Predictors of Mortality among Patients Hospitalized with COVID-19 during the First Wave in India: A Multisite Case-Control Study.

Severe acute respiratory syndrome coronavirus 2 disease (COVID-19) has caused more than 6 million deaths globally. Understanding predictors of mortality will help in prioritizing patient care and preventive approaches. This was a multicentric, unmatched, hospital-based case-control study conducted in nine teaching hospitals in India. Cases were microbiologically confirmed COVID-19 patients who died in the hospital during the period of study and controls were microbiologically confirmed COVID-19 patients who were discharged from the same hospital after recovery. Cases were recruited sequentially from March 2020 until December-March 2021. All information regarding cases and controls was extracted retrospectively from the medical records of patients by trained physicians. Univariable and multivariable logistic regression was done to assess the association between various predictor variables and deaths due to COVID-19. A total of 2,431 patients (1,137 cases and 1,294 controls) were included in the study. The mean age of patients was 52.8 years (SD: 16.5 years), and 32.1% were females. Breathlessness was the most common symptom at the time of admission (53.2%). Increasing age (adjusted odds ratio [aOR]: 46-59 years, 3.4 [95% CI: 1.5-7.7]; 60-74 years, 4.1 [95% CI: 1.7-9.5]; and ≥ 75 years, 11.0 [95% CI: 4.0-30.6]); preexisting diabetes mellitus (aOR: 1.9 [95% CI: 1.2-2.9]); malignancy (aOR: 3.1 [95% CI: 1.3-7.8]); pulmonary tuberculosis (aOR: 3.3 [95% CI: 1.2-8.8]); breathlessness at the time of admission (aOR: 2.2 [95% CI: 1.4-3.5]); high quick Sequential Organ Failure Assessment score at the time of admission (aOR: 5.6 [95% CI: 2.7-11.4]); and oxygen saturation < 94% at the time of admission (aOR: 2.5 [95% CI: 1.6-3.9]) were associated with mortality due to COVID-19. These results can be used to prioritize patients who are at increased risk of death and to rationalize therapy to reduce mortality due to COVID-19.

Characteristics and predictors of Long COVID among diagnosed cases of COVID-19.

BACKGROUND: Long COVID or long-term symptoms after COVID-19 has the ability to affect health and quality of life. Knowledge about the burden and predictors could aid in their prevention and management. Most of the studies are from high-income countries and focus on severe acute COVID-19 cases. We did this study to estimate the incidence and identify the characteristics and predictors of Long COVID among our patients. METHODOLOGY: We recruited adult (≥18 years) patients who were diagnosed as Reverse Transcription Polymerase Chain Reaction (RTPCR) confirmed SARS-COV-2 infection and were either hospitalized or tested on outpatient basis. Eligible participants were followed up telephonically after four weeks and six months of diagnosis of SARS-COV-2 infection to collect data on sociodemographic, clinical history, vaccination history, Cycle threshold (Ct) values during diagnosis and other variables. Characteristics of Long COVID were elicited, and multivariable logistic regression was done to find the predictors of Long COVID. RESULTS: We have analyzed 487 and 371 individual data with a median follow-up of 44 days (Inter quartile range (IQR): 39,47) and 223 days (IQR:195,251), respectively. Overall, Long COVID was reported by 29.2% (95% Confidence interval (CI): 25.3%,33.4%) and 9.4% (95% CI: 6.7%,12.9%) of participants at four weeks and six months of follow-up, respectively. Incidence of Long COVID among patients with mild/moderate disease (n = 415) was 23.4% (95% CI: 19.5%,27.7%) as compared to 62.5% (95% CI: 50.7%,73%) in severe/critical cases(n = 72) at four weeks of follow-up. At six months, the incidence among mild/moderate (n = 319) was 7.2% (95% CI:4.6%,10.6%) as compared to 23.1% (95% CI:12.5%,36.8%) in severe/critical (n = 52). The most common Long COVID symptom was fatigue. Statistically significant predictors of Long COVID at four weeks of follow-up were-Pre-existing medical conditions (Adjusted Odds ratio (aOR) = 2.00, 95% CI: 1.16,3.44), having a higher number of symptoms during acute phase of COVID-19 disease (aOR = 11.24, 95% CI: 4.00,31.51), two doses of COVID-19 vaccination (aOR = 2.32, 95% CI: 1.17,4.58), the severity of illness (aOR = 5.71, 95% CI: 3.00,10.89) and being admitted to hospital (Odds ratio (OR) = 3.89, 95% CI: 2.49,6.08). CONCLUSION: A considerable proportion of COVID-19 cases reported Long COVID symptoms. More research is needed in Long COVID to objectively assess the symptoms and find the biological and radiological markers.

Clinico-Epidemiological Characteristics of Healthcare Workers with SARS-CoV-2 Infection during the First and Second Waves in a Teaching Hospital from Eastern India: A Comparative Analysis.

In this retrospective observational study, we have performed a comparative analysis of the demographic, clinical and epidemiological characteristics of the HCWs affected with SARS-CoV-2 infection during first two waves in India. The overall prevalence of SARS-CoV-2 infection among HCWs was found to be 15.24% (14.20-16.33) and 23.38% (22.14-25.65) during first and second waves respectively. The second wave showed an adjusted odds ratio of 0.04(0.02-0.07) and 2.09(1.49-2.93) for hospitalization and being symptomatic, respectively. We detected significantly higher level of C-reactive protein (CRP) among admitted HCWs during the second wave (5.10 -14.60 mg/dl) as compared to the first wave (2.00 - 2.80 mg/dl). Our study found the relative risk of SARS-CoV-2 reinfection among HCWs during the second wave to be 0.68 [0.57-0.82, p < 0.001)]. Although, the prevalence of SARS CoV-2 infection and risk of being symptomatic was higher during second wave, the risk of hospitalization was less when compared with the first wave.

