Background
To address the problem of resource limitation, biomarkers having a potential for mortality prediction are urgently required. This study was designed to evaluate whether hemogram‐derived ratios could predict in‐hospital deaths in COVID‐19 patients.
Materials and Methods
This multicenter retrospective study included hospitalized COVID‐19 patients from four COVID‐19 dedicated hospitals in Sylhet, Bangladesh. Data on clinical characteristics, laboratory parameters, and survival outcomes were analyzed. Logistic regression models were fitted to identify the predictors of in‐hospital death.
Results
Out of 442 patients, 55 (12.44%) suffered in‐hospital death. The proportion of male was higher in nonsurvivor group (61.8%). The mean age was higher in nonsurvivors (69 ± 13 vs. 59 ± 14 years,
p
< 0.001). Compared to survivors, nonsurvivors exhibited higher frequency of comorbidities, such as chronic kidney disease (34.5% vs. 15.2%,
p
≤ 0.001), chronic obstructive pulmonary disease (23.6% vs. 10.6%,
p
= 0.011), ischemic heart disease (41.8% vs. 19.4%,
p
< 0.001), and diabetes mellitus (76.4% vs. 61.8%,
p
= 0.05). Leukocytosis and lymphocytopenia were more prevalent in nonsurvivors (
p
< 0.05). Neutrophil‐to‐lymphocyte ratio (NLR), derived NLR (d‐NLR), and neutrophil‐to‐platelet ratio (NPR) were significantly higher in nonsurvivors (
p
< 0.05). After adjusting for potential covariates, NLR (odds ratio [OR] 1.05; 95% confidence interval [CI] 1.009‐1.08), d‐NLR (OR 1.08; 95% CI 1.006‐1.14), and NPR (OR 1.20; 95% CI 1.09‐1.32) have been found to be significant predictors of mortality in hospitalized COVID‐19 patients. The optimal cut‐off points for NLR, d‐NLR, and NPR for prediction of in‐hospital mortality for COVID‐19 patients were 7.57, 5.52 and 3.87, respectively.
Conclusion
Initial assessment of NLR, d‐NLR, and NPR values at hospital admission is of good prognostic value for predicting mortality of patients with COVID‐19.