Background: The COVID-19 vaccination program was introduced in India on 16 January 2021. The Government-issued fact sheet was the only source of information regarding Adverse Events Following Immunizations (AEFIs) for these vaccines. The objective of this study was to assess the AEFIs reported following COVID-19 vaccination in a tertiary care teaching hospital. Materials and methods: The spontaneous reporting method was used for data collection for a period of 3 months. A data collection form was designed to collect the data from the study population who reported adverse events. Collected data were analyzed and categorized by severity and seriousness. The causality assessment team performed causality assessment of the AEFIs using the World Health Organization’s causality assessment algorithm. Results: A total of 11,656 doses of COVID-19 vaccine were administered at the study site during the study period, of which 9292 doses were COVISHIELD™ and 2364 doses were COVAXIN™. In all, 445 AEFIs were reported from 269 subjects with an incidence rate of 3.48%. The majority of the subjects with AEFIs belonged to the age group of 18–45 years. Out of the total 445 AEFIs, 418 AEFIs were expected as per the fact sheets, 409 with COVISHIELD™ and 9 with COVAXIN™. Most of the AEFIs [62.02% ( n = 276)] were observed at the system organ class of ‘General disorders and administration site conditions’. After the causality assessment, out of 433 AEFIs to COVISHIELDTM vaccine, 94.22% ( n = 408) of events were categorized to have ‘consistent causal association with immunization’. Out of 12 adverse events following COVAXIN™, 8 (66.66%) events were categorized as ‘consistent causal association with immunization’. All of them recovered from their adverse events without any sequelae. Conclusion: Spontaneous reporting is one of the cheapest methods that can be used for the reporting of AEFI. This method helps health care professionals to identify rare events and potential signals.

Background Implementation of checklists has been shown to be effective in improving patient safety. This study aims to evaluate the effectiveness of implementation of a checklist for daily care processes into clinical practice of pediatric intensive care units (PICUs) with limited resources. Methods Prospective before–after study in eight PICUs from China, Congo, Croatia, Fiji, and India after implementation of a daily checklist into the ICU rounds. Results Seven hundred and thirty-five patients from eight centers were enrolled between 2015 and 2017. Baseline stage had 292 patients and post-implementation 443. The ICU length of stay post-implementation decreased significantly [9.4 (4–15.5) vs. 7.3 (3.4–13.4) days, p = 0.01], with a nominal improvement in the hospital length of stay [15.4 (8.4–25) vs. 12.6 (7.5–24.4) days, p = 0.055]. The hospital mortality and ICU mortality between baseline group and post-implementation group did not show a significant difference, 14.4% vs. 11.3%; p = 0.22 for each. There was a variable impact of checklist implementation on adherence to various processes of care recommendations. A decreased exposure in days was noticed for; mechanical ventilation from 42.6% to 33.8%, p < 0.01; central line from 31.3% to 25.3%, p < 0.01; and urinary catheter from 30.6% to 24.4%, p < 0.01. Although there was an increased utilization of antimicrobials (89.9–93.2%, p < 0.01). Conclusions Checklists for the treatment of acute illness and injury in the PICU setting marginally impacted the outcome and processes of care. The intervention led to increasing adherence with guidelines in multiple ICU processes and led to decreased length of stay.

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Introduction: Coronavirus disease 2019 (COVID-19) pandemic has caused unprecedented mortality and has stretched the health infrastructure thin worldwide, especially in low- and middle-income countries. There is a need to evaluate easily available biomarkers for their clinical relevance for poor outcomes in severe cases of COVID-19. It is also known that comorbidities affect these biomarkers with or without COVID-19. We aimed to unearth the influence of comorbidities on feasible hematological predictive markers for mortality in hospitalized severe COVID-19 patients. Materials and Methods: This is a retrospective study done on severe COVID-19 hospitalized patients, diagnosed with RT polymerase chain reaction (n = 205), were investigated. Comorbidities associated with the patients were tracked and scored according to Charlson comorbidity index (CCI). CCI score of zero was grouped in A, those with CCI score 1–4 into group B and those with CCI scores ≥ 5 into group C. Correlation between hematological parameters and CCI scores was analyzed using Pearson correlation coefficient. Optimal cut-off and odds ratio was derived from receiver operating characteristic (ROC) curve analysis. Results: Among the 205 severe COVID-19 patients age, C-reactive protein (CRP), neutrophil lymphocyte ratio (NLR), derived NLR (dNLR), absolute neutrophil count (ANC) and total leukocyte count (TLC) were found to be statistically significant independent risk factors for predicting COVID-19 mortality (p < 0.01). In group A, cut off for CRP was 51.5 mg/L (odds ratio [OR]: 26.7; area under curve [AUC]: 0.867), TLC was 11,850 cells/mm³ (OR: 11.7; AUC: 0.731), NLR was 11.76 (OR: 14.3; AUC: 0.756), dNLR was 5.77 (OR: 4.89; AUC: 0.659), ANC was 13,110 cells/mm³ (OR: 1.68; AUC: 0.553). In group B, cut off for CRP was 36.5 mg/L (OR: 32.1; AUC: 0.886), TLC was 11,077 cells/mm³ (OR: 12.1; AUC: 0.722), NLR was 8.27 (OR: 18.9; AUC: 0.827), dNLR was 3.79 (OR: 9.26; AUC: 0.727), ANC was 11,420 cells/mm³ (OR: 2.42; AUC: 0.564). In group C, cut-off for CRP was 23.7 mg/L (OR: 32.7; AUC: 0.904), TLC was 10,480 cells/mm³ (OR: 21.2; AUC: 0.651), NLR was 6.29 (OR: 23.5; AUC: 0.647), dNLR was 1.93 (OR: 20.8; AUC: 0.698), ANC was 6650 cells/mm³ (OR: 2.45; AUC: 0.564). Conclusions: In severe COVID-19 patients, CRP was the most reliable biomarker to predict mortality followed by NLR. Presence, type, and number of co-morbidities influence the levels of the biomarkers and the clinically relevant cut-offs associated with mortality.