{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T15:47:07Z","timestamp":1791820027631,"version":"4.3.4"},"reference-count":61,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2023,12,6]],"date-time":"2023-12-06T00:00:00Z","timestamp":1701820800000},"content-version":"vor","delay-in-days":5,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Evidence Based Medicine"],"published-print":{"date-parts":[[2023,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Aim<\/jats:title><jats:p>To assess the performance of validated prediction models for acute respiratory distress syndrome (ARDS) by systematic review and meta\u2010analysis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Eight databases (Medline, CINAHL, Embase, The Cochrane Library, CNKI, WanFang Data, Sinomed, and VIP) were searched up to March 26, 2023. Studies developed and validated a prediction model for ARDS in adult patients were included. Items on study design, incidence, derivation methods, predictors, discrimination, and calibration were collected. The risk of bias was assessed by the Prediction model Risk of Bias Assessment Tool. Models with a reported area under the curve of the receiver operating characteristic (AUC) metric were analyzed.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>A total of 25 studies were retrieved, including 48 unique prediction models. Discrimination was reported in all studies, with AUC ranging from 0.701 to 0.95. Emerged AUC value of the logistic regression model was 0.837 (95% CI: 0.814 to 0.859). Besides, the value in the ICU group was 0.856 (95% CI: 0.812 to 0.899), the acute pancreatitis group was 0.863 (95% CI: 0.844 to 0.882), and the postoperation group was 0.835 (95% CI: 0.808 to 0.861). In total, 24 of the included studies had a high risk of bias, which was mostly due to the improper methods in predictor screening (13\/24), model calibration assessment (9\/24), and dichotomization of continuous predictors (6\/24).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>This study shows that most prediction models for ARDS are at high risk of bias, and the discrimination ability of the model is excellent. Adherence to standardized guidelines for model development is necessary to derive a prediction model of value to clinicians.<\/jats:p><\/jats:sec>","DOI":"10.1111\/jebm.12565","type":"journal-article","created":{"date-parts":[[2023,12,7]],"date-time":"2023-12-07T23:22:36Z","timestamp":1701991356000},"page":"465-476","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Critical evaluation of established risk prediction models for acute respiratory distress syndrome in adult patients: A systematic review and meta\u2010analysis"],"prefix":"10.1111","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9979-5100","authenticated-orcid":false,"given":"Tao","family":"Wei","sequence":"first","affiliation":[{"name":"Anesthesiology Department Affiliated Cancer Hospital of Xiangya School of Medicine Central South University\/Hunan Cancer Hospital  Changsha 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