Machine-learning prediction of 90-day readmission after primary total hip Arthroplasty: Analysis of 1,340 cases from the Michigan Arthroplasty Registry (MARCQI).

Background: Ninety-day readmission after total hip arthroplasty (THA) drives cost and signals sub-optimal recovery, yet existing risk-stratification tools are imprecise. We aimed to develop and validate a machine-learning model to predict 90-day readmissions and to identify modifiable risk factors. Methods: The Michigan Arthroplasty Registry Collaborative Quality Initiative (MARCQI) was queried for all primary THAs performed between 2012 and 2023 at a single institution. All surgeries were performed by fellowship-trained adult reconstruction surgeons. Demographics, comorbidities, peri-operative variables, and discharge dispositions were extracted. Univariate analyses compared patients readmitted within 90 days with those not readmitted. A multilayer perceptron neural network (MPNN) was trained on 70% of the cohort and tested on the remaining 30%. Model discrimination was assessed with area under the receiver-operating-characteristic curve (AUC), and variable importance was calculated. Results: Of 1,340 THA patients, 69 (5.1%) were readmitted within 90 days, with rates climbing from 0% in ASA I to 24% in ASA IV (p < .001). Spearman correlations pinpointed length of stay (LOS) as the strongest readmission predictor (midnights ρ = 0.130; hours ρ = 0.123; both p < .001), followed by discharge to post-acute care (ρ = -0.074; p = .007), smoking (ρ = 0.084; p = .002), and alcohol use (ρ = -0.072; p = .008). No other demographic or comorbidity variables reached significance.An MPNN model achieved 94.7 % training accuracy, 95.2% testing accuracy, and an AUC of 0.71, ranking length of stay, ASA score, and bleeding disorders as its top three predictors. Conclusion: Prolonged hospital stays and higher ASA status are key drivers of 90-day readmission after THA. Integrating machine-learning risk stratification with strategies to shorten LOS, enhance preoperative optimization, and refine discharge planning may reduce readmission rates. Level of evidence: Prognostic Level III.