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 of...
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| Veröffentlicht in: | International journal of stroke 2015-02, Vol.10 (2), p.231-244 |
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| creator | 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. |
| 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, 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 a |
| doi_str_mv | 10.1111/ijs.12411 |
| format | Article |
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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.</description><identifier>ISSN: 1747-4930</identifier><identifier>ISSN: 1747-4949</identifier><identifier>EISSN: 1747-4949</identifier><identifier>DOI: 10.1111/ijs.12411</identifier><identifier>PMID: 25491651</identifier><language>eng</language><publisher>London, England: SAGE Publications</publisher><subject>Algorithms ; Calibration ; Clinical Medicine ; Data Collection - methods ; Humans ; Klinisk medicin ; Medical and Health Sciences ; Medicin och hälsovetenskap ; Mobile Applications ; Netherlands ; Neurologi ; Neurology ; New Zealand ; prevention ; Prognosis ; Risk ; Risk Factors ; Russia ; Sensitivity and Specificity ; Stroke - diagnosis ; stroke prediction ; Stroke Riskometer(TM) App ; validation</subject><ispartof>International journal of stroke, 2015-02, Vol.10 (2), p.231-244</ispartof><rights>2014 The Authors</rights><rights>2014 The Authors. International Journal of Stroke published by John Wiley & Sons Ltd on behalf of World Stroke Organization.</rights><rights>2014 The Authors. International Journal of Stroke published by John Wiley & Sons Ltd on behalf of World Stroke Organization. 2014</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c649t-1da879e3f91fa80504aea0ddee49487f7e6a087030f7d9b8f46fc761aa2f0b893</citedby><cites>FETCH-LOGICAL-c649t-1da879e3f91fa80504aea0ddee49487f7e6a087030f7d9b8f46fc761aa2f0b893</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://journals.sagepub.com/doi/pdf/10.1111/ijs.12411$$EPDF$$P50$$Gsage$$H</linktopdf><linktohtml>$$Uhttps://journals.sagepub.com/doi/10.1111/ijs.12411$$EHTML$$P50$$Gsage$$H</linktohtml><link.rule.ids>230,314,550,776,780,881,21798,27901,27902,43597,43598</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/25491651$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink><backlink>$$Uhttps://lup.lub.lu.se/record/5187192$$DView record from Swedish Publication Index$$Hfree_for_read</backlink><backlink>$$Uhttp://kipublications.ki.se/Default.aspx?queryparsed=id:130614151$$DView record from Swedish Publication Index$$Hfree_for_read</backlink></links><search><creatorcontrib>Parmar, Priya</creatorcontrib><creatorcontrib>Krishnamurthi, Rita</creatorcontrib><creatorcontrib>Ikram, M. Arfan</creatorcontrib><creatorcontrib>Hofman, Albert</creatorcontrib><creatorcontrib>Mirza, Saira S.</creatorcontrib><creatorcontrib>Varakin, Yury</creatorcontrib><creatorcontrib>Kravchenko, Michael</creatorcontrib><creatorcontrib>Piradov, Michael</creatorcontrib><creatorcontrib>Thrift, Amanda G.</creatorcontrib><creatorcontrib>Norrving, Bo</creatorcontrib><creatorcontrib>Wang, Wenzhi</creatorcontrib><creatorcontrib>Mandal, Dipes Kumar</creatorcontrib><creatorcontrib>Barker-Collo, Suzanne</creatorcontrib><creatorcontrib>Sahathevan, Ramesh</creatorcontrib><creatorcontrib>Davis, Stephen</creatorcontrib><creatorcontrib>Saposnik, Gustavo</creatorcontrib><creatorcontrib>Kivipelto, Miia</creatorcontrib><creatorcontrib>Sindi, Shireen</creatorcontrib><creatorcontrib>Bornstein, Natan M.</creatorcontrib><creatorcontrib>Giroud, Maurice</creatorcontrib><creatorcontrib>Béjot, Yannick</creatorcontrib><creatorcontrib>Brainin, Michael</creatorcontrib><creatorcontrib>Poulton, Richie</creatorcontrib><creatorcontrib>Narayan, K. M. Venkat</creatorcontrib><creatorcontrib>Correia, Manuel</creatorcontrib><creatorcontrib>Freire, António</creatorcontrib><creatorcontrib>Kokubo, Yoshihiro</creatorcontrib><creatorcontrib>Wiebers, David</creatorcontrib><creatorcontrib>Mensah, George</creatorcontrib><creatorcontrib>BinDhim, Nasser F.