ABSTRACT
Background Coronaviruses, generally known to cause a mild degree of respiratory illness have in the recent past caused three serious disease outbreaks. The world is yet to be released from the grip of the most recent coronavirus disease 2019 (COVID-19) pandemic due to emerging mutant strains. Age, presence of comorbidities, clinical severity, and laboratory markers such as C-reactive protein and D-dimer are some of the factors being employed to prioritize patients for hospital care. It is known that comorbidities themselves are an outcome of inflammation and can induce a pro-inflammatory state. Our study aims to elucidate the influence of age and comorbidities on laboratory markers in patients with COVID-19. Methodology This is a single-center retrospective study of patients with a laboratory diagnosis of COVID-19 admitted to our hospital between September 21, 2020, and October 1, 2020. A total of 387 patients above the age of 18 years were included in the analysis and categorized based on the age-adjusted Charlson comorbidity index (ACCI) score into group A (score ≤4) and group B (score >4). Demographic, clinical, and laboratory factors as well as outcomes were compared. Results Group B exhibited higher intensive care unit admission and mortality, as well as statistically significant higher mean values of most laboratory markers. A correlation was also observed between the ACCI score and biomarker values. On comparison between the two groups regarding cut-offs predicting mortality for laboratory determinants, no consistent pattern was observed. Conclusions A correlation between age, the number of comorbidities, and laboratory markers was observed in our analysis of COVID-19-affected patients. Aging and comorbid conditions can produce a state of meta-inflammation and can thereby contribute to hyperinflammation in COVID-19. This can be an explanation for the higher risk of COVID-19-related mortality in older individuals and those with underlying comorbidities.
ABSTRACT
Context: Coronavirus disease 2019 (COVID-19) mortality trends can help discern the pattern of outbreak evolution and systemic responses. Aim: This study aimed to explore patterns of COVID-19 deaths in Thiruvananthapuram district from 31 March 2020 to 31 December 2021. Setting and Design: Secondary data analysis of COVID-19 deaths in Thiruvananthapuram district was performed. Materials and Methods: Mortality data were obtained from the district COVID-19 control room, and deaths in the first and second waves of COVID-19 were compared. Statistical Analysis: We summarised data as proportions and medians with the inter-quartile range (IQR) and performed Chi-square tests to make comparisons wherever applicable. Results: As on 31 December 2021, 4587 COVID-19 deaths were reported in Thiruvananthapuram district, with a case fatality rate of 0.91%. We observed high mortality among older persons (66.7%) and men (56.6%). The leading cause of death was bronchopneumonia (60.6%). The majority (88.5%) had co-morbidities, commonly diabetes mellitus (54.9%). The median interval from diagnosis to hospitalisation was 4 days (IQR 2-7), and that from hospitalisation to death was 2 days (IQR 0-6). The deaths reported during the second wave were four times higher than those of the first wave with a higher proportion of deaths in the absence of co-morbidities (p < 0.001). The majority of the deceased were unvaccinated. Ecological analysis with vaccine coverage data indicated 5.4 times higher mortality among unvaccinated than those who received two vaccine doses. Conclusions: The presence of co-morbidities, an unvaccinated status, and delay in hospitalisation were important reasons for COVID-19 deaths. Primary level health providers can potentially help sustaining vaccination, expeditious referral, and monitoring of COVID-19 patients.
ABSTRACT
Introduction The 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. Methodology This 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. Results The 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 non-survivors than in survivors: mean (SD) 11.9 (7.6) vs 7.5 (4.2) (x109/L), 10.5 (7.4) vs 5.3 (4.1) (x109/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) (x109/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. Conclusion Several 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.