Early Prediction of ICU Mortality in Patients with Acute Hypoxemic Respiratory Failure Using Machine Learning: The...

Background: Early prediction of ICU death in acute hypoxemic respiratory failure (AHRF) could inform clinicians for targeting therapies to reduce harm and increase survival. We sought to determine clinical modifiable and non-modifiable features during the first 24 h of AHRF associated with ICU death. Methods: This is a development, testing, and validation study using data from a prospective, multicenter, nation-based, observational cohort of 1241 patients with AHRF (defined as PaO₂/FiO₂ ≤ 300 mmHg on mechanical ventilation [MV] with positive end-expiratory pressure [PEEP] ≥ 5 cmH₂O and FiO₂ ≥ 0.3) from any etiology. Using relevant features captured at AHRF diagnosis and within 24 h, we developed a logistic regression model following variable selection by genetic algorithm and machine learning (ML) approaches. Results: We analyzed 1193 patients, after excluding 48 patients with no data at 24 h after AHRF diagnosis. Using repeated random sampling, we selected 75% (n = 900) for model development and testing, and 25% (n = 293) for final validation. Risk modeling identified six major predictors of ICU death, including patient's age, and values at 24 h of PEEP, FiO₂, plateau pressure, tidal volume, and number of extrapulmonary organ failures. Performance with ML methods was similar to logistic regression and achieved a high area under the receiver operating characteristic curve (AUROC) of 0.88, 95%CI 0.86-0.90. Validation confirmed adequate model performance (AUROC 0.83, 95%CI 0.78-0.88). Conclusions: ML and traditional methods led to an encouraging model to predict ICU death in ventilated AHRF as early as 24 h after diagnosis. More research is needed to identify modifiable factors to prevent ICU deaths.