</creatorcontrib><creatorcontrib>Barber, P. Alan</creatorcontrib><creatorcontrib>Pandian, Jeyaraj Durai</creatorcontrib><creatorcontrib>Hankey, Graeme J.</creatorcontrib><creatorcontrib>Mehndiratta, Man Mohan</creatorcontrib><creatorcontrib>Azhagammal, Shobhana</creatorcontrib><creatorcontrib>Ibrahim, Norlinah Mohd</creatorcontrib><creatorcontrib>Abbott, Max</creatorcontrib><creatorcontrib>Rush, Elaine</creatorcontrib><creatorcontrib>Hume, Patria</creatorcontrib><creatorcontrib>Hussein, Tasleem</creatorcontrib><creatorcontrib>Bhattacharjee, Rohit</creatorcontrib><creatorcontrib>Purohit, Mitali</creatorcontrib><creatorcontrib>Feigin, Valery L.</creatorcontrib><creatorcontrib>Stroke RiskometerTM Collaboration Writing Group</creatorcontrib><creatorcontrib>for the Stroke Riskometer™ Collaboration Writing Group</creatorcontrib><title>The Stroke Riskometer™ App: Validation of a Data Collection Tool and Stroke Risk Predictor</title><title>International journal of stroke</title><addtitle>Int J Stroke</addtitle><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, 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.</description><subject>Algorithms</subject><subject>Calibration</subject><subject>Clinical Medicine</subject><subject>Data Collection - methods</subject><subject>Humans</subject><subject>Klinisk medicin</subject><subject>Medical and Health Sciences</subject><subject>Medicin och hälsovetenskap</subject><subject>Mobile Applications</subject><subject>Netherlands</subject><subject>Neurologi</subject><subject>Neurology</subject><subject>New Zealand</subject><subject>prevention</subject><subject>Prognosis</subject><subject>Risk</subject><subject>Risk Factors</subject><subject>Russia</subject><subject>Sensitivity and Specificity</subject><subject>Stroke - diagnosis</subject><subject>stroke prediction</subject><subject>Stroke Riskometer(TM) App</subject><subject>validation</subject><issn>1747-4930</issn><issn>1747-4949</issn><issn>1747-4949</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>D8T</sourceid><recordid>eNp1ks1u1DAQxyNERUvhwAsgnxAc0tqJY8ccKlXLV6WVimDhhGRN7HGb3Wwc7ATEnSfh0XgS3N1l1UpgaeTR-D8_ezyTZU8YPWFpnbbLeMIKzti97IhJLnOuuLq_90t6mD2McUkpr2QpHmSHRcUVExU7yr4srpF8HINfIfnQxpVf44jh989f5HwYXpLP0LUWxtb3xDsC5BWMQGa-69BsggvvOwK9vY0g7wPa1ow-PMoOHHQRH-_24-zTm9eL2bt8fvn2YnY-z43gasyZhVoqLJ1iDmpaUQ4I1FrEVEctnUQBtJa0pE5a1dSOC2ekYACFo02tyuMs33LjdxymRg-hXUP4oT20ehdaJQ91xYSSMunn_9V305CsSbZJcJWgBZXaYG01F9xoRcFqUxkjaAOGW0y4sy0usdZoDfZjgO4O9e5J317rK_9N87JMeJoAz3eA4L9OGEe9bqPBroMe_RR1alVR1rUQN6W-2EpN8DEGdPtrGNU3w6DTMOjNMCTt09vv2iv_dj8Jnu3-Aa5QL_0U-tSmf5D-AB46vuk</recordid><startdate>20150201</startdate><enddate>20150201</enddate><creator>Parmar, Priya</creator><creator>Krishnamurthi, Rita</creator><creator>Ikram, M. Arfan</creator><creator>Hofman, Albert</creator><creator>Mirza, Saira S.</creator><creator>Varakin, Yury</creator><creator>Kravchenko, Michael</creator><creator>Piradov, Michael</creator><creator>Thrift, Amanda G.</creator><creator>Norrving, Bo</creator><creator>Wang, Wenzhi</creator><creator>Mandal, Dipes Kumar</creator><creator>Barker-Collo, Suzanne</creator><creator>Sahathevan, Ramesh</creator><creator>Davis, Stephen</creator><creator>Saposnik, Gustavo</creator><creator>Kivipelto, Miia</creator><creator>Sindi, Shireen</creator><creator>Bornstein, Natan M.</creator><creator>Giroud, Maurice</creator><creator>Béjot, Yannick</creator><creator>Brainin, Michael</creator><creator>Poulton, Richie</creator><creator>Narayan, K. M. Venkat</creator><creator>Correia, Manuel</creator><creator>Freire, António</creator><creator>Kokubo, Yoshihiro</creator><creator>Wiebers, David</creator><creator>Mensah, George</creator><creator>BinDhim, Nasser F.</creator><creator>Barber, P. Alan</creator><creator>Pandian, Jeyaraj Durai</creator><creator>Hankey, Graeme J.