Treatment drop-in in a contemporary cohort used to derive cardiovascular risk prediction equations.

Cardiovascular Diseases Cohort Studies Electronic Health Records Risk Assessment Treatment Outcome

Journal

Heart (British Cardiac Society)
ISSN: 1468-201X
Titre abrégé: Heart
Pays: England
ID NLM: 9602087

Informations de publication

Date de publication:
02 Jul 2024
Historique:
received: 21 03 2024
accepted: 11 06 2024
medline: 4 7 2024
pubmed: 4 7 2024
entrez: 3 7 2024
Statut: aheadofprint

Résumé

No routinely recommended cardiovascular disease (CVD) risk prediction equations have adjusted for CVD preventive medications initiated during follow-up (treatment drop-in) in their derivation cohorts. This will lead to underestimation of risk when equations are applied in clinical practice if treatment drop-in is common. We aimed to quantify the treatment drop-in in a large contemporary national cohort to determine whether equations are likely to require adjustment. Eight de-identified individual-level national health administrative datasets in Aotearoa New Zealand were linked to establish a cohort of almost all New Zealanders without CVD and aged 30-74 years in 2006. Individuals dispensing blood-pressure-lowering and/or lipid-lowering medications between 1 July 2006 and 31 December 2006 (baseline dispensing), and in each 6-month period during 12 years' follow-up to 31 December 2018 (follow-up dispensing), were identified. Person-years of treatment drop-in were determined. A total of 1 399 348 (80%) out of the 1 746 695 individuals in the cohort were not dispensed CVD medications at baseline. Blood-pressure-lowering and/or lipid-lowering treatment drop-in accounted for 14% of follow-up time in the group untreated at baseline and increased significantly with increasing predicted baseline 5-year CVD risk (12%, 31%, 34% and 37% in <5%, 5-9%, 10-14% and ≥15% risk groups, respectively) and with increasing age (8% in 30-44 year-olds to 30% in 60-74 year-olds). CVD preventive treatment drop-in accounted for approximately one-third of follow-up time among participants typically eligible for preventive treatment (≥5% 5-year predicted risk). Equations derived from cohorts with long-term follow-up that do not adjust for treatment drop-in effect will underestimate CVD risk in higher risk individuals and lead to undertreatment. Future CVD risk prediction studies need to address this potential flaw.

Sections du résumé

BACKGROUND BACKGROUND
No routinely recommended cardiovascular disease (CVD) risk prediction equations have adjusted for CVD preventive medications initiated during follow-up (treatment drop-in) in their derivation cohorts. This will lead to underestimation of risk when equations are applied in clinical practice if treatment drop-in is common. We aimed to quantify the treatment drop-in in a large contemporary national cohort to determine whether equations are likely to require adjustment.
METHODS METHODS
Eight de-identified individual-level national health administrative datasets in Aotearoa New Zealand were linked to establish a cohort of almost all New Zealanders without CVD and aged 30-74 years in 2006. Individuals dispensing blood-pressure-lowering and/or lipid-lowering medications between 1 July 2006 and 31 December 2006 (baseline dispensing), and in each 6-month period during 12 years' follow-up to 31 December 2018 (follow-up dispensing), were identified. Person-years of treatment drop-in were determined.
RESULTS RESULTS
A total of 1 399 348 (80%) out of the 1 746 695 individuals in the cohort were not dispensed CVD medications at baseline. Blood-pressure-lowering and/or lipid-lowering treatment drop-in accounted for 14% of follow-up time in the group untreated at baseline and increased significantly with increasing predicted baseline 5-year CVD risk (12%, 31%, 34% and 37% in <5%, 5-9%, 10-14% and ≥15% risk groups, respectively) and with increasing age (8% in 30-44 year-olds to 30% in 60-74 year-olds).
CONCLUSIONS CONCLUSIONS
CVD preventive treatment drop-in accounted for approximately one-third of follow-up time among participants typically eligible for preventive treatment (≥5% 5-year predicted risk). Equations derived from cohorts with long-term follow-up that do not adjust for treatment drop-in effect will underestimate CVD risk in higher risk individuals and lead to undertreatment. Future CVD risk prediction studies need to address this potential flaw.

Identifiants

pubmed: 38960588
pii: heartjnl-2024-324179
doi: 10.1136/heartjnl-2024-324179
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© Author(s) (or their employer(s)) 2024. No commercial re-use. See rights and permissions. Published by BMJ.

Déclaration de conflit d'intérêts

Competing interests: None declared.

Auteurs

Jingyuan Liang (J)

Section of Epidemiology and Biostatistics, University of Auckland, Auckland, New Zealand jingyuan.liang@auckland.ac.nz.

Rodney T Jackson (RT)

Section of Epidemiology and Biostatistics, University of Auckland, Auckland, New Zealand.

Romana Pylypchuk (R)

Section of Epidemiology and Biostatistics, University of Auckland, Auckland, New Zealand.

Yeunhyang Choi (Y)

Section of Epidemiology and Biostatistics, University of Auckland, Auckland, New Zealand.

Claris Chung (C)

Accounting and Information Systems, University of Canterbury, Christchurch, New Zealand.

Sue Crengle (S)

Ngāi Tahu Māori Health Research Unit, University of Otago, Dunedin, New Zealand.

Pei Gao (P)

Department of Epidemiology and Biostatistics, Peking University, Beijing, China.
Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China.

Corina Grey (C)

Section of Epidemiology and Biostatistics, University of Auckland, Auckland, New Zealand.

Matire Harwood (M)

Department of General Practice and Primary Health Care, University of Auckland, Auckland, New Zealand.

Anders Holt (A)

Section of Epidemiology and Biostatistics, University of Auckland, Auckland, New Zealand.
Department of Cardiology, Copenhagen University Hospital - Herlev and Gentofte, Hellerup, Denmark.

Andrew Kerr (A)

Section of Epidemiology and Biostatistics, University of Auckland, Auckland, New Zealand.
School of Medicine, University of Auckland, Auckland, New Zealand.

Suneela Mehta (S)

Section of Epidemiology and Biostatistics, University of Auckland, Auckland, New Zealand.

Susan Wells (S)

Department of General Practice and Primary Health Care, University of Auckland, Auckland, New Zealand.

Katrina Poppe (K)

Section of Epidemiology and Biostatistics, University of Auckland, Auckland, New Zealand.
School of Medicine, University of Auckland, Auckland, New Zealand.

Classifications MeSH