Role of ivermectin in the prevention of SARS-CoV-2 infection among healthcare workers in India: A matched case-control study.

BACKGROUND: Ivermectin is one among several potential drugs explored for its therapeutic and preventive role in SARS-CoV-2 infection. The study was aimed to explore the association between ivermectin prophylaxis and the development of SARS-CoV-2 infection among healthcare workers. METHODS: A hospital-based matched case-control study was conducted among healthcare workers of AIIMS Bhubaneswar, India, from September to October 2020. Profession, gender, age and date of diagnosis were matched for 186 case-control pairs. Cases and controls were healthcare workers who tested positive and negative, respectively, for COVID-19 by RT-PCR. Exposure was defined as the intake of ivermectin and/or hydroxychloroquine and/or vitamin-C and/or other prophylaxis for COVID-19. Data collection and entry was done in Epicollect5, and analysis was performed using STATA version 13. Conditional logistic regression models were used to describe the associated factors for SARS-CoV-2 infection. RESULTS: Ivermectin prophylaxis was taken by 76 controls and 41 cases. Two-dose ivermectin prophylaxis (AOR 0.27, 95% CI, 0.15-0.51) was associated with a 73% reduction of SARS-CoV-2 infection among healthcare workers for the following month. Those involved in physical activity (AOR 3.06 95% CI, 1.18-7.93) for more than an hour/day were more likely to contract SARS-CoV-2 infection. Type of household, COVID duty, single-dose ivermectin prophylaxis, vitamin-C prophylaxis and hydroxychloroquine prophylaxis were not associated with SARS-CoV-2 infection. CONCLUSION: Two-dose ivermectin prophylaxis at a dose of 300 µg/kg with a gap of 72 hours was associated with a 73% reduction of SARS-CoV-2 infection among healthcare workers for the following month. Chemoprophylaxis has relevance in the containment of pandemic.

Study of respiratory viruses and their coinfection with bacterial and fungal pathogens in acute exacerbation of chronic obstructive pulmonary diseases.

BACKGROUND: Patients with chronic obstructive pulmonary disease (COPD) develop acute exacerbations (AE), with varying natural history. The exacerbation is triggered by infection, leading to increased morbidity and mortality. The study on infectious aetiology of AECOPD is largely restricted to only viral or only bacterial aetiology. There are no studies from India that have investigated multiple viral, bacterial, and fungal associations from the same group of patients. This prospective study was conducted over 2 years to estimate the incidence and profile of viral infections in AECOPD patients, their coinfection with other bacterial and fungal agents, and association of the type and pattern of infective agent with the clinical severity. MATERIALS AND METHODS: Seventy-four AECOPD cases were included in the study. Multiplex polymerase chain reaction was performed from nasopharyngeal swab using Fast Track Diagnostics Respiratory Pathogens 21 Plus Kit. Ziehl-Neelsen (ZN) stain, Modified ZN, and potassium hydroxide (KOH) mount were performed for Mycobacteria, Nocardia, and fungal elements. Bacterial cultures and fungal cultures were done as per the standard techniques. Serum samples were tested for Mycoplasma and Chlamydia pneumoniae immunoglobulin M enzyme-linked immunosorbent assay. RESULTS: The number of AECOPD events involving only viral infection, only bacterial infection, bacterial-viral coinfection, and no infection were 43 (58.1%), 32 (43.2%), 20 (27%), and 19 (25.7%), respectively. Influenza A virus was the most common virus (22/43, 51%) identified. In 26 patients, monoviral infections were found, and in 17 patients, polyviral infections were identified, the most common pattern being influenza A and B virus, followed by human rhinovirus and human parainfluenza. The most common bacteria isolated were Pseudomonas aeruginosa (9/32,28%) followed by Acinetobacter baumanii and Klebsiella pneumoniae (7/32, 21%). Among the viral-bacterial coinfection, human coronavirus NL63 infection was always associated with a bacterial infection. CONCLUSION: This information on the various viral and bacterial etiologies of respiratory infections in AECOPD in this part of India will improve the understanding of the management of AECOPD using a timely institution of antivirals and reduce the overuse of antibiotics and the implementation of routine influenza vaccination.

BCG vaccination induced protection from COVID-19.

There are worldwide urgency, efforts, and uncertainties for the discovery of a vaccine against SARS CoV2. If successful, it will take its own time till useful for the humans. Till the specific vaccine is available, there are evidences for repurposing existing other vaccines. It is observed that countries having a routine BCG vaccination programme, have shown to have lower incidence of COVID-19, suggesting some protective mechanisms of BCG against COVID-19 in such countries. In countries like India despite vast population density and other adversities, and growing numbers of COVID19 infections, the mortality rate and severity of COVID has been low in comparison to some TB non-endemic countries (like Europe and USA). In addition, there are evidences that BCG vaccination offers partial protection and survival in low-income countries where tuberculosis is prevalent. The nonspecific effects (NSEs) of immune responses induced by BCG vaccination protect against other infections seem to be due to its immunological memory eliciting lymphocytes response and trained immunity. The protective effect on other viral infection in humans are believed to be mediated by heterologous lymphocyte activation and the initiation of innate immune memory may be applicable to SARS CoV2. The BCG vaccination at birth does not have a protective effect beyond childhood against COVID-19. In adults, there might be other factors dampening the virulence and pathogenicity of COVID-19. In the TB endemic countries like India, with high population density, similar to BCG vaccination, the environmental Mycobacteria might be imparting some immune-protection from severity and deaths of COVID-19.