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1.
Preprint in English | medRxiv | ID: ppmedrxiv-21261855

ABSTRACT

BackgroundThe second wave of the COVID-19 pandemic hit India from early April 2021 to June 2021 and more than 400,000 cases per day were reported in the country. We describe the clinical features, demography, treatment trends, baseline laboratory parameters of a cohort of patients admitted at the All India Institute of Medical Sciences, New Delhi with SARS-CoV-2 infection and their association with the outcome. MethodsThis was a retrospective cohort study describing the clinical, laboratory and treatment patterns of consecutive patients admitted with SARS-CoV-2 infection. Multivariate logistic regression models were fitted to identify the clinical and biochemical predictors of developing hypoxia, deterioration during the hospital stay and death. FindingsA total of 2080 patients were included in the study. The case fatality rate was 19.5%. Amongst the survivors, the median duration of hospital stay was 8 (5-11) days. Out of 853 (42.3%%) of patients who had COVID-19 Acute respiratory distress syndrome at presentation, 340 (39.9%) died. Patients aged 45-60 years [OR (95% CI): 1.8 (1.2-2.6)p =0.003] and those aged >60 years [OR (95%CI): 3.4 (2.3-5.2), p<0.001] had a higher odds of death as compared to the 18-44 age group. Vaccination reduced the odds of death by 30% [OR (95% CI): 0.7 (0.5-0.9), p=0.036]. Patients with hyper inflammation at baseline as suggested by leucocytosis [OR (95% CI): 2.1 (1.4-3.10), p <0.001], raised d-dimer >500 mg/dL [OR (95% CI): 3.2 (2.2-4.6), p <0.001] and raised C-reactive peptide >0.5 mg/L [OR (95% CI): 3.8 (1.1-13), p=0.037] had higher odds of death. Patients who were admitted in the second week had lower odds of death and those admitted in the third week had higher odds of death. InterpretationThis is the largest cohort of patients admitted with COVID-19 from India reported to date and has shown that vaccination status and early admission during the inflammatory phase can change the course of illness of these patients. Strategies should be made to improve vaccination rates and early admission of patients with moderate and severe COVID-19 to improve outcomes. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSThe COVID-19 pandemic has been ravaging the world since December 2019 and the cases in various regions are being reported in waves. We found that the case fatality rates ranging from 1.4% to 28.3% have been reported in the first wave in India. Older age and the presence of comorbidities are known predictors of mortality. There are no reports regarding the effectiveness of vaccination, correlation of mortality with the timing of admission to the health care facility and inflammatory markers in the second wave of the COVID-19 pandemic in India. Added-value of this studyThis study reports the real-world situation where patients get admitted at varying time points of their illness due to the mismatch between the availability of hospital beds and the rising number of COVID-19 patients during the pandemic. It reports the odds of developing severe hypoxia necessitating oxygen therapy and death thus helping identify priority groups for admission. Implications of all the available evidenceThis study found increased odds of requiring oxygen support or death in patients older than 45 years of age, with comorbidities, and those who had hyper-inflammation with raised C-reactive peptide, d-dimer or leukocytosis. Patients who were admitted in the second week of illness had lower odds of death as compared to those admitted in the third week implying that treatment with corticosteroids in the second week of the illness during the inflammatory phase could lead to reduced mortality. These findings would help triage patients and provide guidance for developing admission policy during times where hospital beds are scarce. Vaccination was found to reduce the odds of deterioration or death and should be fast-tracked to prevent further waves of the pandemic.

2.
Preprint in English | medRxiv | ID: ppmedrxiv-20122580

ABSTRACT

To understand the spread of Covid-19, we analyse an extended Susceptible-Exposed-Infected-Recovered (SEIR) model that accounts for asymptomatic carriers, and explore the effect of different intervention strategies such as social distancing (SD) and testing-quarantining (TQ). The two intervention strategies (SD and TQ) try to reduce the disease reproductive number, R0, to a target value [Formula], but in distinct ways, which we implement in our model equations. We find that for the same [Formula], TQ is more efficient in controlling the pandemic than SD. However, for TQ to be effective, it has to be based on contact tracing and our study quantifies the required ratio of tests-per-day to the number of new cases-per-day. Our analysis shows that the largest eigenvalue of the linearised dynamics provides a simple understanding of the disease progression, both pre- and post-intervention, and explains observed data for many countries. We propose an accurate way of specifying initial conditions for the numerics (from insufficient data) using the fact that the early time exponential growth is well-described by the dominant eigenvector of the linearized equations. Weak intervention strategies (that reduce R0 but not sufficiently) reduce the peak values of infections and the asymptotic affected population and we provide analytic expressions for these in terms of the disease parameters. We apply them in the Indian context to obtain heuristic projections for the course of the pandemic, noting that the predictions strongly depend on the assumed fraction of asymptomatic carriers.

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