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Development and Internal Validation of a Model Predicting the Risk of Recurrent Stroke for Middle-Aged and Elderly
Zhenglong Jin1, Wenying Gao2, Tao Yu3
1The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong Province, P.R. China; Department of Neurology, The Affiliated Jiangmen Traditional Chinese Medicine Hospital of Ji'nan University, Jiangmen, Guangdong Province, P.R. China.
Insights
A new model predicts recurrent stroke risk in older adults. Lifestyle factors like exercise and social activities, along with diastolic blood pressure, are key indicators for stroke recurrence.
Area of Science:
- Neurology
- Public Health
- Gerontology
Background:
- Stroke is a leading cause of disability and mortality in middle-aged and elderly populations.
- Predicting recurrent stroke is crucial for timely intervention and improved patient outcomes.
Purpose of the Study:
- To develop and validate a predictive model for recurrent stroke risk in middle-aged and elderly individuals.
- Identify key clinical and lifestyle factors associated with stroke recurrence.
Main Methods:
- Retrospective cohort study of 1,327 stroke patients from the China Health and Retirement Longitudinal Study (CHARLS).
- Logistic regression model developed using training set (70%) and validated on test set (30%).
- Predictive performance assessed using the Delong test and area under the receiver operating characteristic curve (AUC).
Main Results:
- The incidence of recurrent stroke was 14.47% over an average follow-up of 2.26 years.
- Moderate exercise duration, walking duration, social activities, and diastolic blood pressure were significant predictors of recurrent stroke.
- The developed logistic regression model achieved an AUC of 0.75, indicating good predictive performance.
Conclusions:
- A validated logistic regression model effectively predicts the risk of recurrent stroke in middle-aged and elderly patients after two years.
- The model demonstrates good discrimination and accuracy, offering a valuable tool for clinical risk assessment.
Objective:
To develop and validate a model for predicting the risk of recurrent stroke among middle-aged and elderly stroke patients.
Methods:
A total of 1,327 stroke patients from the China Health and Retirement Longitudinal Study (CHARLS) were included in the retrospective cohort study, and they were randomly divided into the training and test sets at a ratio of 7:3. Univariate and multivariate regression analyses were used to select the predictors in the training set, which were used to develop logistic regression model. The Delong test and area under the receiver operating characteristic curve were adopted to investigate the predicted performance of the model.
Results:
The average follow-up time was 2.26 ± 0.52 years, and the incidence of recurrent stroke was 14.47%. The result indicated that duration of moderate exercise, duration of walking, social activities, and diastolic blood pressure were associated with the risk of recurrent stroke among the middle-aged and elderly stroke patients. A logistic regression model was constructed to predict the risk of recurrent stroke after 2 years: [Logit (PR)=ln (PR/(1-PR) =-1.658-0.841 moderate exercise (<2 hours/day)-0.559∗moderate exercise (≥2 hours/day)-0.906∗walk (<2 hours/day)-1.131∗walk (≥2 hours/day)-0.474∗social activities 1-0.968∗social activities 2-1.248∗social activities 3 + 0.015∗diastolic blood pressure)]. The value of the area under the curve reached 0.75, showing that the logistic regression model performs well in the prediction of the risk of recurrent stroke.
Conclusions:
A logistic regression model for predicting the risk of recurrent stroke was developed among middle-aged and elderly stroke patients after 2 years, and the model showed good discrimination and accuracy via internal validation.
