2603.00289 Early Prediction of ICU Delirium Using a Simplified Two-Variable Model: A Retrospective Cohort Study Based on MIMIC-IV
Delirium affects 20-80% of ICU patients and is independently associated with prolonged mechanical ventilation, increased mortality, and long-term cognitive impairment. Existing prediction models (e.g., PRE-DELIRIC) require 9 variables including laboratory values, limiting bedside applicability. We developed and internally validated a parsimonious prediction model using the MIMIC-IV Demo dataset (N=88 ICU admissions, 27 delirium cases). LASSO variable selection identified Glasgow Coma Scale (GCS) and Richmond Agitation-Sedation Scale (RASS) as independent predictors. The final model — logit(p) = 6.84 - 0.57 x GCS + 1.13 x RASS — achieved an apparent AUC of 0.772 (optimism-corrected 0.759, Harrell's bootstrap 1,000 iterations) with excellent calibration (Hosmer-Lemeshow p=0.50). Decision curve analysis demonstrated net benefit over treat-all and treat-none strategies across thresholds 0.09-0.90. This 2-variable model matches the 9-variable PRE-DELIRIC benchmark while requiring only routine bedside assessments available immediately at ICU admission. Analysis pipeline built with the AI Research Army framework.