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Record W4409244957 · doi:10.1097/aln.0000000000005420

Lost in Transition: New Evidence on the Risks of Underassisted Ventilation on the Diaphragm

2025· article· en· W4409244957 on OpenAlexaff
Martin Dres, Ewan C. Goligher

Bibliographic record

VenueAnesthesiology · 2025
Typearticle
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineDiaphragm (acoustics)Ventilation (architecture)Intensive care medicine

Abstract

fetched live from OpenAlex

“Capdevila et al. therefore confirm and extend the hypothesis that underassistance may cause diaphragm injury by showing that VIDD from overassistance may predispose to further injury from underassistance.”Image: J. P. Rathmell.Transitioning from controlled ventilation to assisted ventilation is a critical step in the process of preparing critically ill patients for weaning from mechanical ventilation, but the optimal timing of this transition remains unclear. Clinicians often rely on simple indicators, such as improvements in gas exchange, to assess whether patients are ready for assisted ventilation. However, switching too early may increase the risk of a relapse of the acute illness,1 while delaying the transition can prolong mechanical ventilation and increase the risk of complications including ventilator-induced diaphragm dysfunction (VIDD). VIDD is the consequence of diaphragm disuse related to the exposition to controlled ventilation and has been established from animal models.2 As such, the decision to switch to assisted ventilation requires careful clinical judgment.3 While it is well established that overassistance is a risk factor of diaphragm atrophy and dysfunction,4,5 less is known about the risks associated with underassistance. To evaluate the impact of underassistance on diaphragm function, Capdevila et al.6 conducted an experimental study involving two groups of piglets exposed to underassisted ventilation. The authors hypothesized that underassisted ventilation would cause diaphragm dysfunction, particularly in the presence of preexisting VIDD. In one group, the authors induced ventilator-induced diaphragm dysfunction (the VIDD group) by exposing the animals to 72 h of controlled mechanical ventilation to mimic the conditions of critical illness, while the other group received only 2 h of controlled mechanical ventilation (the no-VIDD group). Both groups were then exposed to 2 h of underassisted ventilation to study its impact on animals with a normal diaphragm (no-VIDD group) in comparison to animals with VIDD. Underassistance was achieved by stopping sedation until spontaneous respiratory efforts commenced and then carefully titrating drugs to maintain spontaneous breathing while keeping the animal unconscious. After 2 h, diaphragm function was assessed in vivo (diaphragm pressure-generating capacity assessed by magnetic twitch tracheal pressure), and diaphragm structure was evaluated on biopsies. It is important to highlight that the authors assessed diaphragm function using the reference technique of bilateral anterior magnetic phrenic stimulation, which enables standardized measurement independent of patient cooperation. However, this method is not widely available and requires specific technical expertise. Notably, the same research group recently developed an ultrasound-guided transcutaneous phrenic nerve stimulation technique for daily bedside assessment of diaphragm function through targeted electrical phrenic nerve stimulation.7 As expected, the authors found that diaphragm pressure-generating capacity decreased by 22% after 72 h of controlled mechanical ventilation in the first group, confirming the presence of VIDD. More crucially, the animals with established VIDD exhibited a significant further decrease in diaphragm pressure-generating capacity after 2 h of underassisted ventilation. By contrast, there was no decrease in diaphragm pressure-generating capacity in animals without initial VIDD. Interestingly, the decline of diaphragm function during underassisted ventilation was not influenced by the level of inspiratory effort estimated by esophageal pressure. Histologic analysis revealed increased sarcomere injury after underassistance in the VIDD group but not in the no-VIDD group. The authors conclude that a short duration of underassisted ventilation injures the diaphragm and impairs diaphragm function in the presence of preexisting VIDD, similar to intensive care unit conditions. The authors have to be congratulated for their innovative approach and elegant experimental design. A number of points warrant caution in interpreting the findings. First, further studies are required to confirm that the findings in this preclinical porcine model apply in critically ill humans. Sepsis renders the diaphragm susceptible to load-induced injury,8,9 so that development of VIDD may not be necessary to sustain load-induced injury from underassistance in the clinical setting. Second, exposure to sedation is an important potential confounder,10 because the groups did not receive the same amount of sedatives because the no-VIDD group received much less cumulative sedation exposure than the VIDD group. Third, the level of inspiratory effort was not standardized. Esophageal pressure swings varied widely between animals. If underassistance causes load-induced injury, one might expect to observe greater injury in animals with more vigorous respiratory effort, but this was not observed. In fact, the direction of causation is complex, because greater injury would also impair diaphragm function and reduce esophageal pressure swings. Fourth, the modality of underassistance was intended to mimic ineffective efforts11 that are among the most frequent type of asynchrony in patients. However, other patterns can be observed and their impact on the diaphragm remains unclear.12 Previous studies have suggested that underassistance may result in diaphragm injury and dysfunction. In one study in which mice were sensitized to loading from systemic inflammation, the application of mechanical ventilation mitigated diaphragm injury in comparison to spontaneously breathing controls.8 Patients with elevated thickening fraction on ultrasound (a surrogate for diaphragm contractility) and lower levels of ventilatory support exhibited rapid increases in diaphragm thickness (suggesting diaphragm injury), and this was associated with prolonged mechanical ventilation and increased complications.13 Capdevila et al.6 therefore confirm and extend the hypothesis that underassistance may cause diaphragm injury by showing that VIDD from overassistance may predispose to further injury from underassistance. This is a fascinating and clinically important observation for the daily practice, highlighting the need to facilitate the use of monitoring the level of inspiratory effort and diaphragm activity techniques at the bedside (diaphragm ultrasound, esophageal pressure monitoring, airway occlusion pressure).14,15 This may help to guide diaphragm-protective approaches to ventilation and sedation, although the results of ongoing clinical trials regarding the feasibility of such approaches are awaited.16 These data also suggest that if VIDD could be prevented by avoiding diaphragm inactivity during mechanical ventilation altogether,17 this would also reduce the susceptibility to injury during the transition from controlled to assisted ventilation. This hypothesis requires careful study in future clinical trials. Research Support Support was provided solely from institutional and/or departmental sources. Competing Interests Dr. Dres received personal fees from Lungpacer Medical Inc. (Exton, Pennsylvania), GSK (Paris, France), and Fisher & Paykel (Auckland, New Zealand). Dr. Goligher reports receiving personal fees from Lungpacer Medical, Stimit LC, Heecap, Bioage, Vyaire, Drager, Getinge, and Zoll, unrelated to the current work.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.039
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.039
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0000.003
Scholarly communication0.0020.004
Open science0.0020.002
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0110.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.170
GPT teacher head0.366
Teacher spread0.196 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Published2025
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