Dataset Information

Can an arthroplasty risk score predict bundled care events after total joint arthroplasty?

ABSTRACT:

Background

The validated Arthroplasty Risk Score (ARS) predicts the need for postoperative triage to an intensive care setting. We hypothesized that the ARS may also predict hospital length of stay (LOS), discharge disposition, and episode-of-care cost (EOCC).

Methods

We retrospectively reviewed a series of 704 patients undergoing primary total hip and knee arthroplasty over 17 months. Patient characteristics, 90-day EOCC, LOS, and readmission rates were compared before and after ARS implementation.

Results

ARS implementation was associated with fewer patients going to a skilled nursing or rehabilitation facility after discharge (63% vs 74%, P = .002). There was no difference in LOS, EOCC, readmission rates, or complications. While the adoption of the ARS did not change the mean EOCC, ARS >3 was predictive of high EOCC outlier (odds ratio 2.65, 95% confidence interval 1.40-5.01, P = .003). Increased ARS correlated with increased EOCC (P = .003).

Conclusions

Implementation of the ARS was associated with increased disposition to home. It was predictive of high EOCC and should be considered in risk adjustment variables in alternative payment models.

SUBMITTER: Ashley BS

PROVIDER: S-EPMC5859673 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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Publications

Can an arthroplasty risk score predict bundled care events after total joint arthroplasty?

Ashley Blair S BS Courtney Paul Maxwell PM Gittings Daniel J DJ Bernstein Jenna A JA Lee Gwo Chin GC Hume Eric L EL Kamath Atul F AF

Arthroplasty today 20170831 1

<h4>Background</h4>The validated Arthroplasty Risk Score (ARS) predicts the need for postoperative triage to an intensive care setting. We hypothesized that the ARS may also predict hospital length of stay (LOS), discharge disposition, and episode-of-care cost (EOCC).<h4>Methods</h4>We retrospectively reviewed a series of 704 patients undergoing primary total hip and knee arthroplasty over 17 months. Patient characteristics, 90-day EOCC, LOS, and readmission rates were compared before and after ...[more]

PMID: 29560404

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Project description:Venous thromboembolism (VTE) and major bleeding (MBE) are feared complications that are influenced by numerous host and surgical related factors. Using machine learning on contemporary data, our aim was to develop and validate a practical, easy-to-use algorithm to predict risk for VTE and MBE following total joint arthroplasty (TJA). This was a single institutional study of 35,963 primary and revision total hip (THA) and knee arthroplasty (TKA) patients operated between 2009 and 2020. Fifty-six variables related to demographics, comorbidities, operative factors as well as chemoprophylaxis were included in the analysis. The cohort was divided to training (70%) and test (30%) sets. Four machine learning models were developed for each of the outcomes assessed (VTE and MBE). Models were created for all VTE grouped together as well as for pulmonary emboli (PE) and deep vein thrombosis (DVT) individually to examine the need for distinct algorithms. For each outcome, the model that best performed using repeated cross validation was chosen for algorithm development, and predicted versus observed incidences were evaluated. Of the 35,963 patients included, 308 (0.86%) developed VTE (170 PE's, 176 DVT's) and 293 (0.81%) developed MBE. Separate models were created for PE and DVT as they were found to outperform the prediction of VTE. Gradient boosting trees had the highest performance for both PE (AUC-ROC 0.774 [SD 0.055]) and DVT (AUC-ROC 0.759 [SD 0.039]). For MBE, least absolute shrinkage and selection operator (Lasso) analysis had the highest AUC (AUC-ROC 0.803 [SD 0.035]). An algorithm that provides the probability for PE, DVT and MBE for each specific patient was created. All 3 algorithms had good discriminatory capability and cross-validation showed similar probabilities comparing predicted and observed failures indicating high accuracy of the model. We successfully developed and validated an easy-to-use algorithm that accurately predicts VTE and MBE following TJA. This tool can be used in every-day clinical decision making and patient counseling.

Project description:BackgroundThe Comprehensive Care for Joint Replacement (CJR) model provides bundled payments for in-hospital care and care within 90 days following discharge for Medicare beneficiaries undergoing lower-extremity joint replacement involving the hip, knee, or ankle (total hip arthroplasty, total knee arthroplasty, or total ankle arthroplasty [TAA]). The study hypothesis was that patient comorbidities are associated with readmissions, emergency department (ED) utilization, and subspecialist wound-healing consultation, which are examples of costly contributors to postoperative health-care spending.MethodsThe medical records for 1,024 patients undergoing TAAs between June 2007 and December 2016 at a single academic center in the southeastern United States were reviewed for the outcomes of readmissions, ED visitations, and subspecialist wound-healing consultation within the 90-day post-discharge period. All patients undergoing TAA (n = 1,365) were eligible. Of the 1,037 patients who consented to participation in the study and underwent TAA, 1,024 (98.7%) completed the study. Medical comorbidities according to the Elixhauser and Charlson-Deyo comorbidity indices that were present prior to TAA were recorded. Univariate and multivariable tests of significance were used to relate patient and operative characteristics to outcomes.ResultsFour hundred and ninety-six (48.4%) of the 1,024 patients were female, and 964 (94.1%) were white/Caucasian, with an average age (and standard deviation) of 63 ± 10.5 years. Hypertension, obesity, solid tumor, depression, rheumatic disease, cardiac arrhythmia, hypothyroidism, diabetes mellitus, and chronic pulmonary disease had a prevalence of >10%. Fifty-three (5.2%) of the 1,024 patients were readmitted for any cause. Thirty-six (3.5%) of the 1,024 returned to the ED but were not admitted to the hospital. Readmission or ED visitation was most commonly for a wound complication, followed by deep venous thrombosis (DVT) and pulmonary embolism (PE) evaluation, while urgent medical evaluations composed the majority of non-TAA-related ED visitations. No patient comorbidities were significantly associated with 90-day readmission, ED visitation, or wound complications in multivariable models.ConclusionsPatient comorbidities were not associated with 90-day hospital readmissions or ED visitation for patients undergoing TAA. Readmissions were dominated by evaluation of wound compromise as well as DVT and PE. These data suggest that there may be considerable differences between TAA and total hip arthroplasty or total knee arthroplasty that cause surgeons to question the inclusion of TAA in CJR bundled payment models.Level of evidencePrognostic Level IV. See Instructions for Authors for a complete description of levels of evidence.

Dataset Information

Can an arthroplasty risk score predict bundled care events after total joint arthroplasty?

Background

Methods

Results

Conclusions

Publications

Can an arthroplasty risk score predict bundled care events after total joint arthroplasty?

Similar Datasets

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