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Zhu B, Zhang D, Sang M, Zhao L, Wang C, Xu Y. Establishment and evaluation of a predictive model for length of hospital stay after total knee arthroplasty: A single-center retrospective study in China. Front Surg 2023; 10:1102371. [PMID: 37091271 PMCID: PMC10118006 DOI: 10.3389/fsurg.2023.1102371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 03/08/2023] [Indexed: 04/08/2023] Open
Abstract
BackgroundTotal knee arthroplasty (TKA) is the ultimate option for end-stage osteoarthritis, and the demand of this procedure are increasing every year. The length of hospital stay (LOS) greatly affects the overall cost of joint arthroplasty. The purpose of this study was to develop and validate a predictive model using perioperative data to estimate the risk of prolonged LOS in patients undergoing TKA.MethodsData for 694 patients after TKA collected retrospectively in our department were analyzed by logistic regression models. Multi-variable logistic regression modeling with forward stepwise elimination was used to determine reduced parameters and establish a prediction model. The discrimination efficacy, calibration efficacy, and clinical utility of the prediction model were evaluated.ResultsEight independent predictors were identified: non-medical insurance payment, Charlson Comorbidity Index (CCI) ≥ 3, body mass index (BMI) > 25.2, surgery on Monday, age > 67.5, postoperative complications, blood transfusion, and operation time > 120.5 min had a higher probability of hospitalization for ≥6 days. The model had good discrimination [area under the curve (AUC), 0.802 95% CI, 0.754–0.850]] and good calibration (p = 0.929). A decision curve analysis proved that the nomogram was clinically effective.ConclusionThis study identified risk factors for prolonged hospital stay in patients after TKA. It is important to recognize all the factors that affect hospital LOS to try to maximize the use of medical resources, optimize hospital LOS and ultimately optimize the care of our patients.
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Appiah KOB, Khunti K, Kelly BM, Innes AQ, Liao Z, Dymond M, Middleton RG, Wainwright TW, Yates T, Zaccardi F. Patient-rated satisfaction and improvement following hip and knee replacements: Development of prediction models. J Eval Clin Pract 2023; 29:300-311. [PMID: 36172971 DOI: 10.1111/jep.13767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 08/18/2022] [Accepted: 08/21/2022] [Indexed: 12/01/2022]
Abstract
RATIONALE Effective preoperative assessments of determinants of health status and function may improve postoperative outcomes. AIMS AND OBJECTIVES We developed risk scores of preoperative patient factors and patient-reported outcome measures (PROMs) as predictors of patient-rated satisfaction and improvement following hip and knee replacements. PATIENTS AND METHODS Prospectively collected National Health Service and independent sector patient data (n = 30,457), including patients' self-reported demographics, comorbidities, PROMs (Oxford Hip/Knee score (OHS/OKS) and European Quality of Life (EQ5D index and health-scale), were analysed. Outcomes were defined as patient-reported satisfaction and improvement following surgery at 7-month follow-up. Univariable and multivariable-adjusted logistic regressions were undertaken to build prediction models; model discrimination was evaluated with the concordance index (c-index) and nomograms were developed to allow the estimation of probabilities. RESULTS Of the 14,651 subjects with responses for satisfaction following hip replacements 564 (3.8%) reported dissatisfaction, and 1433 (9.2%) of the 15,560 following knee replacement reported dissatisfaction. A total of 14,662 had responses for perceived improvement following hip replacement (lack of improvement in 391; 2.7%) and 15,588 following knee replacement (lack of improvements in 1092; 7.0%). Patients reporting poor outcomes had worse preoperative PROMs. Several factors, including age, gender, patient comorbidities and EQ5D, were included in the final prediction models: C-indices of these models were 0.613 and 0.618 for dissatisfaction and lack of improvement, respectively, for hip replacement and 0.614 and 0.598, respectively, for knee replacement. CONCLUSIONS Using easily accessible preoperative patient factors, including PROMs, we developed models which may help predict dissatisfaction and lack of improvement following hip and knee replacements and facilitate risk stratification and decision-making processes.
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Affiliation(s)
- Karen O B Appiah
- Leicester Diabetes Centre, Leicester General Hospital, University of Leicester, Leicester, UK.,Leicester Real World Evidence Unit, Leicester General Hospital, University of Leicester, Leicester, UK
| | - Kamlesh Khunti
- Leicester Diabetes Centre, Leicester General Hospital, University of Leicester, Leicester, UK.,Leicester Real World Evidence Unit, Leicester General Hospital, University of Leicester, Leicester, UK.,NIHR Applied Research Collaboration-East Midlands (ARC-EM), University Hospitals of Leicester NHS Trust and University of Leicester, Leicester, UK
| | | | | | | | | | - Robert G Middleton
- Nuffield Health, Epsom Gateway, Epsom, UK.,Orthopaedic Research Institute, Bournemouth University, Poole, UK
| | - Thomas W Wainwright
- Nuffield Health, Epsom Gateway, Epsom, UK.,Orthopaedic Research Institute, Bournemouth University, Poole, UK
| | - Thomas Yates
- Leicester Diabetes Centre, Leicester General Hospital, University of Leicester, Leicester, UK.,NIHR Leicester Biomedical Research Centre, University Hospitals of Leicester NHS Trust and University of Leicester, Leicester, UK
| | - Francesco Zaccardi
- Leicester Diabetes Centre, Leicester General Hospital, University of Leicester, Leicester, UK.,Leicester Real World Evidence Unit, Leicester General Hospital, University of Leicester, Leicester, UK
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An orthopaedic intelligence application successfully integrates data from a smartphone-based care management platform and a robotic knee system using a commercial database. INTERNATIONAL ORTHOPAEDICS 2023; 47:485-494. [PMID: 36508053 DOI: 10.1007/s00264-022-05651-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 12/02/2022] [Indexed: 12/14/2022]
Abstract
PURPOSE To evaluate the feasibility of using a smartphone-based care management platform (sbCMP) and robotic-assisted total knee arthroplasty (raTKA) to collect data throughout the episode-of-care and assess if intra-operative measures of soft tissue laxity in raTKA were associated with post-operative outcomes. METHODS A secondary data analysis of 131 patients in a commercial database who underwent raTKA was performed. Pre-operative through six week post-operative step counts and KOOS JR scores were collected and cross-referenced with intra-operative laxity measures. A Kruskal-Wallis test or a Wilcoxon sign-rank was used to assess outcomes. RESULTS There were higher step counts at six weeks post-operatively in knees with increased laxity in both the lateral compartment in extension and medial compartment in flexion (p < 0.05). Knees balanced in flexion within < 0.5 mm had higher KOOS JR scores at six weeks post-operative (p = 0.034) compared to knees balanced within 0.5-1.5 mm. CONCLUSION A smartphone-based care management platform can be integrated with raTKA to passively collect data throughout the episode-of-care. Associations between intra-operative decisions regarding laxity and post-operative outcomes were identified. However, more robust analysis is needed to evaluate these associations and ensure clinical relevance to guide machine learning algorithms.
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Oliveira FMD, Costa LAV, Bastos AMPDA, Paião ID, Ferretti M, Lenza M. Avaliação dos fatores de risco relacionados ao tempo de internação e às complicações pós-operatórias em pacientes submetidos a artroplastia total primária do joelho. Rev Bras Ortop 2022. [DOI: 10.1055/s-0042-1753534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022] Open
Abstract
Resumo
Objetivo Avaliar os fatores de risco relacionados a um tempo de internação mais longo e às complicações pós-operatórias precoces (primeiros 30 dias após a cirurgia) em pacientes submetidos a artroplastia total do joelho (ATJ).
Materiais e Métodos Este é um estudo transversal com coleta de dados de pacientes submetidos a ATJ em um hospital privado entre 2015 e 2019. Os seguintes dados foram coletados: idade, gênero, índice de massa corporal, e comorbidades clínicas. Também coletamos dados intraoperatórios, como o grau na classificação da American Society of Anesthesiologists (ASA) e a duração da cirurgia, além do tempo de internação, as complicações pós-operatórias, e a readmissão em 30 dias. Os possíveis fatores de risco associados a um tempo de internação mais longo e às taxas de complicações pós-operatórias foram investigados por meio de modelos estatísticos.
Resultados Os pacientes mais velhos, com graus mais elevados na classificação da ASA ou que sofreram complicações pós-operatórias, ficaram internados por mais tempo. Para cada aumento em um ano de idade, esperamos que o tempo de internação seja multiplicado por 1,008 (intervalo de confiança de 95% [IC95%]: 1,004 a 1,012; p < 0,001). Em pacientes de grau III na classificação da ASA, espera-se que o tempo seja multiplicado por 1,297 (IC95%: 1,083 a 1,554; p = 0,005) em comparação com pacientes de grau I. Em pacientes com complicações pós-operatórias, espera-se que o tempo seja multiplicado por 1,505 (IC95%: 1,332 a 1,700; p < 0,001) em comparação com pacientes sem complicações.
Conclusão Este estudo demonstrou que, em pacientes submetidos a ATJ primária, características pré-operatórias, como idade avançada e grau ≥ III na classificação da ASA, e o desenvolvimento de complicações pós-operatórias predizem o aumento do tempo de internação hospitalar de forma independente.
