In The Journal of arthroplasty ; h5-index 65.0
BACKGROUND : Postoperative discharge to facilities account for over 33% of the $ 2.7 billion revision total knee arthroplasty (TKA)-associated annual expenditures and are associated with increased complications when compared to home discharges. Prior studies predicting discharge disposition using advanced machine learning (ML) have been limited due to a lack of generalizability and validation. This study aimed to establish ML model generalizability by externally validating its prediction for non-home discharge following revision TKA using national and institutional databases.
METHODS : The national and institutional cohorts comprised 52,533 and 1,628 patients, respectively, with 20.6 and 19.4% non-home discharge rates. Five ML models were trained and internally validated (five-fold cross-validation) on a large national dataset. Subsequently, external validation was performed on our institutional dataset. Model performance was assessed using discrimination, calibration, and clinical utility. Global predictor importance plots and local surrogate models were used for interpretation.
RESULTS : The strongest predictors of non-home discharge were patient age, body mass index, and surgical indication. The area under the receiver operating characteristic curve (AUC) increased from internal to external validation and ranged between 0.77 to 0.79. Artificial neural network was the best predictive model for identifying patients at risk for non-home discharge (AUC = 0.78), and also the most accurate (calibration slope = 0.93, intercept = 0.02, and Brier score = 0.12).
CONCLUSION : All five ML models demonstrated good-to-excellent discrimination, calibration, and clinical utility on external validation, with artificial neural network being the best model for predicting discharge disposition following revision TKA. Our findings establish the generalizability of ML models developed using data from a national database. The integration of these predictive models into clinical workflow may assist in optimizing discharge planning, bed management, and cost containment associated with revision TKA.
Buddhiraju Anirudh, Chen Tony Lin-Wei, Subih Murad Abdullah, Seo Henry Hojoon, Esposito John G, Kwon Young-Min
2023-Feb-25
Discharge Planning, Knee Arthroplasty, Machine Learning, NSQIP, Prediction Model, Validation
