{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T06:58:27Z","timestamp":1785308307618,"version":"3.55.0"},"reference-count":26,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T00:00:00Z","timestamp":1785283200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Acute lung injury (ALI) and acute respiratory distress syndrome (ARDS) remain major complications in trauma and other high-risk acute-care populations. Numerous statistical and machine-learning models have been developed for risk prediction, but their reported performance may vary because of differences in diagnostic criteria, predictor timing, validation strategies, and clinical settings. This systematic review and meta-analysis evaluated the performance of prediction models for ALI\/ARDS and related acute pulmonary complications and explored potential sources of heterogeneity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The Cochrane Library, PubMed, EMBASE, and Web of Science were searched through August 13, 2025. Eligible studies developed or validated prediction models for ALI, ARDS, or related acute pulmonary complications in high-risk acute-care populations. Two reviewers independently screened studies, extracted data, and assessed risk of bias using PROBAST. Pooled C-index and accuracy were calculated when comparable estimates were available. Exploratory subgroup analyses examined diagnostic criteria, study design, and predictor timing. Sensitivity analyses included restriction to trauma- or burn-related studies and leave-one-out analyses.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Fourteen studies involving 5,608 participants were included. Outcome definitions varied and were based on the American-European Consensus Conference criteria, the Berlin definition, or other criteria. Logistic regression was the most commonly evaluated approach. In training datasets, pooled accuracy and C-index were 0.732 (95% CI, 0.694\u20130.768) and 0.80 (95% CI, 0.76\u20130.83), respectively. In test datasets, the corresponding estimates were 0.803 (95% CI, 0.756\u20130.843) and 0.81 (95% CI, 0.77\u20130.86). Penalized regression models, particularly LASSO, showed high discrimination in individual studies but were less frequently evaluated. Subgroup analyses suggested a lower pooled training C-index in studies using AECC criteria than in those using the Berlin definition or other criteria. Sensitivity analyses yielded results consistent with the primary findings.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Prediction models for ALI\/ARDS and related acute pulmonary complications showed generally good discrimination, with logistic regression remaining the most widely used approach. However, heterogeneous outcome definitions, inconsistent predictor timing, limited external validation, and incomplete reporting continue to hinder clinical translation. Future studies should standardize outcome definitions, clearly specify prediction time points and horizons, and prioritize transparent multicenter external validation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Trial registration<\/jats:title>\n                    <jats:p>PROSPERO (Registration No. CRD42024576566).<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2026.1882144","type":"journal-article","created":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T05:58:25Z","timestamp":1785304705000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine learning for predicting acute lung injury after trauma: a systematic review and meta-analysis"],"prefix":"10.3389","volume":"13","author":[{"given":"Zhe","family":"Song","sequence":"first","affiliation":[{"name":"Department of Intensive Care Medicine, The Affiliated Hospital of Yangzhou University, Yangzhou","place":["Jiangsu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Intensive Care Medicine, The Affiliated Hospital of Yangzhou University, Yangzhou","place":["Jiangsu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bing","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Emergency Medicine, The Affiliated Hospital of Yangzhou University, Yangzhou","place":["Jiangsu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sihan","family":"Pang","sequence":"additional","affiliation":[{"name":"Department of Intensive Care Medicine, The Affiliated Hospital of Yangzhou University, Yangzhou","place":["Jiangsu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiyang","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Emergency Medicine, The Affiliated Hospital of Yangzhou University, Yangzhou","place":["Jiangsu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gongke","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Trauma Medicine Center, The Affiliated Hospital of Yangzhou University, Yangzhou","place":["Jiangsu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,29]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"995","DOI":"10.1007\/s00134-022-06809-8","article-title":"Imaging the acute respiratory distress syndrome: past, present and future","volume":"48","author":"Bitker","year":"2022","journal-title":"Intensive Care Med."},{"key":"B2","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1016\/j.jss.2015.11.050","article-title":"Trauma indices for prediction of acute respiratory distress syndrome","volume":"201","author":"Afshar","year":"2016","journal-title":"J Surg Res."},{"key":"B3","doi-asserted-by":"publisher","first-page":"e580","DOI":"10.1097\/SLA.0000000000005741","article-title":"Emergency department pediatric readiness among US trauma centers: a machine learning analysis of components associated with survival","volume":"278","author":"Newgard","year":"2023","journal-title":"Ann Surg."},{"key":"B4","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1016\/j.suc.2012.01.003","article-title":"The American college of surgeons trauma quality improvement program","volume":"92","author":"Nathens","year":"2012","journal-title":"Surg Clinics North America."},{"key":"B5","doi-asserted-by":"publisher","first-page":"111","DOI":"10.55460\/MTVH-ONCM","article-title":"Experience with trauma-induced ARDS: a retrospective study of US Wartime casualties 2003-2015","volume":"22","author":"Nam","year":"2022","journal-title":"J Spec Oper Med."},{"key":"B6","doi-asserted-by":"publisher","first-page":"2309","DOI":"10.1097\/CCM.0b013e318180dc74","article-title":"The impact of development of acute lung injury on hospital mortality in critically ill trauma patients","volume":"36","author":"Shah","year":"2008","journal-title":"Crit Care Med."},{"key":"B7","doi-asserted-by":"publisher","first-page":"e17521","DOI":"10.7717\/peerj.17521","article-title":"Incidence, mortality, and predictive factors associated with acute respiratory distress syndrome in multiple trauma patients living in high-altitude areas: a retrospective study in Shigatse","volume":"12","author":"Tu","year":"2024","journal-title":"PeerJ."},{"key":"B8","doi-asserted-by":"publisher","first-page":"737","DOI":"10.1053\/j.semtcvs.2021.03.033","article-title":"Risk of acute lung injury after esophagectomy","volume":"34","author":"Leng","year":"2022","journal-title":"Semin Thorac Cardiovasc Surg."},{"key":"B9","doi-asserted-by":"publisher","first-page":"818","DOI":"10.1164\/ajrccm.149.3.7509706","article-title":"The American-European consensus conference on ARDS. 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