Background: The role of prophylactic antibiotics in preventing ventilator-associated pneumonia (VAP) in patients undergoing invasive mechanical ventilation (IMV) remains unclear. This network meta-analysis compared the efficacy and safety of antibiotic prophylaxis in preventing VAP in an IMV population in intensive-care units (ICUs). Methods: We searched the PubMed, Web of Science, Embase, and Cochrane Library databases from inception to December 2021, to identify relevant studies assessing the impact of prophylactic antibiotics on the incidence of VAP, the mortality, and the duration of ICU stays and hospitalization to perform a meta-analysis. Results: Thirteen studies (2144 patients) were included, 12 of which were selected for the primary analysis, which revealed that treatment with prophylactic antibiotics resulted in a lower VAP rate compared with control groups [risk ratio (RR) = 0.62]. Bayesian network meta-analysis indicated that aerosolized tobramycin and intravenous ampicillin–sulbactam presented the greatest likelihood being the most efficient regimen for reducing VAP. Conclusions: Antibiotic prophylaxis may reduce the incidence of VAP, but not the mortality, for adult patients undergoing IMV in ICUs. Tobramycin via nebulization and ampicillin–sulbactam via intravenous administration presented the greatest likelihood of being the most efficient regimen for preventing VAP. However, well-designed randomized studies are warranted before definite recommendations can be made.

Background: JAK (Janus kinases) inhibitors have been proposed as a promising treatment option for the coronavirus disease-2019 (COVID-19). However, the benefits of JAK inhibitors and the optimum thereof for COVID-19 have not been adequately defined.Methods: Databases were searched from their inception dates to 17 June 2022. Eligible studies included randomized controlled trials and observational studies. Extracted data were analyzed by pairwise and network meta-analysis. The primary outcome was the coefficient of mortality.Results: Twenty-eight studies of 8,206 patients were included and assessed qualitatively (modified Jadad and Newcastle–Ottawa Scale scores). A pairwise meta-analysis revealed that JAK inhibitors effectively reduced the mortality (OR = 0.54; 95% CI: 0.46–0.63; P < 0.00001; I² = 32%) without increasing the risk of adverse events (OR = 1.02; 95% CI: 0.88–1.18; P = 0.79; I² = 12%). In a network meta-analysis, clinical efficacy benefits were seen among different types of JAK inhibitors (baricitinib, ruxolitinib, and tofacitinib) without the observation of a declined incidence of adverse events. The assessment of rank probabilities indicated that ruxolitinib presented the greatest likelihood of benefits regarding mortality and adverse events.Conclusion: JAK inhibitors appear to be a promising treatment for COVID-19 concerning reducing mortality, and they do not increase the risk of adverse events vs. standard of care. A network meta-analysis suggests that mortality benefits are associated with specific JAK inhibitors, and among these, ruxolitinib presents the greatest likelihood of having benefits for mortality and adverse events.Systematic review registration: [www.crd.york.ac.uk/prospero], identifier [CRD42022343338].

Introduction: Positive airway pressure (PAP) therapy is currently the first-line respiratory support technique for acute respiratory failure (ARF) due to acute cardiogenic pulmonary edema (ACPE), but the accompanied adverse events and patient's intolerance with treatment in some cases limited its use in clinical practice. Some recent trials indicated that high-flow nasal cannula oxygen (HFNO) is a promising alternative to PAP therapy. In order to choose the optimum treatment for patients with ACPE, this network meta-analysis will firstly compares the efficacy of HFNO, PAP, and conventional oxygen therapy (COT).Methods and analysis: The Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols 2015 statement and its extension for network meta-analysis will be followed in the conduct of this investigation. We will examine these databases: PubMed, EMBASE, Cochrane Central Register of Controlled Trials and Web of Science. The ClinicalTrials.gov and World Health Organization International Clinical Trials Registry Platform Search Portal will be used to search ongoing trials. Only randomized controlled trials meeting the eligibility criteria will be included. Through the Cochrane Collaboration's tool, the included studies' risk of bias will be assessed. The pairwise meta-analysis will be performed with RevMan 5.4.1 software. A Bayesian network meta-analysis will use random-effects models to derive odds ratios for the treatment effects of all interventions compared to each other using R software (version 3.6.1), and the rjags and gemtc packages. The Q statistic and I² index will be used for investigating the heterogeneity, and subgroup analysis or sensitivity analysis will be used to explore the source of heterogeneity. In addition, the Grading of Recommendations Assessment, Development and Evaluation system will be used to inspect the quality of evidence.

Noninvasive evaluation of partial pressure of carbon dioxide (PCO2) is clinically important for screening and monitoring of hypercapnia, especially in pati

Chronic obstructive pulmonary disease (COPD) remains a major global health burden and is currently the third leading cause of death worldwide. Acute exacerbations accelerate disease progression and contribute substantially to mortality, underscoring the urgent need for reliable prognostic biomarkers. The endothelial activation and stress index (EASIX), a composite indicator of endothelial dysfunction, has demonstrated prognostic utility across diverse critical illnesses. However, its association with clinical outcomes in critically ill patients with COPD has not been clearly established. In this retrospective cohort study, data of critically ill patients with COPD were extracted from the Medical Information Mart for Intensive Care (MIMIC) database. Participants were stratified into tertiles based on EASIX values, and intergroup differences in clinical characteristics were analyzed. The relationship between EASIX and 28-day all-cause mortality was examined using Kaplan-Meier survival analysis, Cox proportional hazards regression, and restricted cubic spline modeling. The Boruta algorithm was applied to assess the relative importance of candidate predictors, and prognostic models were subsequently developed using six machine learning algorithms. A total of 4,590 patients met the inclusion criteria. The incidence of 28-day ICU mortality increased progressively across higher EASIX tertiles (p < 0.001). EASIX was independently associated with 28-day ICU all-cause mortality, with both unadjusted and fully adjusted Cox models confirming this relationship (unadjusted HR = 1.21, p < 0.001; adjusted HR = 1.082, p < 0.001). Subgroup analyses demonstrated that the association between elevated EASIX and mortality risk remained consistent across demographic and comorbidity categories (p for interaction > 0.05 for all). The Boruta algorithm identified EASIX as one of the most important predictors of 28-day mortality. Among the six machine learning models evaluated, the XGBoost algorithm yielded the highest discriminative (AUC = 0.823), calibration and clinical application. EASIX serves as an independent prognostic marker for 28-day all-cause mortality in critically ill COPD patients. Furthermore, the EASIX-based machine learning model demonstrated strong predictive accuracy, supporting its potential as a valuable clinical tool or early risk stratification and decision-making in intensive care settings.