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External validation of the QLifetime cardiovascular risk prediction tool: population cohort study

BACKGROUND: Prediction of lifetime cardiovascular disease (CVD) risk is recommended in many clinical guidelines, but lifetime risk models are rarely externally validated. The aim of this study was to externally validate the QRiskLifetime incident CVD risk prediction tool. METHODS: Independent extern...

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Autores principales: Livingstone, Shona, Morales, Daniel R., Fleuriot, Jacques, Donnan, Peter T., Guthrie, Bruce
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10105395/
https://www.ncbi.nlm.nih.gov/pubmed/37061672
http://dx.doi.org/10.1186/s12872-023-03209-8
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author Livingstone, Shona
Morales, Daniel R.
Fleuriot, Jacques
Donnan, Peter T.
Guthrie, Bruce
author_facet Livingstone, Shona
Morales, Daniel R.
Fleuriot, Jacques
Donnan, Peter T.
Guthrie, Bruce
author_sort Livingstone, Shona
collection PubMed
description BACKGROUND: Prediction of lifetime cardiovascular disease (CVD) risk is recommended in many clinical guidelines, but lifetime risk models are rarely externally validated. The aim of this study was to externally validate the QRiskLifetime incident CVD risk prediction tool. METHODS: Independent external validation of QRiskLifetime using Clinical Practice Research Datalink data, examining discrimination and calibration in the whole population and stratified by age, and reclassification compared to QRISK3. Since lifetime CVD risk is unobservable, performance was evaluated at 10-years’ follow-up, and lifetime performance inferred in terms of performance for in the different age-groups from which lifetime predictions are derived. RESULTS: One million, two hundreds sixty thousand and three hundreds twenty nine women and 1,223,265 men were included in the analysis. Discrimination was excellent in the whole population (Harrell’s-C = 0.844 in women, 0.808 in men), but moderate to poor stratified by age-group (Harrell’s C in people aged 30–44 0.714 for both men and women, in people aged 75–84 0.578 in women and 0.556 in men). Ten-year CVD risk was under-predicted in the whole population, and in all age-groups except women aged 45–64, with worse under-prediction in older age-groups. Compared to those at highest QRISK3 estimated 10-year risk, those with highest lifetime risk were younger (mean age: women 50.5 vs. 71.3 years; men 46.3 vs. 63.8 years) and had lower systolic blood pressure and prevalence of treated hypertension, but had more family history of premature CVD, and were more commonly minority ethnic. Over 10-years, the estimated number needed to treat (NNT) with a statin to prevent one CVD event in people with QRISK3 ≥ 10% was 34 in women and 37 in men, compared to 99 and 100 for those at highest lifetime risk. CONCLUSIONS: QRiskLifetime underpredicts 10-year CVD risk in nearly all age-groups, so is likely to also underpredict lifetime risk. Treatment based on lifetime risk has considerably lower medium-term benefit than treatment based on 10-year risk. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12872-023-03209-8.
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spelling pubmed-101053952023-04-16 External validation of the QLifetime cardiovascular risk prediction tool: population cohort study Livingstone, Shona Morales, Daniel R. Fleuriot, Jacques Donnan, Peter T. Guthrie, Bruce BMC Cardiovasc Disord Research Article BACKGROUND: Prediction of lifetime cardiovascular disease (CVD) risk is recommended in many clinical guidelines, but lifetime risk models are rarely externally validated. The aim of this study was to externally validate the QRiskLifetime incident CVD risk prediction tool. METHODS: Independent external validation of QRiskLifetime using Clinical Practice Research Datalink data, examining discrimination and calibration in the whole population and stratified by age, and reclassification compared to QRISK3. Since lifetime CVD risk is unobservable, performance was evaluated at 10-years’ follow-up, and lifetime performance inferred in terms of performance for in the different age-groups from which lifetime predictions are derived. RESULTS: One million, two hundreds sixty thousand and three hundreds twenty nine women and 1,223,265 men were included in the analysis. Discrimination was excellent in the whole population (Harrell’s-C = 0.844 in women, 0.808 in men), but moderate to poor stratified by age-group (Harrell’s C in people aged 30–44 0.714 for both men and women, in people aged 75–84 0.578 in women and 0.556 in men). Ten-year CVD risk was under-predicted in the whole population, and in all age-groups except women aged 45–64, with worse under-prediction in older age-groups. Compared to those at highest QRISK3 estimated 10-year risk, those with highest lifetime risk were younger (mean age: women 50.5 vs. 71.3 years; men 46.3 vs. 63.8 years) and had lower systolic blood pressure and prevalence of treated hypertension, but had more family history of premature CVD, and were more commonly minority ethnic. Over 10-years, the estimated number needed to treat (NNT) with a statin to prevent one CVD event in people with QRISK3 ≥ 10% was 34 in women and 37 in men, compared to 99 and 100 for those at highest lifetime risk. CONCLUSIONS: QRiskLifetime underpredicts 10-year CVD risk in nearly all age-groups, so is likely to also underpredict lifetime risk. Treatment based on lifetime risk has considerably lower medium-term benefit than treatment based on 10-year risk. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12872-023-03209-8. BioMed Central 2023-04-15 /pmc/articles/PMC10105395/ /pubmed/37061672 http://dx.doi.org/10.1186/s12872-023-03209-8 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Livingstone, Shona
Morales, Daniel R.
Fleuriot, Jacques
Donnan, Peter T.
Guthrie, Bruce
External validation of the QLifetime cardiovascular risk prediction tool: population cohort study
title External validation of the QLifetime cardiovascular risk prediction tool: population cohort study
title_full External validation of the QLifetime cardiovascular risk prediction tool: population cohort study
title_fullStr External validation of the QLifetime cardiovascular risk prediction tool: population cohort study
title_full_unstemmed External validation of the QLifetime cardiovascular risk prediction tool: population cohort study
title_short External validation of the QLifetime cardiovascular risk prediction tool: population cohort study
title_sort external validation of the qlifetime cardiovascular risk prediction tool: population cohort study
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10105395/
https://www.ncbi.nlm.nih.gov/pubmed/37061672
http://dx.doi.org/10.1186/s12872-023-03209-8
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