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Characterizing Respiratory Mechanics in Lymphangioleiomyomatosis as Measured by Oscillometry

2021· article· en· W4245826276 on OpenAlexafffundabout
Cynthia Nohra, Joyce Wu, A. Rad, Anastasiia Vasileva, John Thenganatt, Jennifer Landry, Ronald J. Dandurand, Stewart B. Gottfried, Zoltán Hantos, R. Clodagh, Chung‐Wai Chow

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicTuberous Sclerosis Complex Research
Canadian institutionsMcGill University Health CentreChristie (Canada)McGill UniversityUniversity Health Network
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchUniversity of TorontoHungarian Scientific Research Fund
KeywordsLymphangioleiomyomatosisRespiratory physiologyRespiratory systemMedicineComputer scienceCardiologyInternal medicineLung

Abstract

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Rationale: Lymphangioleiomyomatosis (LAM) is a rare cystic lung disease almost exclusively affecting women presenting as progressive pulmonary function loss and, in some instances, leading to respiratory failure.Approximately 70% of patients have abnormal lung function with the most common being airflow obstruction at presentation and decreased diffusing capacity (DLCO).Recent reports show a 3-4-fold decrease in the number of small airways in LAM lungs.Oscillometry, a non-invasive rapid pulmonary function test (PFT), is particularly sensitive to changes in respiratory mechanics in the lung periphery.The objective of the current study is to characterize oscillometry measurements in LAM patients, which has not been previously described, and to determine whether parameters derived from standard and intrabreath oscillometry correlate with disease severity.Methods: All LAM patients, diagnosed according to ATS guidelines and followed at the Rare Diseases Clinic at our institutions, were enrolled for sequential oscillometry and conventional PFTs at UHN and Montreal PFT Laboratories.Oscillometry, including the novel intrabreath tracking at 10 Hz was performed using the tremoflo® device (Thorasys Inc., Montreal, QC).Results: From August 2019 to December 2020, we enrolled 25 LAM patients (age=47.2±12.6 years; 24F/1M; 19 at UHN, 6 at McGill).Forced spirometry and single-breath technique, reported as mean ± SD, revealed mild airflow obstruction and decreased DLCO (%FEV1=78.6±24.8,%FVC=96.8±19,FEV 1 /FVC=0.67±0.15;%DLCO=70.1±25.2) with normal lung volumes by plethysmography (%RV=121±38.2,%TLC=99.5±23.2,%RV/TLC=105 ± 29.1).In contrast, oscillometry metrics were all worse than predicted normals for the cohort (Table 1), with higher resistance at 5 Hz (R5), frequency dependence of resistance (R5-19 or difference of resistance between 5 and 19 Hz) and AX (area of reactance) to suggest peripheral airway obstruction and ventilatory inhomogeneity.Intrabreath tracking, reported as median (IQR), reveal minimal tidal changes between endexpiration and end-inspiration in both resistance (ReE-ReI) and reactance (XeE-XeI): ReE-ReI=0.5 (0.8) cmH 2 O.s/L; XeE-XeI=0.1 (0.2) cmH 2 O.s/L.However, the area of reactance-vs-volume loop area (AXV) in the LAM patients [AXV= 0.44 (0.45) cmH 2 O.s] is two times higher than that reported in normal subjects.Conclusion: Data from this small cross-sectional cohort suggests that subtle physiologic abnormalities in LAM patients may be more sensitively assessed with oscillometry than with conventional pulmonary function tests.The increased AXV suggests development of expiratory flow limitation during normal breathing, which is associated with elevated resistance values.Increase in LAM patient recruitment and longitudinal follow-up will facilitate in determining whether oscillometry can accurately assess disease progression.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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

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 head0.059
GPT teacher head0.313
Teacher spread0.254 · 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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Citations0
Published2021
Admission routes3
Has abstractyes

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