Evaluation of the clinical profile, laboratory parameters and outcome of two hundred COVID-19 patients from a tertiary centre in India

Abstract

COVID-19 is a pandemic with over 5 million cases worldwide. The disease has imposed a huge burden on health resources. Evaluation of clinical and epidemiological profiles of such patients can help in understanding and managing the outbreak more efficiently. This study was a prospective observational analysis of 200 diagnosed COVID-19 patients admitted to a tertiary care center from 20th march to 8th May 2020. All these patients were positive for COVID-19 by an oro-nasopharyngeal swab-rtPCR based testing. Analyses of demographic factors, clinical characteristics, comorbidities, laboratory parameters, and the outcomes were performed. The mean age of the population was 40 years with a slight male predominance (116 patients out of 200, 58%). A majority of the patients (147, 73.5 %) were symptomatic, with fever being the most common symptom (109, 54.5%), followed by cough (91, 45.5%). An older age, presence of symptoms and their duration, leukocytosis, a high quick SOFA score, a high modified SOFA score, need for ventilator support, an AST level more than 3 times the upper limit of normal (ULN), and a serum creatinine level of 2 mg/dl or greater were at a significantly higher risk of ICU admission and mortality. Presence of diabetes mellitus, AST > three times ULN, serum creatinine 2 mg/dl or higher, and a qSOFA score of 1 or higher were all associated with significantly greater odds of critical care requirement. Triage and severity assessment helps in deciding the requirement for a hospital stay and ICU admission for COVID-19 which can easily be done using clinical and laboratory parameters. A mild, moderate and severe category approach with defined criteria and treatment guidelines will help in judicious utilization of health-care resources, especially for developing countries like India.
 
 *Other members of the Safdarjung Hospital COVID-19 working group: Balvinder Singh (Microbiology), MK Sen (Pulmonary Medicine), Shibdas Chakrabarti (Pulmonary Medicine), NK Gupta (Pulmonary medicine), AJ Mahendran (Pulmonary Medicine), Ramesh Meena (Medicine), G Usha (Anaesthesiology), Santvana Kohli (Anaesthesiology), Sahil Diwan (Anaesthesiology), Rushika Saksena (Microbiology), Vikramjeet Dutta (Microbiology), Anupam Kr Anveshi (Microbiology)

Clinical characteristics and outcomes of COVID-19: Experience at a major tertiary care center in Pakistan

“…However, there has been wide variability in data reported from different parts of the world. ICU mortality was reported as 26 % from large multicenter case series from Lombardy, Italy [27], 35.8%% from a National cohort study from the UK [28], 31% in a multicenter study from Spain [29], 53% from a large center in New York, USA [30], and 56% from a tertiary care center in India [31]. The heterogeneity in these figures is explained by limited ICU resources particularly in pandemic epicenters and may have been influenced by the age of the affected population [24].…”

Mortality among adult hospitalized patients during the first wave and second wave of COVID-19 pandemic at a tertiary care center in India

“…The risk of severity, need of hospital admission and an unfavorable outcome is increased in the presence of comorbidities. [13] The most common pre-existing illness among all the hospitalized patients as well as deceased patients in our hospital, was Cardiovascular Disorders (including Hypertension) followed by Diabetes Mellitus. This trend is similar in hospitalized COVID -19 Patients across the globe (Supplementary Table 2) except in Japan where the leading comorbidity is Chronic Respiratory Disease followed by Cardiovascular Disorders [8] .…”

SARS-Cov2: a meta-analysis of symptom distribution by continent in 7310 adult COVID-19 infected patients

“…The pooled proportion of male patients was 52% (0.43–0.62). Among the eligible studies, 2 (3.57%) of 56 studies were rated as high risk of bias [ 11 , 44 ], and 54 studies (96.42%) as moderate [ 1 , 4 , 5 , 7 – 11 , 13 – 17 , 19 – 25 , 27 , 28 , 31 – 36 , 38 , 39 , 41 – 48 , 50 , 52 – 59 , 61 – 64 ] risk of bias (Supplementary Table 1).…”