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Prediction of first cardiovascular disease event in 2.9 million individuals using Danish administrative healthcare data: a nationwide, registry-based derivation and validation study

AIMS: The aim of this study was to derive and validate a risk prediction model with nationwide coverage to predict the individual and population-level risk of cardiovascular disease (CVD). METHODS AND RESULTS: All 2.98 million Danish residents aged 30–85 years free of CVD were included on 1 January...

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Autores principales: Christensen, Daniel Mølager, Phelps, Matthew, Gerds, Thomas, Malmborg, Morten, Schjerning, Anne-Marie, Strange, Jarl Emanuel, El-Chouli, Mohamad, Larsen, Lars Bruun, Fosbøl, Emil, Køber, Lars, Torp-Pedersen, Christian, Mehta, Suneela, Jackson, Rod, Gislason, Gunnar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9241501/
https://www.ncbi.nlm.nih.gov/pubmed/35919262
http://dx.doi.org/10.1093/ehjopen/oeab015
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author Christensen, Daniel Mølager
Phelps, Matthew
Gerds, Thomas
Malmborg, Morten
Schjerning, Anne-Marie
Strange, Jarl Emanuel
El-Chouli, Mohamad
Larsen, Lars Bruun
Fosbøl, Emil
Køber, Lars
Torp-Pedersen, Christian
Mehta, Suneela
Jackson, Rod
Gislason, Gunnar
author_facet Christensen, Daniel Mølager
Phelps, Matthew
Gerds, Thomas
Malmborg, Morten
Schjerning, Anne-Marie
Strange, Jarl Emanuel
El-Chouli, Mohamad
Larsen, Lars Bruun
Fosbøl, Emil
Køber, Lars
Torp-Pedersen, Christian
Mehta, Suneela
Jackson, Rod
Gislason, Gunnar
author_sort Christensen, Daniel Mølager
collection PubMed
description AIMS: The aim of this study was to derive and validate a risk prediction model with nationwide coverage to predict the individual and population-level risk of cardiovascular disease (CVD). METHODS AND RESULTS: All 2.98 million Danish residents aged 30–85 years free of CVD were included on 1 January 2014 and followed through 31 December 2018 using nationwide administrative healthcare registries. Model predictors and outcome were pre-specified. Predictors were age, sex, education, use of antithrombotic, blood pressure-lowering, glucose-lowering, or lipid-lowering drugs, and a smoking proxy of smoking-cessation drug use or chronic obstructive pulmonary disease. Outcome was 5-year risk of first CVD event, a combination of ischaemic heart disease, heart failure, peripheral artery disease, stroke, or cardiovascular death. Predictions were computed using cause-specific Cox regression models. The final model fitted in the full data was internally-externally validated in each Danish Region. The model was well-calibrated in all regions. Area under the receiver operating characteristic curve (AUC) and Brier scores ranged from 76.3% to 79.6% and 3.3 to 4.4. The model was superior to an age-sex benchmark model with differences in AUC and Brier scores ranging from 1.2% to 1.5% and −0.02 to −0.03. Average predicted risks in each Danish municipality ranged from 2.8% to 5.9%. Predicted risks for a 66-year old ranged from 2.6% to 25.3%. Personalized predicted risks across ages 30–85 were presented in an online calculator (https://hjerteforeningen.shinyapps.io/cvd-risk-manuscript/). CONCLUSION: A CVD risk prediction model based solely on nationwide administrative registry data provided accurate prediction of personal and population-level 5-year first CVD event risk in the Danish population. This may inform clinical and public health primary prevention efforts.
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spelling pubmed-92415012022-08-01 Prediction of first cardiovascular disease event in 2.9 million individuals using Danish administrative healthcare data: a nationwide, registry-based derivation and validation study Christensen, Daniel Mølager Phelps, Matthew Gerds, Thomas Malmborg, Morten Schjerning, Anne-Marie Strange, Jarl Emanuel El-Chouli, Mohamad Larsen, Lars Bruun Fosbøl, Emil Køber, Lars Torp-Pedersen, Christian Mehta, Suneela Jackson, Rod Gislason, Gunnar Eur Heart J Open Original Article AIMS: The aim of this study was to derive and validate a risk prediction model with nationwide coverage to predict the individual and population-level risk of cardiovascular disease (CVD). METHODS AND RESULTS: All 2.98 million Danish residents aged 30–85 years free of CVD were included on 1 January 2014 and followed through 31 December 2018 using nationwide administrative healthcare registries. Model predictors and outcome were pre-specified. Predictors were age, sex, education, use of antithrombotic, blood pressure-lowering, glucose-lowering, or lipid-lowering drugs, and a smoking proxy of smoking-cessation drug use or chronic obstructive pulmonary disease. Outcome was 5-year risk of first CVD event, a combination of ischaemic heart disease, heart failure, peripheral artery disease, stroke, or cardiovascular death. Predictions were computed using cause-specific Cox regression models. The final model fitted in the full data was internally-externally validated in each Danish Region. The model was well-calibrated in all regions. Area under the receiver operating characteristic curve (AUC) and Brier scores ranged from 76.3% to 79.6% and 3.3 to 4.4. The model was superior to an age-sex benchmark model with differences in AUC and Brier scores ranging from 1.2% to 1.5% and −0.02 to −0.03. Average predicted risks in each Danish municipality ranged from 2.8% to 5.9%. Predicted risks for a 66-year old ranged from 2.6% to 25.3%. Personalized predicted risks across ages 30–85 were presented in an online calculator (https://hjerteforeningen.shinyapps.io/cvd-risk-manuscript/). CONCLUSION: A CVD risk prediction model based solely on nationwide administrative registry data provided accurate prediction of personal and population-level 5-year first CVD event risk in the Danish population. This may inform clinical and public health primary prevention efforts. Oxford University Press 2021-08-02 /pmc/articles/PMC9241501/ /pubmed/35919262 http://dx.doi.org/10.1093/ehjopen/oeab015 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of the European Society of Cardiology. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Original Article
Christensen, Daniel Mølager
Phelps, Matthew
Gerds, Thomas
Malmborg, Morten
Schjerning, Anne-Marie
Strange, Jarl Emanuel
El-Chouli, Mohamad
Larsen, Lars Bruun
Fosbøl, Emil
Køber, Lars
Torp-Pedersen, Christian
Mehta, Suneela
Jackson, Rod
Gislason, Gunnar
Prediction of first cardiovascular disease event in 2.9 million individuals using Danish administrative healthcare data: a nationwide, registry-based derivation and validation study
title Prediction of first cardiovascular disease event in 2.9 million individuals using Danish administrative healthcare data: a nationwide, registry-based derivation and validation study
title_full Prediction of first cardiovascular disease event in 2.9 million individuals using Danish administrative healthcare data: a nationwide, registry-based derivation and validation study
title_fullStr Prediction of first cardiovascular disease event in 2.9 million individuals using Danish administrative healthcare data: a nationwide, registry-based derivation and validation study
title_full_unstemmed Prediction of first cardiovascular disease event in 2.9 million individuals using Danish administrative healthcare data: a nationwide, registry-based derivation and validation study
title_short Prediction of first cardiovascular disease event in 2.9 million individuals using Danish administrative healthcare data: a nationwide, registry-based derivation and validation study
title_sort prediction of first cardiovascular disease event in 2.9 million individuals using danish administrative healthcare data: a nationwide, registry-based derivation and validation study
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9241501/
https://www.ncbi.nlm.nih.gov/pubmed/35919262
http://dx.doi.org/10.1093/ehjopen/oeab015
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