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Background <p>Coronary 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.</p> Objective <p>To 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.</p> Methods <p>Two population-based cohorts were constructed from the Maine Health Information Exchange (HIE): a retrospective cohort for model training and calibration (2015–2017, <i>N</i> = 1,042,124), and a prospective cohort for external validation (2016–2018, <i>N</i> = 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.</p> Results <p>The 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%, <i>N</i> = 960,021), low (6.79%, <i>N</i> = 70,676), medium (0.85%, <i>N</i> = 8,888), high (0.05%, <i>N</i> = 554), and very high (0.002%, <i>N</i> = 19). Among the very high-risk group, 11 individuals (57.89%) developed CHD within one year.</p> Conclusions <p>This 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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月30日

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  • Yaqi Zhang,
  • Yifu Mo,
  • Naoto Ozawa,
  • Takumi Ichikawa,
  • Chao-Jung Huang,
  • Zhi Han,
  • Lu Tian,
  • Shaun T. Alfreds,
  • Karl G. Sylvester,
  • Doff B. McElhinney,
  • Xuefeng B. Ling

摘要

Background

Coronary 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.

Objective

To 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.

Methods

Two 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.

Results

The 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.

Conclusions

This 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.