Machine Learning-Based Predictive Models for 90-Day Readmission of Total Joint Arthroplasty Using Comprehensive Electronic Health Records and Patient-Reported Outcome Measures

Jaeyoung Park; Xiang Zhong; Emilie N Miley; Rachel S Rutledge; Jaquelyn Kakalecik; Matthew C Johnson; Chancellor F Gray

doi:10.1016/j.artd.2023.101308

Machine Learning-Based Predictive Models for 90-Day Readmission of Total Joint Arthroplasty Using Comprehensive Electronic Health Records and Patient-Reported Outcome Measures

Arthroplast Today. 2023 Dec 28:25:101308. doi: 10.1016/j.artd.2023.101308. eCollection 2024 Feb.

Authors

Jaeyoung Park¹, Xiang Zhong², Emilie N Miley³, Rachel S Rutledge³, Jaquelyn Kakalecik³, Matthew C Johnson⁴, Chancellor F Gray⁵

Affiliations

¹ Booth School of Business, University of Chicago, Chicago, IL, USA.
² Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA.
³ Department of Orthopaedic Surgery and Sports Medicine, University of Florida, Gainesville, FL, USA.
⁴ University of Florida College of Medicine, Gainesville, FL, USA.
⁵ Florida Orthopaedic Institute, Gainesville, FL, USA.

Abstract

Background: The Centers for Medicare & Medicaid Services currently incentivizes hospitals to reduce postdischarge adverse events such as unplanned hospital readmissions for patients who underwent total joint arthroplasty (TJA). This study aimed to predict 90-day TJA readmissions from our comprehensive electronic health record data and routinely collected patient-reported outcome measures.

Methods: We retrospectively queried all TJA-related readmissions in our tertiary care center between 2016 and 2019. A total of 104-episode care characteristics and preoperative patient-reported outcome measures were used to develop several machine learning models for prediction performance evaluation and comparison. For interpretability, a logistic regression model was built to investigate the statistical significance, magnitudes, and directions of associations between risk factors and readmission.

Results: Given the significant imbalanced outcome (5.8% of patients were readmitted), our models robustly predicted the outcome, yielding areas under the receiver operating characteristic curves over 0.8, recalls over 0.5, and precisions over 0.5. In addition, the logistic regression model identified risk factors predicting readmission: diabetes, preadmission medication prescriptions (ie, nonsteroidal anti-inflammatory drug, corticosteroid, and narcotic), discharge to a skilled nursing facility, and postdischarge care behaviors within 90 days. Notably, low self-reported confidence to carry out social activities accurately predicted readmission.

Conclusions: A machine learning model can help identify patients who are at substantially increased risk of a readmission after TJA. This finding may allow for health-care providers to increase resources targeting these patients. In addition, a poor response to the "social activities" question may be a useful indicator that predicts a significant increased risk of readmission after TJA.

Keywords: Electronic health records; Hospital readmission; Machine learning; Patient-reported outcome measures; Total joint arthroplasty.