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275. FACTORS ASSOCIATED WITH DISEASE PROGRESSION IN PATIENTS WITH MYELOPEROXIDASE-ANTINEUTROPHIL CYTOPLASMIC ANTIBODY-RELATED PULMONARY FIBROSIS

2019· article· en· W2932748164 on OpenAlexaff
Rahmah Alsilmi, Amornpun Wongkarnjana, Ciaran Scallan, Ehsan Haider, Faten Al-Douri, Hanyan Zou, Karen Beattie, Mallory A Granholm, Martin Kolb, Nader Khalidi, Nathan Hambly, Nima Makhdami, Parameswaran Nair, Gerald Cox

Bibliographic record

VenueLara D. Veeken · 2019
Typearticle
Languageen
FieldMedicine
TopicInterstitial Lung Diseases and Idiopathic Pulmonary Fibrosis
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineAnti-neutrophil cytoplasmic antibodyMyeloperoxidasePulmonary fibrosisAntibodyImmunologyDiseaseFibrosisVasculitisInternal medicinePathologyInflammation

Abstract

fetched live from OpenAlex

Background: Fibrotic interstitial lung disease (ILD) is an established finding in patients with ANCA- associated vasculitis (AAV) with an observed prevalence of 23-45% with a preponderance in patients with microscopic polyangiitis (MPA).1,2 The presence of ILD in patients with AAV appears to be associated with a poor prognosis and reduced survival.3,4 However, the factors associated with the progression of fibrosis or decline in pulmonary function, including systemic treatment, have not been well characterized. We aimed to evaluate factors that may be associated with progression of fibrotic ILD from a cohort of patients with AAV. Methods: Patients with a positive serology for myeloperoxidase antibodies (MPO-ANCA) and a diagnosis of AAV were identified retrospectively from a list of outpatients assessed at our center from 2001 to 2018. All CT chest images were reviewed to identify the presence of fibrotic ILD based on the Fleischner Society recommendations.5 The extent of fibrosis was scored using a method modified from Ooi et al.6 Progression of disease was defined as an increase in the fibrosis score of at least 2 (CT progression) or deterioration of FVC > 10% or DLCO > 15% (PFT decline) at any time in the follow-up period. Patients were categorized as “progressors” or “non-progressors” and characteristics and outcomes were determined. Clinical, radiographic, pulmonary function, and outcome data were extracted. Results: We identified 93 patients of whom 20 (21.5%) had radiographic evidence of fibrotic ILD. Of these 20, 8 (40%) demonstrated CT progression while only 3 (15%) patients had PFT decline. The median time to follow-up CT chest and PFT was 3 years (IQR 1-5) and 2 years (1-3) respectively. Age, sex, and baseline FVC appeared similar between patients with or without progression. Patients with CT progression appeared to have lower mean DLCO at baseline (34.7% vs 46.9%). A similar frequency of UIP pattern (50%) and baseline fibrosis score (6.8 – 8.0) was observed in both groups. Finally, the use of cyclophosphamide or azathioprine was similar in the groups comparing fibrosis score but was less common in patients who demonstrated PFT decline. Conclusion: Our data suggests that a low baseline DLCO may suggest a higher chance of ILD progression in AAV. Conversely, the use of cyclophosphamide may correlate with stability of lung function. Finally, CT characteristics may not be predictors of prognosis. These findings identify an opportunity for additional prospective study. Clinical characteristics of groups comparing progression of ILD by PFT changes or lung fibrosis score Clinical characteristics of groups comparing progression of ILD by PFT changes or lung fibrosis score Disclosures: None

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)0.000
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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.006
GPT teacher head0.231
Teacher spread0.224 · 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
Published2019
Admission routes1
Has abstractyes

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