Objective: Traditional #readmission risk models relying on static discharge data have limited predictive performance and fail to capture patients’ recovery trajectories after hospitalization. We sought to identify optimal modeling parameters for dynamically predicting readmission risk using post-discharge step-count data from remote monitoring devices.
Methods: We combined data for adults aged 55+ from 2 studies that collected longitudinal activity data after discharge. We constructed a patient-day dataset incorporating static demographic and clinical variables and dynamic activity features aggregated over retrospective windows of 3, 5, 7, or 10 days. Models predicted readmission or death over prospective horizons of 3, 5, 7, or 10 days, within follow-up periods of 30-180 days. Logistic regression and LightGBM models were trained using 5-fold cross-validation on an 80:20 patient-level split.
Results: Among 215 participants, LightGBM outperformed logistic regression across all configurations (mean AUC 0.82 vs 0.76). Performance improved with longer prospective horizons but was insensitive to retrospective window length. The LightGBM model was well-calibrated (Hosmer-Lemeshow χ2 = 2.46, P = .96), whereas logistic regression showed miscalibration (χ2 = 51.8, P < .001). In feature-importance analyses, LightGBM ranked static (length of stay, vitals, BMI) and activity (recent steps, distance) features highly, whereas logistic regression emphasized activity variables.
Discussion: Prediction performance was impacted by horizon length and training window, with minimal effect of retrospective window. LightGBM achieved better discrimination and calibration, supporting flexible, non-parametric methods for post-discharge risk prediction.
Conclusion: Post-discharge #step count data enhance dynamic readmission risk prediction. Optimizing temporal windows and model type improves discrimination and calibration.
https://academic.oup.com/jamia/advance-article/doi/10.1093/jamia/ocag057/8662633
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