Mortality Prediction Using Electronic Health Records. JMIR Med Inform 2020; 8:e21798. [PMID: 33084589 PMCID: PMC7641783 DOI: 10.2196/21798] [Citation(s) in RCA: 62] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Revised: 07/25/2020] [Accepted: 07/27/2020] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND Risk scores can be useful in clinical risk stratification and accurate allocations of medical resources, helping health providers improve patient care. Point-based scores are more understandable and explainable than other complex models and are now widely used in clinical decision making. However, the development of the risk scoring model is nontrivial and has not yet been systematically presented, with few studies investigating methods of clinical score generation using electronic health records. OBJECTIVE This study aims to propose AutoScore, a machine learning-based automatic clinical score generator consisting of 6 modules for developing interpretable point-based scores. Future users can employ the AutoScore framework to create clinical scores effortlessly in various clinical applications. METHODS We proposed the AutoScore framework comprising 6 modules that included variable ranking, variable transformation, score derivation, model selection, score fine-tuning, and model evaluation. To demonstrate the performance of AutoScore, we used data from the Beth Israel Deaconess Medical Center to build a scoring model for mortality prediction and then compared the data with other baseline models using the receiver operating characteristic analysis. A software package in R 3.5.3 (R Foundation) was also developed to demonstrate the implementation of AutoScore. RESULTS Implemented on the data set with 44,918 individual admission episodes of intensive care, the AutoScore-created scoring models performed comparably well as other standard methods (ie, logistic regression, stepwise regression, least absolute shrinkage and selection operator, and random forest) in terms of predictive accuracy and model calibration but required fewer predictors and presented high interpretability and accessibility. The nine-variable, AutoScore-created, point-based scoring model achieved an area under the curve (AUC) of 0.780 (95% CI 0.764-0.798), whereas the model of logistic regression with 24 variables had an AUC of 0.778 (95% CI 0.760-0.795). Moreover, the AutoScore framework also drives the clinical research continuum and automation with its integration of all necessary modules. CONCLUSIONS We developed an easy-to-use, machine learning-based automatic clinical score generator, AutoScore; systematically presented its structure; and demonstrated its superiority (predictive performance and interpretability) over other conventional methods using a benchmark database. AutoScore will emerge as a potential scoring tool in various medical applications.
Collapse
Affiliation(s)
- Feng Xie
- Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore
| | - Bibhas Chakraborty
- Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore
- Department of Statistics and Applied Probability, National University of Singapore, Singapore, Singapore
- Department of Biostatistics & Bioinformatics, Duke University, Durham, NC, United States
| | - Marcus Eng Hock Ong
- Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore
- Department of Emergency Medicine, Singapore General Hospital, Singapore, Singapore
- Health Services Research Centre, Singapore Health Services, Singapore, Singapore
| | - Benjamin Alan Goldstein
- Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore
- Department of Biostatistics & Bioinformatics, Duke University, Durham, NC, United States
| | - Nan Liu
- Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore
- Health Services Research Centre, Singapore Health Services, Singapore, Singapore
- Institute of Data Science, National University of Singapore, Singapore, Singapore
| |
Collapse
|
|
48
|
Zisberg A, Shulyaev K, Gur-Yaish N, Agmon M, Pud D. Symptom clusters in hospitalized older adults: Characteristics and outcomes. Geriatr Nurs 2020; 42:240-246. [PMID: 32891441 DOI: 10.1016/j.gerinurse.2020.08.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Revised: 08/16/2020] [Accepted: 08/19/2020] [Indexed: 12/13/2022]
Abstract
Hospital care in medical patients relies mostly on objective measures with limited assessment of subjective symptoms. We subgrouped 331 hospitalized older adults with medical diagnosis (age 75.5 ± 7.1) according to the severity of multiple symptoms to explore if these subgroups differed in health-related characteristics on admission and functional outcomes one month post-discharge. Cluster analysis identified three subgroups based on experiences with five highly distressing symptoms (fatigue, dyspnea, dizziness, sleep disturbance, pain): low levels of all symptoms, high levels of all symptoms; moderate levels of four symptoms with high dyspnea. Belonging in different subgroups was accompanied by different levels of cognitive and mental, but not physical or health status. Patients in the subgroup "Moderate Levels with High Dyspnea" had significantly lower risk of decline in post-discharge instrumental activities of daily living than other subgroups. Better understanding of older hospitalized adults' symptom profiles may yield important information on health condition and recovery.
