External Validation of a Trajectory-Based ICU Discharge Readiness Score: A Retrospective Cohort Study in MIMIC-IV

BACKGROUND Timely identification of ICU patients approaching discharge readiness is critical for hospital throughput optimization and resource allocation. We developed a trajectory-based clinical signal scoring system that integrates multi-domain data streams (vital signs, laboratory values, neurological status, device support, and medication infusions) to generate a continuous readiness score (0-10) with actionable classification states. OBJECTIVE To externally validate the trajectory-based ICU discharge readiness scoring system using the MIMIC-IV critical care database, evaluating detection rates and lead times relative to actual ICU discharge. METHODS Study Design: Retrospective simulated prospective validation Data Source: MIMIC-IV v3.1 (PhysioNet), a publicly available critical care database from Beth Israel Deaconess Medical Center Population: All adult ICU stays with documented vital signs and discharge times (~94,000 encounters) Intervention: Application of the trajectory-based readiness scoring engine at 4-hour intervals throughout each ICU stay Primary Outcomes: - Detection rate: Proportion of patients reaching Monitor (score ≥5.0) and Actionable (score ≥7.5) states before ICU discharge - Lead time: Hours between first detection of Monitor/Actionable state and actual ICU discharge ANALYSIS PLAN 1. Calculate detection rates and lead time distributions for the full cohort 2. Stratify results by discharge disposition (Home, SNF/Rehab, Died/Hospice, LTAC) 3. Generate demographic characteristics (Table 1) stratified by final readiness classification 4. Assess trajectory stability (state transition frequency) 5. Identify blocker patterns (ventilation, vasopressors, renal replacement therapy) EXPECTED OUTCOMES Based on pilot validation (n=97), we anticipate: - Monitor detection rate: 70-80% - Actionable detection rate: 60-70% - Median lead time: 30-50 hours before ICU discharge DATA AVAILABILITY MIMIC-IV is publicly available through PhysioNet (https://physionet.org/content/mimiciv/) with credentialed access. Analysis code will be made available upon publication. FUNDING None CONFLICTS OF INTEREST S.B. Onyambu is the developer of the Cadralis clinical signal infrastructure platform.