These guidelines, written for clinicians, contains evidence-based recommendations for the prevention of hospital acquired infections Hospital acquired infections are a major cause of mortality and morbidity and provide challenge to clinicians. Measures of infection control include identifying patients at risk of nosocomial infections, observing hand hygiene, following standard precautions to reduce transmission and strategies to reduce VAP, CR-BSI, CAUTI. Environmental factors and architectural lay out also need to be emphasized upon. Infection prevention in special subsets of patients - burns patients, include identifying sources of organism, identification of organisms, isolation if required, antibiotic prophylaxis to be used selectively, early removal of necrotic tissue, prevention of tetanus, early nutrition and surveillance. Immunodeficient and Transplant recipients are at a higher risk of opportunistic infections. The post tranplant timetable is divided into three time periods for determining risk of infections. Room ventilation, cleaning and decontamination, protective clothing with care regarding food requires special consideration. Monitoring and Surveillance are prioritized depending upon the needs. Designated infection control teams should supervise the process and help in collection and compilation of data. Antibiotic Stewardship Recommendations include constituting a team, close coordination between teams, audit, formulary restriction, de-escalation, optimizing dosing, active use of information technology among other measure. The recommendations in these guidelines are intended to support, and not replace, good clinical judgment. The recommendations are rated by a letter that indicates the strength of the recommendation and a Roman numeral that indicates the quality of evidence supporting the recommendation, so that readers can ascertain how best to apply the recommendations in their practice environments.

In Coronavirus disease 2019 (COVID-19), early identification of patients with a high risk of mortality can significantly improve triage, bed allocation, timely management, and possibly, outcome. The study objective is to develop and validate individualized mortality risk scores based on the anonymized clinical and laboratory data at admission and determine the probability of Deaths at 7 and 28 days. Data of 1393 admitted patients (Expired—8.54%) was collected from six Apollo Hospital centers (from April to July 2020) using a standardized template and electronic medical records. 63 Clinical and Laboratory parameters were studied based on the patient’s initial clinical state at admission and laboratory parameters within the first 24 h. The Machine Learning (ML) modelling was performed using eXtreme Gradient Boosting (XGB) Algorithm. ‘Time to event’ using Cox Proportional Hazard Model was used and combined with XGB Algorithm. The prospective validation cohort was selected of 977 patients (Expired—8.3%) from six centers from July to October 2020. The Clinical API for the Algorithm is http://20.44.39.47/covid19v2/page1.php being used prospectively. Out of the 63 clinical and laboratory parameters, Age [adjusted hazard ratio (HR) 2.31; 95% CI 1.52–3.53], Male Gender (HR 1.72, 95% CI 1.06–2.85), Respiratory Distress (HR 1.79, 95% CI 1.32–2.53), Diabetes Mellitus (HR 1.21, 95% CI 0.83–1.77), Chronic Kidney Disease (HR 3.04, 95% CI 1.72–5.38), Coronary Artery Disease (HR 1.56, 95% CI − 0.91 to 2.69), respiratory rate > 24/min (HR 1.54, 95% CI 1.03–2.3), oxygen saturation below 90% (HR 2.84, 95% CI 1.87–4.3), Lymphocyte% in DLC (HR 1.99, 95% CI 1.23–2.32), INR (HR 1.71, 95% CI 1.31–2.13), LDH (HR 4.02, 95% CI 2.66–6.07) and Ferritin (HR 2.48, 95% CI 1.32–4.74) were found to be significant. The performance parameters of the current model is at AUC ROC Score of 0.8685 and Accuracy Score of 96.89. The validation cohort had the AUC of 0.782 and Accuracy of 0.93. The model for Mortality Risk Prediction provides insight into the COVID Clinical and Laboratory Parameters at admission. It is one of the early studies, reflecting on ‘time to event’ at the admission, accurately predicting patient outcomes.

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Objective: Chest CT can provide a simple quantitative assessment of the extent of the parenchymal opacities in COVID-19 patients. In this study, we postulate that CT findings can be used to ascertain the overall disease burden and predict the clinical outcome. Methods: In this prospective study undertaken from March 28, 2020, until May 20, 2020, 142 patients with CT features suggestive of viral pneumonia, and positive RT-PCR for COVID-19 were enrolled. A dedicated spiral CT scanner was used for all COVID-19 suspects. CT features were reported as typical, indeterminate, or atypical for COVID-19 pneumonia. A CT involvement score (CT-IS) was given to each scan and assigned mild, moderate, or severe category depending on the score range. The patients were followed up for at least 15 days. Results: Ground glass opacity was present in 100% of the patients. There was a significant association between CT-IS and the final outcome of the patients. A statistically significant increasing trend of mortality and requirement of critical medical attention was observed with the rising value of CT-IS in COVID-19. Conclusion: The severe CT-IS score group has a high mortality. The CT-IS score could be valuable in predicting clinical outcome and could also be useful in triage of patients needing hospital admission. In situations where healthcare resources are limited, and patient load high, a more careful approach for patients with higher CT-IS scores could be indispensable. Advances in knowledge: CT-IS is a simple quantitative method for assessing the disease burden of COVID-19 cases. It can be invaluable in places with limited resources and high patient load to segregate patients requiring critical medical attention.

Complete tracheal resection is extremely rare after blunt chest trauma. A high degree of suspicion is essential to identify these cases and early intervention is associated with better outcome. We report a patient with complete tracheal resection, in whom the airway was secured whilst the patient remained awake, breathing spontaneously under fibreoptic bronchoscopic guidance. As a precautionary measure, we had kept cardiopulmonary bypass set up in readiness. Anaesthetic management needed to be modified during repair of the trachea, by using total intravenous anaesthesia with propofol and rocuronium infusion and insertion of a separate endotracheal tube into the distal portion of the trachea whilst reconstruction of the trachea took place. The usual inhalational technique could not be used. The anaesthesiologist managing such a case should be aware of the difficulties during securing the airway and during repair of the trachea. Proper planning and keeping back-up plans ready helps in successful management of these patients.