Abstract summary 

Early identification of patients at high risk of poor clinical outcomes is the key to success in saving the lives of patients with coronavirus disease 2019 (COVID-19). Neutrophil to Lymphocyte Ratio (NLR) is an easily available and cheap surrogate inflammatory marker, its baseline NLR role in African COVID-19 patients remains to be investigated. The objective of the study aimed to evaluate the role of NLR as a predictor of severity and mortality of COVID-19 patients admitted at the Millennium COVID 19 care center in Addis Ababa, Ethiopia.A cross-sectional study was conducted on patients with COVID-19 admitted to the Millennium COVID-19 care center from August 1 to October 30, 2021. Receiver Operating Characteristic curve analysis was used to calculate the area under the curve to assess the predictive capacity of NLR on mortality and severity. Multivariable logistic regression analysis was done to identify the association between independent variables and disease outcomes with an Adjusted Odds Ratio (AOR), P-value, and 95% CI for AOR were used for testing significance.The NLR of 9.47 was identified as the optimal cut-off value for predicting mortality with a sensitivity of 88.7% and a specificity of 95.4% (Area Under the Curve (AUC):0.95, 95% CI 0.92-98; P<0.001) and the NLR of 5.86 was an effective threshold value in predicting the severity of disease with a sensitivity of 92.2% and a specificity of 75% (AUC:0.85, 95% CI 0.800-0.905; P<0.001). In multivariable logistic regression analysis, after adjusting for confounding factors, NLR of more than 9.47 and 5.86 was significantly associated with all-cause of in-hospital mortality (AOR=4.73, 95% CI, 1.19-33.68; P<0.02), and severity of disease (AOR=12.98, 95% CI 3.85-43.80; P=0.001), respectively.NLR greater than 9.47 and 5.86 effectively predict mortality and severity of the disease, respectively. It provides an objective input for early decision-making in inpatient management especially in resources limited area.

Authors & Co-authors:  Tadesse Zelalem Z Bekele Bayissa Abdi A Diriba Tolesa T Chernet Nahom N Tsegaye Seniat S Tsega Molla M

Study Outcome 

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