The Stroke Riskometer™ App: Validation of a Data Collection Tool and Stroke Risk Predictor
Year: 2014
Type: article
Abstract: 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(TM) , 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(TM) ) 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, R(2) 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(TM) performed well against the FSRS five-year AUROC for both males (FSRS = 75.0% (95% CI 72.3%-77.6%), Stroke Riskometer(TM) = 74.0(95% CI 71.3%-76.7%) and females [FSRS = 70.3% (95% CI 67.9%-72.8%, Stroke Riskometer(TM) = 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(TM) is comparable in performance for stroke prediction with FSRS and QStroke. All three algorithms performed equally poorly in predicting stroke events. The Stroke Riskometer(TM) 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.
Source: International Journal of Stroke
DOI: 10.1111/ijs.12411
Authors: Priya Parmar, Rita Krishnamurthi, M. Arfan Ikram, Albert Hofman, Saira S. Mirza, Yury Varakin, Michael Kravchenko, Michael Piradov, Amanda G. Thrift, Bo Norrving, Wenzhi Wang, Dipes Kumar Mandal, Suzanne Barker-Collo, Ramesh Sahathevan, Stephen Davis, Gustavo Saposnik, Miia Kivipelto, Shireen Sindi, Natan M. Bornstein, Maurice Giroud, Yannick Béjot, Michael Brainin, Richie Poulton, K. M. Venkat Narayan, Manuel Correia, António Freire, Yoshihiro Kokubo, David Wiebers, George Mensah, Nasser F. BinDhim, P. Alan Barber, Jeyaraj Durai Pandian, Graeme J. Hankey, Man Mohan Mehndiratta, Shobhana Azhagammal, Norlinah Mohd Ibrahim, Max Abbott, Elaine Rush, Patria Hume, Tasleem Hussein, Rohit Bhattacharjee, Mitali Purohit, Valery L. Feigin
Institutions: Auckland University of Technology, Erasmus MC, Research Center of Neurology, Monash University, Lund University, Beijing Institute of Neurosurgery, University of Auckland, University Kebangsaan Malaysia Medical Centre, National University of Malaysia, The University of Melbourne, University of Toronto, Karolinska Institutet, Tel Aviv University, CHU Dijon Bourgogne, Université de Bourgogne, Universität für Weiterbildung Krems, University of Otago, Emory University, Hospital de Santo António, Unidade Local de Saúde de Coimbra, National Cerebral and Cardiovascular Center, Mayo Clinic, National Institutes of Health, National Heart, Lung, and Blood Institute, The University of Sydney, Christian Medical College, The University of Western Australia
Language: English
Subfield: Clinical Neurology
Field: Medicine
Domain: Health Sciences
Open Access status: green
Funder: University of Edinburgh
OpenAlex ID: W1966628631