Development and internal validation of a multivariable prediction model for 6-year risk of stroke: a cohort study in middle-aged and elderly Chinese population

Abstract

ObjectiveTo develop and internally validate a prediction model for 6-year risk of stroke and its primary subtypes in middle-aged and elderly Chinese population.DesignThis is a retrospective cohort study from a prospectively collected database.ParticipantsWe included a total 3124 adults aged 45–80 years, free of stroke or myocardial infarction at baseline in the 2009–2015 cohort of China Health and Nutrition Survey.Primary and secondary outcome measuresThe outcome of the prediction model was stroke. Investigated predictors were: age, gender, body mass index (BMI), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), total cholesterol (TC), hypertension (HBP), drinking status, smoking status, diabetes and site. Stepwise multiple Cox regression was applied to identify independent predictors. A nomogram was constructed to predict 6-year risk of stroke based on the multiple analysis results. Bootstraps with 1000 resamples were applied to both C-index and calibration curve.ResultThe overall incidence of overall stroke was 2.98%. Age, gender, HBP and TC were found as significant risk predictors for overall stroke; age, gender, HBP and LDL-C were found as significant risk predictors for ischaemic stroke; age, gender, HBP, BMI and HDL-C were found as significant risk predictors for haemorrhagic stroke. The nomogram was constructed using significant variables included in the model, with a C-index of 0.74 (95% CI: 0.72 to 0.76), 0.74 (95% CI: 0.71 to 0.77), and 0.81 (95% CI: 0.78 to 0.84) for overall stroke, ischaemic stroke, and haemorrhagic stroke model, respectively. The calibration curves demonstrated the good agreements between predicted and observed 6-year risk probability.ConclusionOur nomogram could be convenient, easy to use and effective prognoses for predicting 6-year risk of stroke in middle-aged and elderly Chinese population.

Development of rapid and effective risk prediction models for stroke in the Chinese population: a cross-sectional study

“…We have provided more details of similar studies in recent years in table 5. Compared with other studies,14 19 20 37–41 the models we used have stronger prediction and discrimination performances with higher AUCs, which may be due to the inclusion of more variables in this study. Many machine learning models are sensitive to imbalanced data.…”

“…This also indicates that each region should establish a prediction model with its own geographic and ethnic characteristics based on its own data 43. In addition, studies19 40 have reported that age was a significant risk predictor for stroke, whereas it was not highly predictive of stroke in our models. Age group may obscure the contribution of age to stroke in this study.…”

“…Zhang et al 18 employed a variety of filter-based feature selection models to improve the ineffective feature selection in existing research on stroke risk detection. Yu et al 19 developed a simple, convenient model to predict the risk of stroke among middle-aged and elderly Chinese adults using retrospective cohort datasets. Nevertheless, the sample size is relatively small for developing a prediction model, and the only variables used to build the model are sex, age, hypertension and total cholesterol (TC).…”

Adjustment of the GRACE score by the triglyceride glucose index improves the prediction of clinical outcomes in patients with acute coronary syndrome undergoing percutaneous coronary intervention

“…The differences in performance indicated the expected optimism, which were calculated by subtracting the optimism from the apparent model performance [ 21 ]. Calibration curves, which describe the calibration of the model in terms of the agreement between the predicted risks of adverse cardiovascular events and observed frequency of adverse cardiovascular events, were also adopted to estimate the model performance [ 22 ]. The y-axis represents the actual adverse cardiovascular events rate.…”

Health Promotion and Wellness in Neurologic Physical Therapy: Strategies to Advance Practice

“…11,12 However, the 4 other health behaviors also represent critical modifiable risk factors for the onset of noncommunicable diseases (NCDs) such as heart disease and diabetes, which are important considerations in HPW neurologic PT practice. 13 Although underexplored in PT literature and practice, these 5 health behaviors are modifiable risk factors that have been associated with the onset or worsening of neurologic conditions and injuries such as stroke, 12,[14][15][16][17][18][19][20][21][22] spinal cord injury (SCI), [23][24][25][26][27][28][29] traumatic brain injury (TBI), [30][31][32][33][34] multiple sclerosis (MS), [35][36][37][38] and Parkinson disease (PD). [39][40][41][42][43][44] PT screening and health promotion activities targeting all 5 of these health behaviors may reduce risk of NCD and improve the health, wellness, as well as disease-specific and functional outcomes, of clients with neurologic conditions and injuries.…”