Laboratory findings in predicting intensive care need and death of COVID-19 patients

Abstract

Objectives: The ability to predict the course of COVID-19 is very valuable in terms of the optimal use of health resourc-es. The aim of this study was to examine the value of biochemical and hematological parameters in the estimation of hospital stay, disease severity, and likelihood of death. Methods: Routine blood analysis data of confirmed COVID-19 cases (n=222) were collected and analyzed. The patients were divided into 3 groups: outpatient, inpatient, and patients requiring intensive care. Results: There were significant differences between the 3 groups in terms of age, lymphocyte, neutrophil, hemoglobin, hematocrit, mean corpuscular volume (MCV), red blood cell distribution width (RDW), neutrophil-to-lymphocyte ratio (NLR), neutrophil-to-monocyte ratio (NMR), platelet-to-lymphocyte ratio (PLR), procalcitonin, C-reactive protein (CRP), and D-dimer values. Univariate analysis for mortality revealed significant differences in neutrophil, NLR, PLR, NMR, procalcitonin, and CRP values. Multivariable logistic regression yielded significant differences in only NMR and procal-citonin values. A positive correlation was determined between the length of hospital stay and age, MPV, procalcitonin, and D-dimer values. Conclusion: The neutrophil count was the most appropriate parameter to predict the need for intensive care (area under the curve: 0.782, sensitivity: 73%, specificity: 75%, with a cutoff of 4.43). The NMR and procalcitonin values were significant to predict death in multivariate analysis. Age, CRP, and D-dimer values were the parameters most associated with the duration of hospitalization.