Early ICU Mortality Prediction and Survival Analysis for Respiratory Failure
This work addresses timely clinical support and resource management for ICU patients with respiratory failure, representing an incremental improvement in prediction accuracy.
The study tackled early mortality risk prediction for respiratory failure patients in the ICU using the first 24 hours of physiological data, achieving an AUROC of 80-83% and a 4% improvement in AUCPR on Day 5 compared to state-of-the-art models.
Respiratory failure is the one of major causes of death in critical care unit. During the outbreak of COVID-19, critical care units experienced an extreme shortage of mechanical ventilation because of respiratory failure related syndromes. To help this, the early mortality risk prediction in patients who suffer respiratory failure can provide timely support for clinical treatment and resource management. In the study, we propose a dynamic modeling approach for early mortality risk prediction of the respiratory failure patients based on the first 24 hours ICU physiological data. Our proposed model is validated on the eICU collaborate database. We achieved a high AUROC performance (80-83%) and significantly improved AUCPR 4% on Day 5 since ICU admission, compared to the state-of-art prediction models. In addition, we illustrated that the survival curve includes the time-varying information for the early ICU admission survival analysis.