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Affiliation(s)
| | | | | | - Isabela Dias Paião
- Departamento de Ortopedia, Hospital Israelita Albert Einstein, São Paulo, SP, Brasil
| | - Mário Ferretti
- Departamento de Ortopedia, Hospital Israelita Albert Einstein, São Paulo, SP, Brasil
| | - Mário Lenza
- Departamento de Ortopedia, Hospital Israelita Albert Einstein, São Paulo, SP, Brasil
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Xu K, Zhang L, Ren Z, Wang T, Zhang Y, Zhao X, Yu T. Development and validation of a nomogram to predict complications in patients undergoing simultaneous bilateral total knee arthroplasty: A retrospective study from two centers. Front Surg 2022; 9:980477. [PMID: 36189401 PMCID: PMC9515415 DOI: 10.3389/fsurg.2022.980477] [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: 06/28/2022] [Accepted: 08/30/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose Complications were significantly increased 30 days after Simultaneous bilateral total knee arthroplasty (SBTKA). In this study, an individualized nomogram was established and validated to predict the complications within 30 days after SBTKA. Methods The general data of 861 patients (training set) who received SBTKA in The Affiliated Hospital of Qingdao University between January 1, 2012 and March 31, 2017 were retrospectively analyzed. All patients were divided into complication group (n = 96) and non-complication group (n = 765) according to the incidence of complications within 30 years after SBTKA. Independent risk factors for postoperative SBTKA complications were identified and screened by binary logistic regression analyses, and then a nomogram prediction model was constructed using R software. The area under curve (AUC), calibration curve, and decision curve analysis (DCA) were selected to evaluate the line-chart. Meanwhile, 396 patients receiving SBTKA in the Third Hospital of Hebei Medical University who met the inclusion and exclusion criteria (test set) were selected to verify the nomogram. Results Five independent predictors were identified by binary logistic regression analyses and a nomogram was established. The AUC of this nomogram curve is 0.851 (95% CI: 0.819-0.883) and 0.818 (95% CI: 0.735-0.900) in the training and testing sets, respectively. In the training set and test set, calibration curves show that nomogram prediction results are in good agreement with actual observation results, and DCA shows that nomogram prediction results have good clinical application value. Conclusion Older age, lower preoperative hemoglobin level, higher preoperative blood urea nitrogen (BUN) level, longer operation time, ASA grade ≥ III are independent predictors of SBTKA complications within 30 days after surgery. A nomogram containing these five predictors can accurately predict the risk of complications within 30 days after SBTKA.
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Affiliation(s)
- Kuishuai Xu
- Department of Sports Medicine, the Affiliated Hospital of Qingdao University, Qingdao, China
| | - Liang Zhang
- Department of Abdominal Ultrasound, the Affiliated Hospital of Qingdao University, Qingdao, China
| | - Zhongkai Ren
- Department of Sports Medicine, the Affiliated Hospital of Qingdao University, Qingdao, China
| | - Tianrui Wang
- Department of Traumatology, the Affiliated Hospital of Qingdao University, Qingdao, China
| | - Yingze Zhang
- Department of Sports Medicine, the Affiliated Hospital of Qingdao University, Qingdao, China
| | - Xia Zhao
- Department of Sports Medicine, the Affiliated Hospital of Qingdao University, Qingdao, China
| | - Tengbo Yu
- Department of Sports Medicine, the Affiliated Hospital of Qingdao University, Qingdao, China
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Chang WJ, Naylor J, Natarajan P, Liu V, Adie S. Evaluating methodological quality of prognostic prediction models on patient reported outcome measurements after total hip replacement and total knee replacement surgery: a systematic review protocol. Syst Rev 2022; 11:165. [PMID: 35948989 PMCID: PMC9364604 DOI: 10.1186/s13643-022-02039-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 07/28/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Prediction models for poor patient-reported surgical outcomes after total hip replacement (THR) and total knee replacement (TKR) may provide a method for improving appropriate surgical care for hip and knee osteoarthritis. There are concerns about methodological issues and the risk of bias of studies producing prediction models. A critical evaluation of the methodological quality of prediction modelling studies in THR and TKR is needed to ensure their clinical usefulness. This systematic review aims to (1) evaluate and report the quality of risk stratification and prediction modelling studies that predict patient-reported outcomes after THR and TKR; (2) identify areas of methodological deficit and provide recommendations for future research; and (3) synthesise the evidence on prediction models associated with post-operative patient-reported outcomes after THR and TKR surgeries. METHODS MEDLINE, EMBASE, and CINAHL electronic databases will be searched to identify relevant studies. Title and abstract and full-text screening will be performed by two independent reviewers. We will include (1) prediction model development studies without external validation; (2) prediction model development studies with external validation of independent data; (3) external model validation studies; and (4) studies updating a previously developed prediction model. Data extraction spreadsheets will be developed based on the CHARMS checklist and TRIPOD statement and piloted on two relevant studies. Study quality and risk of bias will be assessed using the PROBAST tool. Prediction models will be summarised qualitatively. Meta-analyses on the predictive performance of included models will be conducted if appropriate. A narrative review will be used to synthesis the evidence if there are insufficient data to perform meta-analyses. DISCUSSION This systematic review will evaluate the methodological quality and usefulness of prediction models for poor outcomes after THR or TKR. This information is essential to provide evidence-based healthcare for end-stage hip and knee osteoarthritis. Findings of this review will contribute to the identification of key areas for improvement in conducting prognostic research in this field and facilitate the progress in evidence-based tailored treatments for hip and knee osteoarthritis. SYSTEMATIC REVIEW REGISTRATION PROSPERO registration number CRD42021271828.
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Affiliation(s)
- Wei-Ju Chang
- Centre for Pain IMPACT, Neuroscience Research Australia (NeuRA), 139 Barker St, Randwick, NSW 2031 Australia
- School of Health Sciences, College of Health, Medicine and Wellbeing, The University of Newcastle, Callaghan, NSW 2038 Australia
| | - Justine Naylor
- School of Clinical Medicine, UNSW Medicine & Health, South West Clinical Campuses, Discipline of Surgery, Faculty of Medicine and Health, UNSW, Sydney, NSW Australia
- Whitlam Orthopaedic Research Centre, Ingham Institute for Applied Medical Research, 1 Campbell St, Liverpool, NSW 2170 Australia
| | - Pragadesh Natarajan
- St George and Sutherland Clinical School, University of New South Wales, Clinical Sciences (WRPitney) Building, Short Street, St George Hospital, Kogarah, NSW 2217 Australia
| | - Victor Liu
- St George and Sutherland Clinical School, University of New South Wales, Clinical Sciences (WRPitney) Building, Short Street, St George Hospital, Kogarah, NSW 2217 Australia
| | - Sam Adie
- St George and Sutherland Clinical School, University of New South Wales, Clinical Sciences (WRPitney) Building, Short Street, St George Hospital, Kogarah, NSW 2217 Australia
- St. George and Sutherland Centre for Clinical Orthopaedic Research (SCORe), Suite 201, Level 2 131 Princes Highway, Kogarah, NSW 2217 Australia
- School of Clinical Medicine, UNSW Medicine & Health, UNSW, New South Wales Sydney, Australia
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Sun Z, Liu W, Liu H, Li J, Hu Y, Tu B, Wang W, Fan C. A new prognostic nomogram for heterotopic ossification formation after elbow trauma : the Shanghai post-Traumatic Elbow Heterotopic Ossification Prediction (STEHOP) model. Bone Joint J 2022; 104-B:963-971. [PMID: 35909382 DOI: 10.1302/0301-620x.104b8.bjj-2022-0206.r2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
AIMS Heterotopic ossification (HO) is a common complication after elbow trauma and can cause severe upper limb disability. Although multiple prognostic factors have been reported to be associated with the development of post-traumatic HO, no model has yet been able to combine these predictors more succinctly to convey prognostic information and medical measures to patients. Therefore, this study aimed to identify prognostic factors leading to the formation of HO after surgery for elbow trauma, and to establish and validate a nomogram to predict the probability of HO formation in such particular injuries. METHODS This multicentre case-control study comprised 200 patients with post-traumatic elbow HO and 229 patients who had elbow trauma but without HO formation between July 2019 and December 2020. Features possibly associated with HO formation were obtained. The least absolute shrinkage and selection operator regression model was used to optimize feature selection. Multivariable logistic regression analysis was applied to build the new nomogram: the Shanghai post-Traumatic Elbow Heterotopic Ossification Prediction model (STEHOP). STEHOP was validated by concordance index (C-index) and calibration plot. Internal validation was conducted using bootstrapping validation. RESULTS Male sex, obesity, open wound, dislocations, late definitive surgical treatment, and lack of use of non-steroidal anti-inflammatory drugs were identified as adverse predictors and incorporated to construct the STEHOP model. It displayed good discrimination with a C-index of 0.80 (95% confidence interval 0.75 to 0.84). A high C-index value of 0.77 could still be reached in the internal validation. The calibration plot showed good agreement between nomogram prediction and observed outcomes. CONCLUSION The newly developed STEHOP model is a valid and convenient instrument to predict HO formation after surgery for elbow trauma. It could assist clinicians in counselling patients regarding treatment expectations and therapeutic choices. Cite this article: Bone Joint J 2022;104-B(8):963-971.
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Affiliation(s)
- Ziyang Sun
- Department of Orthopaedics, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China.,Shanghai Engineering Research Center for Orthopaedic Material Innovation and Tissue Regeneration, Shanghai, China
| | - Weixuan Liu
- Department of Orthopaedics, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China.,Shanghai Engineering Research Center for Orthopaedic Material Innovation and Tissue Regeneration, Shanghai, China
| | - Hang Liu
- Department of Orthopaedics, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China.,Shanghai Engineering Research Center for Orthopaedic Material Innovation and Tissue Regeneration, Shanghai, China
| | - Juehong Li
- Department of Orthopaedics, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China.,Shanghai Engineering Research Center for Orthopaedic Material Innovation and Tissue Regeneration, Shanghai, China
| | - Yuehao Hu
- Shanghai Key Laboratory of Orthopaedic Implants, Department of Orthopaedic Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Bing Tu
- Department of Orthopaedics, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China.,Shanghai Engineering Research Center for Orthopaedic Material Innovation and Tissue Regeneration, Shanghai, China
| | - Wei Wang
- Department of Orthopaedics, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China.,Shanghai Engineering Research Center for Orthopaedic Material Innovation and Tissue Regeneration, Shanghai, China
| | - Cunyi Fan
- Department of Orthopaedics, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China.,Shanghai Engineering Research Center for Orthopaedic Material Innovation and Tissue Regeneration, Shanghai, China
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Tan XJ, Gu XX, Ge FM, Li ZY, Zhang LQ. Nomogram to predict postoperative complications in elderly with total hip replacement. World J Clin Cases 2022; 10:3720-3728. [PMID: 35647152 PMCID: PMC9100714 DOI: 10.12998/wjcc.v10.i12.3720] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 02/22/2022] [Accepted: 03/26/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND By analyzing the risk factors of postoperative complications in elderly patients with hip replacement, We aimed to develop a nomogram model based on preoperative and intraoperative variables and verified the sensitivity and specificity for risk stratification of postoperative complications in elderly with total hip replacement patients.
AIM To develop a nomogram model for risk stratification of postoperative complications in elderly with total hip replacement patients.
