Background COVID-19 is rapidly spreading causing extensive burdens across the world. Effective vaccines to prevent COVID-19 are urgently needed. Methods and findings Our objective was to assess the effectiveness and safety of COVID-19 vaccines through analyses of all currently available randomized clinical trials. We searched the databases CENTRAL, MEDLINE, Embase, and other sources from inception to June 17, 2021 for randomized clinical trials assessing vaccines for COVID-19. At least two independent reviewers screened studies, extracted data, and assessed risks of bias. We conducted meta-analyses, network meta-analyses, and Trial Sequential Analyses (TSA). Our primary outcomes included all-cause mortality, vaccine efficacy, and serious adverse events. We assessed the certainty of evidence with GRADE. We identified 46 trials; 35 trials randomizing 219 864 participants could be included in our analyses. Our meta-analyses showed that mRNA vaccines (efficacy, 95% [95% confidence interval (CI), 92% to 97%]; 71 514 participants; 3 trials; moderate certainty); inactivated vaccines (efficacy, 61% [95% CI, 52% to 68%]; 48 029 participants; 3 trials; moderate certainty); protein subunit vaccines (efficacy, 77% [95% CI, −5% to 95%]; 17 737 participants; 2 trials; low certainty); and viral vector vaccines (efficacy 68% [95% CI, 61% to 74%]; 71 401 participants; 5 trials; low certainty) prevented COVID-19. Viral vector vaccines decreased mortality (risk ratio, 0.25 [95% CI 0.09 to 0.67]; 67 563 participants; 3 trials, low certainty), but comparable data on inactivated, mRNA, and protein subunit vaccines were imprecise. None of the vaccines showed evidence of a difference on serious adverse events, but observational evidence suggested rare serious adverse events. All the vaccines increased the risk of non-serious adverse events. Conclusions The evidence suggests that all the included vaccines are effective in preventing COVID-19. The mRNA vaccines seem most effective in preventing COVID-19, but viral vector vaccines seem most effective in reducing mortality. Further trials and longer follow-up are necessary to provide better insight into the safety profile of these vaccines.

OBJECTIVES: We sought to update our 2015 work in the Second Pediatric Acute Lung Injury Consensus Conference (PALICC-2) guidelines for the diagnosis and management of pediatric acute respiratory distress syndrome (PARDS), considering new evidence and topic areas that were not previously addressed. DESIGN: International consensus conference series involving 52 multidisciplinary international content experts in PARDS and four methodology experts from 15 countries, using consensus conference methodology, and implementation science. SETTING: Not applicable. PATIENTS: Patients with or at risk for PARDS. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Eleven subgroups conducted systematic or scoping reviews addressing 11 topic areas: 1) definition, incidence, and epidemiology; 2) pathobiology, severity, and risk stratification; 3) ventilatory support; 4) pulmonary-specific ancillary treatment; 5) nonpulmonary treatment; 6) monitoring; 7) noninvasive respiratory support; 8) extracorporeal support; 9) morbidity and long-term outcomes; 10) clinical informatics and data science; and 11) resource-limited settings. The search included MEDLINE, EMBASE, and CINAHL Complete (EBSCOhost) and was updated in March 2022. Grading of Recommendations, Assessment, Development, and Evaluation methodology was used to summarize evidence and develop the recommendations, which were discussed and voted on by all PALICC-2 experts. There were 146 recommendations and statements, including: 34 recommendations for clinical practice; 112 consensus-based statements with 18 on PARDS definition, 55 on good practice, seven on policy, and 32 on research. All recommendations and statements had agreement greater than 80%. CONCLUSIONS: PALICC-2 recommendations and consensus-based statements should facilitate the implementation and adherence to the best clinical practice in patients with PARDS. These results will also inform the development of future programs of research that are crucially needed to provide stronger evidence to guide the pediatric critical care teams managing these patients.

There is low-quality evidence for the effects of nutrition support on mortality and serious adverse events. Based on the results of our review, it does not appear to lead to a risk ratio reduction of approximately 10% or more in either all-cause mortality or serious adverse events at short-term and long-term follow-up.There is very low-quality evidence for an increase in weight with nutrition support at the end of treatment in hospitalised adults determined to be at nutritional risk. The effects of nutrition support on all remaining outcomes are unclear.Despite the clinically heterogenous population and the high risk of bias of all included trials, our analyses showed limited signs of statistical heterogeneity. Further trials may be warranted, assessing enteral nutrition (tube-feeding) for different patient groups. Future trials ought to be conducted with low risks of systematic errors and low risks of random errors, and they also ought to assess health-related quality of life.

Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19) which has rapidly spread worldwide. Several human randomized clinical trials assessing potential vaccines are currently underway. There is an urgent need for a living systematic review that continuously assesses the beneficial and harmful effects of all available vaccines for COVID-19. Methods/design We will conduct a living systematic review based on searches of major medical databases (e.g., MEDLINE, EMBASE, CENTRAL) and clinical trial registries from their inception onwards to identify relevant randomized clinical trials. We will update the literature search once a week to continuously assess if new evidence is available. Two review authors will independently extract data and conduct risk of bias assessments. We will include randomized clinical trials comparing any vaccine aiming to prevent COVID-19 (including but not limited to messenger RNA; DNA; non-replicating viral vector; replicating viral vector; inactivated virus; protein subunit; dendritic cell; other vaccines) with any comparator (placebo; “active placebo;” no intervention; standard care; an “active” intervention; another vaccine for COVID-19) for participants in all age groups. Primary outcomes will be all-cause mortality; a diagnosis of COVID-19; and serious adverse events. Secondary outcomes will be quality of life and non-serious adverse events. The living systematic review will include aggregate data meta-analyses, trial sequential analyses, network meta-analyses, and individual patient data meta-analyses. Within-study bias will be assessed using Cochrane risk of bias tool. The Grading of Recommendations, Assessment, Development and Evaluations (GRADE) and Confidence in Network Meta-Analysis (CINeMA) approaches will be used to assess certainty of evidence. Observational studies describing harms identified during the search for trials will also be included and described and analyzed separately. Discussion COVID-19 has become a pandemic with substantial mortality. A living systematic review assessing the beneficial and harmful effects of different vaccines is urgently needed. This living systematic review will regularly inform best practice in vaccine prevention and clinical research of this highly prevalent disease. Systematic review registration PROSPERO CRD42020196492

This article describes the methodology used for The Second Pediatric Acute Lung Injury Consensus Conference (PALICC-2). The PALLIC-2 sought to develop evidence-based clinical recommendations and when evidence was lacking, expert-based consensus statements and research priorities for the diagnosis and management of pediatric acute respiratory distress syndrome (PARDS).DATA SOURCES: Electronic searches were conducted using PubMed, Embase, and Cochrane Library (CENTRAL) databases from 2012 to March 2022. STUDY SELECTION:Content was divided into 11 sections related to PARDS, with abstract and full text screening followed by data extraction for studies which met inclusion with no exclusion criteria. DATA EXTRACTION:We used a standardized data extraction form to construct evidence tables, grade the evidence, and formulate recommendations or statements using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system.DATA SYNTHESIS: This consensus conference was comprised of a multidisciplinary group of international experts in pediatric critical care, pulmonology, respiratory care, and implementation science which followed standards set by the Institute of Medicine, using the GRADE system and Research And Development/

Objective To externally validate various prognostic models and scoring rules for predicting short term mortality in patients admitted to hospital for covid-19. Design Two stage individual participant data meta-analysis. Setting Secondary and tertiary care. Participants 46 914 patients across 18 countries, admitted to a hospital with polymerase chain reaction confirmed covid-19 from November 2019 to April 2021. Data sources Multiple (clustered) cohorts in Brazil, Belgium, China, Czech Republic, Egypt, France, Iran, Israel, Italy, Mexico, Netherlands, Portugal, Russia, Saudi Arabia, Spain, Sweden, United Kingdom, and United States previously identified by a living systematic review of covid-19 prediction models published in The BMJ , and through PROSPERO, reference checking, and expert knowledge. Model selection and eligibility criteria Prognostic models identified by the living systematic review and through contacting experts. A priori models were excluded that had a high risk of bias in the participant domain of PROBAST (prediction model study risk of bias assessment tool) or for which the applicability was deemed poor. Methods Eight prognostic models with diverse predictors were identified and validated. A two stage individual participant data meta-analysis was performed of the estimated model concordance (C) statistic, calibration slope, calibration-in-the-large, and observed to expected ratio (O:E) across the included clusters. Main outcome measures 30 day mortality or in-hospital mortality. Results Datasets included 27 clusters from 18 different countries and contained data on 46 914patients. The pooled estimates ranged from 0.67 to 0.80 (C statistic), 0.22 to 1.22 (calibration slope), and 0.18 to 2.59 (O:E ratio) and were prone to substantial between study heterogeneity. The 4C Mortality Score by Knight et al (pooled C statistic 0.80, 95% confidence interval 0.75 to 0.84, 95% prediction interval 0.72 to 0.86) and clinical model by Wang et al (0.77, 0.73 to 0.80, 0.63 to 0.87) had the highest discriminative ability. On average, 29% fewer deaths were observed than predicted by the 4C Mortality Score (pooled O:E 0.71, 95% confidence interval 0.45 to 1.11, 95% prediction interval 0.21 to 2.39), 35% fewer than predicted by the Wang clinical model (0.65, 0.52 to 0.82, 0.23 to 1.89), and 4% fewer than predicted by Xie et al’s model (0.96, 0.59 to 1.55, 0.21 to 4.28). Conclusion The prognostic value of the included models varied greatly between the data sources. Although the Knight 4C Mortality Score and Wang clinical model appeared most promising, recalibration (intercept and slope updates) is needed before implementation in routine care.

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Analysis 4.1. Comparison 4: Meropenem versus standard care (ampicillin plus gentamicin or cefotaxime plus gentamicin), Outcome