A Slightly Different Tune: The Multipart Harmony of Noisy Life Support
Bibliographic record
Abstract
University of Manitoba, Winnipeg, Manitoba, Canada. amutch@cc.umanitoba.caI would like to bring to the attention of readers of Anesthesiology some inaccuracies in the Editorial View “Noisy Mechanical Ventilation: Listen to the Melody” by Drs. Shimabukuro and Gropper.1This article provided an overview of the research published in the journal by Spieth et al., 2“Effects of different levels of pressure support variability in experimental lung injury.” As stated by the editorialists, “Using variability in breathing patterns is not a completely new concept. It was probably first elucidated by Suki et al. 3in Nature in 1998.” In fact such an approach, subsequently called biologically variable ventilation, was first elucidated in 1996 in work done in my laboratory by Lefevre et al .4Careful reading of the article in Nature indicates that stochastic resonance was advanced as an explanation for the improvement in compliance and oxygenation with a noisy end-inspiratory pressure based on analysis of data from our publication. We have since extended these observations to more generally indicate where noisy ventilation will be efficacious and where it may be detrimental.5This analysis is based on Jensen’s inequality–a mathematical proof stating that a variable input will enhance an output when a smoothly continuous curvilinear function is convex.6In contrast, if the function describing a relationship is concave, the addition of noise can diminish output and potentially be detrimental. Noisy ventilation is especially advantageous with tidal volume protocols according to the Acute Respiratory Distress Syndrome Network trial because the volume-pressure curve is convex curvilinear in the range of low tidal volumes.7The lone negative animal study examining noisy ventilation in an acute respiratory distress syndrome model may be a consequence of large tidal volumes administered over the concave region of the sigmoidal volume-pressure curve—the portion of the static compliance curve where it may be deleterious to provide positive pressure ventilation based on modeling using Jensen’s inequality.8The editorial further raises the question of the use of noisy ventilation in normal lungs. My laboratory has examined the use of noisy ventilation in the experimental setting of baseline anesthesia without lung injury9and during ventilation of the dependent lung during one-lung anesthesia (an article published in Anesthesiology).10Colleagues published, again in Anesthesiology, the first clinical trial examining the application of variable ventilation during abdominal aneurysmectomy.11In each of these studies better gas exchange and respiratory mechanics were seen with noisy ventilation, as compared with conventional monotonous control mode ventilation, similar to the findings by Spieth et al. I do concur with the editorialists that “The importance of variability, whether referring to the respiratory system or DNA, in biologic systems should not be underestimated.” Based on the nonlinear amplification of output in convex curvilinear systems as outlined above, we have shown that noisy perfusion can improve cerebral oxygenation,12diminish renal injury,13and enhance delivery of cardioplegia14during cardiopulmonary bypass. These improvements are likely a consequence of better flow with noisy perfusion pressure over the convex curvilinear flow-pressure curve manifest by the microvasculature. An overview of some of these issues is discussed in a short review.15I also concur that “the time is here to translate these studies to the bedside.” The article by Spieth et al. reinforces prior work by this European group, the group in Boston, and the group in Canada, all demonstrating the advantage of adding noise to life support systems.University of Manitoba, Winnipeg, Manitoba, Canada. amutch@cc.umanitoba.ca
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".