Key result
Support vector machine model detects obstructive CAD in hypertensive patients with an AUC of ~0.79.
Why the study?
Early diagnosis of obstructive coronary artery disease in hypertensive patients is limited by costly or invasive imaging, prompting exploration of low-cost diagnostic alternatives using routine blood test markers.
Does a machine learning model using age, dyslipidemia, LMR, and MHR accurately diagnose obstructive coronary artery disease in hypertensive patients?
Cohort (n=427)
No
Does a machine learning model using age, dyslipidemia, LMR, and MHR accurately diagnose obstructive coronary artery disease in hypertensive patients?
Effect estimate: AUC 0.786 (95% CI 0.701-0.862)
A support vector machine model incorporating age, dyslipidemia, LMR, and MHR can effectively and non-invasively identify obstructive coronary artery disease in hypertensive patients.
May aid non-invasive CAD screening in hypertensive patients; leaves open prospective validation before clinical adoption.
Background Hypertensive patients face high risk of obstructive coronary artery disease (CAD), but early diagnosis is limited by costly or invasive gold-standard imaging. Composite inflammatory and lipid markers from routine blood tests may offer a low-cost alternative. We aimed to develop and validate a machine learning model using such markers for obstructive CAD in hypertensive patients. Methods This retrospective cohort comprised 427 hypertensive patients treated at the Affiliated Hospital of Changchun University of Chinese Medicine between 2023 and 2025. Of these individuals, 195 patients (45.7%) received a diagnosis of obstructive coronary artery disease, characterized by coronary artery diameter stenosis equal to or greater than 50% as confirmed by coronary angiography. Through the integration of Boruta feature selection and variance inflation factor analysis, a set of four key diagnostic variables was identified: age, dyslipidemia, Lymphocyte-to-monocyte ratio (LMR), and Monocyte-to-HDL-C ratio (MHR). The dataset was partitioned into training and validation subsets at a ratio of 7:3. Five distinct machine learning models were constructed, and model performance was assessed using metrics such as the area under the curve (AUC), accuracy, F1 score, calibration, and SHAP analysis for both evaluation and interpretation of outcomes. Results The support vector machine (SVM) model demonstrated the highest generalization performance, achieving a validation AUC of 0.786 (95% CI: 0.701, 0.862), an accuracy of 0.756, and an F1 score of 0.73. LMR exhibited a protective effect with a linear correlation, whereas MHR was identified as a risk factor. Notably, the age of 60 years was a critical threshold for disease susceptibility. Conclusion Developed in this study, the parsimonious support vector machine model incorporates age, dyslipidemia,LMR and MHR. It enables effective diagnostic identification of obstructive coronary artery disease among hypertensive patients via routine clinical indicators. Being interpretable and non-invasive, this model is suitable for first-line screening before expensive invasive imaging tests and helps implement early risk stratification in primary healthcare settings.
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Liu et al. (2026) conducted a cohort in Hypertension with suspected obstructive coronary artery disease (n=427). Support vector machine (SVM) diagnostic model vs. Other machine learning models (Logistic regression, Decision Tree, XGBoost, ANN) was evaluated on Area under the curve (AUC) for diagnosing obstructive coronary artery disease in the validation set (AUC 0.786, 95% CI 0.701-0.862). A support vector machine model incorporating age, dyslipidemia, lymphocyte-to-monocyte ratio, and monocyte-to-HDL-C ratio achieved an AUC of 0.786 for diagnosing obstructive coronary artery disease in hypertensive patients.
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