Vitamin D deficiency (VDD) owing to its immunomodulatory effects is believed to influence outcomes in COVID-19. We conducted a prospective, observational study of patients, hospitalized with COVID-19. Serum 25-OHD level < 20 ng/mL was considered VDD. Patients were classified as having mild and severe disease on basis of the WHO ordinal scale for clinical improvement (OSCI). Of the 410 patients recruited, patients with VDD (197,48.2%) were significantly younger and had lesser comorbidities. The levels of PTH were significantly higher in the VDD group (63.5 ± 54.4 vs. 47.5 ± 42.9 pg/mL). The proportion of severe cases (13.2% vs.14.6%), mortality (2% vs. 5.2%), oxygen requirement (34.5% vs.43.4%), ICU admission (14.7% vs.19.8%) was not significantly different between patients with or without VDD. There was no significant correlation between serum 25-OHD levels and inflammatory markers studied. Serum parathormone levels correlated with D-dimer (r 0.117, p- 0.019), ferritin (r 0.132, p-0.010), and LDH (r 0.124, p-0.018). Amongst VDD patients, 128(64.9%) were treated with oral cholecalciferol (median dose of 60,000 IU). The proportion of severe cases, oxygen, or ICU admission was not significantly different in the treated vs. untreated group. In conclusion, serum 25-OHD levels at admission did not correlate with inflammatory markers, clinical outcomes, or mortality in hospitalized COVID-19 patients. Treatment of VDD with cholecalciferol did not make any difference to the outcomes.

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ObjectiveTo describe the clinical profile and factors leading to increased mortality in coronavirus disease (COVID-19) patients admitted to a group of hospitals in India.DesignA records-based study of the first 1000 patients with a positive result on real-time reverse transcriptase-polymerase-chain-reaction assay for SARS-CoV-2 admitted to our facilities. Various factors such as demographics, presenting symptoms, co-morbidities, ICU admission, oxygen requirement and ventilator therapy were studied.ResultsOf the 1000 patients, 24 patients were excluded due to lack of sufficient data. Of the remaining 976 in the early phase of the epidemic, males were admitted twice as much as females (67.1% and 32.9%, respectively). Mortality in this initial phase was 10.6% and slightly higher for males and steeply higher for older patients. More than 8% reported no symptoms and the most common presenting symptoms were fever (78.3%), productive cough (37.2%), and dyspnea (30.64%). More than one-half (53.6%) had no co-morbidity. The major co-morbidities were hypertension (23.7%), diabetes without (15.4%), and with complications (9.6%). The co-morbidities were associated with higher ICU admissions, greater use of ventilators as well as higher mortality. A total of 29.9% were admitted to the ICU, with a mortality rate of 32.2%. Mortality was steeply higher in those requiring ventilator support (55.4%) versus those who never required ventilation (1.4%). The total duration of hospital stay was just a day longer in patients admitted to the ICU than those who remained in wards.ConclusionMale patients above the age of 60 and with co-morbidities faced the highest rates of mortality. They should be admitted to the hospital in early stage of the disease and given aggressive treatment to help reduce the morbidity and mortality associated with COVID-19.

Second wave of COVID 19 pandemic in India came with unexpected quick speed and intensity, creating an acute shortage of beds, ventilators, and oxygen at the peak of occurrence. This may have been partly caused by emergence of new variant delta. Clinical experience with the cases admitted to hospitals suggested that it is not merely a steep rise in cases but also possibly the case profile is different. This study was taken up to investigate the differentials in the characteristics of the cases admitted in the second wave versus those admitted in the first wave. Records of a total of 14398 cases admitted in the first wave (2020) to our network of hospitals in north India and 5454 cases admitted in the second wave (2021) were retrieved, making it the largest study of this kind in India. Their demographic profile, clinical features, management, and outcome was studied. Age sex distribution of the cases in the second wave was not much different from those admitted in the first wave but the patients with comorbidities and those with greater severity had larger share. Level of inflammatory markers was more adverse. More patients needed oxygen and invasive ventilation. ICU admission rate remained nearly the same. On the positive side, readmissions were lower, and the duration of hospitalization was slightly less. Usage of drugs like remdesivir and IVIG was higher while that of favipiravir and tocilizumab was lower. Steroid and anticoagulant use remained high and almost same during the two waves. More patients had secondary bacterial and fungal infections in Wave 2. Mortality increased by almost 40% in Wave 2, particularly in the younger patients of age less than 45 years. Higher mortality was observed in those admitted in wards, ICU, with or without ventilator support and those who received convalescent plasma. No significant demographic differences in the cases in these two waves, indicates the role of other factors such as delta variant and late admissions in higher severity and more deaths. Comorbidity and higher secondary bacterial and fungal infections may have contributed to increased mortality.