METHODS A total of 414 elderly patients who underwent surgical treatment for total hip replacement hospitalized at the Affiliated Hospital of Guangdong Medical University from March 1, 2017 to August 31, 2019 were included into this study. Univariate and multivariate logistic regression were conducted to identify independent risk factors of postoperative complication in the 414 patients. A nomogram was developed by R software and validated to predict the risk of postoperative complications.
RESULTS Multivariate logistic regression analysis revealed that age (OR = 1.05, 95%CI: 1.00-1.09), renal failure (OR = 0.90, 95%CI: 0.83-0.97), Type 2 diabetes (OR = 1.05, 95%CI: 1.00-1.09), albumin (ALB) (OR = 0.91, 95%CI: 0.83-0.99) were independent risk factors of postoperative complication in elderly patients with hip replacement (P < 0.05). For validation of the nomogram, receive operating characteristic curve revealed that the model predicting postoperative complication in elderly patients with hip replacement was the area under the curve of 0.8254 (95%CI: 0.78-0.87), the slope of the calibration plot was close to 1 and the model passed Hosmer-Lemeshow goodness of fit test (χ2 = 10.16, P = 0.4264), calibration in R Emax = 0.176, Eavg = 0.027, which all demonstrated that the model was of good accuracy.
CONCLUSION The nomogram predicting postoperative complications in patients with total hip replacement constructed based on age, type 2 diabetes, renal failure and ALB is of good discrimination and accuracy, which was of clinical significance.
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Affiliation(s)
- Xiu-Juan Tan
- Department of Anesthesiology, The First Affiliated Hospital, Jinan University, Guangzhou 510630, Guangdong Province, China
| | - Xiao-Xia Gu
- Department of Anesthesiology, The Affiliated Hospital of Guangdong Medical University, Zhanjiang 524001, Guangdong Province, China
| | - Feng-Min Ge
- Department of Anesthesiology, The Affiliated Hospital of Guangdong Medical University, Zhanjiang 524001, Guangdong Province, China
| | - Zhi-Yi Li
- Department of Anesthesiology, The Affiliated Hospital of Guangdong Medical University, Zhanjiang 524001, Guangdong Province, China
| | - Liang-Qing Zhang
- Department of Anesthesiology, The Affiliated Hospital of Guangdong Medical University, Zhanjiang 524001, Guangdong Province, China
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Wu R, Ma Y, Yang Y, Li M, Zheng Q, Fu G. A clinical model for predicting knee replacement in early-stage knee osteoarthritis: data from osteoarthritis initiative. Clin Rheumatol 2022; 41:1199-1210. [PMID: 34802087 DOI: 10.1007/s10067-021-05986-z] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 11/04/2021] [Accepted: 11/05/2021] [Indexed: 02/05/2023]
Abstract
BACKGROUND Knee osteoarthritis (OA) progresses in a heterogeneous way, as a majority of the patients gradually worsen over decades while some undergo rapid progression and require knee replacement. The aim of this study was to develop a predictive model that enables quantified risk prediction of future knee replacement in patients with early-stage knee OA. METHODS Patients with early-stage knee OA, intact MRI measurements, and a follow-up time larger than 108 months were retrieved from the Osteoarthritis Initiative database. Twenty-five candidate predictors including demographic data, clinical outcomes, and radiographic parameters were selected. The presence or absence of knee replacement during the first 108 months of the follow-up was regarded as the primary outcome. Patients were randomly divided into derivation and validation groups in the ratio of three to one. Nomograms were developed based on multivariable logistic regressions of derivation group via R language. Those models were further tested in the validation group for external validation. RESULTS A total of 839 knees were enrolled, with 98 knees received knee replacement during the first 108 months. Glucocorticoid injection history, knee OA in the contralateral side, extensor muscle strength, area of cartilage deficiency, bone marrow lesion, and meniscus extrusion were selected to develop the nomogram after multivariable logistic regression analysis. The bias-corrected C-index and AUC of our nomogram in the validation group were 0.804 and 0.822, respectively. CONCLUSION Our predicting model provided simplified identification of patients with high risk of rapid progression in knee OA, which showed adequate predictive discrimination and calibration. KEY POINTS • Knee OA progresses in a heterogeneous way and rises to a challenge when making treatment strategies. • Our predicting model provided simplified identification of patients with high risk of rapid progression in knee OA.
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Affiliation(s)
- Rongjie Wu
- Department of Orthopedics, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Yuexiu District, 106, Zhongshan Road, Guangzhou, Guangdong Province, People's Republic of China
- Shantou University Medical College, Shantou, Guangdong Province, People's Republic of China
| | - Yuanchen Ma
- Department of Orthopedics, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Yuexiu District, 106, Zhongshan Road, Guangzhou, Guangdong Province, People's Republic of China
| | - Yuhui Yang
- Department of Orthopedics, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Yuexiu District, 106, Zhongshan Road, Guangzhou, Guangdong Province, People's Republic of China
| | - Mengyuan Li
- Department of Orthopedics, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Yuexiu District, 106, Zhongshan Road, Guangzhou, Guangdong Province, People's Republic of China
| | - Qiujian Zheng
- Department of Orthopedics, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Yuexiu District, 106, Zhongshan Road, Guangzhou, Guangdong Province, People's Republic of China.
| | - Guangtao Fu
- Department of Orthopedics, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Yuexiu District, 106, Zhongshan Road, Guangzhou, Guangdong Province, People's Republic of China.
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10
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Liu W, Sun Z, Xiong H, Liu J, Lu J, Cai B, Wang W, Fan C. Development and validation of a prognostic nomogram for open elbow arthrolysis : the Shanghai Prediction model for Elbow Stiffness Surgical Outcome. Bone Joint J 2022; 104-B:486-494. [PMID: 35360939 PMCID: PMC9020519 DOI: 10.1302/0301-620x.104b4.bjj-2021-1326.r2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
AIMS The aim of this study was to develop and internally validate a prognostic nomogram to predict the probability of gaining a functional range of motion (ROM ≥ 120°) after open arthrolysis of the elbow in patients with post-traumatic stiffness of the elbow. METHODS We developed the Shanghai Prediction Model for Elbow Stiffness Surgical Outcome (SPESSO) based on a dataset of 551 patients who underwent open arthrolysis of the elbow in four institutions. Demographic and clinical characteristics were collected from medical records. The least absolute shrinkage and selection operator regression model was used to optimize the selection of relevant features. Multivariable logistic regression analysis was used to build the SPESSO. Its prediction performance was evaluated using the concordance index (C-index) and a calibration graph. Internal validation was conducted using bootstrapping validation. RESULTS BMI, the duration of stiffness, the preoperative ROM, the preoperative intensity of pain, and grade of post-traumatic osteoarthritis of the elbow were identified as predictors of outcome and incorporated to construct the nomogram. SPESSO displayed good discrimination with a C-index of 0.73 (95% confidence interval 0.64 to 0.81). A high C-index value of 0.70 could still be reached in the interval validation. The calibration graph showed good agreement between the nomogram prediction and the outcome. CONCLUSION The newly developed SPESSO is a valid and convenient model which can be used to predict the outcome of open arthrolysis of the elbow. It could assist clinicians in counselling patients regarding the choice and expectations of treatment. Cite this article: Bone Joint J 2022;104-B(4):486-494.
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Affiliation(s)
- Weixuan Liu
- Department of Orthopaedics, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
- Shanghai Engineering Research Center for Orthopedic Material Innovation and Tissue Regeneration, Shanghai, China
| | - Ziyang Sun
- Department of Orthopaedics, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
- Shanghai Engineering Research Center for Orthopedic Material Innovation and Tissue Regeneration, Shanghai, China
| | - Hao Xiong
- Department of Orthopaedics, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
- Shanghai Engineering Research Center for Orthopedic Material Innovation and Tissue Regeneration, Shanghai, China
| | - Junjian Liu
- Department of Orthopedics, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Jiuzhou Lu
- Department of Hand Surgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Bin Cai
- Department of Rehabilitation Medicine, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wei Wang
- Department of Orthopaedics, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
- Shanghai Engineering Research Center for Orthopedic Material Innovation and Tissue Regeneration, Shanghai, China
| | - Cunyi Fan
- Department of Orthopaedics, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
- Shanghai Engineering Research Center for Orthopedic Material Innovation and Tissue Regeneration, Shanghai, China
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11
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Xie C, Ou S, Lin Z, Zhang J, Li Q, Lin L. Prediction of 90-Day Local Complications in Patients After Total Knee Arthroplasty: A Nomogram With External Validation. Orthop J Sports Med 2022; 10:23259671211073331. [PMID: 35224115 PMCID: PMC8873555 DOI: 10.1177/23259671211073331] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 11/08/2021] [Indexed: 11/16/2022] Open
Abstract
Background: Local complications after total knee arthroplasty (TKA) significantly affect the patient’s prognosis. Nomograms can be a useful tool for predicting such complications. Purpose: To compare the preoperative and intraoperative factors of patients who underwent TKA with and without complications and to construct and validate a nomogram based on selective predictors of local complications within 90 days postoperatively. Study Design: Case-control study; Level of evidence, 3. Methods: The nomogram was developed in a primary cohort that consisted of 410 patients who underwent primary TKA at the authors’ institution between January 2015 and September 2018. Predictor variables included 4 major local complications that can occur within 90 days: reoperation (including implant revision or removal for any reason and manipulation under anesthesia), infection, bleeding requiring ≥4 unit transfusion of red blood cells within 72 hours of surgery, and peripheral nerve injury. The authors used least absolute shrinkage and selection operator (LASSO) regression analysis for data dimension reduction and feature selection. Multivariable logistic regression analysis was used to develop the nomogram. Performance of the nomogram was assessed using C-index, calibration plot, area under the receiver operating characteristic curve (AUC), and decision curve analysis (DCA). The model was subjected to bootstrap validation and external validation using a prospective cohort of 249 patients. Results: Four significantly prognostic factors were incorporated into the nomogram: age-adjusted Charlson Comorbidity Index, American Society of Anesthesiologists score, tourniquet time, and estimated intraoperative blood loss. The model displayed good discrimination, with a C-index of 0.819 and an AUC of 0.819. The calibration curves showed optimal agreement between nomogram prediction and actual observation. A high C-index value of 0.801 could still be reached in bootstrap validation. Application of the nomogram in the validation cohort showed good discrimination (C-index, 0.731) and good calibration. DCA demonstrated that the nomogram was clinically useful. Conclusion: The authors developed and validated a novel nomogram that can provide individual prediction of local complications within 90 days for patients after TKA. This practical tool may be conveniently used to estimate individual risk and help clinicians take measures to minimize or prevent the incidence of complications.
