Credibility of subgroup findings in clinical trials and meta-analyses

Effect of positive end expiratory pressure on non-hypoxic apnea time and atelectasis during induction of anesthesia in infant: A randomized controlled trial

INTRODUCTION

This study aimed to assess the impact of positive-end-expiratory pressure (PEEP) on the non-hypoxic apnea time in infants during anesthesia induction with an inspired oxygen fraction of 0.8.

METHODS

This age stratified randomized controlled trial included patients under 1 year of age. Preoxygenation was performed using an inspired oxygen fraction of 0.8 for 2 min. Inspired oxygen fraction of 0.8 was administered via a face mask with volume-controlled ventilation at a tidal volume of 6 mL.kg-1, with or without 7 cmH2O of PEEP. Tracheal intubation was performed after 3 min of ventilation; however, it was disconnected from the breathing circuit. Ventilation was resumed once the pulse oximetry readings reached 95%. The primary outcome was the non-hypoxic apnea time, defined as the time from the cessation of ventilation to achieving a pulse oximeter reading of 95%. The secondary outcome measures included the degree of atelectasis assessed by ultrasonography and the presence of gastric air insufflation.

RESULTS

Eighty-four patients were included in the final analysis. In the positive end-expiratory pressure group, the atelectasis score decreased (17.0 vs. 31.5, p < .001; mean difference and 95% CI of 11.6, 7.5-15.6), while the non-hypoxic apnea time increased (80.1 s vs. 70.6 s, p = .005; mean difference and 95% CI of -9.4, -16.0 to -2.9), compared to the zero end-expiratory pressure group, among infants who are 6 months old or younger, not in those aged older than 6 months.

DISCUSSION

The application of positive end-expiratory pressure reduced the incidence of atelectasis and extended the non-hypoxic apnea time in infants who are 6 months old or younger. However, it did not affect the incidence of atelectasis nor the non-hypoxic apnea time in patients aged older than 6 months.

A Methodological Framework for Meta-analysis and Clinical Interpretation of Subgroup Data: The Case of Major Adverse Cardiovascular Events With GLP-1 Receptor Agonists and SGLT2 Inhibitors in Type 2 Diabetes

We present a methodological framework for conducting and interpreting subgroup meta-analyses. Methodological steps comprised evaluation of clinical heterogeneity regarding the definition of subpopulations, credibility assessment of subgroup meta-analysis, and translation of relative into absolute treatment effects. We used subgroup data from type 2 diabetes cardiovascular outcomes trials (CVOTs) with glucagon-like peptide 1 (GLP-1) receptor agonists and sodium-glucose cotransporter 2 (SGLT2) inhibitors for patients with established cardiovascular disease and those at high cardiovascular risk without manifest cardiovascular disease. First, we evaluated the variability in definitions of the subpopulations across CVOTs using major adverse cardiovascular events (MACE) incidence in the placebo arm as a proxy for baseline cardiovascular risk. As baseline risk did not differ considerably across CVOTs, we conducted subgroup meta-analyses of hazard ratios (HRs) for MACE and assessed the credibility of a potential effect modification. Results suggested using the same overall relative effect for each of the two subpopulations (HR 0.85, 95% CI 0.80-0.90, for GLP-1 receptor agonists and HR 0.91, 95% CI 0.85-0.97, for SGLT2 inhibitors). Finally, we calculated 5-year absolute treatment effects (number of fewer patients with event per 1,000 patients). Treatment with GLP-1 receptor agonists resulted in 30 fewer patients with event in the subpopulation with established cardiovascular disease and 14 fewer patients with event in patients without manifest cardiovascular disease. For SGLT2 inhibitors, the respective absolute effects were 18 and 8 fewer patients with event per 1,000 patients. This framework can be applied to subgroup meta-analyses regardless of outcomes or modification variables.

Systematic reviews and meta-analysis with sub-groups - A commentary

As the outcomes of systematic reviews and meta-analysis are used to inform clinical practice it is imperative that the review and meta-analysis conducted is accurate and rigorous. In this commentary the authors wish to review the recommended approaches to be used when conducting a meta-analysis of quantitative data in a systematic review and outline the common errors and risks. The authors of this commentary aim to highlight the consequence of potential errors when conducting a meta-analysis using sub-groups with an example from a systematic review published in Research in Social and Administrative Pharmacy in 2019.

Inflammation risk before cardiac surgery and the treatment effect of intraoperative dexamethasone

Patients who exhibit high systemic inflammation after cardiac surgery may benefit most from pre-emptive anti-inflammatory treatments. In this secondary analysis (n = 813) of the randomised, double-blind Intraoperative High-Dose Dexamethasone for Cardiac Surgery trial, we set out to develop an inflammation risk prediction model and assess whether patients at higher risk benefit from a single intraoperative dose of dexamethasone (1 mg/kg). Inflammation risk before surgery was quantified from a linear regression model developed in the placebo arm, relating preoperatively available covariates to peak postoperative C-reactive protein. The primary endpoint was the interaction between inflammation risk and the peak postoperative C-reactive protein reduction associated with dexamethasone treatment. The impact of dexamethasone on the main clinical outcome (a composite of death, myocardial infarction, stroke, renal failure, or respiratory failure within 30 days) was also explored in relation to inflammation risk. Preoperatively available covariates explained a minority of peak postoperative C-reactive protein variation and were not suitable for clinical application (R2 = 0.058, P = 0.012); C-reactive protein before surgery (excluded above 10 mg/L) was the most predictive covariate (P < 0.001). The anti-inflammatory effect of dexamethasone increased as the inflammation risk increased (-0.689 mg/L per unit predicted peak C-reactive protein, P = 0.002 for interaction). No treatment-effect heterogeneity was detected for the main clinical outcome (P = 0.167 for interaction). Overall, risk predictions from a model of inflammation after cardiac surgery were associated with the degree of peak postoperative C-reactive protein reduction derived from dexamethasone treatment. Future work should explore the impact of this phenomenon on clinical outcomes in larger surgical populations.