Development and Validation of a Risk Prediction Model to Estimate the Risk of Stroke Among Hypertensive Patients in University of Gondar Comprehensive Specialized Hospital, Gondar, 2012 to 2022

Abstract

Background

A risk prediction model to predict the risk of stroke has been developed for hypertensive patients. However, the discriminating power is poor, and the predictors are not easily accessible in low-income countries. Therefore, developing a validated risk prediction model to estimate the risk of stroke could help physicians to choose optimal treatment and precisely estimate the risk of stroke.

Objective

This study aims to develop and validate a risk prediction model to estimate the risk of stroke among hypertensive patients at the University of Gondar Comprehensive Specialized Hospital.

Methods

A retrospective follow-up study was conducted among 743 hypertensive patients between September 01/2012 and January 31/2022. The participants were selected using a simple random sampling technique. Model performance was evaluated using discrimination, calibration, and Brier scores. Internal validity and clinical utility were evaluated using bootstrapping and a decision curve analysis.

Results

Incidence of stroke was 31.4 per 1000 person-years (95% CI: 26.0, 37.7). Combinations of six predictors were selected for model development (sex, residence, baseline diastolic blood pressure, comorbidity, diabetes, and uncontrolled hypertension). In multivariable logistic regression, the discriminatory power of the model was 0.973 (95% CI: 0.959, 0.987). Calibration plot illustrated an overlap between the probabilities of the predicted and actual observed risks after 10,000 times bootstrap re-sampling, with a sensitivity of 92.79%, specificity 93.51%, and accuracy of 93.41%. The decision curve analysis demonstrated that the net benefit of the model was better than other intervention strategies, starting from the initial point.

Conclusion

An internally validated, accurate prediction model was developed and visualized in a nomogram. The model is then changed to an offline mobile web-based application to facilitate clinical applicability. The authors recommend that other researchers eternally validate the model.

Keywords:

• a risk prediction model
• stroke
• nomogram
• hypertensive patients
• Gondar

Abbreviations

ANN, Artificial Neural Network; AUC, Area under the Curve; BP, Blood Pressure; CI, Confidence Interval; CVD, Cardiovascular Disease; DBP, Diastolic Blood pressure; DCA, Decision Curve Analysis; DM, Diabetes Mellitus; HTN, Hypertension; LASSO, Least Absolute Shrinkage and Selection Operator; ROC, Receiver Operator Characteristics; SBP, Systolic Blood Pressure; UoGCSH, University of Gondar Comprehensive Specialized Hospital; WHO, World Health Organization.

Acknowledgments

YMC would like to thank the University of Gondar, College of Medicine and Health Science, Institute of Public Health, for giving them the opportunity to develop this thesis and for providing Internet access, and Mizan Aman College of Health Science, which gave them a chance to learn a second degree.

Disclosure

The authors report no conflicts of interest in this work.