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Affiliation(s)
- Chao Xie
- Department of Joint and Orthopedics, Zhujiang Hospital of Southern Medical University, Guangzhou, China
| | - Songwen Ou
- Department of Joint and Orthopedics, Zhujiang Hospital of Southern Medical University, Guangzhou, China
- The Eighth People’s Hospital of Dongguan, Guangdong Medical University, Dongguan City, China
| | - Zhaowei Lin
- Department of Joint and Orthopedics, Zhujiang Hospital of Southern Medical University, Guangzhou, China
| | - Jinwei Zhang
- Department of Joint and Orthopedics, Zhujiang Hospital of Southern Medical University, Guangzhou, China
| | - Qi Li
- Department of Joint and Orthopedics, Zhujiang Hospital of Southern Medical University, Guangzhou, China
| | - Lijun Lin
- Department of Joint and Orthopedics, Zhujiang Hospital of Southern Medical University, Guangzhou, China
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12
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MacLean IS, Lu Y, Patel BH, Agarwalla A, Nolte MT, Lavoie-Gagne O, Romeo AA, Forsythe B. A Risk Stratification Nomogram to Predict Inpatient Admissions After Total Shoulder Arthroplasty Among Patients Eligible for Medicare. Orthopedics 2022; 45:43-49. [PMID: 34734779 DOI: 10.3928/01477447-20211101-09] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
The goal of this study was to establish a risk stratification nomogram to aid in determining the need for inpatient admission among patients who were eligible for Medicare and were undergoing primary total shoulder arthroplasty (TSA). The American College of Surgeons National Surgical Quality Improvement Program database was queried to identify all patients older than 65 years who underwent primary TSA between 2006 and 2016. The primary outcome measure was inpatient admission, as defined by hospital length of stay longer than 2 days. Multiple demographic, comorbid, and peri-operative variables were used in a multivariate logistic regression model to yield a risk stratification nomogram. A total of 1514 inpatient and 6020 out-patient admissions were analyzed. Age older than 80 years (odds ratio [OR], 2.69; P<.0001; 95% CI, 2.21-3.27), female sex (OR, 2.18; P<.0001; 95% CI, 1.90-2.51), dependent functional status (OR, 1.69; P<.0001; 95% CI, 1.2-2.38), dialysis (OR, 3.48; P=.029; 95% CI, 1.14-10.63), admission from an inpatient facility (OR, 1.76; P<.0001; 95% CI, 1.70-1.82), and inflammatory arthritis (OR, 1.69; P<.02; 95% CI, 1.25-13.78) were the greatest determinants of inpatient stay. The resulting predictive model showed acceptable discrimination and calibration. Our model enabled reliable and straightforward identification of the most suitable candidates for inpatient admission among patients who were eligible for Medicare and were undergoing primary TSA. Patients who were receiving dialysis, who had dyspnea at rest, and who had bleeding disorders were more likely to be admitted as inpatients after TSA. Larger multicenter studies are necessary to externally validate the proposed predictive nomogram. [Orthopedics. 2022;45(1):43-49.].
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13
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Fu G, Li M, Xue Y, Wang H, Zhang R, Ma Y, Zheng Q. Rapid preoperative predicting tools for 1-year mortality and walking ability of Asian elderly femoral neck fracture patients who planned for hip arthroplasty. J Orthop Surg Res 2021; 16:455. [PMID: 34271974 PMCID: PMC8283892 DOI: 10.1186/s13018-021-02605-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 07/08/2021] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Femoral neck fractures in elderly patients typically warrant operative treatment and are related to high risks of mortality and morbidity. As early hip arthroplasties for elderly femoral neck fractures are widely accepted, rapid predicting models that allowed quantitative and individualized prognosis assessments are strongly needed as references for orthopedic surgeons during preoperative conversations. METHODS Data of patients aged ≥ 65 years old who underwent primary unilateral hemiarthroplasty or total hip arthroplasty due to femoral neck fracture between January 1st, 2012 and June 30th, 2019 in our center were collected. Candidate variables included demographic data, comorbidities, and routine preoperative screening tests. The main outcomes included 1-year mortality and free walking rate after hip arthroplasty. Patients were randomly divided into derivation and validation groups in the ratio of three to one. Nomograms were developed based on multivariable logistic regressions of derivation group via R language. One thousand bootstraps were used for internal validation. Those models were further tested in the validation group for external validation. RESULTS The final analysis was performed on 702 patients after exclusion and follow-up. All-cause 1-year mortality of the entire data set was 23.4%, while the free walking rate was 57.3%. Preoperative walking ability showed the biggest impact on predicting 1-year mortality and walking ability. Static nomograms were created from the final multivariable models, which allowed simplified graphical computations for the risks of 1-year mortality and walking ability in a certain patient. The bias-corrected C index of those nomograms for predicting 1-year mortality in the derivation group and the validation group were 0.789 and 0.768, while they were 0.807 and 0.759 for predicting postoperative walking ability. The AUC of the mortality and walking ability predicting models were 0.791 and 0.818, respectively. CONCLUSIONS Our models enabled rapid preoperative 1-year mortality and walking ability predictions in Asian elderly femoral neck fracture patients who planned for hip arthroplasty, with adequate predictive discrimination and calibration. Those rapid assessment models could help surgeons in making more reasonable clinical decisions and subsequently reducing the risk of potential medical dispute via quantitative and individualized prognosis assessments.
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Affiliation(s)
- Guangtao Fu
- Department of Orthopedics, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong Province, People's Republic of China
| | - Mengyuan Li
- Department of Orthopedics, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong Province, People's Republic of China
| | - Yunlian Xue
- Division of Statistics, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong Province, People's Republic of China
| | - Hao Wang
- Department of Orthopedics, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong Province, People's Republic of China
| | - Ruiying Zhang
- Department of Orthopedics, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong Province, People's Republic of China.
| | - Yuanchen Ma
- Department of Orthopedics, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong Province, People's Republic of China.
| | - Qiujian Zheng
- Department of Orthopedics, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong Province, People's Republic of China.
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14
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Shah AA, Devana SK, Lee C, Kianian R, van der Schaar M, SooHoo NF. Development of a Novel, Potentially Universal Machine Learning Algorithm for Prediction of Complications After Total Hip Arthroplasty. J Arthroplasty 2021; 36:1655-1662.e1. [PMID: 33478891 PMCID: PMC10371358 DOI: 10.1016/j.arth.2020.12.040] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 11/19/2020] [Accepted: 12/22/2020] [Indexed: 02/02/2023] Open
Abstract
BACKGROUND As the prevalence of hip osteoarthritis increases, the number of total hip arthroplasty (THA) procedures performed is also projected to increase. Accurately risk-stratifying patients who undergo THA would be of great utility, given the significant cost and morbidity associated with developing perioperative complications. We aim to develop a novel machine learning (ML)-based ensemble algorithm for the prediction of major complications after THA, as well as compare its performance against standard benchmark ML methods. METHODS This is a retrospective cohort study of 89,986 adults who underwent primary THA at any California-licensed hospital between 2015 and 2017. The primary outcome was major complications (eg infection, venous thromboembolism, cardiac complication, pulmonary complication). We developed a model predicting complication risk using AutoPrognosis, an automated ML framework that configures the optimally performing ensemble of ML-based prognostic models. We compared our model with logistic regression and standard benchmark ML models, assessing discrimination and calibration. RESULTS There were 545 patients who had major complications (0.61%). Our novel algorithm was well-calibrated and improved risk prediction compared to logistic regression, as well as outperformed the other four standard benchmark ML algorithms. The variables most important for AutoPrognosis (eg malnutrition, dementia, cancer) differ from those that are most important for logistic regression (eg chronic atherosclerosis, renal failure, chronic obstructive pulmonary disease). CONCLUSION We report a novel ensemble ML algorithm for the prediction of major complications after THA. It demonstrates superior risk prediction compared to logistic regression and other standard ML benchmark algorithms. By providing accurate prognostic information, this algorithm may facilitate more informed preoperative shared decision-making.
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Affiliation(s)
- Akash A Shah
- Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, Los Angeles, CA
| | - Sai K Devana
- Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, Los Angeles, CA
| | - Changhee Lee
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA
| | - Reza Kianian
- Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, Los Angeles, CA
| | - Mihaela van der Schaar
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA; Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom
| | - Nelson F SooHoo
- Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, Los Angeles, CA
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15
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Lavoie-Gagne O, Siow M, Harkin W, Flores AR, Girard PJ, Schwartz AK, Kent WT. Characterization of electric scooter injuries over 27 months at an urban level 1 trauma center. Am J Emerg Med 2021; 45:129-136. [PMID: 33690079 DOI: 10.1016/j.ajem.2021.02.019] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 01/04/2021] [Accepted: 02/08/2021] [Indexed: 10/22/2022] Open
Abstract
BACKGROUND Electric scooters (e-scooters) have become a widespread method of transportation. The purpose of this study is to provide risk stratification tools for modifiable risk factors associated with e-scooter injury morbidity. METHODS Patients at an urban Level 1 Trauma center sustaining e-scooter injuries between November 2017 through March 2020 were identified. Primary outcomes of interest were major trauma, as defined by an Injury Severity Score (ISS) >15, or hospital admission. RESULTS A total of 442 patients sustained orthopaedic (51%), facial (31%), cranial (13%), and chest/abdominal injuries (4.5%). Rate of helmet use was 2.5%, hospital admission was 40.7%, and intensive care was 3%. Patients with facial injuries were half as likely to sustain major trauma as compared to orthopaedic injuries (p < 0.05). Factors with higher likelihood of hospital admission included age > 40 years (OR 4.20, p < 0.01), alcohol or other substance intoxication (OR 4.14 and 9.87, p < 0.001), loss of consciousness (OR 2.72, p < 0.003), or transport to the hospital by ambulance (OR 4.47, p < 0.001). CONCLUSIONS There is a substantial proportion of major trauma within e-scooter injuries. Modifiable risk factors for hospital admission include use of head protection and substance use while riding e-scooters.
