Early Detection of Clinical Deterioration in ICU Patients With Respiratory Failure Receiving Vasoactive Support: A Machine Learning Approach to Identifying High-Risk Individuals.

BACKGROUND AND AIMS: Acute respiratory failure requiring intensive care is frequently accompanied by hemodynamic instability, necessitating vasoactive pharmacologic support, and elevated mortality. We aimed to develop and validate a machine learning model to stratify all-cause in-hospital mortality risk in ICU patients with respiratory failure receiving vasoactive therapy.

METHODS

We performed a secondary analysis of the MIMIC-IV database, including adult ICU patients (2017-2022) with respiratory failure who received vasoactive medications. A random forest survival model was constructed to estimate individualized survival probabilities. Patients were subsequently stratified into two cohorts based on median predicted survival. A multivariable logistic regression model was used to profile clinical and laboratory characteristics associated with low- and high-risk groups.

RESULTS

The final cohort comprised 1951 adult patients. Using the random forest survival model (concordance index = 0.769), patients were categorized into low-risk and high-risk groups, with significantly different survival outcomes (log-rank test p < 0.001). The logistic regression model (p < 0.001, Nagelkerke R 2 = 0.714, Brier score = 0.102) achieved high performance (AUC = 0.924) in identifying high-risk individuals. High-risk patients were characterized by biomarkers of multiorgan dysfunction, including renal insufficiency, hepatic impairment, coagulopathy, acid-base disturbances, and systemic inflammation. In contrast, higher levels of hemoglobin, albumin, and arterial oxygen tension were associated with lower risk, indicative of preserved physiological reserve.

CONCLUSIONS

A machine learning model incorporating early-phase ICU data effectively stratified mortality risk in patients with respiratory failure receiving vasoactive support. This approach provides a clinically interpretable framework to inform prognostication, optimize resource allocation, and support data-driven decision-making.