Timing of 30-Day Hospital Readmission Risk and Its Implications for Clinical Utility

Area of Science:

• Healthcare Analytics
• Prognostic Modeling
• Patient Outcomes

Background:

• Thirty-day hospital readmission is a key metric for post-discharge care, but a single estimate doesn't pinpoint actionable intervention times.
• Current models lack granularity in identifying specific windows of heightened readmission risk post-discharge.

Purpose of the Study:

• To evaluate time-structured hospital readmission risk within 30 days after discharge.
• To assess the clinical utility of time-structured risk prediction for resource allocation.

Main Methods:

• Retrospective prognostic study using adult hospital discharge data from 2017-2024 (development cohort) and 2008-2019 (external cohort).
• Analysis included landmark intervals (0-7, 8-14, 15-21, 22-30 days) and cumulative horizons (7, 14, 21, 30 days).
• Performance assessed using discrimination, calibration, decision curves, and capacity-constrained analyses.

Main Results:

• Over 617,000 discharges analyzed; 16.3% 30-day readmission rate, with 68.2% occurring within 14 days.
• Landmark analyses showed decreasing positive predictive values (PPVs) beyond 7 days (e.g., 16.0% for days 8-14 at 5% capacity).
• Numbers needed to prevent one readmission increased significantly after the first week.

Conclusions:

• Hospital readmission risk is dynamic and not adequately represented by a single 30-day estimate.
• Time-structured modeling can characterize readmission risk timing.
• Further research is required to develop models that reliably guide interventions during specific postdischarge intervals.