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

Authors

Mohammad Fathi, Hamed Markazi Moghadam, Mahdis Fathi, Mohammadreza Hajiesmaeili, Navid Nooraei, Nasser Malekpour Alamdari, Sanaz Zargar Balaye Jame, Farhad Hashemnezhad Khataee, Nader Markazi Moghaddam

Published in

Health science reports. Volume 9. Issue 9. Pages e72848. Epub Sep 10, 2026.

Abstract

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.
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.
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.
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.

PMID:
42724649
Bibliographic data and abstract were imported from PubMed on 11 Sep 2026.

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