</creator><creator>Mehndiratta, Man Mohan</creator><creator>Azhagammal, Shobhana</creator><creator>Ibrahim, Norlinah Mohd</creator><creator>Abbott, Max</creator><creator>Rush, Elaine</creator><creator>Hume, Patria</creator><creator>Hussein, Tasleem</creator><creator>Bhattacharjee, Rohit</creator><creator>Purohit, Mitali</creator><creator>Feigin, Valery L.</creator><general>SAGE Publications</general><general>BlackWell Publishing Ltd</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope><scope>ADTPV</scope><scope>AGCHP</scope><scope>AOWAS</scope><scope>D8T</scope><scope>D95</scope><scope>ZZAVC</scope></search><sort><creationdate>20150201</creationdate><title>The Stroke Riskometer™ App: Validation of a Data Collection Tool and Stroke Risk Predictor</title><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></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c649t-1da879e3f91fa80504aea0ddee49487f7e6a087030f7d9b8f46fc761aa2f0b893</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Algorithms</topic><topic>Calibration</topic><topic>Clinical Medicine</topic><topic>Data Collection - methods</topic><topic>Humans</topic><topic>Klinisk medicin</topic><topic>Medical and Health Sciences</topic><topic>Medicin och hälsovetenskap</topic><topic>Mobile Applications</topic><topic>Netherlands</topic><topic>Neurologi</topic><topic>Neurology</topic><topic>New Zealand</topic><topic>prevention</topic><topic>Prognosis</topic><topic>Risk</topic><topic>Risk Factors</topic><topic>Russia</topic><topic>Sensitivity and Specificity</topic><topic>Stroke - diagnosis</topic><topic>stroke prediction</topic><topic>Stroke Riskometer(TM) App</topic><topic>validation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Parmar, Priya</creatorcontrib><creatorcontrib>Krishnamurthi, Rita</creatorcontrib><creatorcontrib>Ikram, M. Arfan</creatorcontrib><creatorcontrib>Hofman, Albert</creatorcontrib><creatorcontrib>Mirza, Saira S.</creatorcontrib><creatorcontrib>Varakin, Yury</creatorcontrib><creatorcontrib>Kravchenko, Michael</creatorcontrib><creatorcontrib>Piradov, Michael</creatorcontrib><creatorcontrib>Thrift, Amanda G.</creatorcontrib><creatorcontrib>Norrving, Bo</creatorcontrib><creatorcontrib>Wang, Wenzhi</creatorcontrib><creatorcontrib>Mandal, Dipes Kumar</creatorcontrib><creatorcontrib>Barker-Collo, Suzanne</creatorcontrib><creatorcontrib>Sahathevan, Ramesh</creatorcontrib><creatorcontrib>Davis, Stephen</creatorcontrib><creatorcontrib>Saposnik, Gustavo</creatorcontrib><creatorcontrib>Kivipelto, Miia</creatorcontrib><creatorcontrib>Sindi, Shireen</creatorcontrib><creatorcontrib>Bornstein, Natan M.</creatorcontrib><creatorcontrib>Giroud, Maurice</creatorcontrib><creatorcontrib>Béjot, Yannick</creatorcontrib><creatorcontrib>Brainin, Michael</creatorcontrib><creatorcontrib>Poulton, Richie</creatorcontrib><creatorcontrib>Narayan, K. M. Venkat</creatorcontrib><creatorcontrib>Correia, Manuel</creatorcontrib><creatorcontrib>Freire, António</creatorcontrib><creatorcontrib>Kokubo, Yoshihiro</creatorcontrib><creatorcontrib>Wiebers, David</creatorcontrib><creatorcontrib>Mensah, George</creatorcontrib><creatorcontrib>BinDhim, Nasser F.</creatorcontrib><creatorcontrib>Barber, P. Alan</creatorcontrib><creatorcontrib>Pandian, Jeyaraj Durai</creatorcontrib><creatorcontrib>Hankey, Graeme J.</creatorcontrib><creatorcontrib>Mehndiratta, Man Mohan</creatorcontrib><creatorcontrib>Azhagammal, Shobhana</creatorcontrib><creatorcontrib>Ibrahim, Norlinah Mohd</creatorcontrib><creatorcontrib>Abbott, Max</creatorcontrib><creatorcontrib>Rush, Elaine</creatorcontrib><creatorcontrib>Hume, Patria</creatorcontrib><creatorcontrib>Hussein, Tasleem</creatorcontrib><creatorcontrib>Bhattacharjee, Rohit</creatorcontrib><creatorcontrib>Purohit, Mitali</creatorcontrib><creatorcontrib>Feigin, Valery L.