Prediction models for mortality in ICU patients with delirium show fair discriminationStudy develops models to predict mortality risk in ICU patients with delirium

PURPOSE: Pre-admission functional status affects patients' ability to overcome the deteriorating effects of acute critical illness. We aimed to develop a clinical prediction model for 90-day and 1-year mortality based on pre-admission data, including functional status, in adult delirious ICU patients. METHODS: We included participants randomized to the three highest-enrolling hospitals in the Agents Intervening against Delirium in the Intensive Care Unit (AID-ICU) trial, with pre-admission data on the Clinical Frailty Scale, Comorbidity-Polypharmacy Score, and Barthel-20 score. Ten candidate models were evaluated using multiple modeling approaches. Final models were chosen based on the hyperparameter setting maximizing the Cox-Snell pseudo R. All baseline variables and trial allocation were included. Final models were retrained on the full dataset, with internal validation performed using bootstrapping validation adjusting for optimism. RESULTS: Of 1000 participants in AID-ICU, 632 were included: 630 provided data on 90-day mortality, and 610 on 1-year mortality. The elastic net regression models demonstrated stable, robust performance. The optimism-adjusted areas under the receiver operating characteristic curves were 0.74 (95% confidence interval [CI]: 0.70-0.78) and 0.74 (95% CI: 0.70-0.77) for the 90-day and 1-year mortality models, respectively. Calibration was good across the risk spectrum. Frailty, age, the Simplified Mortality Score for the Intensive Care Unit (SMS-ICU), advanced cancer, and surgical admission contributed most to the prediction models. CONCLUSIONS: We developed models to predict 90-day and 1-year mortality at ICU admission in patients enrolled in the AID-ICU trial, using baseline variables, including functional status measures. The models showed fair discrimination and good calibration, with frailty, age, SMS-ICU, advanced cancer, and surgical admission as key predictors. Future studies are needed to test whether the model is valid in other ICU settings and whether its performance is sufficient to have clinical value. EDITORIAL COMMENT: This article presents mortality prediction models for ICU patients with delirium that incorporate pre-admission functional status and apply several modern statistical learning approaches, providing an instructive and transparent example of contemporary prediction modeling. The resulting elastic net regression models showed fair discrimination and good calibration for predicting 90-day and 1-year mortality, with frailty, age, and illness severity emerging as the strongest predictors. However, such models should be interpreted cautiously at the individual patient level and may be most useful for identifying patients at increased risk who may benefit from careful clinical assessment, individualized treatment, and close follow-up.