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Development of an Australian cardiovascular disease mortality risk score using multiple imputation and recalibration from national statistics
OBJECTIVE: To develop and recalibrate an Australian 5-year cardiovascular disease (CVD) mortality risk score to produce contemporary predictions of risk. METHODS: Data were pooled from six Australian cohort studies (n = 54,829), with baseline data collected between 1989 and 2003. Participants includ...
Autores principales: | , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5219754/ https://www.ncbi.nlm.nih.gov/pubmed/28061760 http://dx.doi.org/10.1186/s12872-016-0462-5 |
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author | Backholer, Kathryn Hirakawa, Yoichiro Tonkin, Andrew Giles, Graham Magliano, Dianna J. Colagiuri, Stephen Harris, Mark Mitchell, Paul Nelson, Mark Shaw, Jonathan E. Simmons, David Simons, Leon Taylor, Anne Harding, Jessica Gopinath, Bamini Woodward, Mark |
author_facet | Backholer, Kathryn Hirakawa, Yoichiro Tonkin, Andrew Giles, Graham Magliano, Dianna J. Colagiuri, Stephen Harris, Mark Mitchell, Paul Nelson, Mark Shaw, Jonathan E. Simmons, David Simons, Leon Taylor, Anne Harding, Jessica Gopinath, Bamini Woodward, Mark |
author_sort | Backholer, Kathryn |
collection | PubMed |
description | OBJECTIVE: To develop and recalibrate an Australian 5-year cardiovascular disease (CVD) mortality risk score to produce contemporary predictions of risk. METHODS: Data were pooled from six Australian cohort studies (n = 54,829), with baseline data collected between 1989 and 2003. Participants included were aged 40–74 years and free of CVD at baseline. Variables were harmonised across studies and missing data were imputed using multiple imputation. Cox proportional hazards models were used to estimate the risk of CVD mortality associated with factors mutually independently predictive (p < 0.05) and a 5-year risk prediction algorithm was constructed. This algorithm was recalibrated to reflect contemporary national levels of CVD mortality and risk factors using national statistics. RESULTS: Over a mean 16.6 years follow-up, 1375 participants in the six studies died from CVD. The prediction model included age, sex, smoking, diabetes, systolic blood pressure, total and high-density lipoprotein cholesterol (HDLC), a social deprivation score, estimated glomerular filtration rate and its square and interactions of sex with diabetes, HDLC and deprivation score, and of age with systolic blood pressure and smoking. This model discriminated well when applied to a Scottish study population (c-statistic (95% confidence interval): 0.751 (0.709, 0.793)). Recalibration generally increased estimated risks, but well below those predicted by the European SCORE models. CONCLUSIONS: The resulting risk score, which includes markers of both chronic kidney disease and socioeconomic deprivation, is the first CVD mortality risk prediction tool for Australia to be derived using Australian data. The primary model, and the method of recalibration, is applicable elsewhere. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12872-016-0462-5) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5219754 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-52197542017-01-10 Development of an Australian cardiovascular disease mortality risk score using multiple imputation and recalibration from national statistics Backholer, Kathryn Hirakawa, Yoichiro Tonkin, Andrew Giles, Graham Magliano, Dianna J. Colagiuri, Stephen Harris, Mark Mitchell, Paul Nelson, Mark Shaw, Jonathan E. Simmons, David Simons, Leon Taylor, Anne Harding, Jessica Gopinath, Bamini Woodward, Mark BMC Cardiovasc Disord Research Article OBJECTIVE: To develop and recalibrate an Australian 5-year cardiovascular disease (CVD) mortality risk score to produce contemporary predictions of risk. METHODS: Data were pooled from six Australian cohort studies (n = 54,829), with baseline data collected between 1989 and 2003. Participants included were aged 40–74 years and free of CVD at baseline. Variables were harmonised across studies and missing data were imputed using multiple imputation. Cox proportional hazards models were used to estimate the risk of CVD mortality associated with factors mutually independently predictive (p < 0.05) and a 5-year risk prediction algorithm was constructed. This algorithm was recalibrated to reflect contemporary national levels of CVD mortality and risk factors using national statistics. RESULTS: Over a mean 16.6 years follow-up, 1375 participants in the six studies died from CVD. The prediction model included age, sex, smoking, diabetes, systolic blood pressure, total and high-density lipoprotein cholesterol (HDLC), a social deprivation score, estimated glomerular filtration rate and its square and interactions of sex with diabetes, HDLC and deprivation score, and of age with systolic blood pressure and smoking. This model discriminated well when applied to a Scottish study population (c-statistic (95% confidence interval): 0.751 (0.709, 0.793)). Recalibration generally increased estimated risks, but well below those predicted by the European SCORE models. CONCLUSIONS: The resulting risk score, which includes markers of both chronic kidney disease and socioeconomic deprivation, is the first CVD mortality risk prediction tool for Australia to be derived using Australian data. The primary model, and the method of recalibration, is applicable elsewhere. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12872-016-0462-5) contains supplementary material, which is available to authorized users. BioMed Central 2017-01-06 /pmc/articles/PMC5219754/ /pubmed/28061760 http://dx.doi.org/10.1186/s12872-016-0462-5 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Backholer, Kathryn Hirakawa, Yoichiro Tonkin, Andrew Giles, Graham Magliano, Dianna J. Colagiuri, Stephen Harris, Mark Mitchell, Paul Nelson, Mark Shaw, Jonathan E. Simmons, David Simons, Leon Taylor, Anne Harding, Jessica Gopinath, Bamini Woodward, Mark Development of an Australian cardiovascular disease mortality risk score using multiple imputation and recalibration from national statistics |
title | Development of an Australian cardiovascular disease mortality risk score using multiple imputation and recalibration from national statistics |
title_full | Development of an Australian cardiovascular disease mortality risk score using multiple imputation and recalibration from national statistics |
title_fullStr | Development of an Australian cardiovascular disease mortality risk score using multiple imputation and recalibration from national statistics |
title_full_unstemmed | Development of an Australian cardiovascular disease mortality risk score using multiple imputation and recalibration from national statistics |
title_short | Development of an Australian cardiovascular disease mortality risk score using multiple imputation and recalibration from national statistics |
title_sort | development of an australian cardiovascular disease mortality risk score using multiple imputation and recalibration from national statistics |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5219754/ https://www.ncbi.nlm.nih.gov/pubmed/28061760 http://dx.doi.org/10.1186/s12872-016-0462-5 |
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