Evidence map›Paper›PMID 39506551›Full record

ArticleEuropean journal of preventive cardiology2025

Comparing 5-year and 10-year predicted cardiovascular disease risks in Aotearoa New Zealand: national data linkage study of 1.7 million adults.

Jingyuan Liang, Susan Wells, Rod Jackson, Yeunhyang Choi, Suneela Mehta, Claris Chung, Pei Gao, Katrina Poppe

Abstract readComparative Study
PubMed Publisher
In one paragraph

Article in European journal of preventive cardiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Cardiovascular Risk Prediction in Older Adults.Current atherosclerosis reports · 2025
    Review
  3. Article
  4. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Jingyuan LiangSection of Epidemiology and Biostatistics, School of Population Health, Faculty of Medical and Health Sciences, University of Auckland, Private Bag 92019, Auckland 1142, New Zealand.ORCID 0000-0002-3489-5776
Susan WellsDepartment of General Practice and Primary Health Care, School of Population Health, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand.
Rod JacksonSection of Epidemiology and Biostatistics, School of Population Health, Faculty of Medical and Health Sciences, University of Auckland, Private Bag 92019, Auckland 1142, New Zealand.
Yeunhyang ChoiSection of Epidemiology and Biostatistics, School of Population Health, Faculty of Medical and Health Sciences, University of Auckland, Private Bag 92019, Auckland 1142, New Zealand.
Suneela MehtaSection of Epidemiology and Biostatistics, School of Population Health, Faculty of Medical and Health Sciences, University of Auckland, Private Bag 92019, Auckland 1142, New Zealand.
Claris ChungUC Business School, Accounting and Information Systems, University of Canterbury, Christchurch, New Zealand.
Pei GaoDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China.ORCID 0000-0001-8649-1290
Katrina PoppeSection of Epidemiology and Biostatistics, School of Population Health, Faculty of Medical and Health Sciences, University of Auckland, Private Bag 92019, Auckland 1142, New Zealand.

Funding

Chinese Scholarship Council 202106010103New Zealand Health Research Council grant number: 21/712New Zealand Heart Foundation Heart Health Research Trust 1886
6 · The paper itself

Abstract

aimsThere is no consensus on the optimal time horizon for predicting cardiovascular disease (CVD) risk to inform treatment decisions. New Zealand and Australia recommend 5 years, whereas most countries recommend 10 years. We compared predicted risk and treatment-eligible groups using 5-year and 10-year equations. METHODS AND

resultsIndividual-level linked administrative data sets identified 1 746 665 New Zealanders without CVD, aged 30-74 years in 2006, with follow-up to 2018. Participants were randomly allocated to derivation and validation cohorts. Sex-specific 5-year and 10-year risk prediction models were developed in the derivation cohort and applied in the validation cohort. There were 28 116 (3.2%) and 62 027 (7.1%) first CVD events that occurred during 5-year and 10-year follow-ups, respectively (cumulative risk, derivation cohort). Median predicted 10-year CVD risk (3.8%) was approximately 2.5 times 5-year risk (1.6%), and 95% of individuals in the top quintile of 5-year risk were also in the top quintile of 10-year risk, across age/gender groups (validation cohort). Using common guideline-recommended treatment thresholds (5% 5-year and 10% 10-year risk), approximately 14% and 28% of women and men, respectively, were identified as treatment-eligible applying 5-year equations compared with 17% and 32% of women and men applying 10-year equations. Older age was the major contributor to treatment eligibility in both sexes.

conclusionPredicted 10-year CVD risk was approximately 2.5 times 5-year risk. Both equations identified mostly the same individuals in the highest risk quintile. Conversely, commonly used treatment thresholds identified more treatment-eligible individuals using 10-year equations, and both equations identified approximately twice as many treatment-eligible men as women. The treatment threshold, rather than the risk horizon, is the main determinant of treatment eligibility.

Indexed as

Cardiovascular DiseasesAdultAgedDatabases, FactualFemaleHeart Disease Risk FactorsHumansInformation Storage and RetrievalMaleMiddle AgedNew ZealandPrognosisRisk AssessmentRisk FactorsTime FactorsCardiovascular diseasePrimary preventionRisk prediction horizonRoutine data

Identifiers

PMID39506551

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.