The Stroke RiskometerTM App: Validation of a data collection tool and stroke risk predictor

P.G. Parmar, R.V.M. Krishnamurthi, M.A. Ikram, A.F. Hofman, S.S. Mirza, Y.Y.A. Varakin, M.A. Kravchenko, M.A. Piradov, A.G. Thrift, B. Norrving, W. Wang, D.K. Mandal, S.L. Barker-Collo, R.A. Sahathevan, S.M. Davis, G. Saposnik, M. Kivipelto, S. Sindi, N.M. Bornstein, M. GiroudY. Béjot, M. Brainin, R.G. Poulton, K.M.V. Narayan, M.M. Correia, A.J. Freire, Y. Kokubo, D.O. Wiebers, G.A. Mensah, N.F. Bindhim, P.A. Barber, J.D. Pandian, Graeme J. Hankey, M.M.O. Mehndiratta, S. Azhagammal, N.M.O. Ibrahim, M. Abbott, E.C. Rush, P.A. Hume, T. Hussein, R. Bhattacharjee, M. Purohit, V.L. Feǐgin

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    Abstract

    © 2015 World Stroke Organization 10 2 February 2015 10.1111/ijs.12411 Research Research © 2014 The Authors. International Journal of Stroke published by John Wiley & Sons Ltd on behalf of World Stroke Organization.

     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 RiskometerTM, 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 RiskometerTM) 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 RiskometerTM performed well against the FSRS five-year AUROC for both males (FSRS=75·0% (95% CI 72·3%-77·6%), Stroke RiskometerTM=74·0(95% CI 71·3%-76·7%) and females [FSRS=70·3% (95% CI 67·9%-72·8%, Stroke RiskometerTM=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

    Original languageEnglish
    Pages (from-to)231-244
    JournalInternational Journal of Stroke
    Volume10
    Issue number2
    DOIs
    Publication statusPublished - Feb 2015

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