{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T02:26:19Z","timestamp":1790216779404,"version":"4.1.0"},"reference-count":26,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T00:00:00Z","timestamp":1778112000000},"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>Objective<\/jats:title>\n                    <jats:p>This study aimed to create and validate a machine learning (ML) model for predicting the risk of multidrug-resistant (MDR) infection in critically ill patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD) admitted to the intensive care unit (ICU).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Data from patients diagnosed with AECOPD were retrospectively extracted from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database. A total of 1,018 patients were split at a 7:3 ratio into a training set (\n                      <jats:italic>n<\/jats:italic>\n                      \u202f=\u202f712) for model development and an internal validation set (\n                      <jats:italic>n<\/jats:italic>\n                      \u202f=\u202f306). MDR occurrence served as the outcome. Features were selected with the Boruta algorithm and least absolute shrinkage and selection operator (LASSO). Five ML algorithms\u2014XGBoost, support vector machine (SVM), LightGBM, CatBoost, and Gaussian na\u00efve Bayes (NB)\u2014were trained with 10-fold cross-validation. Model performance was evaluated by the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, F1 score, calibration plot, decision curve, and clinical impact curve. An external validation cohort of 324 AECOPD patients admitted to the Affiliated Hospital of Jiangsu University between January 2024 and October 2025 was collected. The best-performing model was interpreted with Shapley Additive exPlanations (SHAP) to clarify feature importance and decision logic, and a nomogram was constructed to increase readability.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Boruta identified SpO\u2082, RBC, hemoglobin, hematocrit, blood urea nitrogen, creatinine, Nbpd, and Nbpm as significant predictors. The LightGBM outperformed the other algorithms: in the internal validation set, it achieved 78.9% accuracy, 90.5% sensitivity, 87.3% specificity, 60.5% F1 score, and an AUC of 0.966 (95% CI 0.951\u20130.981); in the external validation set, it reached 74.4% accuracy, 71.1% sensitivity, 77.8% specificity, 58.7% F1 score, and an AUC of 0.926 (95% CI 0.895\u20130.958). SHAP analysis indicated that hematocrit and SpO\u2082 were the primary drivers of model decisions. Interactive and dynamic nomograms were successfully developed.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Multidrug-resistant occurrence in AECOPD patients was associated with SpO\u2082, RBC, hemoglobin, hematocrit, blood urea nitrogen, creatinine, Nbpd, and Nbpm. The LightGBM model demonstrated good discriminative ability but limited sensitivity for detecting positive cases, offering potential value as a rule-out screening tool for MDR risk in critically ill AECOPD patients admitted to ICU.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2026.1806672","type":"journal-article","created":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T05:42:39Z","timestamp":1778132559000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Construction and internal\u2013external validation of a machine learning-based risk prediction model for multidrug resistance in ICU patients with acute exacerbation of chronic obstructive pulmonary disease"],"prefix":"10.3389","volume":"13","author":[{"given":"Yu","family":"Gu","sequence":"first","affiliation":[{"name":"Department of Clinical Laboratory, Affiliated Hospital of Jiangsu University","place":["Zhenjiang, Jiangsu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiming","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Clinical Laboratory, Affiliated Hospital of Jiangsu University","place":["Zhenjiang, Jiangsu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Clinical Laboratory, Affiliated Hospital of Jiangsu University","place":["Zhenjiang, Jiangsu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Clinical Laboratory, Affiliated Hospital of Jiangsu University","place":["Zhenjiang, Jiangsu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiang","family":"Xin","sequence":"additional","affiliation":[{"name":"Department of Clinical Pharmacy, Baoying People's Hospital, Baoying Clinical Medical College of Yangzhou University","place":["Yangzhou, Jiangsu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minxuan","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Hospital-Acquired Infection Control, Affiliated Hospital of Jiangsu University","place":["Zhenjiang, Jiangsu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,5,7]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"e18743064279064","DOI":"10.2174\/0118743064279064231227070344","article-title":"Global initiative for chronic obstructive lung disease (GOLD) 2023 guidelines reviewed","volume":"18","author":"Sharma","year":"2024","journal-title":"Open Respir Med J"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"2671","DOI":"10.2147\/COPD.S527263","article-title":"Global, regional, and National Burden Trends in chronic obstructive pulmonary disease attributable to particulate matter pollution: 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