Risk factors and prediction models for recurrent acute ischemic stroke: a retrospective analysis
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 manag...
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PeerJ Inc.
2024-11-01
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| author | Liuhua Ke Hongyu Zhang Kang Long Zheng Peng Yongjun Huang Xingxuan Ma Wanjun Wu |
| author_facet | Liuhua Ke Hongyu Zhang Kang Long Zheng Peng Yongjun Huang Xingxuan Ma Wanjun Wu |
| author_sort | Liuhua Ke |
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| description | 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]; P = 0.001), history of cerebral infarction (HR = 1.709; 95% CI [1.066–2.738]; P = 0.026), cerebral artery stenosis (HR = 0.534; 95% CI [0.306–0.931]; P = 0.027), carotid arteriosclerosis (HR = 1.823; 95% CI [1.137–2.924]; P = 0.013), systolic blood pressure (HR = 0.981; 95% CI [0.971–0.991]; P < 0.0001), red cell distribution width-coefficient of variation (RDW-CV) (HR = 1.251; 95% CI [1.019–1.536]; P = 0.033), mean platelet volume (MPV) (HR = 1.506; 95% CI [1.148–1.976]; P = 0.003), uric acid (UA) (HR = 0.995; 95% CI [0.991–1.000]; P = 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. 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. |
| format | Article |
| id | doaj-art-2daf224ee77d498b83a64f1209418d7d |
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| language | English |
| publishDate | 2024-11-01 |
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| spelling | doaj-art-2daf224ee77d498b83a64f1209418d7d2025-08-20T02:28:28ZengPeerJ Inc.PeerJ2167-83592024-11-0112e1860510.7717/peerj.18605Risk factors and prediction models for recurrent acute ischemic stroke: a retrospective analysisLiuhua Ke0Hongyu Zhang1Kang Long2Zheng Peng3Yongjun Huang4Xingxuan Ma5Wanjun Wu6Department of Clinical Laboratory, Liuzhou Traditional Chinese Medical Hospital, Liuzhou, Guangxi, ChinaDepartment of Clinical Laboratory, Liuzhou Traditional Chinese Medical Hospital, Liuzhou, Guangxi, ChinaDepartment of Clinical Laboratory, Liuzhou Traditional Chinese Medical Hospital, Liuzhou, Guangxi, ChinaDepartment of Clinical Laboratory, Liuzhou Traditional Chinese Medical Hospital, Liuzhou, Guangxi, ChinaDepartment of Neurology, Liuzhou Traditional Chinese Medical Hospital, Liuzhou, Guangxi, ChinaDepartment of Clinical Laboratory, Liuzhou Traditional Chinese Medical Hospital, Liuzhou, Guangxi, ChinaDepartment of Clinical Laboratory, Liuzhou Traditional Chinese Medical Hospital, Liuzhou, Guangxi, ChinaBackground 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]; P = 0.001), history of cerebral infarction (HR = 1.709; 95% CI [1.066–2.738]; P = 0.026), cerebral artery stenosis (HR = 0.534; 95% CI [0.306–0.931]; P = 0.027), carotid arteriosclerosis (HR = 1.823; 95% CI [1.137–2.924]; P = 0.013), systolic blood pressure (HR = 0.981; 95% CI [0.971–0.991]; P < 0.0001), red cell distribution width-coefficient of variation (RDW-CV) (HR = 1.251; 95% CI [1.019–1.536]; P = 0.033), mean platelet volume (MPV) (HR = 1.506; 95% CI [1.148–1.976]; P = 0.003), uric acid (UA) (HR = 0.995; 95% CI [0.991–1.000]; P = 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. 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.https://peerj.com/articles/18605.pdfAcute ischemic strokeRecurrenceUnivariate analysisLASSO regressionMachine learning |
| spellingShingle | Liuhua Ke Hongyu Zhang Kang Long Zheng Peng Yongjun Huang Xingxuan Ma Wanjun Wu Risk factors and prediction models for recurrent acute ischemic stroke: a retrospective analysis PeerJ Acute ischemic stroke Recurrence Univariate analysis LASSO regression Machine learning |
| title | Risk factors and prediction models for recurrent acute ischemic stroke: a retrospective analysis |
| title_full | Risk factors and prediction models for recurrent acute ischemic stroke: a retrospective analysis |
| title_fullStr | Risk factors and prediction models for recurrent acute ischemic stroke: a retrospective analysis |
| title_full_unstemmed | Risk factors and prediction models for recurrent acute ischemic stroke: a retrospective analysis |
| title_short | Risk factors and prediction models for recurrent acute ischemic stroke: a retrospective analysis |
| title_sort | risk factors and prediction models for recurrent acute ischemic stroke a retrospective analysis |
| topic | Acute ischemic stroke Recurrence Univariate analysis LASSO regression Machine learning |
| url | https://peerj.com/articles/18605.pdf |
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