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The Stroke Riskometer (TM) App: Validation of a data collection tool and stroke risk predictor

Parmar, Priya; Krishnamurthi, Rita; Ikram, M. Arfan; Hofman, Albert; Mirza, Saira S.; Varakin, Yury; Kravchenko, Michael; Piradov, Michael; Thrift, Amanda G. and Norrving, Bo LU , et al. (2015) In International Journal of Stroke 10(2). p.231-244
Abstract
BackgroundThe 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... (More)
BackgroundThe 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. Methods752 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. ResultsThe Stroke Riskometer(TM) performed well against the FSRS five-year AUROC for both males (FSRS=750% (95% CI 723%-776%), Stroke Riskometer(TM)=740(95% CI 713%-767%) and females [FSRS=703% (95% CI 679%-728%, Stroke Riskometer(TM)=715% (95% CI 690%-739%)], and better than QStroke [males - 597% (95% CI 573%-620%) and comparable to females=711% (95% CI 690%-731%)]. Discriminative ability of all algorithms was low (C-statistic ranging from 051-056, D-statistic ranging from 001-012). Hosmer-Lemeshow illustrated that all of the predicted risk scores were not well calibrated with the observed event data (P<0006). ConclusionsThe 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. (Less)
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published
subject
keywords
prevention, stroke prediction, Stroke Riskometer(TM) App, validation
in
International Journal of Stroke
volume
10
issue
2
pages
231 - 244
publisher
Wiley-Blackwell
external identifiers
  • wos:000348713500025
  • scopus:84921613442
ISSN
1747-4949
DOI
10.1111/ijs.12411
language
English
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yes
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5f560207-ce8d-464c-90ad-c5cc60bac4de (old id 5187192)
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2015-04-01 07:32:10
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2017-11-05 03:23:26
@article{5f560207-ce8d-464c-90ad-c5cc60bac4de,
  abstract     = {BackgroundThe 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. Methods752 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. ResultsThe Stroke Riskometer(TM) performed well against the FSRS five-year AUROC for both males (FSRS=750% (95% CI 723%-776%), Stroke Riskometer(TM)=740(95% CI 713%-767%) and females [FSRS=703% (95% CI 679%-728%, Stroke Riskometer(TM)=715% (95% CI 690%-739%)], and better than QStroke [males - 597% (95% CI 573%-620%) and comparable to females=711% (95% CI 690%-731%)]. Discriminative ability of all algorithms was low (C-statistic ranging from 051-056, D-statistic ranging from 001-012). Hosmer-Lemeshow illustrated that all of the predicted risk scores were not well calibrated with the observed event data (P&lt;0006). ConclusionsThe 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.},
  author       = {Parmar, Priya and Krishnamurthi, Rita and Ikram, M. Arfan and Hofman, Albert and Mirza, Saira S. and Varakin, Yury and Kravchenko, Michael and Piradov, Michael and Thrift, Amanda G. and Norrving, Bo and Wang, Wenzhi and Mandal, Dipes Kumar and Barker-Collo, Suzanne and Sahathevan, Ramesh and Davis, Stephen and Saposnik, Gustavo and Kivipelto, Miia and Sindi, Shireen and Bornstein, Natan M. and Giroud, Maurice and Bejot, Yannick and Brainin, Michael and Poulton, Richie and Narayan, K. M. Venkat and Correia, Manuel and Freire, Antonio and Kokubo, Yoshihiro and Wiebers, David and Mensah, George and BinDhim, Nasser F. and Barber, P. Alan and Pandian, Jeyaraj Durai and Hankey, Graeme J. and Mehndiratta, Man Mohan and Azhagammal, Shobhana and Ibrahim, Norlinah Mohd and Abbott, Max and Rush, Elaine and Hume, Patria and Hussein, Tasleem and Bhattacharjee, Rohit and Purohit, Mitali and Feigin, Valery L.},
  issn         = {1747-4949},
  keyword      = {prevention,stroke prediction,Stroke Riskometer(TM) App,validation},
  language     = {eng},
  number       = {2},
  pages        = {231--244},
  publisher    = {Wiley-Blackwell},
  series       = {International Journal of Stroke},
  title        = {The Stroke Riskometer (TM) App: Validation of a data collection tool and stroke risk predictor},
  url          = {http://dx.doi.org/10.1111/ijs.12411},
  volume       = {10},
  year         = {2015},
}