Collapse
Affiliation(s)
- Anna Zisberg
- The Cheryl Spencer Department of Nursing Faculty of Social Welfare and Health Science, University of Haifa, Mount Carmel, Israel.
| | - Ksenya Shulyaev
- The Cheryl Spencer Department of Nursing Faculty of Social Welfare and Health Science, University of Haifa, Mount Carmel, Israel
| | | | - Maayan Agmon
- The Cheryl Spencer Department of Nursing Faculty of Social Welfare and Health Science, University of Haifa, Mount Carmel, Israel
| | - Dorit Pud
- The Cheryl Spencer Department of Nursing Faculty of Social Welfare and Health Science, University of Haifa, Mount Carmel, Israel.
| |
Collapse
|
|
49
|
Ivic R, Kurland L, Vicente V, Castrén M, Bohm K. Serious conditions among patients with non-specific chief complaints in the pre-hospital setting: a retrospective cohort study. Scand J Trauma Resusc Emerg Med 2020; 28:74. [PMID: 32727586 PMCID: PMC7391698 DOI: 10.1186/s13049-020-00767-0] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Accepted: 07/16/2020] [Indexed: 12/03/2022] Open
Abstract
Background Emergency Medical Services (EMS) are faced daily with patients presenting with a non-specific chief complaints (NSC); i.e. decreased general condition, general malaise, sense of illness, or just being unable to cope with usual daily activities. Patients presenting with NSCs often have normal vital signs. It has previously been established that however, NSCs may have a serious underlying condition that has yet to be identified. The primary outcome of this study was to determine the prevalence of serious conditions in patients presenting with NSCs to the EMS. Method A retrospective cohort study of patients ≥18 years of age who were reported as presenting with chief complaints compatible with NSCs to the EMS in Stockholm Region and transported to an emergency department between January 1st, 2013 and December 31st, 2013. Patients were identified via the EMS electronic health care record and followed via records from the National Patient Registry and Causes of Death Registry at Sweden’s National Board for Health and Welfare. The definition of serious condition was defined by expert consensus. Descriptive statistics as well as regression analyses were used. Results A total of 3780 patients were included, with a median age of 77 years. A serious condition was present in 35.3% of the patients. The in-hospital mortality rate for the group with serious conditions was 10.1% (OR 6.8, CI 95%, 4.1–11.3), and the 30-day mortality rate was 20.2% (OR 3.1, CI 95%, 2.3–4.0). In the group with no serious conditions the rates were 1.0 and 4.2%, respectively. The total hospitalization rate was 67.6%. The presence of serious conditions as well as increased mortality rates were associated with Rapid Emergency Triage and Treatment system (RETTS) as well as National Early Warning Score (NEWS) irrespective of triage score. Conclusion More than one-third of the patients presenting with NSCs to EMS had a serious underlying condition which was associated with increased mortality and hospitalization rates. Trial registration Not applicable.
Collapse
Affiliation(s)
- Robert Ivic
- Karolinska Institute, Department of Clinical Science and Education, Södersjukhuset, Stockholm, Sweden. .,Academic Emergency Medical Service, Region Stockholm, Stockholm, Sweden.
| | - Lisa Kurland
- Karolinska Institute, Department of Clinical Science and Education, Södersjukhuset, Stockholm, Sweden.,Department of Medical Sciences and Department of Emergency Medicine, Örebro University, Örebro, Sweden
| | - Veronica Vicente
- Karolinska Institute, Department of Clinical Science and Education, Södersjukhuset, Stockholm, Sweden.,Academic Emergency Medical Service, Region Stockholm, Stockholm, Sweden
| | - Maaret Castrén
- Karolinska Institute, Department of Clinical Science and Education, Södersjukhuset, Stockholm, Sweden.,Emergency Medicine, Helsinki University and Department of Emergency Medicine and Services, Helsinki University Hospital, Helsinki, Finland
| | - Katarina Bohm
- Karolinska Institute, Department of Clinical Science and Education, Södersjukhuset, Stockholm, Sweden.,Academic Emergency Medical Service, Region Stockholm, Stockholm, Sweden.,Department of Emergency Medicine, Södersjukhuset, Stockholm, Sweden
| |
Collapse
|
|
50
|
Affiliation(s)
- Joanne Cleverley
- Department of Radiology, Royal Free Hospital NHS Trust, London, UK
| | - James Piper
- Royal Free Hospital NHS Trust, London, UK
- UCL Medical School, Royal Free Campus, London, UK
| | - Melvyn M Jones
- Research Department of Primary Care & Population Health, UCL, Royal Free Campus, London, UK
- Institute of Biomedical Education, St George's University of London, London, UK
| |
Collapse
|