The effect of perioperative benzodiazepine administration on postoperative nausea and vomiting: a systematic review and meta-analysis of randomised controlled trials.

BACKGROUND: Despite recent systematic reviews suggesting their benefit for postoperative nausea, vomiting, or both (PONV) prevention, benzodiazepines have not been incorporated into guidelines for PONV prophylaxis because of concerns about possible adverse effects. We conducted an updated meta-analysis to inform future practice guidelines. METHODS: We included randomised controlled trials (RCTs) of all languages comparing benzodiazepines with non-benzodiazepine comparators in adults undergoing inpatient surgery. Our outcomes were postoperative nausea, vomiting, or both. We assessed risk of bias for RCTs using the Cochrane Risk of Bias tool. We pooled data using a random-effects model and assessed the quality of evidence for each outcome using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach. RESULTS: We screened 31 413 abstracts and 950 full texts. We included 119 RCTs; 104 were included in quantitative synthesis. Based on moderate certainty evidence, we found that perioperative benzodiazepine administration reduced the incidence of PONV (52 studies, n=5086, relative risk [RR]: 0.77, 95% confidence interval [CI] 0.66-0.89; number needed to treat [NNT] 16; moderate certainty), postoperative nausea (55 studies, n=5916, RR: 0.72, 95% CI 0.62-0.83; NNT 21; moderate certainty), and postoperative vomiting (52 studies, n=5909, RR: 0.74, 95% CI 0.60-0.91; NNT 55; moderate certainty). CONCLUSIONS: Moderate quality evidence shows that perioperative benzodiazepine administration decreases the incidence of PONV. The results of this systematic review and meta-analysis will inform future clinical practice guidelines. SYSTEMATIC REVIEW PROTOCOL: The protocol for this systematic review was pre-registered with PROSPERO International Prospective Register of Systematic Reviews (CRD42022361088) and published in BMJ Open (PMID 31831540).

Small-Volume Blood Collection Tubes to Reduce Transfusions in Intensive Care: The STRATUS Randomized Clinical Trial.

Importance: Blood collection for laboratory testing in intensive care unit (ICU) patients is a modifiable contributor to anemia and red blood cell (RBC) transfusion. Most blood withdrawn is not required for analysis and is discarded. Objective: To determine whether transitioning from standard-volume to small-volume vacuum tubes for blood collection in ICUs reduces RBC transfusion without compromising laboratory testing procedures. Design, Setting, and Participants: Stepped-wedge cluster randomized trial in 25 adult medical-surgical ICUs in Canada (February 5, 2019 to January 21, 2021). Interventions: ICUs were randomized to transition from standard-volume (n = 10 940) to small-volume tubes (n = 10 261) for laboratory testing. Main Outcomes and Measures: The primary outcome was RBC transfusion (units per patient per ICU stay). Secondary outcomes were patients receiving at least 1 RBC transfusion, hemoglobin decrease during ICU stay (adjusted for RBC transfusion), specimens with insufficient volume for testing, length of stay in the ICU and hospital, and mortality in the ICU and hospital. The primary analysis included patients admitted for 48 hours or more, excluding those admitted during a 5.5-month COVID-19-related trial hiatus. Results: In the primary analysis of 21 201 patients (mean age, 63.5 years; 39.9% female), which excluded 6210 patients admitted during the early COVID-19 pandemic, there was no significant difference in RBC units per patient per ICU stay (relative risk [RR], 0.91 [95% CI, 0.79 to 1.05]; P = .19; absolute reduction of 7.24 RBC units/100 patients per ICU stay [95% CI, -3.28 to 19.44]). In a prespecified secondary analysis (n = 27 411 patients), RBC units per patient per ICU stay decreased after transition from standard-volume to small-volume tubes (RR, 0.88 [95% CI, 0.77 to 1.00]; P = .04; absolute reduction of 9.84 RBC units/100 patients per ICU stay [95% CI, 0.24 to 20.76]). Median decrease in transfusion-adjusted hemoglobin was not statistically different in the primary population (mean difference, 0.10 g/dL [95% CI, -0.04 to 0.23]) and lower in the secondary population (mean difference, 0.17 g/dL [95% CI, 0.05 to 0.29]). Specimens with insufficient quantity for analysis were rare (≤0.03%) before and after transition. Conclusions and Relevance: Use of small-volume blood collection tubes in the ICU may decrease RBC transfusions without affecting laboratory analysis. Trial Registration: ClinicalTrials.gov Identifier: NCT03578419.

Machine learning decision tree algorithm role for predicting mortality in critically ill adult COVID-19 patients admitted to the ICU.

BACKGROUND: Coronavirus disease-19 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and is currently a major cause of intensive care unit (ICU) admissions globally. The role of machine learning in the ICU is evolving but currently limited to diagnostic and prognostic values. A decision tree (DT) algorithm is a simple and intuitive machine learning method that provides sequential nonlinear analysis of variables. It is simple and might be a valuable tool for bedside physicians during COVID-19 to predict ICU outcomes and help in critical decision-making like end-of-life decisions and bed allocation in the event of limited ICU bed capacities. Herein, we utilized a machine learning DT algorithm to describe the association of a predefined set of variables and 28-day ICU outcome in adult COVID-19 patients admitted to the ICU. We highlight the value of utilizing a machine learning DT algorithm in the ICU at the time of a COVID-19 pandemic. METHODS: This was a prospective and multicenter cohort study involving 14 hospitals in Saudi Arabia. We included critically ill COVID-19 patients admitted to the ICU between March 1, 2020, and October 31, 2020. The predictors of 28-day ICU mortality were identified using two predictive models: conventional logistic regression and DT analyses. RESULTS: There were 1468 critically ill COVID-19 patients included in the study. The 28-day ICU mortality was 540 (36.8 %), and the 90-day mortality was 600 (40.9 %). The DT algorithm identified five variables that were integrated into the algorithm to predict 28-day ICU outcomes: need for intubation, need for vasopressors, age, gender, and PaO2/FiO2 ratio. CONCLUSION: DT is a simple tool that might be utilized in the ICU to identify critically ill COVID-19 patients who are at high risk of 28-day ICU mortality. However, further studies and external validation are still required.

The GRADE evidence-to-decision framework: a report of its testing and application in 15 international guideline panels.

BACKGROUND: Judgments underlying guideline recommendations are seldom recorded and presented in a systematic fashion. The GRADE Evidence-to-Decision Framework (EtD) offers a transparent way to record and report guideline developers' judgments. In this paper, we report the experiences with the EtD frameworks in 15 real guideline panels. METHODS: Following the guideline panel meetings, we asked methodologists participating in the panel to provide feedback regarding the EtD framework. They were instructed to consider their own experience and the feedback collected from the rest of the panel. Two investigators independently summarized the responses and jointly interpreted the data using pre-specified domains as coding system. We asked methodologists to review the results and provide further input to improve the structure of the EtDs iteratively. RESULTS: The EtD framework was well received, and the comments were generally positive. Methodologists felt that in a real guideline panel, the EtD framework helps structuring a complex process through relatively simple steps in an explicit and transparent way. However, some sections (e.g., "values and preferences" and "balance between benefits and harms") required further development and clarification that were considered in the current version of the EtD framework. CONCLUSIONS: The use of an EtD framework in guideline development offers a structured and explicit way to record and report the judgments and discussion of guideline panels during the formulation of recommendations. In addition, it facilitates the formulation of recommendations, assessment of their strength, and identifying gaps in research.