The Stroke Riskometer™ App: Validation of a Data Collection Tool and Stroke Risk Predictor
Authors: Parmar Priya , Krishnamurthi Rita , Ikram M. Arfan , Hofman Albert , Mirza Saira S. , Varakin Yury , Kravchenko Michael , Piradov Michael , Thrift Amanda G. , Norrving Bo , Wang Wenzhi , Mandal Dipes Kumar , Barker-Collo Suzanne , Sahathevan Ramesh , Davis Stephen , Saposnik Gustavo , Kivipelto Miia , Sindi Shireen , Bornstein Natan M. , Giroud Maurice , Béjot Yannick , Brainin Michael , Poulton Richie , Narayan K. M. Venkat , Correia Manuel , Freire António , Kokubo Yoshihiro , Wiebers David , Mensah George , BinDhim Nasser F. , Barber P. Alan , Pandian Jeyaraj Durai , Hankey Graeme J. , Mehndiratta Man Mohan , Azhagammal Shobhana , Ibrahim Norlinah Mohd , Abbott Max , Rush Elaine , Hume Patria , Hussein Tasleem , Bhattacharjee Rohit , Purohit Mitali , Feigin Valery L. ,   for the Stroke Riskometer™ Collaboration Writing Group†
Published: December 2014

Background The greatest potential to reduce the burden of stroke is by primary prevention of first-ever stroke, which constitutes three quarters of all stroke. In addition to population-wide prevention strategies (the ‘mass’ approach), the ‘high risk’ approach aims to identify individuals at risk of stroke and to modify their risk factors, and risk, accordingly. Current methods of assessing and modifying stroke risk are difficult to access and implement by the general population, amongst whom most future strokes will arise. To help reduce the burden of stroke on individuals and the population a new app, the Stroke Riskometer™, has been developed. We aim to explore the validity of the app for predicting the risk of stroke compared with current best methods. Methods 752 stroke outcomes from a sample of 9501 individuals across three countries (New Zealand, Russia and the Netherlands) were utilized to investigate the performance of a novel stroke risk prediction tool algorithm (Stroke Riskometer™) compared with two established stroke risk score prediction algorithms (Framingham Stroke Risk Score [FSRS] and QStroke). We calculated the receiver operating characteristics (ROC) curves and area under the ROC curve (AUROC) with 95% confidence intervals, Harrels C-statistic and D-statistics for measure of discrimination, R2 statistics to indicate level of variability accounted for by each prediction algorithm, the Hosmer-Lemeshow statistic for calibration, and the sensitivity and specificity of each algorithm. Results The Stroke Riskometer™ performed well against the FSRS five-year AUROC for both males (FSRS = 75·0% (95% CI 72·3%–77·6%), Stroke Riskometer™ = 74·0(95% CI 71·3%–76·7%) and females [FSRS = 70·3% (95% CI 67·9%–72·8%, Stroke Riskometer™ = 71·5% (95% CI 69·0%–73·9%)], and better than QStroke [males–59·7% (95% CI 57·3%–62·0%) and comparable to females = 71·1% (95% CI 69·0%–73·1%)]. Discriminative ability of all algorithms was low (C-statistic ranging from 0·51–0·56, D-statistic ranging from 0·01–0·12). Hosmer-Lemeshow illustrated that all of the predicted risk scores were not well calibrated with the observed event data ( P < 0·006). Conclusions The Stroke Riskometer™ is comparable in performance for stroke prediction with FSRS and QStroke. All three algorithms performed equally poorly in predicting stroke events. The Stroke Riskometer™ will be continually developed and validated to address the need to improve the current stroke risk scoring systems to more accurately predict stroke, particularly by identifying robust ethnic/race ethnicity group and country specific risk factors.