ArticlePeerJ2024
Risk factors and prediction models for recurrent acute ischemic stroke: a retrospective analysis.
Article in PeerJ, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Dynamic Prediction of 3-Month Recurrence After First-Ever Ischemic Stroke Using a Two-Stream Attention-LSTM Model - Henan Province, China, 2025.China CDC weekly · 2026Article
- Prognostic value of circulating neuron-specific enolase combined with erythrocyte and platelet distribution indices in patients with severe traumatic brain injury.Journal of medical biochemistry · 2026Article
- Intra-Session recurrence in intradialytic hypotension prediction: evaluation implications and recurrence-aware modeling.Frontiers in digital health · 2026Article
- Analysis of factors influencing poor neurological outcomes in patients with acute ischemic stroke.Annals of medicine · 2025Article
- Association between the systemic inflammatory response index and acute ischemic stroke in young people.Frontiers in neurology · 2025Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: 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. Methods: 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). Results: Multivariate COX regression analysis revealed that history of hypertension (Hazard Ratio [HR] = 2.549; 95% Confidence Interval (CI) [1.503-4.321]; Conclusions: 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.
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