</creatorcontrib><creatorcontrib>Stroke RiskometerTM Collaboration Writing Group</creatorcontrib><creatorcontrib>for the Stroke Riskometer™ Collaboration Writing Group</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>SwePub</collection><collection>SWEPUB Lunds universitet full text</collection><collection>SwePub Articles</collection><collection>SWEPUB Freely available online</collection><collection>SWEPUB Lunds universitet</collection><collection>SwePub Articles full text</collection><jtitle>International journal of stroke</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Parmar, Priya</au><au>Krishnamurthi, Rita</au><au>Ikram, M. Arfan</au><au>Hofman, Albert</au><au>Mirza, Saira S.</au><au>Varakin, Yury</au><au>Kravchenko, Michael</au><au>Piradov, Michael</au><au>Thrift, Amanda G.</au><au>Norrving, Bo</au><au>Wang, Wenzhi</au><au>Mandal, Dipes Kumar</au><au>Barker-Collo, Suzanne</au><au>Sahathevan, Ramesh</au><au>Davis, Stephen</au><au>Saposnik, Gustavo</au><au>Kivipelto, Miia</au><au>Sindi, Shireen</au><au>Bornstein, Natan M.</au><au>Giroud, Maurice</au><au>Béjot, Yannick</au><au>Brainin, Michael</au><au>Poulton, Richie</au><au>Narayan, K. M. Venkat</au><au>Correia, Manuel</au><au>Freire, António</au><au>Kokubo, Yoshihiro</au><au>Wiebers, David</au><au>Mensah, George</au><au>BinDhim, Nasser F.</au><au>Barber, P. Alan</au><au>Pandian, Jeyaraj Durai</au><au>Hankey, Graeme J.</au><au>Mehndiratta, Man Mohan</au><au>Azhagammal, Shobhana</au><au>Ibrahim, Norlinah Mohd</au><au>Abbott, Max</au><au>Rush, Elaine</au><au>Hume, Patria</au><au>Hussein, Tasleem</au><au>Bhattacharjee, Rohit</au><au>Purohit, Mitali</au><au>Feigin, Valery L.</au><aucorp>Stroke RiskometerTM Collaboration Writing Group</aucorp><aucorp>for the Stroke Riskometer™ Collaboration Writing Group</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>The Stroke Riskometer™ App: Validation of a Data Collection Tool and Stroke Risk Predictor</atitle><jtitle>International journal of stroke</jtitle><addtitle>Int J Stroke</addtitle><date>2015-02-01</date><risdate>2015</risdate><volume>10</volume><issue>2</issue><spage>231</spage><epage>244</epage><pages>231-244</pages><issn>1747-4930</issn><issn>1747-4949</issn><eissn>1747-4949</eissn><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™, 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.</abstract><cop>London, England</cop><pub>SAGE Publications</pub><pmid>25491651</pmid><doi>10.1111/ijs.12411</doi><tpages>14</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1747-4930 |
| ispartof | International journal of stroke, 2015-02, Vol.10 (2), p.231-244 |
| issn | 1747-4930 1747-4949 1747-4949 |
| language | eng |
| recordid | cdi_swepub_primary_oai_swepub_ki_se_516977 |
| source | MEDLINE; Wiley Online Library Journals Frontfile Complete; SAGE Complete; SWEPUB Freely available online |
| subjects | Algorithms Calibration Clinical Medicine Data Collection - methods Humans Klinisk medicin Medical and Health Sciences Medicin och hälsovetenskap Mobile Applications Netherlands Neurologi Neurology New Zealand prevention Prognosis Risk Risk Factors Russia Sensitivity and Specificity Stroke - diagnosis stroke prediction Stroke Riskometer(TM) App validation |
| title | The Stroke Riskometer™ App: Validation of a Data Collection Tool and Stroke Risk Predictor |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-10T06%3A54%3A35IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_swepu&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=The%20Stroke%20Riskometer%E2%84%A2%20App:%20Validation%20of%20a%20Data%20Collection%20Tool%20and%20Stroke%20Risk%20Predictor&rft.jtitle=International%20journal%20of%20stroke&rft.au=Parmar,%20Priya&rft.aucorp=Stroke%20RiskometerTM%20Collaboration%20Writing%20Group&rft.date=2015-02-01&rft.volume=10&rft.issue=2&rft.spage=231&rft.epage=244&rft.pages=231-244&rft.issn=1747-4930&rft.eissn=1747-4949&rft_id=info:doi/10.1111/ijs.12411&rft_dat=%3Cproquest_swepu%3E1652388669%3C/proquest_swepu%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1652388669&rft_id=info:pmid/25491651&rft_sage_id=10.1111_ijs.12411&rfr_iscdi=true |