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Record W4398171198 · doi:10.1186/s13054-024-04953-9

Expiratory flow limitation during mechanical ventilation: real-time detection and physiological subtypes

2024· article· en· W4398171198 on OpenAlexaff
Detajin Junhasavasdikul, Akarawut Kasemchaiyanun, Tanakorn Tassaneyasin, Tananchai Petnak, Frank Silva Bezerra, Ricard Mellado Artigas, Lü Chen, Yuda Sutherasan, Pongdhep Theerawit, Laurent Brochard

Bibliographic record

VenueCritical Care · 2024
Typearticle
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
FundersMahidol University
KeywordsMedicineMechanical ventilationVentilation (architecture)Intensive care medicineEmergency medicineCardiologyInternal medicineMechanical engineering

Abstract

fetched live from OpenAlex

Abstract Background Tidal expiratory flow limitation (EFL T ) complicates the delivery of mechanical ventilation but is only diagnosed by performing specific manoeuvres. Instantaneous analysis of expiratory resistance (Rex) can be an alternative way to detect EFL T without changing ventilatory settings. This study aimed to determine the agreement of EFL T detection by Rex analysis and the PEEP reduction manoeuvre using contingency table and agreement coefficient. The patterns of Rex were explored. Methods Medical patients ≥ 15-year-old receiving mechanical ventilation underwent a PEEP reduction manoeuvre from 5 cmH 2 O to zero for EFL T detection. Waveforms were recorded and analyzed off-line. The instantaneous Rex was calculated and was plotted against the volume axis, overlapped by the flow-volume loop for inspection. Lung mechanics, characteristics of the patients, and clinical outcomes were collected. The result of the Rex method was validated using a separate independent dataset. Results 339 patients initially enrolled and underwent a PEEP reduction. The prevalence of EFL T was 16.5%. EFL T patients had higher adjusted hospital mortality than non-EFL T cases. The Rex method showed 20% prevalence of EFL T and the result was 90.3% in agreement with PEEP reduction manoeuvre. In the validation dataset, the Rex method had resulted in 91.4% agreement. Three patterns of Rex were identified: no EFL T , early EFL T , associated with airway disease, and late EFL T , associated with non-airway diseases, including obesity. In early EFL T , external PEEP was less likely to eliminate EFL T . Conclusions The Rex method shows an excellent agreement with the PEEP reduction manoeuvre and allows real-time detection of EFL T . Two subtypes of EFL T are identified by Rex analysis. Trial registration : Clinical trial registered with www.thaiclinicaltrials.org (TCTR20190318003). The registration date was on 18 March 2019, and the first subject enrollment was performed on 26 March 2019.

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)0.002
metaresearch head score (Gemma)0.006
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.036
GPT teacher head0.309
Teacher spread0.273 · 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".

Quick stats

Citations5
Published2024
Admission routes1
Has abstractyes

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