2MuÅŸ State Hospital, Department of Emergency Medicine, MuÅŸ, Turkey DOI : 10.29400/tjgeri.2024.386 Introduction: We evaluated the predictive power of systemic inflammatory parameters, including the neutrophil/lymphocyte ratio, C-reactive protein/ albumin ratio, monocyte/eosinophil ratio, and platelet/lymphocyte ratio, for allcause in-hospital mortality in elderly.
Materials and Method: This single-center and retrospective study enrolled 46,563 patients aged ?65 years who presented to the emergency department due to various complaints from June 2019 to June 2022. We evaluated the demographics, clinical characteristics, laboratory data, and clinical outcomes of the patients.
Results: A total of 3,385 hospitalized patients, 1,808 males and 1,577 females, were included in the study. The average age was 76.25±7.35 years. The overall mortality rate was 11.73%. Nonsurvivors had significantly elevated neutrophil/lymphocyte, C-reactive protein/albumin, monocyte/eosinophil, and platelet/lymphocyte ratios compared to survivors (p=0.0001 for all comparisons). Elevated neutrophil/lymphocyte and C-reactive protein/albumin ratios were determined as independent predictors of mortality. A neutrophil/lymphocyte ratio ≥9.41 had 46.97% sensitivity and 79.99% specificity for predicting mortality. While the positive predictive value was 23.7%, the negative predictive value was 91.9%. Additionally, a C-reactive protein/albumin ratio ≥13.18 was identified as the cut-off for mortality, with 57.07% sensitivity and 69.91% specificity. Its positive and negative predictive values were 20.1% and 92.5%, respectively.
Conclusion: Mean serum neutrophil/lymphocyte and C-reactive protein/ albumin ratios on hospital admission were associated with all-cause mortality in hospitalized patients aged ≥65 years. However, their sensitivity and positive predictive value were relatively low. Nevertheless, negative predictive value for both were significantly high. This implies that these parameters could be used to determine the elderly at a lower risk of mortality.
Keywords : Aged; Biomarkers; Decision Making; Mortality