Monocyte-to-High Density Lipoprotein Cholesterol Ratio Sebagai Prediktor Keparahan Stemi Berdasarkan Killip Score

Abstract

Introduction: ST-elevation myocardial infarction (STEMI) is a global health challenge that significantly impacts morbidity, mortality, and disability in patients. The Killip score is the most commonly used classification system to stratify the severity of heart failure in patients with STEMI. The monocyte-to-high-density lipoprotein cholesterol ratio (MHR) is a potential biomarker that can be used to assess mortality in patients with STEMI. This study aimed to assess the role of MHR as a predictor of STEMI severity, as classified using the Killip score.

Aim: To evaluate the monocyte-to-high density lipoprotein cholesterol ratio (MHR) as a predictor of disease severity in patients with STEMI treated at Haji Adam Malik General Hospital,Medan.

Methods: This was a prospective cohort study conducted in the Cardiovascular Care Unit (CVCU) of Haji Adam Malik Hospital, Medan. This study involved patients diagnosed with STEMI and treated in the CVCU between July 2025 and December 2025. Data were collected from laboratory test results and heart failure severity were recorded for each patient based on the Killip score. Data analysis was performed using SPSS version 26.0.

Results: Hematological parameters including hemoglobin, erythrocytes,leukocytes, hematocrit, platelets, MCV, MCHC,RDW,MPV, PCT, PDW, monocytes, neutrophils,lymphocytes, eosinophils, basophils, and HDLC did not show statistically significant differences between groups based on the Killip Score (p>0.05), although there was a tendency toward increased leukocyte/neutrophil and RDW levels in higher Killip classes. ROC curve analysis showed that MHR had an AUC value of 0,991 with a significant p-value and a wide confidence interval, indicating excellent discriminatory ability.

Conclusion: Most hematological parameters did not show significant differences between Killip classes. Further studies with prospective designs, larger and more representative sample sizes, balanced Killip distributions, and multivariate analysis capable of controlling for confounding factors are needed.