A Novel Afrocentric Stroke Risk Assessment Score: Models from the Siren Study

Abstract

BackgroundStroke risk can be quantified using risk factors whose effect sizes vary by geography and race. No stroke risk assessment tool exists to estimate aggregate stroke risk for indigenous African. ObjectivesTo develop Afrocentric risk-scoring models for stroke occurrence. Materials and MethodsWe evaluated 3533 radiologically confirmed West African stroke cases paired 1:1 with age-, and sex-matched stroke-free controls in the SIREN study. The 7,066 subjects were randomly split into a training and testing set at the ratio of 85:15. Conditional logistic regression models were constructed by including 17 putative factors linked to stroke occurrence using the training set. Significant risk factors were assigned constant and standardized statistical weights based on regression coefficients (β) to develop an additive risk scoring system on a scale of 0–100%. Using the testing set, Receiver Operating Characteristics (ROC) curves were constructed to obtain a total score to serve as cut-off to discriminate between cases and controls. We calculated sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) at this cut-off. ResultsFor stroke occurrence, we identified 15 traditional vascular factors. Cohen's kappa for validity was maximal at a total risk score of 56% using both statistical weighting approaches to risk quantification and in both datasets. The risk score had a predictive accuracy of 76% (95%CI: 74–79%), sensitivity of 80.3%, specificity of 63.0%, PPV of 68.5% and NPV of 76.2% in the test dataset. For ischemic strokes, 12 risk factors had predictive accuracy of 78% (95%CI: 74–81%). For hemorrhagic strokes, 7 factors had a predictive accuracy of 79% (95%CI: 73–84%). ConclusionsThe SIREN models quantify aggregate stroke risk in indigenous West Africans with good accuracy. Prospective studies are needed to validate this instrument for stroke prevention.