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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 < 80%) during the procedure.

Preoxygenation is a cornerstone of airway management, designed to extend the “safe apnea time” by denitrogenating the lungs and optimizing oxygen reservoirs. The aim is to minimize peri-intubation hypoxia, which is a known predictor of morbidity and mortality.

Commonly used pre-oxygenation strategies include:

  • Conventional oxygen therapy (COT), such as non-rebreather masks.
  • High-flow nasal cannula (HFNC) provides warmed, humidified oxygen at high flow rates and can generate low levels of positive end-expiratory pressure (PEEP).
  • Non-invasive ventilation (NIV) provides pressure support to enhance alveolar ventilation and decrease the work of breathing.
  • Combinations of strategies like HFNC with NIV or bag-valve mask.

Despite the widespread use of these techniques, clinical uncertainty persists regarding the most effective and safest strategy for preoxygenation in AHRF. This knowledge gap has led to multiple randomized controlled trials (RCTs) on the subject.

Clinical Question: What is the optimal pre-oxygenation strategy in patients requiring intubation for acute hypoxic respiratory failure?

Reference: Ye et al. Preoxygenation strategies before intubation in patients with acute hypoxic respiratory failure: a network meta-analysis. Frontiers in Medicine. 2025 Feb

  • Population: Adults with AHRF defined as a respiratory rate >30/min, FiO₂ requirement ≥50% to maintain SpO₂ ≥90%, or PaO₂/FiO₂ < 300 mmHg within four hours of enrollment.
    • Exclusions: Studies involving reviews, conference abstracts, case reports, or lacking full text.
  • Intervention: Pre-oxygenation with Noninvasive Mechanical Ventilation, High Flow Oxygen via Nasal Cannula, or some combination of the above.
  • Comparison: Conventional oxygen therapy (COT) or other preoxygenation. strategies.
  • Outcome: There was no primary outcome explicitly stated. Outcomes included incidence of desaturation (SpO₂ < 80%) during intubation, lowest SpO2 during intubation, post-intubation complication rate, intensive care unit (ICU) length of stay (LOS) and ICU Mortality
  • Type of Study: Network Meta-Analysis (NMA)

Authors’ Conclusions: “Preoxygenation with HFNC appears to be the safest and most effective approach prior to intubation in patients with AHRF compared to other strategies”.

Quality Checklist for Therapeutic Systematic Reviews:

  1. The clinical question is sensible and answerable. Yes
  2. The search for studies was detailed and exhaustive. Yes
  3. The primary studies were of high methodological quality. Yes
  4. The assessment of studies were reproducible. Unsure
  5. The outcomes were clinically relevant. No
  6. The treatment effect was large enough and precise enough to be clinically significant. Unsure
  7. Who funded the trial? Not stated
  8. Did the authors declare any conflicts of interest? No conflicts declared

Results: Their search found 11 RCTs containing 2,874 patients with average ages ranging from mid-40s to 60s.

Key Result:

  • Outcomes:
    • Incidence of Severe Hypoxia (SpO2 <80%): NIV>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 too heterogeneous, and no inconsistencies are found, you can compute the rank order. This is done with a tool called SUCRA – Surface Under Cumulative Ranking Curve. This gives you a percentage of how likely a given flavour is to be the best. It’s normalized, so the percentages will not add up to 100%. In our Gum Challenge, Cherry might score 90% and Grape 75%, Cheese 10%, and Sewage 1%. What this tells you is that both Cherry and Grape are pretty good, but there’s a small margin between the two.

First, you assess heterogeneity within each direct comparison. Then you consider whether the network appears transitive by comparing PICO elements. Finally, you check for inconsistency, which is the statistical signal that transitivity might not hold.

3. Effect Sizes vs Rank Order: One key advantage of NMAs is that they not only estimate effect size but are also able to rank the efficacy and/or safety of an intervention. Ranking of treatments can provide clinicians, guideline writers and policy-makers choices based on the probability of each intervention being the most effective or safest option.

Ranking can also be a weakness of the NMA method if there is over-interpretation of the rank order. This may lead to a conclusion which is inaccurate. It doesn’t just matter that a given treatment was better than another, but by how much. Both Grape and Cheese flavours taste better than sewage, but let’s be honest, Cheese-flavoured gum is only a little better than sewage, and you may not capture that effect size if you only look at the rank orders.

The study we are reviewing today looks at several different treatments that were not different from placebo (in this case, conventional oxygen therapy), yet it generated rank orders and included these results in its conclusions. And in the one outcome to truly have a meaningful statistical effect size, the Incidence of severe hypoxia, NIV was a clear winner, but the clinical effect was marginal. Effect size is best described in terms of an odds ratio or absolute risk reduction and should be displayed contextually alongside the rank-order, but this was not performed. This paper reported the effect sizes, albeit in relative risk reduction and mean differences, for discrete and continuous variables, respectively.

4. Outcomes: It is strongly recommended that a primary outcome in NMAs be defined in advance (a priori). This helps to focus the research question, reduce the risk of selective outcome reporting, and maintain the integrity of treatment comparisons and rankings. When a primary outcome is specified from the start, it ensures that the analysis is guided by clinical relevance rather than post hoc decisions.

In this NMA, they did not explicitly identify any of these as the primary outcome. Nor did they justify analyzing multiple outcomes or describe how they intended to interpret or prioritize them collectively.

