{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T07:56:20Z","timestamp":1791186980160,"version":"4.1.0"},"reference-count":34,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T00:00:00Z","timestamp":1788998400000},"content-version":"vor","delay-in-days":9,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Health Science Reports"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background and Aims<\/jats:title>\n                    <jats:p>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\u2010cause in\u2010hospital mortality risk in ICU patients with respiratory failure receiving vasoactive therapy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We performed a secondary analysis of the MIMIC\u2010IV database, including adult ICU patients (2017\u20132022) 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\u2010 and high\u2010risk groups.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The final cohort comprised 1951 adult patients. Using the random forest survival model (concordance index\u2009=\u20090.769), patients were categorized into low\u2010risk and high\u2010risk groups, with significantly different survival outcomes (log\u2010rank test\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001). The logistic regression model (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001, Nagelkerke\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      \u2009=\u20090.714, Brier score\u2009=\u20090.102) achieved high performance (AUC\u2009=\u20090.924) in identifying high\u2010risk individuals. High\u2010risk patients were characterized by biomarkers of multiorgan dysfunction, including renal insufficiency, hepatic impairment, coagulopathy, acid\u2013base 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.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>A machine learning model incorporating early\u2010phase 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\u2010driven decision\u2010making.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/hsr2.72848","type":"journal-article","created":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T11:27:14Z","timestamp":1789039634000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Early Detection of Clinical Deterioration in ICU Patients With Respiratory Failure Receiving Vasoactive Support: A Machine Learning Approach to Identifying High\u2010Risk Individuals"],"prefix":"10.1002","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9214-724X","authenticated-orcid":false,"given":"Mohammad","family":"Fathi","sequence":"first","affiliation":[{"name":"Critical Care Quality Improvement Research Center, Shahid Modarres Hospital Shahid Beheshti University of Medical Sciences Tehran Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5416-1534","authenticated-orcid":false,"given":"Hamed","family":"Markazi Moghadam","sequence":"additional","affiliation":[{"name":"GBD Collaborator Network, Global Labor Organization (GLO), Faculty of Economics and Management Leibniz University Hannover Hannover Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mahdis","family":"Fathi","sequence":"additional","affiliation":[{"name":"Critical Care Quality Improvement Research Center, Shahid Modarres Hospital Shahid Beheshti University of Medical Sciences Tehran Iran"},{"name":"School of Pharmacy Shahid Beheshti University of Medical Sciences Tehran Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammadreza","family":"Hajiesmaeili","sequence":"additional","affiliation":[{"name":"Critical Care Quality Improvement Research Center, Loghman Hakim Hospital Shahid Beheshti University of Medical Sciences Tehran Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Navid","family":"Nooraei","sequence":"additional","affiliation":[{"name":"Critical Care Quality Improvement Research Center, Shahid Modarres Hospital Shahid Beheshti University of Medical Sciences Tehran Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nasser Malekpour","family":"Alamdari","sequence":"additional","affiliation":[{"name":"Critical Care Quality Improvement Research Center, Shahid Modarres Hospital Shahid Beheshti University of Medical Sciences Tehran Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8393-7314","authenticated-orcid":false,"given":"Sanaz Zargar Balaye","family":"Jame","sequence":"additional","affiliation":[{"name":"Department of Health Management and Economics Faculty of Medicine, Aja University of Medical Sciences Tehran Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Farhad Hashemnezhad","family":"Khataee","sequence":"additional","affiliation":[{"name":"Critical Care Quality Improvement Research Center, Shahid Modarres Hospital Shahid Beheshti University of Medical Sciences Tehran Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2861-2765","authenticated-orcid":false,"given":"Nader Markazi","family":"Moghaddam","sequence":"additional","affiliation":[{"name":"Critical Care Quality Improvement Research Center, Shahid Modarres Hospital Shahid Beheshti University of Medical Sciences Tehran Iran"},{"name":"Department of Health Management and Economics Faculty of Medicine, Aja University of Medical Sciences Tehran 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