The variability of clinical course and prognosis of COVID-19 highlights the necessity of patient sub-group risk stratification based on clinical data. In this study, clinical data from a cohort of Indian COVID-19 hospitalized patients is used to develop risk stratification and mortality prediction models. We analyzed a set of 70 clinical parameters including physiological and hematological for developing machine learning models to identify biomarkers. We also compared the Indian and Wuhan cohort, and analyzed the role of steroids. A bootstrap averaged ensemble of Bayesian networks was also learned to construct an explainable model for discovering actionable influences on mortality and days to outcome. We discovered blood parameters, diabetes, co-morbidity and SpO2 levels as important risk stratification features, whereas mortality prediction is dependent only on blood parameters. XGboost and logistic regression model yielded the best performance on risk stratification and mortality prediction, respectively (AUC score 0.83, AUC score 0.92). Blood coagulation parameters (ferritin, D-Dimer and INR), immune and inflammation parameters IL6, LDH and Neutrophil (%) are common features for both risk and mortality prediction. Compared with Wuhan patients, Indian patients with extreme blood parameters indicated higher survival rate. Analyses of medications suggest that a higher proportion of survivors and mild patients who were administered steroids had extreme neutrophil and lymphocyte percentages. The ensemble averaged Bayesian network structure revealed serum ferritin to be the most important predictor for mortality and Vitamin D to influence severity independent of days to outcome. The findings are important for effective triage during strains on healthcare infrastructure.

Co-infection with ancillary pathogens is a significant modulator of morbidity and mortality in infectious diseases. There have been limited reports of co-infections accompanying SARS-CoV-2 infections, albeit lacking India specific study. The present study has made an effort toward elucidating the prevalence, diversity and characterization of co-infecting respiratory pathogens in the nasopharyngeal tract of SARS-CoV-2 positive patients. Two complementary metagenomics based sequencing approaches, Respiratory Virus Oligo Panel (RVOP) and Holo-seq, were utilized for unbiased detection of co-infecting viruses and bacteria. The limited SARS-CoV-2 clade diversity along with differential clinical phenotype seems to be partially explained by the observed spectrum of co-infections. We found a total of 43 bacteria and 29 viruses amongst the patients, with 18 viruses commonly captured by both the approaches. In addition to SARS-CoV-2, Human Mastadenovirus, known to cause respiratory distress, was present in a majority of the samples. We also found significant differences of bacterial reads based on clinical phenotype. Of all the bacterial species identified, ∼60% have been known to be involved in respiratory distress. Among the co-pathogens present in our sample cohort, anaerobic bacteria accounted for a preponderance of bacterial diversity with possible role in respiratory distress. Clostridium botulinum, Bacillus cereus and Halomonas sp. are anaerobes found abundantly across the samples. Our findings highlight the significance of metagenomics based diagnosis and detection of SARS-CoV-2 and other respiratory co-infections in the current pandemic to enable efficient treatment administration and better clinical management. To our knowledge this is the first study from India with a focus on the role of co-infections in SARS-CoV-2 clinical sub-phenotype.

COVID-19 is invariably a disease of diverse clinical manifestation, with multiple facets involved in modulating the progression and outcome. In this regard, we investigated the role of transcriptionally active microbial co-infections as possible modulators of disease pathology in hospital admitted SARS-CoV-2 infected patients.