Key result
Vitamin D levels predict STEMI versus NSTEMI presentation as the leading classification factor.
Why the study?
ACS, including STEMI and NSTEMI/USAP, is a major cause of cardiovascular morbidity and mortality, prompting an evaluation of clinical and biochemical predictors of ACS type using statistical and machine learning methods.
Do serum vitamin D levels and other clinical and biochemical parameters predict and classify ST-elevation myocardial infarction versus NSTEMI/unstable angina pectoris in patients presenting with acute coronary syndrome?
Case-Control (n=100)
No
Do serum vitamin D levels and other clinical and biochemical parameters predict and classify ST-elevation myocardial infarction versus NSTEMI/unstable angina pectoris in patients presenting with acute coronary syndrome?
Vitamin D deficiency, low HDL cholesterol, dyslipidemia, and lower oxygen saturation are significantly associated with STEMI presentation compared to NSTEMI/USAP, and machine learning models incorporating these features show moderate discriminative ability (Random Forest AUC 0.792) for classifying ACS subtype.
Vitamin D should not guide ACS subtype decisions; leaves open prospective validation of ML models.
Background Acute coronary syndrome (ACS), including ST-elevation myocardial infarction (STEMI) and non-ST-elevation myocardial infarction/unstable angina pectoris (NSTEMI/USAP) is a major cause of cardiovascular morbidity and mortality. Objective To determine clinical and biochemical predictors of ACS type and how well they predict using statistical and machine learning methods. Methodology A Case-Control study was performed at the Hayatabad Medical Complex, Peshawar, between November 2024 and March 2025 with 100 patients participating in the study with an equal number of STEMI and NSTEMI/USAP patients. IBM SPSS software version 27 was used to analyze the data. The Shapiro-Wilk test was used to determine the normality of the data; the Mann-Whitney U test and Chi-square test were used to compare the groups. Binary logistic regression was used to screen the predictors. Supervised machine learning models ( random forest, support vector machine, k nearest neighbors) were trained to classify ACS type. Results There were statistically significant group differences in oxygen saturation and HDL. The main predictors were vitamin D levels, HDL, oxygen saturation and dyslipidemia. Machine learning models exhibited moderate performance with Logistic Regression and Support Vector Machine having the highest accuracy and Random Forest having an AUC of 0.792. Furthermore, vitamin D deficiency was observed more frequently among STEMI patients compared to NSTEMI/USAP patients. Conclusion Machine learning models moderately predicted ACS classification, with vitamin D deficiency, dyslipidemia, and oxygen saturation identified as key predictors. Vitamin D deficiency was more common in STEMI patients, suggesting a role in ACS severity. Trial registration not applicable
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Hussain et al. (2026) conducted a case-control in Acute coronary syndrome (STEMI and NSTEMI/USAP) (n=100). Vitamin D levels (deficiency/insufficiency) vs. Sufficient Vitamin D levels / NSTEMI+USAP group was evaluated on Association of Vitamin D levels with Acute Coronary Syndrome type (STEMI vs. NSTEMI/USAP) (p=0.002). Vitamin D levels significantly predicted STEMI versus NSTEMI/USAP presentation (p=0.002) and ranked as the top predictor in Random Forest classification (Gini importance 0.235, AUC 0.792).
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