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Affiliation(s)
- Ophelie Lavoie-Gagne
- University of California, San Diego Department of Orthopaedic Surgery, San Diego, CA, USA.
| | - Matthew Siow
- University of California, San Diego Department of Orthopaedic Surgery, San Diego, CA, USA.
| | - William Harkin
- University of California, San Diego Department of Orthopaedic Surgery, San Diego, CA, USA.
| | - Alec R Flores
- University of California, San Diego Department of Orthopaedic Surgery, San Diego, CA, USA.
| | - Paul J Girard
- University of California, San Diego Department of Orthopaedic Surgery, San Diego, CA, USA.
| | - Alexandra K Schwartz
- University of California, San Diego Department of Orthopaedic Surgery, San Diego, CA, USA.
| | - William T Kent
- University of California, San Diego Department of Orthopaedic Surgery, San Diego, CA, USA.
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16
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Fu G, Li M, Xue Y, Li Q, Deng Z, Ma Y, Zheng Q. Perioperative patient-specific factors-based nomograms predict short-term periprosthetic bone loss after total hip arthroplasty. J Orthop Surg Res 2020; 15:503. [PMID: 33138840 PMCID: PMC7607681 DOI: 10.1186/s13018-020-02034-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Accepted: 10/20/2020] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Although medical intervention of periprosthetic bone loss in the immediate postoperative period was recommended, not all the patients experienced periprosthetic bone loss after total hip arthroplasty (THA). Prediction tools that enrolled all potential risk factors to calculate an individualized prediction of postoperative periprosthetic bone loss were strongly needed for clinical decision-making. METHODS Data of the patients who underwent primary unilateral cementless THA between April 2015 and October 2017 in our center were retrospectively collected. Candidate variables included demographic data and bone mineral density (BMD) in spine, hip, and periprosthetic regions that measured 1 week after THA. Outcomes of interest included the risk of postoperative periprosthetic bone loss in Gruen zone 1, 7, and total zones in the 1st postoperative year. Nomograms were presented based on multiple logistic regressions via R language. One thousand Bootstraps were used for internal validation. RESULTS Five hundred sixty-three patients met the inclusion criteria were enrolled, and the final analysis was performed in 427 patients (195 male and 232 female) after the exclusion. The mean BMD of Gruen zone 1, 7, and total were decreased by 4.1%, 6.4%, and 1.7% at the 1st year after THA, respectively. 61.1% of the patients (261/427) experienced bone loss in Gruen zone 1 at the 1st postoperative year, while there were 58.1% (248/427) in Gruen zone 7 and 63.0% (269/427) in Gruen zone total. Bias-corrected C-index for risk of postoperative bone loss in Gruen zone 1, 7, and total zones in the 1st postoperative year were 0.700, 0.785, and 0.696, respectively. The most highly influential factors for the postoperative periprosthetic bone loss were primary diagnosis and BMD in the corresponding Gruen zones at the baseline. CONCLUSIONS To the best of our knowledge, our study represented the first time to use the nomograms in estimating the risk of postoperative periprosthetic bone loss with adequate predictive discrimination and calibration. Those predictive models would help surgeons to identify high-risk patients who may benefit from anti-bone-resorptive treatment in the early postoperative period effectively. It is also beneficial for patients, as they can choose the treatment options based on a reasonable expectation following surgery.
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Affiliation(s)
- Guangtao Fu
- Division of Orthopedics, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong Province People’s Republic of China
| | - Mengyuan Li
- Division of Orthopedics, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong Province People’s Republic of China
| | - Yunlian Xue
- Division of Statistics, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong Province People’s Republic of China
| | - Qingtian Li
- Division of Orthopedics, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong Province People’s Republic of China
| | - Zhantao Deng
- Division of Orthopedics, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong Province People’s Republic of China
| | - Yuanchen Ma
- Division of Orthopedics, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong Province People’s Republic of China
| | - Qiujian Zheng
- Division of Orthopedics, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong Province People’s Republic of China
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17
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Xu C, Sun YM, Chai W, Chen JY. C-reactive protein may rise after closed-suction drainage removal in patients undergoing revision arthroplasty: a retrospective study. ANZ J Surg 2020; 90:1062-1066. [PMID: 32418318 DOI: 10.1111/ans.15915] [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: 05/20/2019] [Revised: 03/16/2020] [Accepted: 03/31/2020] [Indexed: 12/12/2022]
Abstract
BACKGROUND Serum C-reactive protein (CRP) has been one of the most commonly used markers to rule out early post-operative infection following total joint arthroplasty. The phenomenon that CRP values rise after prolonged drainage removal may occur in clinical settings. The purpose of this study is to investigate (i) the prevalence and risk factors of such a phenomenon and (ii) whether the raised CRP is associated with post-operative infection. METHODS A retrospective review of 72 revision arthroplasties of the knee and hip with prolonged drainage from 2011 to 2016 was conducted. Perioperative CRP levels were obtained, and patients were divided into two groups according to whether CRP levels were elevated after drainage removal (raised CRP group and control group). Multivariate logistic regression analyses were performed to identify risk factors of raised CRP after drainage removal. The incidence of post-operative wound complications and infection was compared between groups. RESULTS Overall, the prevalence of raised CRP after drainage removal was 29.17% (21/72). CRP level before drainage removal was associated with the occurrence of such a phenomenon (adjusted odds ratio per 10-mg/L increase 0.92, 95% confidence interval 0.87-0.97). The raised CRP levels decrease again within 3 days after drainage removal. There was no significant difference in the incidence of wound complications and infection between the raised CRP group and control group. CONCLUSION Almost one in three patients have raised CRP values after prolonged drainage removal following revision arthroplasty. However, CRP values can decrease again within 3 days after drainage removal without specific management. Almost one in three patients have raised C-reactive protein values after prolonged drainage removal following revision arthroplasty. However, C-reactive protein values can decrease again within 3 days after drainage removal without specific management. Surgeons should remember that such a phenomenon may be not be a proxy for post-operative infection following revision arthroplasty.
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Affiliation(s)
- Chi Xu
- Department of Orthopaedic Surgery, General Hospital of People's Liberation Army, Beijing, China
| | - Yun-Ming Sun
- Department of Orthopaedics, Shengli Oilfield Central Hospital, Dongying, China
| | - Wei Chai
- Department of Orthopaedic Surgery, General Hospital of People's Liberation Army, Beijing, China
| | - Ji-Ying Chen
- Department of Orthopaedic Surgery, General Hospital of People's Liberation Army, Beijing, China
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Abstract
Health policy is a complex and fluid topic that addresses care delivery with the goal of improving patient care. Understanding health policy initiatives, their motivation, and their effects, can help ensure hand surgeons are prepared for the changing health care landscape.
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Affiliation(s)
- Lauren M Shapiro
- Department of Orthopaedic Surgery, Stanford University, 300 Pasteur Drive, Room R1444, Mail Code: 5341, Stanford, CA 94305, USA
| | - Robin N Kamal
- Department of Orthopaedic Surgery, Stanford University, 450 Broadway Street MC: 6342, Redwood City, CA 94603, USA.
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Finch DJ, Pellegrini VD, Franklin PD, Magder LS, Pelt CE, Martin BI. The Effects of Bundled Payment Programs for Hip and Knee Arthroplasty on Patient-Reported Outcomes. J Arthroplasty 2020; 35:918-925.e7. [PMID: 32001083 PMCID: PMC8218221 DOI: 10.1016/j.arth.2019.11.028] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Revised: 11/10/2019] [Accepted: 11/17/2019] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND Patient-reported outcomes are essential to demonstrate the value of hip and knee arthroplasty, a common target for payment reforms. We compare patient-reported global and condition-specific outcomes after hip and knee arthroplasty based on hospital participation in Medicare's bundled payment programs. METHODS We performed a prospective observational study using the Comparative Effectiveness of Pulmonary Embolism Prevention after Hip and Knee Replacement trial. Differences in patient-reported outcomes through 6 months were compared between bundle and nonbundle hospitals using mixed-effects regression, controlling for baseline patient characteristics. Outcomes were the brief Knee Injury and Osteoarthritis Outcomes Score or the brief Hip Disability and Osteoarthritis Outcomes Score, the Patient-Reported Outcomes Measurement Information System Physical Health Score, and the Numeric Pain Rating Scale, measures of joint function, overall health, and pain, respectively. RESULTS Relative to nonbundled hospitals, arthroplasty patients at bundled hospitals had slightly lower improvement in Knee Injury and Osteoarthritis Outcomes Score (-1.8 point relative difference at 6 months; 95% confidence interval -3.2 to -0.4; P = .011) and Hip Disability and Osteoarthritis Outcomes Score (-2.3 point relative difference at 6 months; 95% confidence interval -4.0 to -0.5; P = .010). However, these effects were small, and the proportions of patients who achieved a minimum clinically important difference were similar. Preoperative to postoperative change in the Patient-Reported Outcomes Measurement Information System Physical Health Score and Numeric Pain Rating Scale demonstrated a similar pattern of slightly worse outcomes at bundled hospitals with similar rates of achieving a minimum clinically important difference. CONCLUSIONS Patients receiving care at hospitals participating in Medicare's bundled payment programs do not have meaningfully worse improvements in patient-reported measures of function, health, or pain after hip or knee arthroplasty.
