Evidence map›Paper›PMID 42410287›Full record

ArticleJournal of cardiovascular translational research2026

Predicting Mortality After Percutaneous Coronary Intervention in a Multiethnic Southeast Asian Population: Insights From Machine Learning.

Yih Miin Liew, Yin Kia Chiam, Pei Ling Ngo, Hui Yee Tan, Nor Ashikin Md Sari, Li Kuo Tan, Wan Azman Wan Ahmad, Kok Han Chee

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

Article in Journal of cardiovascular translational research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Yih Miin LiewDepartment of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, 50603, Malaysia. liewym@um.edu.my.ORCID 0000-0003-2487-3933
Yin Kia ChiamDepartment of Software Engineering, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, 50603, Malaysia. yinkia@um.edu.my.
Pei Ling NgoDepartment of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, 50603, Malaysia.
Hui Yee TanDepartment of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, 50603, Malaysia.
Nor Ashikin Md SariDepartment of Medicine, University Malaya Medical Centre, Kuala Lumpur, 50603, Malaysia.
Li Kuo TanDepartment of Biomedical Imaging, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia.
Wan Azman Wan AhmadDepartment of Medicine, University Malaya Medical Centre, Kuala Lumpur, 50603, Malaysia.
Kok Han CheeDepartment of Medicine, University Malaya Medical Centre, Kuala Lumpur, 50603, Malaysia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ischemic heart disease remains a major contributor to mortality in Malaysia, with non-elective percutaneous coronary intervention (PCI) frequently performed in high-risk acute coronary syndrome (ACS) patients. Using nationwide registry data (2007-2020), we evaluated 29,521 patients and compared seven machine learning (ML) models for predicting in-hospital, 30-day, and 1-year mortality. Models were developed in a training cohort and externally validated using hospital-level (TEST1) and prospective temporal (TEST2) cohorts. After logistic recalibration, discrimination for in-hospital mortality ranged from 0.927 to 0.943 (TEST1) and 0.865-0.884 (TEST2). For 30-day mortality, ROC-AUC ranged from 0.902 to 0.923 (TEST1) and 0.753-0.838 (TEST2), and for 1-year mortality from 0.833 to 0.859 (TEST1) and 0.750-0.801 (TEST2). Calibration remained acceptable, and decision curve analysis demonstrated positive net benefit across clinically relevant thresholds. Cross-model stability analysis consistently identified age, haemodynamic status, and renal function as key predictors.

Indexed as

Acute Coronary SyndromeCoronary Artery DiseaseDecision Support TechniquesMachine LearningPercutaneous Coronary InterventionPredictive Learning ModelsAgedClassification AlgorithmsFemaleHospital MortalityHumansMalaysiaMaleMiddle AgedPrediction AlgorithmsPredictive Value of TestsCardiovascular diseaseMachine learningPatient mortality predictionPercutaneous coronary interventionRisk factors analysis

Identifiers

PMID42410287
PMCPMC13337946

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.