Summarising and synthesising regression coefficients through systematic review and meta-analysis for improving hypertension prediction using metamodelling: protocol
Introduction Hypertension is one of the most common medical conditions and represents a major risk factor for heart attack, stroke, kidney disease and mortality. The risk of progression to hypertension depends on several factors, and combining these risk factors into a multivariable model for risk s...
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BMJ Publishing Group
2020-04-01
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| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/10/4/e036388.full |
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| author | Alexander A Leung Hude Quan Tanvir C Turin Mohammad Ziaul Islam Chowdhury Iffat Naeem Khokan C Sikdar Maeve O'Beirne |
| author_facet | Alexander A Leung Hude Quan Tanvir C Turin Mohammad Ziaul Islam Chowdhury Iffat Naeem Khokan C Sikdar Maeve O'Beirne |
| author_sort | Alexander A Leung |
| collection | DOAJ |
| description | Introduction Hypertension is one of the most common medical conditions and represents a major risk factor for heart attack, stroke, kidney disease and mortality. The risk of progression to hypertension depends on several factors, and combining these risk factors into a multivariable model for risk stratification would help to identify high-risk individuals who should be targeted for healthy behavioural changes and/or medical treatment to prevent the development of hypertension. The risk prediction models can be further improved in terms of accuracy by using a metamodel updating technique where existing hypertension prediction models can be updated by combining information available in existing models with new data. A systematic review and meta-analysis will be performed of hypertension prediction models in order to identify known risk factors for high blood pressure and to summarise the magnitude of their association with hypertension.Methods and analysis MEDLINE, Embase, Web of Science, Scopus and grey literature will be systematically searched for studies predicting the risk of hypertension among the general population. The search will be based on two key concepts: hypertension and risk prediction. The summary statistics from the individual studies will be the regression coefficients of the hypertension risk prediction models, and random-effect meta-analysis will be used to obtain pooled estimates. Heterogeneity and publication bias will be assessed, along with study quality, which will be assessed using the Prediction Model Risk of Bias Assessment Tool checklist.Ethics and dissemination Ethics approval is not required for this systematic review and meta-analysis. We plan to disseminate the results of our review through journal publications and presentations at applicable platforms. |
| format | Article |
| id | doaj-art-9b6103fc568d43eb80b1d9d7a05cfaab |
| institution | OA Journals |
| issn | 2044-6055 |
| language | English |
| publishDate | 2020-04-01 |
| publisher | BMJ Publishing Group |
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| series | BMJ Open |
| spelling | doaj-art-9b6103fc568d43eb80b1d9d7a05cfaab2025-08-20T02:30:34ZengBMJ Publishing GroupBMJ Open2044-60552020-04-0110410.1136/bmjopen-2019-036388Summarising and synthesising regression coefficients through systematic review and meta-analysis for improving hypertension prediction using metamodelling: protocolAlexander A Leung0Hude Quan1Tanvir C Turin2Mohammad Ziaul Islam Chowdhury3Iffat Naeem4Khokan C Sikdar5Maeve O'Beirne6Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada1 Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada4 Department of Family Medicine, University of Calgary, Calgary, Alberta, CanadaDepartment of Community Health Sciences, Cumming School of Medicine, Universit of Calgary, Calgary, Albert, Canada1 Department of Community Health Sciences, University of Calgary Cumming School of Medicine, Calgary, Alberta, Canada3 Health Status Assessment, Surveillance and Reporting, Public Health Surveillance and Infrastructure, Population, Public and Indigenous Health, Alberta Health Services, Calgary, Alberta, Canada4 Department of Family Medicine, University of Calgary Cumming School of Medicine, Calgary, Alberta, CanadaIntroduction Hypertension is one of the most common medical conditions and represents a major risk factor for heart attack, stroke, kidney disease and mortality. The risk of progression to hypertension depends on several factors, and combining these risk factors into a multivariable model for risk stratification would help to identify high-risk individuals who should be targeted for healthy behavioural changes and/or medical treatment to prevent the development of hypertension. The risk prediction models can be further improved in terms of accuracy by using a metamodel updating technique where existing hypertension prediction models can be updated by combining information available in existing models with new data. A systematic review and meta-analysis will be performed of hypertension prediction models in order to identify known risk factors for high blood pressure and to summarise the magnitude of their association with hypertension.Methods and analysis MEDLINE, Embase, Web of Science, Scopus and grey literature will be systematically searched for studies predicting the risk of hypertension among the general population. The search will be based on two key concepts: hypertension and risk prediction. The summary statistics from the individual studies will be the regression coefficients of the hypertension risk prediction models, and random-effect meta-analysis will be used to obtain pooled estimates. Heterogeneity and publication bias will be assessed, along with study quality, which will be assessed using the Prediction Model Risk of Bias Assessment Tool checklist.Ethics and dissemination Ethics approval is not required for this systematic review and meta-analysis. We plan to disseminate the results of our review through journal publications and presentations at applicable platforms.https://bmjopen.bmj.com/content/10/4/e036388.full |
| spellingShingle | Alexander A Leung Hude Quan Tanvir C Turin Mohammad Ziaul Islam Chowdhury Iffat Naeem Khokan C Sikdar Maeve O'Beirne Summarising and synthesising regression coefficients through systematic review and meta-analysis for improving hypertension prediction using metamodelling: protocol BMJ Open |
| title | Summarising and synthesising regression coefficients through systematic review and meta-analysis for improving hypertension prediction using metamodelling: protocol |
| title_full | Summarising and synthesising regression coefficients through systematic review and meta-analysis for improving hypertension prediction using metamodelling: protocol |
| title_fullStr | Summarising and synthesising regression coefficients through systematic review and meta-analysis for improving hypertension prediction using metamodelling: protocol |
| title_full_unstemmed | Summarising and synthesising regression coefficients through systematic review and meta-analysis for improving hypertension prediction using metamodelling: protocol |
| title_short | Summarising and synthesising regression coefficients through systematic review and meta-analysis for improving hypertension prediction using metamodelling: protocol |
| title_sort | summarising and synthesising regression coefficients through systematic review and meta analysis for improving hypertension prediction using metamodelling protocol |
| url | https://bmjopen.bmj.com/content/10/4/e036388.full |
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