The Relationship Between Mortality and Leuko-Glycemic Index in Coronary Care Unit Patients (MORCOR-TURK LGI)

Abstract

Introduction&Objective: Identifying high-risk patients with a poor prognosis in coronary care unit (CCU) patients can assist physicians in providing optimal care and implementing preventive strategies. Leuko-glycaemic index (LGI), synthesized by multiplying the blood glucose level by the leukocyte count, has gained popularity in risk stratification of myocardial infarction patients. In this context, this study was carried out to investigate the relationship between LGI assessed at admission and in-hospital mortality in CCU patients. Methods: This is a multi-center, cross-sectional and observational study. (MORCOR-TURK LGI: Mortality Predictors in Coronary Care in Turkey, ClinicalTrials.gov number NCT05296694). The population of this study consisted of 2917 consecutive patients admitted to the CCU. Blood samples were collected into serum separator tubes in the immediate admission to the CCU. LGI was calculated by multiplying both values and dividing them by a thousand. LGI units were expressed in mg/dl. mm³. The sample was divided into two groups based on the LGI cut-off value of 1.23. Logistic regression analysis was used to find the significant predictors of mortality. Receiver operating characteristics (ROC) curve was to find out the cut-off value of LGI. A p value less than 0.05 was considered to be statistically significant in all analyses. Results: Univariable logistic regression analysis revealed that age, heart failure (HF), LGI, coronary artery disease, hypertension, diabetes mellitus and atrial fibrillation are clinically and statistically significant predictors. Further analysis of these variables using the multivariable logistic regression analysis indicated that age (Odds Ratio [OR]: 1.040, 95% confidence interval [CI]: 1.017-1.063; p=0.001), HF (OR: 2.426, 95% CI: 1.419-4.149; p:0.001) and LGI (OR: 1.349, 95% CI: 1.176-1.549; p<0.001), were independent predictors for the development of in-hospital mortality in CCU. LGI score optimal cut-off value of >3.72 predicted in-CCU mortality with 95.56% sensitivity and 49.19% specificity ([AUC]: 0.659 [95% CI: 0.641–0.676, p<0.001]). Conclusion: LGI, a simple and inexpensive index, was associated with in-hospital mortality in CCU patients. Aggressive treatment strategies should be adopted for these patients with higher LGI upon admission. Prospective studies are needed to clarify the prognostic relevance of LGI and CCU patients' mortality in terms of future cardiovascular events.