The absence of a prespecified primary outcome introduces several problems. It raises the risk of multiplicity bias, where statistical significance could emerge by chance due to multiple comparisons. It also complicates interpretation, especially when different outcomes suggest different conclusions about the effectiveness of treatments. Furthermore, the lack of prioritization opens the door for selective emphasis on whichever outcome appears most favourable after the analysis, even if that outcome was not originally the most clinically important.

Even more fun, though the outcome that the authors chose was a composite. HFNC was deemed “safer” (we infer, they do not state) because it involved fewer complications. But when we investigate the elements that constitute a “complication”, it includes things such as a new infiltrate on CXR as a proxy for peri-intubation aspiration,  and cardiac arrest, all lumped in together. While both are bad, they are not the same thing at all.

This NMA can also add to the SGEM lexicon. The SGEM is all about the POO (patient-oriented outcome) and this study’s main positive result of “severe hypoxia” is best described as a MOO (monitor-oriented outcome). Defining a “new infiltrate” as a complication could be considered a ROO (radiology-oriented outcome). In addition, hospital administrators might endorse “ICU length of stay” as an HOO (hospital-oriented outcome).

5. Limitations: This paper compares five different modalities of pre-oxygenation: HFNC, NIV, COT, and (confusingly) HFNC + NIV, and HFNC + COT. In comparing different techniques, it’s critical to understand if they are being implemented the same way, because particularly with the combined modalities, that can make a substantial difference and invalidate the comparison. The authors seemed to make no effort to describe the techniques.

It would be tempting to dive into the cited sources, which would have been a difficult but potentially important exercise. We recently did an SGEM Xtra with Nicholas Peoples. He was the lead author of a study published in the BMJ (Burden of proof: combating inaccurate citation in biomedical literature). Nick quantified that up to 25% of all citations in the general scientific literature are inaccurate and can be considered misleading. I’m working on an artificial intelligence solution to address this problem, and could check the references for readers.

The authors note that for four of the five measured outcomes, the level of evidence was low (the other, moderate). For three of the five outcomes that were analyzed, the comparison groups were not statistically different in effect size, which raises the question as to whether the NMA ranked analysis should be considered useful. While most of the comparison arms had a reasonable number of included patients, the overall numbers were generally low, and two of the arms consisted of only a single trial with very small numbers of participants. Additionally, not all trials measured all of the outcomes of interest. No information is given about the patient recruitment (ED, ICU, etc), which may impact generalizability.

Comment on Authors’ Conclusion Compared to SGEM Conclusion: The authors’ conclusion appears to overstate the certainty of the data.

SGEM Bottom Line: While both NIV and HFNC appear to offer advantages over COT, there is a lack of clear data as to which approach is optimal, and further research is needed.

Dr. Aine Yore

Case Resolution: You decide that, based on this specific patient’s presentation, the increased positive pressure afforded by NIV is more likely to be beneficial and request that respiratory therapy initiate BiPAP for pre-oxygenation.

Clinical Application: This article, while interesting and ambitious, should not be viewed as practice-changing.

What Do I Tell the Patient? Listen to the SGEM Podcast. 

Keener Kontest: Last week’s winner was Steven Stelts. He knew the scaphoid is at risk for avascular necrosis because it has an isolated distal blood supply that can cause loss of blood supply proximally if the scaphoid is fractured. The blood supply is considered retrograde, and there is little collateral circulation.

To hear this week’s keener question, listen to the SGEM podcast. If you know the answer, email TheSGEM@gmail.com with “keener” in the subject line. The first correct answer will get a shout-out on the next episode.

Other SGEM Episodes:

  • SGEM#186: Apneic and the O, O, O2 for Rapid Sequence Intubation
  • SGEM#447: Just What I Needed – Preoxygenation Prior To Intubation

Remember to be skeptical of anything you learn, even if you heard it on the Skeptics’ Guide to Emergency Medicine.

References:

  1. Chaimani A, Caldwell DM, Li T, Higgins JPT, Salanti G. Chapter 11: Undertaking network meta-analyses [last updated October 2019]. In: Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, Welch VA (editors). Cochrane Handbook for Systematic Reviews of Interventions version 6.5. Cochrane, 2024.
  2. Salanti G, Nikolakopoulou A, Efthimiou O, Mavridis D, Egger M, White IR. Introducing the Treatment Hierarchy Question in Network Meta-Analysis. Am J Epidemiol. 2022 Mar 24;191(5):930-938. doi: 10.1093/aje/kwab278. PMID: 35146500; PMCID: PMC9071581.
  3.  https://www.prisma-statement.org/nma
  4. Nikolakopoulou A, Higgins JPT, Papakonstantinou T, Chaimani A, Del Giovane C, Egger M, Salanti G. CINeMA: An approach for assessing confidence in the results of a network meta-analysis. PLoS Med. 2020 Apr 3;17(4):e1003082. doi: 10.1371/journal.pmed.1003082. PMID: 32243458; PMCID: PMC7122720.
  5. Spineli LM, Kalyvas C, Yepes-Nuñez JJ, García-Sierra AM, Rivera-Pinzón DC, Seide SE, Papadimitropoulou K. Low awareness of the transitivity assumption in complex networks of interventions: a systematic survey from 721 network meta-analyses. BMC Med. 2024 Mar 13;22(1):112. doi: 10.1186/s12916-024-03322-1. PMID: 38475826; PMCID: PMC10935945.