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Affiliation(s)
- Daniel J Finch
- Department of Orthopaedics, University of Utah School of Medicine, Salt Lake City, UT; Tufts University School of Medicine, Boston, MA
| | | | - Patricia D Franklin
- Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Laurence S Magder
- Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD
| | - Christopher E Pelt
- Department of Orthopaedics, University of Utah School of Medicine, Salt Lake City, UT
| | - Brook I Martin
- Department of Orthopaedics, University of Utah School of Medicine, Salt Lake City, UT
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20
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Finch DJ, Martin BI, Franklin PD, Magder LS, Pellegrini VD. Patient-Reported Outcomes Following Total Hip Arthroplasty: A Multicenter Comparison Based on Surgical Approaches. J Arthroplasty 2020; 35:1029-1035.e3. [PMID: 31926776 PMCID: PMC8218222 DOI: 10.1016/j.arth.2019.10.017] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2019] [Revised: 09/29/2019] [Accepted: 10/08/2019] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND Comparisons of patient-reported outcomes (PROs) based on surgical approach for total hip arthroplasty (THA) in the United States are limited to series from single surgeons or institutions. Using prospective data from a large, multicenter study, we compare preoperative to postoperative changes in PROs between posterior, transgluteal, and anterior surgical approaches to THA. METHODS Patient-reported function, global health, and pain were systematically collected preoperatively and at 1, 3, and 6 months postoperatively from patients undergoing primary THA at 26 sites participating in the Comparative Effectiveness of Pulmonary Embolism Prevention After Hip and Knee Replacement (ClinicalTrials.gov: NCT02810704). Outcomes consisted of the brief Hip disability and Osteoarthritis Outcome Score, the Patient-Reported Outcomes Measurement Information System Physical Health score, and the Numeric Pain Rating Scale. Operative approaches were grouped by surgical plane relative to the abductor musculature as being either anterior, transgluteal, or posterior. RESULTS Between 12/12/2016 and 08/31/2019, outcomes from 3018 eligible participants were examined. At 1 month, the transgluteal cohort had a 2.2-point lower improvement in Hip disability and Osteoarthritis Outcomes Score (95% confidence interval, 0.40-4.06; P = .017) and a 1.3-point lower improvement in Patient-Reported Outcomes Measurement Information System Physical Health score (95% confidence interval, 0.48-2.04; P = .002) compared to posterior approaches. There was no significant difference in improvement between anterior and posterior approaches. At 3 and 6 months, no clinically significant differences in PRO improvement were observed between groups. CONCLUSION PROs 6 months following THA dramatically improved regardless of the plane of surgical approach, suggesting that choice of surgical approach can be left to the discretion of surgeons and patients without fear of differential early outcomes.
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Affiliation(s)
- Daniel J Finch
- Department of Orthopaedics, University of Utah School of Medicine, Salt Lake City, UT; Tufts University School of Medicine, Boston, MA
| | - Brook I Martin
- Department of Orthopaedics, University of Utah School of Medicine, Salt Lake City, UT
| | - Patricia D Franklin
- Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Laurence S Magder
- Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD
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Xie C, Li Q. A simple nomogram for predicting early complications in patients after primary knee arthroplasty. Knee 2020; 27:518-526. [PMID: 31926676 DOI: 10.1016/j.knee.2019.11.015] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Revised: 09/12/2019] [Accepted: 11/25/2019] [Indexed: 02/02/2023]
Abstract
BACKGROUND This study sought to construct a nomogram for patients based on preoperative and intraoperative variables to individually predict the likelihood of complications within 30 days after primary knee arthroplasty. METHODS Data were obtained from the medical record of patients who underwent primary knee arthroplasty at our institution from 2015 to 2018. Preoperative and intraoperative factors were collected critically. Predictor variables include 15 common complications occurring within 30 days. The predictive model was developed using multivariable logistic regression and least absolute shrinkage and selection operator regression. Clinical usefulness and calibration of the predicting model were assessed using C-index, calibration plot, receiver operating curve, and decision curve analysis. Internal validation was assessed using the bootstrapping validation. RESULTS The prediction nomogram identified six variables associated with complications, including hemoglobin, tourniquet time, operative time, estimated intraoperative blood loss, American Society of Anesthesiologists Classification (ASA class) and type of anesthesia. The model displayed good discrimination with a C-index of 0.822 (95% confidence interval: 0.760-0.884), an area under the curve of 0.822 and good calibration. High C-index value of 0.810 could still be reached in the interval validation. Decision curve analysis showed that the nomogram was clinically useful when intervention was decided at the complications possibility threshold in the three percent to 100% range. CONCLUSION We constructed and validated a nomogram for predicting the probability of postoperative complications within 30 days after primary knee arthroplasty. Our nomogram may prove to be a useful tool for guiding physicians in terms of their decisions.
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Affiliation(s)
- Chao Xie
- Department of Orthopedics, ZhuJiang Hospital of Southern Medical University, Guangzhou, China
| | - Qi Li
- Department of Orthopedics, ZhuJiang Hospital of Southern Medical University, Guangzhou, China.
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Gronbeck C, Cote MP, Halawi MJ. Predicting Inpatient Status After Primary Total Knee Arthroplasty in Medicare-Aged Patients. J Arthroplasty 2019; 34:1322-1327. [PMID: 30930154 DOI: 10.1016/j.arth.2019.03.009] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2018] [Revised: 02/22/2019] [Accepted: 03/04/2019] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND The Centers for Medicare and Medicaid Services (CMS) removed total knee arthroplasty (TKA) from its inpatient only (IPO) list as of January 1, 2018. The purpose of this study was to establish a risk-stratifying nomogram to aid in determining the need for inpatient admission among Medicare-aged patients undergoing primary TKA. METHODS The American College of Surgeons National Surgical Quality Improvement Program database was queried to identify all patients aged ≥65 years who underwent primary TKA between 2006 and 2015. The primary outcome measure was inpatient admission, as defined by hospital length of stay longer than 2 days. Multiple demographic, comorbid, and perioperative variables were incorporated in a multivariate logistic regression model to yield a risk stratification nomogram. RESULTS Sixty-one thousand two hundred eighty-four inpatient and 26,066 outpatient admissions were analyzed. Age >80 years (odds ratio [OR] = 2.27, P < .0001, 95% confidence interval [CI] = 2.13-2.42), simultaneous bilateral TKA (OR = 2.02, P < .0001, 95% CI = 1.77-2.30), dependent functional status (OR = 1.95, P < .0001, 95% CI = 1.62-2.35), metastatic cancer (OR = 1.91, P = .055, 95% CI = 0.99-3.73), and female gender (OR = 1.76, P < .0001, 95% CI = 1.70-1.82) were the greatest determinants of inpatient stay. The resulting predictive model demonstrated acceptable discrimination and excellent calibration. CONCLUSION Our model enabled a reliable and straightforward identification of the most suitable candidates for inpatient admission in Medicare aged-patients undergoing primary TKA. Larger multicenter studies are necessary to externally validate the proposed predictive nomogram.
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Affiliation(s)
| | - Mark P Cote
- Department of Orthopaedic Surgery, University of Connecticut Health Center, Farmington, CT
| | - Mohamad J Halawi
- Department of Orthopaedic Surgery, University of Connecticut Health Center, Farmington, CT
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Snyder DJ, Bienstock DM, Keswani A, Tishelman JC, Ahn A, Molloy IB, Koenig KM, Jevsevar DS, Poeran J, Moucha CS. Preoperative Patient-Reported Outcomes and Clinical Characteristics as Predictors of 90-Day Cost/Utilization and Complications. J Arthroplasty 2019; 34:839-845. [PMID: 30814027 DOI: 10.1016/j.arth.2019.01.059] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/23/2018] [Revised: 01/04/2019] [Accepted: 01/22/2019] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND With the advent of mandatory bundle payments for total joint arthroplasty (TJA), assessing patients' risk for increased 90-day complications and resource utilization is crucial. This study assesses the degree to which preoperative patient-reported outcomes predict 90-day complications, episode costs, and utilization in TJA patients. METHODS All TJA cases in 2017 at 2 high-volume hospitals were queried. Preoperative HOOS/KOOS JR (Hip Injury and Osteoarthritis Outcome Score/Knee Injury and Osteoarthritis Outcome Score) and Veterans RAND 12-item health survey (VR-12) were administered to patients preoperatively via e-collection platform. For patients enrolled in the Medicare bundle, cost data were extracted from claims. Bivariate and multivariate regression analyses were performed. RESULTS In total, 2108 patients underwent TJA in 2017; 1182 (56%) were missing patient-reported outcome data and were excluded. The final study population included 926 patients, 199 (21%) of which had available cost data. Patients with high bundle costs tended to be older, suffer from vascular disease and anemia, and have higher Charlson scores (P < .05 for all). These patients also had lower baseline VR-12 Physical Component Summary Score (PCS; 24 vs 30, P ≤ .001) and higher rates of extended length of stay, skilled nursing facility discharge, 90-day complications, and 90-day readmission (P ≤ .04 for all). In multivariate analysis, higher baseline VR-12 PCS was protective against extended length of stay, skilled nursing facility discharge, >75th percentile bundle cost, and 90-day bundle cost exceeding target bundle price (P < .01 for all). Baseline VR-12 Mental Component Summary Score and HOOS/KOOS JR were not predictive of complications or bundle cost. CONCLUSION Low baseline VR-12 PCS is predictive of high 90-day bundle costs. Baseline HOOS/KOOS JR scores were not predictive of utilization or cost. Neither VR-12 nor HOOS/KOOS JR was predictive of 90-day readmission or complications.
