Development of a Machine Learning Algorithm for Prediction of Complications and Unplanned Readmission Following Primary Anatomic Total Shoulder Replacements.
Shah, Akash A
Jensen, Andrew R
van der Schaar, Mihaela
SooHoo, Nelson F
J Shoulder Elb Arthroplast
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Devana, S. K., Shah, A. A., Lee, C., Jensen, A. R., Cheung, E., van der Schaar, M., & SooHoo, N. F. (2022). Development of a Machine Learning Algorithm for Prediction of Complications and Unplanned Readmission Following Primary Anatomic Total Shoulder Replacements.. J Shoulder Elb Arthroplast, 6 https://doi.org/10.1177/24715492221075444
Background: The demand and incidence of anatomic total shoulder arthroplasty (aTSA) procedures is projected to increase substantially over the next decade. There is a paucity of accurate risk prediction models which would be of great utility in minimizing morbidity and costs associated with major post-operative complications. Machine learning is a powerful predictive modeling tool and has become increasingly popular, especially in orthopedics. We aimed to build a ML model for prediction of major complications and readmission following primary aTSA. Methods: A large California administrative database was retrospectively reviewed for all adults undergoing primary aTSA between 2015 to 2017. The primary outcome was any major complication or readmission following aTSA. A wide scope of standard ML benchmarks, including Logistic regression (LR), XGBoost, Gradient boosting, AdaBoost and Random Forest were employed to determine their power to predict outcomes. Additionally, important patient features to the prediction models were indentified. Results: There were a total of 10,302 aTSAs with 598 (5.8%) having at least one major post-operative complication or readmission. XGBoost had the highest discriminative power (area under receiver operating curve AUROC of 0.689) of the 5 ML benchmarks with an area under precision recall curve AURPC of 0.207. History of implant complication, severe chronic kidney disease, teaching hospital status, coronary artery disease and male sex were the most important features for the performance of XGBoost. In addition, XGBoost identified teaching hospital status and male sex as markedly more important predictors of outcomes compared to LR models. Conclusion: We report a well calibrated XGBoost ML algorithm for predicting major complications and 30-day readmission following aTSA. History of prior implant complication was the most important patient feature for XGBoost performance, a novel patient feature that surgeons should consider when counseling patients.
Original Scientific Research
H. H. LEE (FAU 441489-3H-62252)
External DOI: https://doi.org/10.1177/24715492221075444
This record's URL: https://www.repository.cam.ac.uk/handle/1810/336370