Evidence map›Paper›PMID 41249035›Full record

ArticleHeart (British Cardiac Society)2026

Estimating 5-year absolute risk of cardiovascular disease using routinely collected electronic medical records from Australian general practices.

Nicholas I-Hsien Kuo, Sebastiano Barbieri, Clare Arnott, Blanca Gallego, Ziba Gandomkar, Shahana Ferdousi, Kirsty Douglas, Mark Woodward, Louisa Jorm

Abstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Heart (British Cardiac Society), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Nicholas I-Hsien KuoCentre for Big Data Research in Health, University of New South Wales, Sydney, New South Wales, Australia.ORCID 0000-0001-8749-7280
Sebastiano BarbieriCentre for Big Data Research in Health, University of New South Wales, Sydney, New South Wales, Australia.ORCID 0000-0002-5919-372X
Clare ArnottThe George Institute for Global Health, University of New South Wales, Sydney, New South Wales, Australia.
Blanca GallegoCentre for Big Data Research in Health, University of New South Wales, Sydney, New South Wales, Australia.
Ziba GandomkarSydney School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.
Shahana FerdousiWentWest Primary Health Network, Westmead, New South Wales, Australia.
Kirsty DouglasSchool of Medicine and Psychology, Australian National University, Canberra, Australian Capital Territory, Australia.
Mark WoodwardThe George Institute for Global Health, University of New South Wales, Sydney, New South Wales, Australia.ORCID 0000-0001-9800-5296
Louisa JormCentre for Big Data Research in Health, University of New South Wales, Sydney, New South Wales, Australia l.jorm@unsw.edu.au.ORCID 0000-0003-0390-661X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRecent cardiovascular risk equations from the USA and United Kingdom use routinely collected electronic medical records (EMRs), while current equations used in Australia (AusCVDRisk) have not been validated locally. We assessed the feasibility and performance of using routinely collected EMRs from Australian primary care software systems to predict absolute risk of cardiovascular disease (CVD).

methodsWe used primary care EMR data from the New South Wales Health Lumos programme, covering 680 general practices, linked with hospital and death records. Individuals aged 30-74 years on 1 January 2017 with no prior CVD history and at least one record for an anthropometric measurement or pathology test were included. Sex-specific Cox proportional hazards models were used to estimate 5-year risk of a fatal or non-fatal CVD event. Predictors included demographics, smoking, chronic conditions, clinical variables and medications. Modelling used a 5×2 cross-validation approach. Discrimination, calibration and reclassification performance were assessed.

resultsOver a mean follow-up of 4.91 years, 33 578 CVD events were recorded in 850 216 patients. Full models with 28 predictors had Harrell's C of 0.803 (95% CI 0.801 to 0.804) for females and 0.772 (95% CI 0.770 to 0.773) for males. Least absolute shrinkage and selection operator models with 12-15 predictors performed similarly. Models were well calibrated across age, socioeconomic and smoking strata. Geographic (internal-external) validation across 10 Primary Health Networks confirmed consistent discrimination (C-index range 0.747-0.788 for males; 0.772-0.827 for females). Percentile-based net reclassification improvement showed enhanced event detection compared with a model based on AusCVDRisk variables (event-Net Reclassification Improvement up to 0.185).

conclusionsCVD risk equations based on routine Australian primary care data performed strongly and generalised well across diverse settings. These models offer potential for automated integration into general practice software to support real-time CVD risk assessment.

Indexed as

Cardiovascular DiseasesElectronic Health RecordsGeneral PracticeAdultAgedAustraliaFeasibility StudiesFemaleHeart Disease Risk FactorsHumansMaleMiddle AgedNew South WalesRisk AssessmentRisk FactorsElectronic Health RecordsEpidemiologyRisk AssessmentRisk Factors

Identifiers

PMID41249035
PMCPMC13217029

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LicenceCC BY-NC
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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.