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Affiliation(s)
- Daniel J Snyder
- Department of Orthopedics, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Dennis M Bienstock
- Department of Orthopedics, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Aakash Keswani
- Department of Orthopedics, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Jared C Tishelman
- Department of Orthopedics, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Amy Ahn
- Department of Orthopedics, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Ilda B Molloy
- Department of Orthopaedics, Dartmouth-Hitchcock Medical Center, Lebanon, NH
| | - Karl M Koenig
- Department of Surgery and Perioperative Care, The University of Texas at Austin Dell Seton Medical Center, Austin, TX
| | - David S Jevsevar
- Department of Orthopaedics, Dartmouth-Hitchcock Medical Center, Lebanon, NH
| | - Jashvant Poeran
- Department of Orthopedics, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Calin S Moucha
- Department of Orthopedics, Icahn School of Medicine at Mount Sinai, New York, NY
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Can Machine Learning Methods Produce Accurate and Easy-to-use Prediction Models of 30-day Complications and Mortality After Knee or Hip Arthroplasty? Clin Orthop Relat Res 2019; 477:452-460. [PMID: 30624314 PMCID: PMC6370104 DOI: 10.1097/corr.0000000000000601] [Citation(s) in RCA: 108] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
BACKGROUND Existing universal and procedure-specific surgical risk prediction models of death and major complications after elective total joint arthroplasty (TJA) have limitations including poor transparency, poor to modest accuracy, and insufficient validation to establish performance across diverse settings. Thus, the need remains for accurate and validated prediction models for use in preoperative management, informed consent, shared decision-making, and risk adjustment for reimbursement. QUESTIONS/PURPOSES The purpose of this study was to use machine learning methods and large national databases to develop and validate (both internally and externally) parsimonious risk-prediction models for mortality and complications after TJA. METHODS Preoperative demographic and clinical variables from all 107,792 nonemergent primary THAs and TKAs in the 2013 to 2014 American College of Surgeons-National Surgical Quality Improvement Program (ACS-NSQIP) were evaluated as predictors of 30-day death and major complications. The NSQIP database was chosen for its high-quality data on important outcomes and rich characterization of preoperative demographic and clinical predictors for demographically and geographically diverse patients. Least absolute shrinkage and selection operator (LASSO) regression, a type of machine learning that optimizes accuracy and parsimony, was used for model development. Tenfold validation was used to produce C-statistics, a measure of how well models discriminate patients who experience an outcome from those who do not. External validation, which evaluates the generalizability of the models to new data sources and patient groups, was accomplished using data from the Veterans Affairs Surgical Quality Improvement Program (VASQIP). Models previously developed from VASQIP data were also externally validated using NSQIP data to examine the generalizability of their performance with a different group of patients outside the VASQIP context. RESULTS The models, developed using LASSO regression with diverse clinical (for example, American Society of Anesthesiologists classification, comorbidities) and demographic (for example, age, gender) inputs, had good accuracy in terms of discriminating the likelihood a patient would experience, within 30 days of arthroplasty, a renal complication (C-statistic, 0.78; 95% confidence interval [CI], 0.76-0.80), death (0.73; 95% CI, 0.70-0.76), or a cardiac complication (0.73; 95% CI, 0.71-0.75) from one who would not. By contrast, the models demonstrated poor accuracy for venous thromboembolism (C-statistic, 0.61; 95% CI, 0.60-0.62) and any complication (C-statistic, 0.64; 95% CI, 0.63-0.65). External validation of the NSQIP- derived models using VASQIP data found them to be robust in terms of predictions about mortality and cardiac complications, but not for predicting renal complications. Models previously developed with VASQIP data had poor accuracy when externally validated with NSQIP data, suggesting they should not be used outside the context of the Veterans Health Administration. CONCLUSIONS Moderately accurate predictive models of 30-day mortality and cardiac complications after elective primary TJA were developed as well as internally and externally validated. To our knowledge, these are the most accurate and rigorously validated TJA-specific prediction models currently available (http://med.stanford.edu/s-spire/Resources/clinical-tools-.html). Methods to improve these models, including the addition of nonstandard inputs such as natural language processing of preoperative clinical progress notes or radiographs, should be pursued as should the development and validation of models to predict longer term improvements in pain and function. LEVEL OF EVIDENCE Level III, diagnostic study.
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Gronbeck CJ, Cote MP, Halawi MJ. Predicting Inpatient Status After Total Hip Arthroplasty in Medicare-Aged Patients. J Arthroplasty 2019; 34:249-254. [PMID: 30466961 DOI: 10.1016/j.arth.2018.10.031] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2018] [Revised: 10/12/2018] [Accepted: 10/24/2018] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND The Centers for Medicare and Medicaid Services has solicited comments regarding the removal of total hip arthroplasty (THA) from its inpatient-only list. The goal of this study is to develop and internally validate a risk stratification nomogram to aid in the identification of optimal inpatient candidates in this patient population. METHODS The American College of Surgeons National Surgical Quality Improvement Program database was utilized to identify all patients >65 years of age who underwent primary THA between 2006 and 2015. Inpatient stay was the primary outcome measure, as defined by stay >2 days in length. The impact of numerous demographic, comorbid, and perioperative variables was assessed through a multivariable logistic regression analysis to construct a predictive nomogram. RESULTS In total, 30,587 inpatient THAs and 17,024 outpatient THAs were analyzed. Heart failure (odds ratio [OR] 2.11, P = .001), simultaneous bilateral THA (OR 2.47, P < .0001), age >80 years (OR 2.91, P < .0001), female gender (OR 1.90, P < .0001), and dependent functional status (OR 1.89, P < .0001) were the most influential determinants of inpatient status. The final prediction algorithm showed good accuracy, excellent calibration, and internal validation (bias-corrected concordance index of 0.69). CONCLUSION Our model enabled accurate and simple identification of the best candidates for inpatient admission after THA in Medicare-aged patients. Given the increasing feasibility of outpatient THA coupled with the likelihood of THA being removed from the Centers for Medicare and Medicaid Services inpatient-only list, this model provides a framework to guide discussion and decision-making for stakeholders.
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Affiliation(s)
| | - Mark P Cote
- Department of Orthopaedic Surgery, University of Connecticut Health Center, Farmington, CT
| | - Mohamad J Halawi
- Department of Orthopaedic Surgery, University of Connecticut Health Center, Farmington, CT
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Harris AHS, Kuo AC, Bowe T, Gupta S, Nordin D, Giori NJ. Prediction Models for 30-Day Mortality and Complications After Total Knee and Hip Arthroplasties for Veteran Health Administration Patients With Osteoarthritis. J Arthroplasty 2018; 33:1539-1545. [PMID: 29398261 PMCID: PMC6508537 DOI: 10.1016/j.arth.2017.12.003] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2017] [Revised: 11/30/2017] [Accepted: 12/01/2017] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Statistical models to preoperatively predict patients' risk of death and major complications after total joint arthroplasty (TJA) could improve the quality of preoperative management and informed consent. Although risk models for TJA exist, they have limitations including poor transparency and/or unknown or poor performance. Thus, it is currently impossible to know how well currently available models predict short-term complications after TJA, or if newly developed models are more accurate. We sought to develop and conduct cross-validation of predictive risk models, and report details and performance metrics as benchmarks. METHODS Over 90 preoperative variables were used as candidate predictors of death and major complications within 30 days for Veterans Health Administration patients with osteoarthritis who underwent TJA. Data were split into 3 samples-for selection of model tuning parameters, model development, and cross-validation. C-indexes (discrimination) and calibration plots were produced. RESULTS A total of 70,569 patients diagnosed with osteoarthritis who received primary TJA were included. C-statistics and bootstrapped confidence intervals for the cross-validation of the boosted regression models were highest for cardiac complications (0.75; 0.71-0.79) and 30-day mortality (0.73; 0.66-0.79) and lowest for deep vein thrombosis (0.59; 0.55-0.64) and return to the operating room (0.60; 0.57-0.63). CONCLUSIONS Moderately accurate predictive models of 30-day mortality and cardiac complications after TJA in Veterans Health Administration patients were developed and internally cross-validated. By reporting model coefficients and performance metrics, other model developers can test these models on new samples and have a procedure and indication-specific benchmark to surpass.
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Affiliation(s)
- Alex HS. Harris
- Center for Innovation to Implementation, VA Palo Alto Health Care System, Palo Alto, CA,Department of Surgery, Stanford —Surgical Policy Improvement Research and Education Center, Stanford University School of Medicine, Stanford, CA,Reprint requests: Alex H. S. Harris, PhD, M.S., Center for Innovation to Implementation, VA Palo Alto Health Care System, 795 Willow Road (152-MPD), Menlo Park, California 94025
| | - Alfred C. Kuo
- San Francisco Veterans Affairs Medical Center, University of California, San Francisco, CA
| | - Thomas Bowe
- Center for Innovation to Implementation, VA Palo Alto Health Care System, Palo Alto, CA
| | - Shalini Gupta
- Center for Innovation to Implementation, VA Palo Alto Health Care System, Palo Alto, CA
| | - David Nordin
- Minneapolis Veterans Affairs Medical Center, Minneapolis, MN
| | - Nicholas J. Giori
- Center for Innovation to Implementation, VA Palo Alto Health Care System, Palo Alto, CA,Department of Orthopedic Surgery, Stanford University School of Medicine, Stanford, CA
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Ngarambe C, Smart BJ, Nagarajan N, Rickard J. Validation of the Surgical Apgar Score After Laparotomy at a Tertiary Referral Hospital in Rwanda. World J Surg 2018; 41:1734-1742. [PMID: 28255629 DOI: 10.1007/s00268-017-3951-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
BACKGROUND The surgical Apgar score (SAS) has demonstrated utility in predicting postoperative outcomes in a variety of surgical disciplines. However, there has not been a study validating the utility of the SAS in surgical patients in low-income countries. We conducted a prospective, observational study of patients undergoing laparotomy at a tertiary referral hospital in Rwanda and determined the ability of SAS to predict inpatient major complications and mortality. METHODS All adult patients undergoing laparotomy in a tertiary referral hospital in Rwanda from October 2014 to January 2015 were included. Data were collected on patient and operative characteristics. SAS was calculated and patients were divided into four SAS categories. Primary outcomes were in-hospital mortality and major complications. Rates and odds of in-hospital mortality and major complications were examined across the four SAS categories. Logistic regression modeling and calculation of c-statistics was used to determine the discriminative ability of SAS. RESULTS 218 patients underwent laparotomy during the study period. One hundred and forty-three (65.6%) were male, and the median age was 34 years (IQR 27-51 years). The most common diagnosis was intestinal obstruction (97 [44.5%]). A high proportion of patients (170 [78%]) underwent emergency surgery. Thirty-nine (18.3%) patients died, and 61 (28.6%) patients had a major complication. In-hospital mortality occurred in 25 (50%) patients in the high-risk group, 12 (16%) in the moderate-risk group, 2 (3%) in the mild-risk group and there were no deaths in the low-risk group. Major complications occurred in 32 (64%) patients in the high-risk group, 22 (29%) in the moderate-risk group, 7 (11%) in the mild-risk group and there were no complications in the low-risk group. SAS was a good predictor of postoperative mortality (c-statistic 0.79) and major complications (c-statistic 0.75). CONCLUSIONS SAS can be used to predict in-hospital mortality and major complications after laparotomy in a Rwandan tertiary referral hospital.
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Affiliation(s)
- Christian Ngarambe
- Department of Surgery, University Teaching Hospital of Butare, Butare, Rwanda
| | - Blair J Smart
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Neeraja Nagarajan
- Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jennifer Rickard
- Department of Surgery, University Teaching Hospital of Kigali, Kigali, Rwanda. .,Department of Surgery, University of Minnesota, 516 Delaware St SE, 11-145E, Minneapolis, MN, 55455, USA.
