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1.
J Orthop ; 58: 135-139, 2024 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-39100544

RESUMO

Introduction: Revision hip and knee total joint arthroplasty (TJA) carries a high burden of postoperative complications, including surgical site infections (SSI), venous thromboembolism (VTE), reoperation, and readmission, which negatively affect postoperative outcomes and patient satisfaction. Socioeconomic area-level composite indices such as the area deprivation index (ADI) are increasingly important measures of social determinants of health (SDoH). This study aims to determine the potential association between ADI and SSI, VTE, reoperation, and readmission occurrence 90 days following revision TJA. Methods: 1047 consecutive revision TJA patients were retrospectively reviewed. Complications, including SSI, VTE, reoperation, and readmission, were combined into one dependent variable. ADI rankings were extracted using residential zip codes and categorized into quartiles. Univariate and multivariate logistic regressions were performed to analyze the association of ADI as an independent factor for complication following revision TJA. Results: Depression (p = 0.034) and high ASA score (p < 0.001) were associated with higher odds of a combined complication postoperatively on univariate logistic regression. ADI was not associated with the occurrence of any of the complications recorded following surgery (p = 0.092). ASA remained an independent risk factor for developing postoperative complications on multivariate analysis. Conclusion: An ASA score of 3 or higher was significantly associated with higher odds of developing postoperative complications. Our findings suggest that ADI alone may not be a sufficient tool for predicting postoperative outcomes following revision TJA, and other area-level indices should be further investigated as potential markers of social determinants of health.

2.
Arch Orthop Trauma Surg ; 144(7): 3045-3052, 2024 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-38953943

RESUMO

INTRODUCTION: Length of stay (LOS) has been extensively assessed as a marker for healthcare utilization, functional outcomes, and cost of care for patients undergoing arthroplasty. The notable patient-to-patient variation in LOS following revision hip and knee total joint arthroplasty (TJA) suggests a potential opportunity to reduce preventable discharge delays. Previous studies investigated the impact of social determinants of health (SDoH) on orthopaedic conditions and outcomes using deprivation indices with inconsistent findings. The aim of the study is to compare the association of three publicly available national indices of social deprivation with prolonged LOS in revision TJA patients. MATERIALS AND METHODS: 1,047 consecutive patients who underwent a revision TJA were included in this retrospective study. Patient demographics, comorbidities, and behavioral characteristics were extracted. Area deprivation index (ADI), social deprivation index (SDI), and social vulnerability index (SVI) were recorded for each patient, following which univariate and multivariate logistic regression analyses were performed to determine the relationship between deprivation measures and prolonged LOS (greater than five days postoperatively). RESULTS: 193 patients had a prolonged LOS following surgery. Categorical ADI was significantly associated with prolonged LOS following surgery (OR = 2.14; 95% CI = 1.30-3.54; p = 0.003). No association with LOS was found using SDI and SVI. When accounting for other covariates, only ASA scores (ORrange=3.43-3.45; p < 0.001) and age (ORrange=1.00-1.03; prange=0.025-0.049) were independently associated with prolonged LOS. CONCLUSION: The varying relationship observed between the length of stay and socioeconomic markers in this study indicates that the selection of a deprivation index could significantly impact the outcomes when investigating the association between socioeconomic deprivation and clinical outcomes. These results suggest that ADI is a potential metric of social determinants of health that is applicable both clinically and in future policies related to hospital stays including bundled payment plan following revision TJA.


Assuntos
Artroplastia de Quadril , Artroplastia do Joelho , Tempo de Internação , Reoperação , Determinantes Sociais da Saúde , Humanos , Artroplastia de Quadril/estatística & dados numéricos , Tempo de Internação/estatística & dados numéricos , Artroplastia do Joelho/estatística & dados numéricos , Masculino , Feminino , Idoso , Estudos Retrospectivos , Pessoa de Meia-Idade , Reoperação/estatística & dados numéricos , Idoso de 80 Anos ou mais
3.
Med Biol Eng Comput ; 62(7): 2073-2086, 2024 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-38451418

RESUMO

Revision total knee arthroplasty (TKA) is associated with a higher risk of readmission than primary TKA. Identifying individual patients predisposed to readmission can facilitate proactive optimization and increase care efficiency. This study developed machine learning (ML) models to predict unplanned readmission following revision TKA using a national-scale patient dataset. A total of 17,443 revision TKA cases (2013-2020) were acquired from the ACS NSQIP database. Four ML models (artificial neural networks, random forest, histogram-based gradient boosting, and k-nearest neighbor) were developed on relevant patient variables to predict readmission following revision TKA. The length of stay, operation time, body mass index (BMI), and laboratory test results were the strongest predictors of readmission. Histogram-based gradient boosting was the best performer in distinguishing readmission (AUC: 0.95) and estimating the readmission probability for individual patients (calibration slope: 1.13; calibration intercept: -0.00; Brier score: 0.064). All models produced higher net benefit than the default strategies of treating all or no patients, supporting the clinical utility of the models. ML demonstrated excellent performance for the prediction of readmission following revision TKA. Optimization of important predictors highlighted by our model may decrease preventable hospital readmission following surgery, thereby leading to reduced financial burden and improved patient satisfaction.


Assuntos
Artroplastia do Joelho , Aprendizado de Máquina , Readmissão do Paciente , Humanos , Readmissão do Paciente/estatística & dados numéricos , Feminino , Masculino , Idoso , Pessoa de Meia-Idade , Reoperação , Estudos de Coortes , Tempo de Internação/estatística & dados numéricos , Redes Neurais de Computação
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