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Record W4296896602 · doi:10.1186/s13054-022-04156-0

A novel capnogram analysis to guide ventilation during cardiopulmonary resuscitation: clinical and experimental observations

2022· article· en· W4296896602 on OpenAlexafffund
Arnaud Lesimple, Caroline Fritz, Alice Hutin, Emmanuel Charbonney, Dominique Savary, Stéphane Delisle, Paul Ouellet, Gilles Bronchti, Fanny Lidouren, Thomas Piraino, François Beloncle, Nathan Prouvez, Alexandre Broc, Alain Mercat, Laurent Brochard, Renaud Tissier, Jean‐Christophe Richard

Bibliographic record

VenueCritical Care · 2022
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsVitalité Health NetworkSt. Michael's HospitalUniversité de MontréalUniversity of TorontoUniversité du Québec à Trois-Rivières
FundersSociété de Réanimation de Langue FrançaiseUniversité du Québec à Trois-Rivières
KeywordsDistensionMedicineCardiopulmonary resuscitationTidal volumeAirwayResuscitationInsufflationVentilation (architecture)AnesthesiaRespiratory physiologyLung volumesLungCardiologyRespiratory systemInternal medicine

Abstract

fetched live from OpenAlex

Abstract Background Cardiopulmonary resuscitation (CPR) decreases lung volume below the functional residual capacity and can generate intrathoracic airway closure. Conversely, large insufflations can induce thoracic distension and jeopardize circulation. The capnogram (CO 2 signal) obtained during continuous chest compressions can reflect intrathoracic airway closure, and we hypothesized here that it can also indicate thoracic distension. Objectives To test whether a specific capnogram may identify thoracic distension during CPR and to assess the impact of thoracic distension on gas exchange and hemodynamics. Methods (1) In out-of-hospital cardiac arrest patients, we identified on capnograms three patterns: intrathoracic airway closure, thoracic distension or regular pattern. An algorithm was designed to identify them automatically. (2) To link CO 2 patterns with ventilation, we conducted three experiments: (i) reproducing the CO 2 patterns in human cadavers, (ii) assessing the influence of tidal volume and respiratory mechanics on thoracic distension using a mechanical lung model and (iii) exploring the impact of thoracic distension patterns on different circulation parameters during CPR on a pig model. Measurements and main results (1) Clinical data: 202 capnograms were collected. Intrathoracic airway closure was present in 35%, thoracic distension in 22% and regular pattern in 43%. (2) Experiments: (i) Higher insufflated volumes reproduced thoracic distension CO 2 patterns in 5 cadavers. (ii) In the mechanical lung model, thoracic distension patterns were associated with higher volumes and longer time constants. (iii) In six pigs during CPR with various tidal volumes, a CO 2 pattern of thoracic distension, but not tidal volume per se, was associated with a significant decrease in blood pressure and cerebral perfusion. Conclusions During CPR, capnograms reflecting intrathoracic airway closure, thoracic distension or regular pattern can be identified. In the animal experiment, a thoracic distension pattern on the capnogram is associated with a negative impact of ventilation on blood pressure and cerebral perfusion during CPR, not predicted by tidal volume per se.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.423

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.057
GPT teacher head0.392
Teacher spread0.335 · 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 teacher head, 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".

Quick stats

Citations17
Published2022
Admission routes2
Has abstractyes

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