Clinical Profile and Determinants of Mortality in Patients With COVID-19: A Retrospective Analytical Cross-Sectional Study in a Tertiary Care Center in South India

Abstract

IntroductionThe COVID-19 pandemic gained ground in India, starting from a few cases and spreading to the whole country; eventually becoming the second-most affected country worldwide. Here, we present the clinical and laboratory profile and the risk factors associated with mortality in COVID-19. The study comes from Kerala, a region that reported the first case in India. Kerala has the second-highest case burden in the country but also has managed to keep the case fatality rate down below the national average. MethodologyThis is a single-center retrospective cross-sectional study on 391 laboratory-confirmed COVID-19 positive inpatients between September 2020 and October 2020. Hematological parameters, coagulation parameters, liver function tests (LFT), and renal function tests (RFT) results were collected and compared among survivors and non-survivors to identify predictive biomarkers of mortality. ResultsThe mean age of all patients was 53.2 years (SD 17.0). On bivariate analyses, the mean values of total leukocyte count (TLC), absolute neutrophil count (ANC), neutrophil-to-lymphocyte ratio (NLR), C-reactive protein (CRP), ferritin, lactate dehydrogenase (LDH), D-dimer at admission, prothrombin time international normalized ratio (PT INR), blood urea nitrogen (BUN), and creatinine were significantly higher in nonsurvivors than in survivors: mean (SD) 11.9 (7.6) vs 7.5 (4.2) (x10 9 /L), 10.5 (7.4) vs 5.3 (4.1) (x10 9 /L), 11.6 (13.5) vs 3.4 (3.5), 185 (117) vs 48 (85) (mg/L), 829.4 (551.2) vs 323.6 (374.1) (ng/ml), 905.5 (589.1) vs 485.1 (353.9) (U/L), 4.01 (3.53) vs 1.29 (2.08) (µg/ml), 1.21 (0.42) vs 0.99 (0.18), 105.1 (91.4) vs 33.6 (31.0) (mg/dl), 3.6 (4.1) vs 1.1 (1.6) (mg/dl), respectively, p < 0.001. Absolute lymphocyte count, serum albumin, and albumin/globulin (A/G) ratio were lower in non-survivors than in survivors (mean (SD) 1.3 (1.0) vs 2.0 (0.9) (x10 9 /L), p < 0.001; 3.0 (0.7) vs 3.8 (2.1) (g/dl), p 0.005; 0.9 (0.3) vs 1.2 (0.4), p < 0.001). Multivariate analysis identified ANC, D-dimer at admission, CRP, and BUN as independent prognostic factors associated with mortality. ConclusionSeveral accessible tests like TLC, ANC, NLR, and BUN can be used in low-resource settings to assess severity in patients with COVID-19. In addition, ANC, D-dimer at admission, CRP, and BUN can be used as independent predictors of in-patient mortality in COVID-19 patients in hospital settings.

Effect of Cytomegalovirus Reactivation on Inflammatory Status and Mortality of Older COVID-19 Patients

“…Moreover, CMV positive patients younger than 87 years had a higher risk of in-hospital mortality than those without CMV reactivation. Consistent with other investigations, we also found that CRP, neutrophil count and frailty status were significantly associated with in-hospital death [ 33 , 34 , 35 ], whereas in patients older than 87 years, who were more susceptible to mortality from SARS-CoV-2 infection with respect to the younger group (45% vs. 17%), CMV DNAemia was not associated with death. In accordance with previous evidence, in the oldest group the main factors related to in-hospital mortality were age and an increased neutrophil count [ 34 , 35 , 36 ].…”

Correlation Between Biomarkers and Age-Adjusted Charlson Comorbidity Index in Patients With COVID-19: A Cross-Sectional Study in a Tertiary Care Center in South India

“…In our earlier analysis, ANC, CRP, D-dimer, and BUN were shown to be independent predictors of COVID-19-related mortality [ 20 ]. Cut-off values predicting mortality were derived for the same biomarkers in the present analysis in both group A and group B. ANC and D dimer showed lower cut-off values in group B when compared to group A while CRP and BUN demonstrated a higher cut-off in group B.…”

“…This is one of the limitations of our study. In fact, hypertension was the second highest comorbidity in our study population [ 20 ]. Other limitations that could have affected the results are the smaller sample size and the retrospective nature of our study.…”

“…The study population was laboratory-confirmed consecutive cases of COVID-19 admitted to our hospital between September 21, 2020, and October 1, 2020. We had determined the laboratory predictors of COVID-19-related mortality in our earlier analysis, the detailed methodology and findings of which have been published [ 20 ]. For the current analysis, we further categorized patients according to the ACCI score, as shown in Table 1 .…”