Evidence mapPaperPMID 42437302Full record

ReviewMalaysian family physician : the official journal of the Academy of Family Physicians of Malaysia2026

Narrative review of the development of an ischaemic heart disease prognostic scoring tool (i-IHD score) among patients with type 2 diabetes mellitus in Malaysia.

Muhammad Muzzammil Mohamad Salleh, Sazzli Shahlan Kasim, Tajul Rosli Razak, Nazar Mohd Azahar, Mohamad Rodi Isa

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Review in Malaysian family physician : the official journal of the Academy of Family Physicians of Malaysia, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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2 · The registry

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Muhammad Muzzammil Mohamad SallehDepartment of Public Health, Faculty of Medicine, Universiti Teknologi MARA, Sungai Buloh, Selangor, Malaysia.
Sazzli Shahlan KasimCardiovascular Advancement and Research Excellence Institute (CARE Institute), Universiti Teknologi MARA, Sungai Buloh, Selangor, Malaysia.
Tajul Rosli RazakFaculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia.
Nazar Mohd AzaharDepartment of Medical Laboratory Technology, Faculty of Health Sciences, Universiti Teknologi MARA (UiTM) Cawangan Pulau Pinang, Kampus Bertam, Kepala Batas, Pulau Pinang, Malaysia.
Mohamad Rodi IsaDepartment of Public Health, Faculty of Medicine, Universiti Teknologi MARA, Sungai Buloh, Selangor, Malaysia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Ischaemic heart disease (IHD) remains a major cause of mortality among individuals with type 2 diabetes mellitus (T2DM) in Malaysia. Conventional cardiovascular risk models, such as the Framingham risk score, often show limited calibration in Asian populations. Artificial intelligence (Al)-calibrated models have emerged as potential alternatives, yet their generalisability and clinical utility across different populations remain uncertain. This narrative review aimed to summarise existing prognostic models for IHD in patients with T2DM and identify methodological gaps relevant to the development of a locally calibrated model. Methods: This narrative review employed a structured search strategy guided by PRISMA principles but was not conducted as a full systematic review. We synthesised evidence from epidemiological and prognostic research. Studies comparing conventional statistical approaches (e.g. logistic regression and Cox models) with AI-calibrated models such as extreme gradient boosting, random forest and support vector machines were reviewed. Results: Eleven studies met the inclusion criteria; four used conventional statistical methods, and seven applied AI or machine learning algorithms. The reported discrimination (area under the curve=0.66-0.94) varied widely. Conventional models commonly lacked external validation and demonstrated restricted applicability beyond their original cohorts. AI-calibrated models showed promising discrimination in some datasets but similarly experienced limited validation and lacked benchmarking against traditional statistical methods. Across the studies, limited calibration and validation reduced generalisability to heterogeneous Malaysian populations. Conclusion: Developing a locally AI-calibrated i-IHD score could enable early risk identification, guide targeted interventions and support national health initiatives, including the Health White Paper 2023 and 13th Malaysia Plan.

Indexed as

Artificial intelligenceDiabetes mellitusMalaysiaMyocardial ischaemiaPrognosisType 2

Identifiers

PMID42437302
PMCPMC13355864

What Socratic 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 Socratic graph.