Clinicoepidemiological profile of COVID-19 patients admitted at a teaching institute in a hilly area of India during the second wave-A retrospective observational study

Abstract

Introduction: COVID-19 ongoing pandemic has resulted in millions of deaths globally, and India has recorded the second highest number of confirmed cases till now. In the absence of effective treatment, it becomes crucial to know about the course of the disease in hospital settings for effective patient care. The present study discusses the clinicoepidemiological, haematological and biochemical determinants among survivors and non-survivors of COVID-19 patients admitted to a tertiary care hospital in a hilly area.

Methods: A record-based cross-sectional study was carried out at the government hospital from March 2021 to June 2021, which included all confirmed cases of 18 years and above. Demographic details, delayed admission, co-morbidities and laboratory parameters were collected.

Results: Out of a total of 1267 COVID-19 patients, the mean age of survived and succumbed was 50.77 ± 16.1 and 60.50 ± 14.2 years, respectively (P < 0.001). The mean survival time in males (17.7 days) was lesser compared to that in females (20.3 days). Two hundred and twelve of them practised self-medication. The mean duration of delayed testing (2.95 ± 2.3 vs 3.36 ± 2.2 days), mean values of haemoglobin (11.39 ± 2.1 vs 12.5 ± 1.7), platelet count (193.8 ± 94.6 vs 253.1 ± 105.9), leucocyte count (11.53 ± 5.72 vs 9.11 ± 5.21), neutrophil-lymphocyte ratio (10.0 ± 2.9 vs 7.3 ± 3.5), urea (61.16 ± 51.8 vs 30.2 ± 21.2) and creatinine (2.13 ± 2.9 vs. 1.1 ± 0.3) among the two groups were statistically significant (P < 0.001). Increasing age, contact history, hypertension [OR 3.2 (95% CI, 1.40-7.39)], diabetes [OR 1.9 (95% CI, 0.81-4.40)] and chronic kidney disease [OR 15.4 (95% CI, 5.23-45.71)] were found to be associated with increased risk of mortality (P < 0.005).

Conclusions: Public health interventions like contact tracing, testing and early identification of laboratory parameters and treatment on priority would help in providing effective care so that the mortality can be reduced.

Keywords: COVID-19; epidemiological; mortality; risk factors; survival analysis.

Figure 1

Kaplan-Meier estimate of survival probability of patients based on their admission time after their onset of symptoms

Figure 2

Kaplan-Meier estimate of survival probability of patients based on their admission time after their onset of symptoms among males and females