{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T16:09:13Z","timestamp":1792080553651,"version":"4.4.0"},"reference-count":34,"publisher":"PeerJ","license":[{"start":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T00:00:00Z","timestamp":1732492800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0"}],"funder":[{"name":"Research Project of Guangxi Zhuang Autonomous Region Health Commission","award":["Z20210530"],"award-info":[{"award-number":["Z20210530"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:sec>\n<jats:title>Background<\/jats:title>\n<jats:p>Ischemic stroke is one of the leading causes of disability and death worldwide, with a high risk of recurrence that severely impacts the quality of life of patients. Therefore, identifying and analyzing the risk factors for recurrent ischemic stroke is crucial for the prevention and management of this disease.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title>Methods<\/jats:title>\n<jats:p>A total of 114 cases of recurrent acute ischemic stroke patients admitted from July 2017 to March 2021 were selected as the observation group, and another 409 cases of initial ischemic stroke patients from the same period as the control group. The clinical data of the observation group and the control group were compared to analyze the risk factors associated with the readmission of ischemic stroke. A single-factor analysis (Model 1), Least Absolute Shrinkage and Selection Operator (LASSO) regression, and machine learning methods (Model 2) were used to screen important variables, and a multi-factor COX Proportional Hazards Model regression stroke recurrence risk prediction model was constructed. The predictive performance of the model was evaluated by the consistency index (C-index).<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title>Results<\/jats:title>\n<jats:p>Multivariate COX regression analysis revealed that history of hypertension (Hazard Ratio [HR] = 2.549; 95% Confidence Interval (CI) [1.503\u20134.321]; <jats:italic>P<\/jats:italic> = 0.001), history of cerebral infarction (HR = 1.709; 95% CI [1.066\u20132.738]; <jats:italic>P<\/jats:italic> = 0.026), cerebral artery stenosis (HR = 0.534; 95% CI [0.306\u20130.931]; <jats:italic>P<\/jats:italic> = 0.027), carotid arteriosclerosis (HR = 1.823; 95% CI [1.137\u20132.924]; <jats:italic>P<\/jats:italic> = 0.013), systolic blood pressure (HR = 0.981; 95% CI [0.971\u20130.991]; <jats:italic>P<\/jats:italic> &lt; 0.0001), red cell distribution width-coefficient of variation (RDW-CV) (HR = 1.251; 95% CI [1.019\u20131.536]; <jats:italic>P<\/jats:italic> = 0.033), mean platelet volume (MPV) (HR = 1.506; 95% CI [1.148\u20131.976]; <jats:italic>P<\/jats:italic> = 0.003), uric acid (UA) (HR = 0.995; 95% CI [0.991\u20131.000]; <jats:italic>P<\/jats:italic> = 0.049) were found significantly associated with acute ischemic stroke. The C-index of the full COX model was 0.777 (0.732~0.821), showing a good discrimination between Model 1 and Model 2.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title>Conclusions<\/jats:title>\n<jats:p>History of hypertension, history of cerebral infarction, cerebral artery stenosis, carotid atherosclerosis, systolic blood pressure, UA, RDW-CV, and MPV were identified as risk factors for acute ischemic stroke recurrence. The model can be used to predict the recurrence of acute ischemic stroke.<\/jats:p>\n<\/jats:sec>","DOI":"10.7717\/peerj.18605","type":"journal-article","created":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T11:41:03Z","timestamp":1732534863000},"page":"e18605","source":"Crossref","is-referenced-by-count":8,"title":["Risk factors and prediction models for recurrent acute ischemic stroke: a retrospective analysis"],"prefix":"10.7717","volume":"12","author":[{"given":"Liuhua","family":"Ke","sequence":"first","affiliation":[{"name":"Department of Clinical Laboratory, Liuzhou Traditional Chinese Medical Hospital, Liuzhou, Guangxi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Clinical Laboratory, Liuzhou Traditional Chinese Medical Hospital, Liuzhou, Guangxi, 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