Evidence map›Paper›PMID 40760476›Full record

ArticleBMC medical informatics and decision making2025

Performance of CAC-prob in predicting coronary artery calcium score: an external validation study in a high-CAC burden population.

Pakpoom Wongyikul, Phichayut Phinyo, Pannipa Suwannasom, Apichat Tantraworasin, Chanikan Srikuenkaew, Pichyapa Jira, Kempiya Pornpipatsakul, Arachaporn Ngachuea, Phansa Chanthanom, Kanogphol Prayongkul and 3 more

Abstract readValidation Study
In one paragraph

Article in BMC medical informatics and decision making, 2025. 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. A Nomogram for Predicting Coronary Artery Calcification Using Achilles Tendon Cross-Sectional Area, Mechanical Parameters, and TCBI.Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis
    Article
4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

13 authors.

Pakpoom WongyikulDepartment of Biomedical Informatics and Clinical Epidemiology (BioCE), Faculty of Medicine, Chiang Mai University, Chiang Mai, 50200, Thailand.
Phichayut PhinyoDepartment of Biomedical Informatics and Clinical Epidemiology (BioCE), Faculty of Medicine, Chiang Mai University, Chiang Mai, 50200, Thailand. phichayutphinyo@gmail.com.
Pannipa SuwannasomDivision of Cardiology, Department of Internal Medicine, Faculty of Medicine, Chiang Mai University, Chiang Mai, 50200, Thailand.
Apichat TantraworasinGeneral Thoracic Unit, Department of Surgery, Faculty of Medicine, Chiang Mai University Hospital, Chiang Mai, 50200, Thailand.
Chanikan SrikuenkaewFaculty of Medicine, Chiang Mai University, Chiang Mai, 50200, Thailand.
Pichyapa JiraFaculty of Medicine, Chiang Mai University, Chiang Mai, 50200, Thailand.
Kempiya PornpipatsakulFaculty of Medicine, Chiang Mai University, Chiang Mai, 50200, Thailand.
Arachaporn NgachueaFaculty of Medicine, Chiang Mai University, Chiang Mai, 50200, Thailand.
Phansa ChanthanomFaculty of Medicine, Chiang Mai University, Chiang Mai, 50200, Thailand.
Kanogphol PrayongkulFaculty of Medicine, Chiang Mai University, Chiang Mai, 50200, Thailand.
Warisara ChavalitjiraphanFaculty of Medicine, Chiang Mai University, Chiang Mai, 50200, Thailand.
Thitirat RattananalinFaculty of Medicine, Chiang Mai University, Chiang Mai, 50200, Thailand.
Surasak SaokaewDivision of Social and Administrative Pharmacy, Department of Pharmaceutical Care, School of Pharmaceutical Sciences, University of Phayao, Phayao, 56000, Thailand.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAlthough CAC screening is gaining recognition in developing countries such as Thailand, official guidelines for using the CAC score in cardiovascular risk assessment remain lacking. This study aims to externally validate CAC-prob, a recently developed prediction model that can estimate the probability of CAC > 0 and CAC ≥ 100, to confirm its robustness.

methodThis study externally validated the CAC-prob model using retrospective data from a tertiary care centre in northern Thailand. Patients who underwent CAC screening between 2019 and 2022 were included. CAC-prob consists of two models: one predicting the probability of CAC > 0 (Model 1) and another predicting the probability of CAC ≥ 100 (Model 2). Model performance was assessed in terms of discrimination (Ordinal C-index), calibration slope, and diagnostic indices for each model.

resultsA total of 329 patients were included. The patient characteristics observed in this study indicated a higher prevalence of DM, hypertension, dyslipidaemia, CKD, and CAC ≥ 100 compared to the development study. The ordinal C-index derived from the validation study showed a slight decline (0.78). The calibration slope for Model 1 and Model 2 was 1.28 (95% CI 0.95-1.63) and 1.06 (95% CI 0.78-1.36), respectively. In Model 1, CAC-prob demonstrated comparable diagnostic performance. However, in Model 2, it showed slightly better performance, with significantly improved sensitivity compared to the development study.

conclusionThis external validation study confirms the predictive performance of CAC-prob in Northern Thai patients. The findings support the integration of CAC-prob into routine clinical practice to aid physicians in making recommendations for CAC screening.

Indexed as

Coronary Artery DiseaseVascular CalcificationAdultAgedFemaleHumansMaleMiddle AgedRetrospective StudiesRisk AssessmentThailandClinical prediction modelCoronary Artery CalciumOutpatients

Identifiers

PMID40760476
PMCPMC12323013

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