{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T23:32:01Z","timestamp":1770679921690,"version":"3.49.0"},"reference-count":51,"publisher":"Wiley","issue":"12","license":[{"start":{"date-parts":[[2021,10,21]],"date-time":"2021-10-21T00:00:00Z","timestamp":1634774400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Clinical Laboratory Analysis"],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Sepsis\u2010associated thrombocytopenia (SAT) is common in critical patients and results in the elevation of mortality. Red cell distribution width (RDW) can reflect body response to inflammation and oxidative stress. We try to investigate the relationship between the RDW and the prognosis of patients with SAT through machine learning.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>809 patients were retrospectively analyzed from the Medical Information Mart for Intensive Care III (MIMIC\u2010III) database. The eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) were used to analyze the impact of each feature. Logistic regression analysis, propensity score matching (PSM), receiver\u2010operating characteristics (ROC) curve analysis, and the Kaplan\u2010Meier method were used for data processing.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The patients with thrombocytopenia had higher 28\u2010day mortality (48.2%). Machine learning indicated that RDW was the second most important in predicting 28\u2010day mortality. The RDW was significantly increased in non\u2010survivors by logistic regression and PSM. ROC curve shows that RDW has moderate predictive power for 28\u2010day mortality. The patients with RDW&gt;16.05 exhibited higher mortality through Kaplan\u2010Meier analysis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Interpretable machine learning can be applied in clinical research. Elevated RDW is not only common in patients with SAT but is also associated with a poor prognosis.<\/jats:p><\/jats:sec>","DOI":"10.1002\/jcla.24053","type":"journal-article","created":{"date-parts":[[2021,10,21]],"date-time":"2021-10-21T21:42:38Z","timestamp":1634852558000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Predictive value of red blood cell distribution width in septic shock patients with thrombocytopenia: A retrospective study using machine learning"],"prefix":"10.1002","volume":"35","author":[{"given":"Jianmin","family":"Ling","sequence":"first","affiliation":[{"name":"Department of Emergency and Intensive Care Unit Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tongzhou","family":"Liao","sequence":"additional","affiliation":[{"name":"Services Computing Technology and System Lab, 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