Abstract
Introduction: Effective triage of COVID-19 patients, especially in resource-limited settings, requires cost-effective and readily available markers. The present study looks at the prognostic role of three such laboratory parameters, absolute lymphocyte count (ALC), absolute neutrophil count (ANC), and neutrophil-to-lymphocyte ratio (NLR). Methodology: A retrospective cohort study was done including 328 COVID-19 reverse transcriptase–polymerase chain reaction-confirmed hospitalized patients aged 18 and above in a tertiary center in northern India. Baseline demographic, clinical, and laboratory parameters were collected on the day of admission. Statistical analysis included descriptive statistics, sensitivity–specificity analysis for optimum cutoffs, multiple logistic regression, and Cox proportional hazards regression. Results: The median age of the patients was 45 with 266 (81.1%) males and 62 (18.9%) females. A total of 109 (33.2%) patients were affected with moderate to severe disease. Forty-nine (14.9%) patients had fatal outcomes. Median ALC was lower in patients with moderate to severe disease compared to mild disease (895 vs. 1554.2) and in nonsurvivors compared to survivors (732.0 vs. 1423.9). Median ANC (5182.8 vs. 3057.6) and NLR (5.38 vs. 2.03) were significantly raised in patients with moderate to severe disease as against mild disease and in nonsurvivors(ANC 7040.25 vs. 3448.5, NLR 10.05 vs. 2.35). ALC 3907, and NLR >2.74 showed considerable sensitivity and specificity for disease severity at admission. ALC and ANC were significantly associated with the odds of moderate to severe disease at admission in the multivariable logistic regression analysis. ALC 4612, and NLR >3.76 had good sensitivity and specificity as predictors of mortality and emerged as independent risk factors for mortality in the multivariable Cox proportional hazards regression. Conclusion: ALC, ANC, and NLR are relatively cost-effective and readily available routine investigations obtained as a part of complete blood count. These indices show good prognostic significance. Their utility in clinical algorithms can better guide management of patients.
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