Behavioral Telemetry for ICU Mortality Prediction: Documentation Pattern Analysis in 46,002 Low-Acuity MIMIC-IV Patients

ObjectiveTo develop and validate a predictive model incorporating behavioral telemetry signals--documentation pattern anomalies derived from routine EHR charting--alongside clinical variables for ICU mortality prediction in patients with low acute physiologic derangement. Materials and MethodsRetrospective cohort study of 46,002 adult ICU stays from MIMIC-IV v3.1 (2008-2022) with SOFA scores 0-2, excluding neurological units. We extracted 66 variables spanning demographics, acuity, behavioral telemetry, clinical enrichment, and temporal factors. Progressive logistic regression models (M1-M7) were compared using cross-validation, DeLong tests, net reclassification improvement, and calibration analysis. ResultsOverall mortality was 9.34% (4,295 deaths). The clinical model (M5) achieved cross-validated AUROC 0.691 versus 0.639 for demographics alone (M2; {Delta}AUROC = 0.052, DeLong p = 4.41x10-47). NRI was 24.3%. Discordant care patients received 30.5% more chart events than concordant patients, with the sole deficit in neurological assessments (-15.4%), refuting the neglect hypothesis. Kaplan-Meier analysis confirmed survival separation (log-rank {chi}2 = 138.6, p = 5.32x10-32). In the most conservative subgroup (SOFA 0, no sedation, no ventilation, N = 11,158), orientation omission remained associated with mortality (adjusted OR 1.52, p = 0.027). DiscussionDeep sedation and mechanical ventilation function as mediators on the causal pathway rather than traditional confounders; the discordant care signal retains significance after full sedation adjustment. ConclusionDocumentation pattern analysis adds measurable predictive value for ICU mortality risk stratification and represents a novel signal for real-time EHR-based clinical decision support.