{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T11:13:36Z","timestamp":1774610016826,"version":"3.50.1"},"reference-count":29,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2026,3,22]],"date-time":"2026-03-22T00:00:00Z","timestamp":1774137600000},"content-version":"vor","delay-in-days":21,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100000050","name":"National Heart, Lung, and Blood Institute","doi-asserted-by":"publisher","award":["R00HL141685"],"award-info":[{"award-number":["R00HL141685"]}],"id":[{"id":"10.13039\/100000050","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000050","name":"National Heart, Lung, and Blood Institute","doi-asserted-by":"publisher","award":["R56HL163607"],"award-info":[{"award-number":["R56HL163607"]}],"id":[{"id":"10.13039\/100000050","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["physoc.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Physiological Reports"],"published-print":{"date-parts":[[2026,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Laboratory\u2010guided methods have the potential to provide robust mortality prediction for acute respiratory distress syndrome (ARDS) which could improve timely intervention. The objective of this investigation was to predict mortality using regression and machine learning techniques using biomarkers linked to ARDS pathophysiology, including matrix metalloproteinase\u20103 (MMP\u20103) and club cell\u2010secretory protein\u201016 (CC16), and general inflammatory biomarkers. 89 adult patients from the \u201cAerosolized \u03b2\u2082\u2010agonist for treatment of acute lung injury\u201d (ALTA) trial were randomly separated into training (\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u200953), validation (\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u20098), and test (\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u200928) sets. Logistic regression and supervised machine learning (ML) models were developed. In total, 20 ICU predictors including baseline characteristics (age, sex, APACHE III score, sepsis, vasoactive agent, PaO\n                    <jats:sub>2<\/jats:sub>\n                    \/FiO\n                    <jats:sub>2<\/jats:sub>\n                    ) and baseline and serial biomarkers were included. The primary outcome was area under the receiver operating characteristic (AUROC) for 90\u2009day mortality. Random Forest, Support Vector Machine (SVM), and XGBoost achieved AUROCs of 0.917, 0.705, and 0.955, respectively. Stepwise regression achieved an AUROC of 0.508. For the highest performing model (XGBoost), MMP3\u2010based variables were the most important features. ML had high predictive ability for 90\u2010day mortality, and MMP\u20103 demonstrates moderate\u2010to\u2010high feature importance in ML models. These findings support using pathophysiology\u2010derived biomarkers in ML models for ARDS prediction.\n                  <\/jats:p>","DOI":"10.14814\/phy2.70807","type":"journal-article","created":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T06:33:36Z","timestamp":1774247616000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Predicting mortality with machine learning &amp; biomarkers: A cohort analysis from aerosolized \u03b2\u2082\u2010agonist for treatment of acute lung injury (\n                    <scp>ALTA<\/scp>\n                    ) trial"],"prefix":"10.14814","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5891-4492","authenticated-orcid":false,"given":"Aaron M.","family":"Chase","sequence":"first","affiliation":[{"name":"Department of Clinical and Experimental Therapeutics University of Georgia College of Pharmacy  Augusta Georgia USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4353-6043","authenticated-orcid":false,"given":"Sultan","family":"Almuntashiri","sequence":"additional","affiliation":[{"name":"Department of Pharmacy University of Hail College of Pharmacy  Ha'il Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0361-4174","authenticated-orcid":false,"given":"Duo","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Clinical and Experimental Therapeutics University of Georgia College of Pharmacy  Augusta Georgia USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0651-4304","authenticated-orcid":false,"given":"Timothy W.","family":"Jones","sequence":"additional","affiliation":[{"name":"Department of Clinical and Experimental Therapeutics University of Georgia College of Pharmacy  Augusta Georgia USA"},{"name":"Piedmont Hospital  Atlanta Georgia USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1806-0883","authenticated-orcid":false,"given":"Xianyan","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Statistics University of Georgia Franklin College of Arts and Sciences  Athens Georgia USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3017-0230","authenticated-orcid":false,"given":"Payaningal R.","family":"Somanath","sequence":"additional","affiliation":[{"name":"Department of Clinical and Experimental Therapeutics University of Georgia College of Pharmacy  Augusta Georgia USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6686-1079","authenticated-orcid":false,"given":"Kelli","family":"Henry","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics University of Colorado Anschutz Medical Campus School of Medicine  Aurora Colorado USA"},{"name":"University of Georgia College of Pharmacy  Augusta Georgia USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2020-0571","authenticated-orcid":false,"given":"Andrea","family":"Sikora","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics University of Colorado Anschutz Medical Campus School of Medicine  Aurora Colorado USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2026,3,22]]},"reference":[{"issue":"1","key":"e_1_2_13_2_1","article-title":"MMP3 in severe COVID\u201019: A biomarker or therapeutic target?","volume":"23","author":"Almuntashiri S.","year":"2022","journal-title":"Infectious Disorders Drug 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