STROKE RISK PREDICTION MODEL FOR AFRICAN POPULATIONS: AN APPROACH FOR EARLY IDENTIFICATION OF POPULATIONS AT RISK

Abstract

Background & Aim Early identification and treatment of populations at risk of stroke in Africa have been delimited by the data to design a risk prediction model tailor-made for indigenous Africans. This study developed and tested an Afrocentric risk scoring model to predict stroke occurrence among West Africans. Method 7066 case-control pairs (identified in the SIREN study) were randomly partitioned into testing and validation datasets (85:15), and conditional logistic regression models were developed from seventeen traditional factors of stroke in the training dataset. Significant factors were assigned constant, statistically weighted (based on regression coefficient), and receiver operating characteristics curves were constructed (from the testing dataset) to estimate cut-off points for discriminate stroke cases from healthy controls (to build an additive risk scoring model for stroke). Sensitivity, specificity, positive and negative predicted values were estimated to assess the strength of the stroke risk prediction model at P < 0.05. Results Cohen's kappa for validity was maximal at a total risk score of 56% using statistical weighting approaches for risk quantification in both datasets after identifying 15 traditional vascular factors for stroke occurrence. The stroke risk prediction model had a strong predictive accuracy (76%, 95%CI: 74%–79%), sensitivity (80.3%), specificity (63.0%), positive predictive value (68.5%) and negative predictive value (76.2%) in the validation dataset. Conclusion The Afrocentric stroke risk models had good accuracy and might be helpful for stroke prediction as a viable approach in de-escalating the rising burden of stroke across multi-ethnic indigenous African populations. Early identification and treatment of populations at risk of stroke in Africa have been delimited by the data to design a risk prediction model tailor-made for indigenous Africans. This study developed and tested an Afrocentric risk scoring model to predict stroke occurrence among West Africans. 7066 case-control pairs (identified in the SIREN study) were randomly partitioned into testing and validation datasets (85:15), and conditional logistic regression models were developed from seventeen traditional factors of stroke in the training dataset. Significant factors were assigned constant, statistically weighted (based on regression coefficient), and receiver operating characteristics curves were constructed (from the testing dataset) to estimate cut-off points for discriminate stroke cases from healthy controls (to build an additive risk scoring model for stroke). Sensitivity, specificity, positive and negative predicted values were estimated to assess the strength of the stroke risk prediction model at P < 0.05. Cohen's kappa for validity was maximal at a total risk score of 56% using statistical weighting approaches for risk quantification in both datasets after identifying 15 traditional vascular factors for stroke occurrence. The stroke risk prediction model had a strong predictive accuracy (76%, 95%CI: 74%–79%), sensitivity (80.3%), specificity (63.0%), positive predictive value (68.5%) and negative predictive value (76.2%) in the validation dataset. The Afrocentric stroke risk models had good accuracy and might be helpful for stroke prediction as a viable approach in de-escalating the rising burden of stroke across multi-ethnic indigenous African populations.