{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T07:26:24Z","timestamp":1786951584668,"version":"build-2736575974"},"reference-count":30,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T00:00:00Z","timestamp":1786492800000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/501100018537","name":"National Science and Technology Major Project","doi-asserted-by":"publisher","award":["2025ZD01902400"],"award-info":[{"award-number":["2025ZD01902400"]}],"id":[{"id":"10.13039\/501100018537","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013064","name":"Key Research and Development Program of Jiangxi Province","doi-asserted-by":"publisher","award":["20232BBG70022CY"],"award-info":[{"award-number":["20232BBG70022CY"]}],"id":[{"id":"10.13039\/501100013064","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>To develop and validate machine learning\u2013based diagnostic models for invasive pulmonary aspergillosis (IPA) using data available within 24\u2005hours of intensive care unit (ICU) admission, construct 3 model versions for different healthcare settings, and derive a bedside score for rapid risk stratification.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This prospective multicenter cohort study enrolled immunocompromised patients with pneumonia complicated by respiratory failure admitted to the ICU at China-Japan Friendship Hospital (formed the derivation cohort) and 15 additional centers nationwide (comprised the external validation cohort).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The derivation cohort included 508 patients (121 IPA) and the external validation cohort 229 patients (53 IPA). Nine independent predictors were identified: CD4+ T-cell count (T), characteristic computed tomography findings (C), dry rales (D), D-dimer, temperature above 38\u00b0C (T), white blood count (W), 7-day cumulative corticosteroid dose (C), diabetes mellitus (D), and lymphocyte count. We tested 3 hierarchical models with 6 machine learning algorithms. In internal validation, logistic regression showed the best discrimination (area under the curve [AUC] 0.915, 95% CI .850\u2013.980) and calibration (Brier score 0.094) for the completed model. In external validation, CatBoost achieved an AUC of 0.844 (0.760\u20130.928), and logistic regression remained robust in simplified (AUC 0.863) and minimal versions (AUC 0.791). The IPA-2T2C2DW score stratified patients into low- (\u22647 points), intermediate- (8\u20139 points), and high-risk (\u226510 points) groups, with observed IPA rates of 8.2%, 28.6%, and 62.4%, respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The IPA-2T2C2DW score enables rapid bedside IPA risk stratification to guide monitoring intensity and empirical antifungal therapy across diverse healthcare settings. Prospective validation in unselected cohorts is warranted before clinical implementation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/infdis\/jiag404","type":"journal-article","created":{"date-parts":[[2026,8,9]],"date-time":"2026-08-09T11:32:56Z","timestamp":1786275176000},"source":"Crossref","is-referenced-by-count":0,"title":["Development and Validation of a Machine Learning\u2013Based Bedside Score (IPA-2T2C2DW) for Invasive Pulmonary Aspergillosis in Critically Ill Nonneutropenic Immunocompromised Hosts"],"prefix":"10.1093","author":[{"given":"Linna","family":"Huang","sequence":"first","affiliation":[{"name":"National Center for Respiratory Medicine, State Key Laboratory of Respiratory Health and Multimorbidity, National Clinical Research Center for Respiratory Diseases, Institute of Respiratory Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital , Beijing ,","place":["P.R. 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