Evidence map›Paper›PMID 41029641›Full record

ArticleBMC public health2025

Real-world risk stratification for coronary heart disease: a one-year prediction model using health information exchange data.

Yaqi Zhang, Yifu Mo, Naoto Ozawa, Takumi Ichikawa, Chao-Jung Huang, Zhi Han, Lu Tian, Shaun T Alfreds, Karl G Sylvester, Doff B McElhinney and 1 more

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In one paragraph

Article in BMC public health, 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. Article
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

11 authors.

Yaqi ZhangCollege of Automation, Guangdong Polytechnic Normal University, Guangzhou, China.
Yifu MoChina Southern Power Grid Company Limited, Guangzhou, China.
Naoto OzawaSchool of Medicine, Stanford University, Stanford, CA, USA.
Takumi IchikawaSchool of Medicine, Stanford University, Stanford, CA, USA.
Chao-Jung HuangJoint Research Center for Artificial Intelligence Technology and All Vista Healthcare, National Taiwan University, Taipei, Taiwan.
Zhi HanSchool of Medicine, Stanford University, Stanford, CA, USA.
Lu TianSchool of Medicine, Stanford University, Stanford, CA, USA.
Shaun T AlfredsHealthInfoNet, Portland, ME, USA.
Karl G SylvesterSchool of Medicine, Stanford University, Stanford, CA, USA.
Doff B McElhinneySchool of Medicine, Stanford University, Stanford, CA, USA.
Xuefeng B LingSchool of Medicine, Stanford University, Stanford, CA, USA. bxling@stanford.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCoronary heart disease (CHD), the most common form of heart disease, progresses over years before culminating in serious cardiac events. Early prediction and intervention are critical to reducing CHD-related morbidity, mortality, and healthcare burden.

objectiveTo develop and validate a machine learning model using statewide electronic health records (EHRs) to predict 1-year risk of CHD in the general population of Maine, enabling targeted preventive strategies.

methodsTwo population-based cohorts were constructed from the Maine Health Information Exchange (HIE): a retrospective cohort for model training and calibration (2015–2017, N = 1,042,124), and a prospective cohort for external validation (2016–2018, N = 1,040,158). EHR features included demographics, diagnoses, procedures, medications, labs, and utilization metrics. A multistage modeling pipeline—comprising statistical filtering, XGBoost-based feature selection, risk prediction, and isotonic regression calibration—was used to construct the final model. Validation included discrimination, calibration, and survival analysis.

resultsThe final XGBoost model achieved strong discrimination: AUC = 0.952 (95% CI: 0.950–0.954) in the retrospective cohort and 0.888 (95% CI: 0.885–0.890) in the prospective cohort. Based on calibrated risk probabilities, the population was stratified into five risk categories: very low (92.30%, N = 960,021), low (6.79%, N = 70,676), medium (0.85%, N = 8,888), high (0.05%, N = 554), and very high (0.002%, N = 19). Among the very high-risk group, 11 individuals (57.89%) developed CHD within one year.

conclusionsThis statewide, HIE-based CHD risk prediction model demonstrates robust performance and real-world applicability. It enables early identification of high-risk individuals and supports population-scale precision prevention through evidence-informed, proactive care.

Indexed as

Coronary DiseaseHealth Information ExchangeMachine LearningAgedElectronic Health RecordsFemaleHumansMaineMaleMiddle AgedPrediction AlgorithmsPredictive Learning ModelsProspective StudiesRetrospective StudiesRisk AssessmentCoronary heart diseaseElectronic health recordsMultiple multivariate machine learningPredictive algorithmRisk stratification

Identifiers

PMID41029641
PMCPMC12486789

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LicenceCC BY
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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.