Dataset Information

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

ABSTRACT:

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.

SUBMITTER: Christensen DM

PROVIDER: S-EPMC9241501 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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 DM Phelps Matthew M Gerds Thomas T Malmborg Morten M Schjerning Anne-Marie AM Strange Jarl Emanuel JE El-Chouli Mohamad M Larsen Lars Bruun LB Fosbøl Emil E Køber Lars L Torp-Pedersen Christian C Mehta Suneela S Jackson Rod R Gislason Gunnar G

European heart journal open 20210802 2

<h4>Aims</h4>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).<h4>Methods and results</h4>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 ...[more]

PMID: 35919262

Similar Datasets

Project description:BackgroundThe possible association between β-adrenoceptor antagonists (β-blockers) and risk of COPD is controversial. The objective of the present study was to test whether β-blocker use is associated with susceptibility to the disease.MethodsA total of 301,542 new users of β-blockers and 1,000,633 new users of any other antihypertensive drugs aged 30-90 years without any history of COPD hospitalizations were included in the present study and followed in the Danish National Patient Registry for incident admissions for COPD and COPD death between 1995 and 2015. Multiple adjusted cox regression models were used to examine the association between use of β-blockers and COPD hospitalization. Additionally, subgroup analyses based on underlying diseases at baseline or duration of treatment were performed.FindingsPeople treated with β-blockers continuously for more than 6 months had a lower risk of COPD hospitalization during follow-up compared to people treated with any other antihypertensive drugs (adjusted hazard ratio [HRadjusted] 0·80, 95% CI 0·79-0·82). Risk of COPD hospitalization was lowered in the groups treated with β-blockers among patients with ischemic heart disease (0·72, 0·69-0·75), cardiac arrhythmias (0·76, 0·72-0·80), asthma (0·69, 0·61-0·79), hypertension (0·91, 0·86-0·96), and diseases of the pulmonary circulation (pulmonary embolism and cor pulmonale) (0·72, 0·59-0·87). All-cause mortality as well as risk of COPD death during follow-up was lower in the group treated with β-blockers compared to the group treated with any other antihypertensive drugs (0·56, 0·53-0·59).InterpretationTreatment with β-blockers seems to reduce risk of COPD hospitalization and mortality compared to treatment with any other antihypertensive drugs.FundingThe Danish Council for Independent Research in Denmark (grant no. 4183-00569B), The Research Foundation of Health Science in Region Zealand (grant no. RSSF2017000661 and no. 15-000342), The Research Foundation of Medical Science (A.P. Møller Foundation, grant no. 16-68), The Research Foundation in memory of King Christian 10th (grant no. 142/2017), Aase & Ejnar Danielsen's Research Foundation (grant no. 10-001946), and Lundbeck Foundation (grant no. R252-2017-1690).

Project description:BackgroundPatients with multimorbidity are frequent users of healthcare, but fragmented care may lead to suboptimal treatment. Yet, this has never been examined across healthcare sectors on a national scale. We aimed to quantify care fragmentation using various measures and to analyze the associations with patient outcomes.MethodsWe conducted a register-based nationwide cohort study with 4.7 million Danish adult citizens. All healthcare contacts to primary care and hospitals during 2018 were recorded. Clinical fragmentation indicators included number of healthcare contacts, involved providers, provider transitions, and hospital trajectories. Formal fragmentation indices assessed care concentration, dispersion, and contact sequence. The patient outcomes were potentially inappropriate medication and all-cause mortality adjusted for demographics, socioeconomic factors, and morbidity level.ResultsThe number of involved healthcare providers, provider transitions, and hospital trajectories rose with increasing morbidity levels. Patients with 3 versus 6 conditions had a mean of 4.0 versus 6.9 involved providers and 6.6 versus 13.7 provider transitions. The proportion of contacts to the patient's own general practice remained stable across morbidity levels. High levels of care fragmentation were associated with higher rates of potentially inappropriate medication and increased mortality on all fragmentation measures after adjusting for demographic characteristics, socioeconomic factors, and morbidity. The strongest associations with potentially inappropriate medication and mortality were found for ≥ 20 contacts versus none (incidence rate ratio 2.83, 95% CI 2.77-2.90) and ≥ 20 hospital trajectories versus none (hazard ratio 10.8, 95% CI 9.48-12.4), respectively. Having less than 25% of contacts with your usual provider was associated with an incidence rate ratio of potentially inappropriate medication of 1.49 (95% CI 1.40-1.58) and a mortality hazard ratio of 2.59 (95% CI 2.36-2.84) compared with full continuity. For the associations between fragmentation measures and patient outcomes, there were no clear interactions with number of conditions.ConclusionsSeveral clinical indicators of care fragmentation were associated with morbidity level. Care fragmentation was associated with higher rates of potentially inappropriate medication and increased mortality even when adjusting for the most important confounders. Frequent contact to the usual provider, fewer transitions, and better coordination were associated with better patient outcomes regardless of morbidity level.

Dataset Information

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

Methods and results

Conclusion

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets