Reference: Ye et al. Preoxygenation strategies before intubation in patients with acute hypoxic respiratory failure: a network meta-analysis. Frontiers in Medicine. 2025 Feb Date: June 12, 2025 Guest Skeptic: Dr. Aine Yore is an Emergency Physician, practicing in the Seattle, Washington area for over twenty years. She is the former president of the Washington chapter of ACEP, and her career focus outside of clinical practice has been largely devoted to health care policy. Case: A 68-year-old woman presents in acute respiratory distress. She is febrile, hypoxemic, and meets criteria for sepsis. A chest x-ray reveals multilobar pneumonia. After managing her sepsis, her oxygenation remains poor, with saturations in the 88-92% range despite supplemental oxygen via a nonrebreather mask, and she now shows signs of worsening fatigue. You determine she requires endotracheal intubation, but note that she is at high risk for peri-intubation complications or even death, and wonder if there is a strategy you can utilize to reduce this risk? Background: Acute hypoxic respiratory failure (AHRF) represents a life-threatening emergency where pulmonary gas exchange becomes insufficient to maintain adequate oxygenation. It commonly arises from a variety of conditions, including pneumonia, acute respiratory distress syndrome (ARDS), sepsis, and exacerbations of chronic lung disease (ex, chronic obstructive lung disease). In such patients, intubation is often required, but the procedure itself introduces additional risk. Nearly 25% of patients undergoing emergency intubation in the context of AHRF experience profound desaturation (SpO₂ 30/min, FiO₂ requirement ≥50% to maintain SpO₂ ≥90%, or PaO₂/FiO₂ HFNC>COT, meaningful effect size Lowest SpO2 during intubation: HFNC+NIV>HFNC+COT>NIV>HFNC>COT, effect size not meaningful Post-intubation Complication Rate: HFNC>HFNC+COT>HFNC+NIV>NIV>COT, effect size not statistically significant ICU Length of Stay: HFNC>COT>NFNC+NIV>NIV, effect size not statistically significant ICU Mortality: HFNC>HFNC+NIV>HFNC+COT>NIV>COT, effect size not statistically significant 1. What Is A Network Meta-Analysis (NMA)? An NMA is an analytical method that allows for the comparison of multiple treatments simultaneously when some or all the treatments have never been directly compared in a head-to-head trial [1]. A key advantage of NMAs is that they can also rank treatments based on their effectiveness or safety. It provides outputs such as surface under the cumulative ranking (SUCRA) curves, which help identify the most effective or safest option [2]. Here’s a way to understand an NMA. Let’s say you want to compare four flavours of chewing gum: Cherry, Grape, Cheese, and Sewage. You have lots of market data comparing them, but, like the Highlander, there can be only one! But nobody has ever compared Cherry and Grape directly! And we need to prove which is best. An NMA can use a combination of direct and indirect evidence to compare them and determine the Ultimate Champion. Direct evidence would include the head-to-head comparisons. Cherry was a lot better than Sewage, of course. And Grape was marginally better than Cheese, and everything was better than Sewage, which is just objectively bad. The NMA will indirectly compare Cherry to Grape and can give you a reasonable sense of confidence as to which is better. 2. How Do You Critically Appraise An NMA? It is like the structured critical appraisal used to probe an SRMA for its validity. There are quality checklists for NMA (PRISMA and CINeMA) [3,4]. What’s the PICO question? How exhaustive was the search? What was the quality of the included studies (Risk of bias assessment) Transitivity assumption? What statistical model was used (Bayesian/Frequentist), and what was the heterogeneity? How precise were the results? Was the effect size clinically relevant? Were there any COIs? One thing specific to NMAs is transitivity, which is different than heterogeneity? Heterogeneity refers to statistical variability in results among studies comparing the same interventions. In contrast, transitivity is the idea that we can validly compare two treatments indirectly through a common comparator [5]. Heterogeneity is assessed, not globally, but within each treatment arm (direct comparisons). If there were three studies comparing Grape to Cheese flavoured gum, those studies themselves need to be similar. There are some highly quantitative statistical tools for this, and also some that are more vibe-based, as in this study. Additionally, an NMA requires internal cross-checking to ensure there is little inconsistency for the results to be valid. If Grape scored higher than Cheese flavour, and Cherry scored higher than Grape, yet Cheese scored higher than Cherry, the data is inconsistent, and an NMA may not be able to provide valid indirect evidence. The tests for inconsistency are also technical, and there are multiple methods of performing them. Assuming that your data is not t
