{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T01:38:52Z","timestamp":1783474732977,"version":"3.55.0"},"reference-count":36,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2023,11,28]],"date-time":"2023-11-28T00:00:00Z","timestamp":1701129600000},"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":["Euro J of Neurology"],"published-print":{"date-parts":[[2024,3]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>The 30\u2010day hospital re\u2010admission rate is a quality measure of hospital care to monitor the efficiency of the healthcare system. The hospital re\u2010admission of acute stroke (AS) patients is often associated with higher mortality rates, greater levels of disability and increased healthcare costs. The aim of our study was to identify predictors of unplanned 30\u2010day hospital re\u2010admissions after discharge of AS patients and define an early re\u2010admission risk score (RRS).<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>This observational, retrospective study was performed on AS patients who were discharged between 2014 and 2019. Early re\u2010admission predictors were identified by machine learning models. The performances of these models were assessed by receiver operating characteristic curve analysis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Of 7599 patients with AS, 3699 patients met the inclusion criteria, and 304 patients (8.22%) were re\u2010admitted within 30\u2009days from discharge. After identifying the predictors of early re\u2010admission by logistic regression analysis, RRS was obtained and consisted of seven variables: hemoglobin level, atrial fibrillation, brain hemorrhage, discharge home, chronic obstructive pulmonary disease, one and more than one hospitalization in the previous year. The cohort of patients was then stratified into three risk categories: low (RRS\u2009=\u20090\u20131), medium (RRS\u2009=\u20092\u20133) and high (RRS\u2009&gt;3) with re\u2010admission rates of 5%, 8% and 14%, respectively.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The identification of risk factors for early re\u2010admission after AS and the elaboration of a score to stratify at discharge time the risk of re\u2010admission can provide a tool for clinicians to plan a personalized follow\u2010up and contain healthcare costs.<\/jats:p><\/jats:sec>","DOI":"10.1111\/ene.16153","type":"journal-article","created":{"date-parts":[[2023,11,28]],"date-time":"2023-11-28T18:59:34Z","timestamp":1701197974000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["A novel risk score predicting 30\u2010day hospital re\u2010admission of patients with acute stroke by machine learning model"],"prefix":"10.1111","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3915-298X","authenticated-orcid":false,"given":"Giovanna","family":"Mercurio","sequence":"first","affiliation":[{"name":"Department of Emergency Science, Anesthesiology and Intensive Care Fondazione Policlinico Universitario A. 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Gemelli IRCCS Rome Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anna Benedetta","family":"Del\u00a0Signore","sequence":"additional","affiliation":[{"name":"Department of Emergency Science, Anesthesiology and Intensive Care Fondazione Policlinico Universitario A. Gemelli IRCCS Rome Italy"},{"name":"Global Medical Department\u2010Primary Care Unit, Angelini Pharma Rome Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Riccardo","family":"Maviglia","sequence":"additional","affiliation":[{"name":"Department of Emergency Science, Anesthesiology and Intensive Care Fondazione Policlinico Universitario A. Gemelli IRCCS Rome Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria Grazia","family":"Bocci","sequence":"additional","affiliation":[{"name":"Department of Emergency Science, Anesthesiology and Intensive Care Fondazione Policlinico Universitario A. 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