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The Stroke Riskometer™ App: Validation of a data collection tool and stroke risk predictor
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 o...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BlackWell Publishing Ltd
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4335600/ https://www.ncbi.nlm.nih.gov/pubmed/25491651 http://dx.doi.org/10.1111/ijs.12411 |
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author | 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 |
author_facet | 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 |
author_sort | Parmar, Priya |
collection | PubMed |
description | 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, 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™ 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. |
format | Online Article Text |
id | pubmed-4335600 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | BlackWell Publishing Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-43356002015-03-04 The Stroke Riskometer™ 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 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 Int J Stroke Research 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, 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™ 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. BlackWell Publishing Ltd 2015-02 2014-12-10 /pmc/articles/PMC4335600/ /pubmed/25491651 http://dx.doi.org/10.1111/ijs.12411 Text en © 2014 The Authors. International Journal of Stroke published by John Wiley & Sons Ltd on behalf of World Stroke Organization. http://creativecommons.org/licenses/by/4.0/ This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research 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 The Stroke Riskometer™ App: Validation of a data collection tool and stroke risk predictor |
title | The Stroke Riskometer™ App: Validation of a data collection tool and stroke risk predictor |
title_full | The Stroke Riskometer™ App: Validation of a data collection tool and stroke risk predictor |
title_fullStr | The Stroke Riskometer™ App: Validation of a data collection tool and stroke risk predictor |
title_full_unstemmed | The Stroke Riskometer™ App: Validation of a data collection tool and stroke risk predictor |
title_short | The Stroke Riskometer™ App: Validation of a data collection tool and stroke risk predictor |
title_sort | stroke riskometer™ app: validation of a data collection tool and stroke risk predictor |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4335600/ https://www.ncbi.nlm.nih.gov/pubmed/25491651 http://dx.doi.org/10.1111/ijs.12411 |
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