Gram-positive pathogens mainly, Staphylococcus aureus, Enterococcus and coagulase-negative Staphylococcus, are developing increasing resistance to glycopeptides that pose a problem in treating infections caused by these pathogens. Vancomycin is the treatment of choice in treating methicillin-resistant S. aureus (MRSA). Community-acquired MRSA is associated with infections in patients without recent history of hospital admission and without the classical risk factors for MRSA carriage (including healthcare personnel). MRSA poses new threats and challenges beyond the hospital with the emergence of community-acquired MRSA. Indiscriminate use of vancomycin leads to the emergence and spread of vancomycin resistance in multidrug resistant strains is of growing concern in the recent years. Minimum Inhibitory concentration (MIC) remains an important determinant in choosing the right antibiotics. Infections caused by MRSA strains with vancomycin MIC > 4 μg/mL leads to the vancomycin treatment failure. The Clinical Laboratory Standards Institute had also lowered the cut-off susceptibility and resistance breakpoints for vancomycin. Despite the availability of newer antimicrobial agents (Linezolid, Daptomycin, Tigecycline) for drug-resistant Gram-positive pathogens, clinicians and patients still need options for treatment of MRSA infection. There is a need to reduce the global burden of infections caused by Gram-positive pathogens and its resistant strains (mainly MRSA). Continuous efforts should be made to prevent the spread and the emergence of glycopeptide resistance by early detection of the resistant strains and using the proper infection control measures in the hospital setting.
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.
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