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Runner RP, Bellamy JL, Vu CCL, Erens GA, Schenker ML, Guild GN. Modified Frailty Index Is an Effective Risk Assessment Tool in Primary Total Knee Arthroplasty. J Arthroplasty 2017; 32:S177-S182. [PMID: 28442185 DOI: 10.1016/j.arth.2017.03.046] [Citation(s) in RCA: 83] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2016] [Revised: 02/10/2017] [Accepted: 03/20/2017] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND "Frailty" is a marker of physiological decline of multiple organ systems, and the frailty index identifies patients who are more susceptible to postoperative complications. The purpose of this study is to validate the modified frailty index (MFI) as a predictor of postoperative complications, reoperations, and readmissions in patients who underwent primary total knee arthroplasty (TKA). METHODS The American College of Surgeons National Surgical Quality Improvement Program database from 2005 to 2014 was queried by the Current Procedural Terminology code for primary TKA (27447). A previously described MFI was used to summate 11 variables in 5 organ systems. Bivariate analysis was performed for postoperative complications. A multiple logistic regression model was used to determine the relationship between MFI, American Society of Anesthesiologists score, and 30-day reoperation, controlling for age, gender, and body mass index. RESULTS A total of 90,260 patients underwent primary TKA during the study period. As MFI score increased, 30-day mortality significantly increased (P < .001). In addition, significantly higher rates of postoperative complications (all P < .001) were observed with increasing MFI: infection, wound, cardiac, pulmonary, and renal complications; and any occurrence. More frail patients also had increasing odds of adverse hospital discharge disposition, reoperation, and readmission (all P < .001). Length of hospital stay increased from 3.10 to 5.16 days (P < .001), while length of intensive care unit stay increased from 3.47 to 5.07 days (P < .001) between MFI score 0 and ≥0.36. MFI predicts 30-day reoperation with an adjusted odds ratio of 3.32 (95% confidence interval, 1.36-8.11; P < .001). Comparatively, MFI was a stronger predictor of reoperation compared with American Society of Anesthesiologists score and age with adjustment for gender and body mass index. CONCLUSION Utilization of the MFI is a valid method in predicting postoperative complications, reoperations, and readmissions in patients undergoing primary TKA and can provide an effective and robust risk assessment tool to appropriately counsel patients and aid in preoperative optimization.
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Affiliation(s)
- Robert P Runner
- Department of Orthopaedics, Emory University School of Medicine, Atlanta, Georgia
| | - Jaime L Bellamy
- Department of Orthopaedics, Emory University School of Medicine, Atlanta, Georgia
| | - CatPhuong Cathy L Vu
- Department of Orthopaedics, Emory University School of Medicine, Atlanta, Georgia
| | - Greg A Erens
- Department of Orthopaedics, Emory University School of Medicine, Atlanta, Georgia
| | - Mara L Schenker
- Department of Orthopaedics, Emory University School of Medicine, Atlanta, Georgia; Department of Orthopaedics, Grady Memorial Hospital, Atlanta, Georgia
| | - George N Guild
- Department of Orthopaedics, Emory University School of Medicine, Atlanta, Georgia
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Shin JI, Keswani A, Lovy AJ, Moucha CS. Simplified Frailty Index as a Predictor of Adverse Outcomes in Total Hip and Knee Arthroplasty. J Arthroplasty 2016; 31:2389-2394. [PMID: 27240960 DOI: 10.1016/j.arth.2016.04.020] [Citation(s) in RCA: 84] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/02/2015] [Revised: 04/04/2016] [Accepted: 04/15/2016] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND The modified frailty index (mFI) has been shown to predict adverse outcomes in multiple nonorthopedic surgical specialties. This study aimed to assess whether mFI is a predictor of adverse events in patients undergoing primary total hip arthroplasty (THA) and total knee arthroplasty (TKA). METHODS Patients who underwent THA and TKA from 2005-2012 were identified in the National Surgical Quality Improvement Program database. mFI was calculated for each patient using 15 variables found in National Surgical Quality Improvement Program. Bivariate and multivariate analyses of postoperative adverse events, including Clavien-Dindo grade IV complications, were performed. RESULTS A total of 14,583 THA and 25,223 TKA patients were included for analysis. The mean (standard deviation, range) mFIs were 0.083 (0.080, 0-0.55) for THA and 0.097 (0.080, 0-0.64) for TKA cohorts. On bivariate analyses, incidence of Clavien-Dindo grade IV complications (cardiac arrest, myocardial infarction, septic shock, pulmonary embolism, postoperative dialysis, reintubation, and prolonged ventilator requirement), hospital-acquired conditions (surgical site infection, venous thromboembolism, and urinary tract infection), any complications, and mortality increased significantly with increase in mFI (P < .0001 for all). Adjusting for demographics, age ≥ 75, body mass index ≥40, American Society of Anesthesiologists class ≥4, and nonclean wound status, mFI ≥0.45 was shown to be the strongest independent predictor of Clavien-Dindo grade IV complications for both THA and TKA cohorts with odds ratios of 5.140 and 4.183, respectively. CONCLUSION mFI ≥0.45 is an independent predictor of Clavien-Dindo grade IV complications in TKA/THA patients with greater odds ratios than age >75, body mass index ≥40, American Society of Anesthesiologists class ≥4. mFI should be considered for risk stratifying joint arthroplasty patients preoperatively and perhaps determining immediate postoperative destination.
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Affiliation(s)
- John I Shin
- Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Aakash Keswani
- Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Andrew J Lovy
- Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Calin S Moucha
- Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, New York
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The impact of acute perioperative myocardial infarction on clinical outcomes after total joint replacement. CURRENT ORTHOPAEDIC PRACTICE 2016. [DOI: 10.1097/bco.0000000000000400] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Abstract
The current healthcare environment in America is driven by the concepts of quality, cost containment, and value. In this environment, primary hip and knee arthroplasty procedures have been targeted for cost containment through quality improvement initiatives intended to reduce the incidence of costly complications and readmissions. Accordingly, risk prediction tools have been developed in an attempt to quantify the patient-specific assessment of risk. Risk prediction tools may be useful for the informed consent process, for enhancing risk mitigation efforts, and for risk-adjusting data used for reimbursement and the public reporting of outcomes. The evaluation of risk prediction tools involves statistical measures such as discrimination and calibration to assess accuracy and utility. Furthermore, prediction tools are tuned to the source dataset from which they are derived, require validation with external datasets, and should be recalibrated over time. However, a high-quality, externally validated risk prediction tool for adverse outcomes after primary total joint arthroplasty remains an elusive goal.
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Halawi MJ, Vovos TJ, Green CL, Wellman SS, Attarian DE, Bolognesi MP. Opioid-Based Analgesia: Impact on Total Joint Arthroplasty. J Arthroplasty 2015. [PMID: 26220104 DOI: 10.1016/j.arth.2015.06.046] [Citation(s) in RCA: 45] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
The objective of this study was to characterize the impact of opioid-based analgesia in total joint arthroplasty. The primary outcomes were incidence of in-hospital complications, length of stay, and discharge destination. Six hundred and seventy-three primary total hip and knee arthroplasties were retrospectively reviewed. The incidence of opioid-related adverse drug events was 8.5%, which accounted for 58.2% of all postoperative complications. Age, anesthesia technique, ASA score, and surgery type were significant risk factors for complications. After adjusting for these confounders, opioid-related adverse drug events were significantly associated with increased length of stay (P < 0.001) and discharge to extended care facilities (P = 0.014).
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Affiliation(s)
- Mohamad J Halawi
- Department of Orthopaedic Surgery, Duke University Medical Center, Durham, North Carolina
| | - Tyler J Vovos
- Duke University School of Medicine, Durham, North Carolina
| | - Cindy L Green
- Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, North Carolina
| | - Samuel S Wellman
- Department of Orthopaedic Surgery, Duke University Medical Center, Durham, North Carolina
| | - David E Attarian
- Department of Orthopaedic Surgery, Duke University Medical Center, Durham, North Carolina
| | - Michael P Bolognesi
- Department of Orthopaedic Surgery, Duke University Medical Center, Durham, North Carolina
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Affiliation(s)
- James T Ninomiya
- Department of Orthopaedic Surgery, FMLH Specialty Clinics Building, Medical College of Wisconsin, 5200 West Wisconsin Avenue, Milwaukee, WI 53226. E-mail address:
| | - John C Dean
- West Texas Orthopedics, 10 Desta Drive, Suite 100E, Midland, TX 79705
| | - Stephen J Incavo
- Houston Methodist Hospital, Smith Tower, 6550 Fannin Street, Suite 2600, Houston, TX 77030
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Konopka JF, Hansen VJ, Rubash HE, Freiberg AA. Risk assessment tools used to predict outcomes of total hip and total knee arthroplasty. Orthop Clin North Am 2015; 46:351-62, ix-x. [PMID: 26043049 DOI: 10.1016/j.ocl.2015.02.004] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
This article reviews recently proposed clinical tools for predicting risks and outcomes in total hip arthroplasty and total knee arthroplasty patients. Additionally, we share the Massachusetts General Hospital experience with using the Risk Assessment and Prediction Tool to predict the need for an extended care facility after total joint arthroplasty.
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Affiliation(s)
- Joseph F Konopka
- Department of Orthopedic Surgery, Yawkey Center for Outpatient Care, Massachusetts General Hospital, Suite 3B, 55 Fruit Street, Boston, MA 02114-2696, USA.
| | - Viktor J Hansen
- Department of Orthopedic Surgery, Yawkey Center for Outpatient Care, Massachusetts General Hospital, Suite 3B, 55 Fruit Street, Boston, MA 02114-2696, USA
| | - Harry E Rubash
- Department of Orthopedic Surgery, Yawkey Center for Outpatient Care, Massachusetts General Hospital, Suite 3B, 55 Fruit Street, Boston, MA 02114-2696, USA
| | - Andrew A Freiberg
- Department of Orthopedic Surgery, Yawkey Center for Outpatient Care, Massachusetts General Hospital, Suite 3B, 55 Fruit Street, Boston, MA 02114-2696, USA
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Development and validation of nomograms to provide individualised predictions of seizure outcomes after epilepsy surgery: a retrospective analysis. Lancet Neurol 2015; 14:283-90. [DOI: 10.1016/s1474-4422(14)70325-4] [Citation(s) in RCA: 128] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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