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P223 Predictors of mortality in idiopathic inflammatory myopathy-associated interstitial lung disease - a systematic review and meta-analysis

2022· review· en· W4224319986 on OpenAlexaboutno aff
Jennifer Hannah, Tanya Gordon, Michael Rooney, James Galloway, Patrick Gordon

Bibliographic record

VenueLara D. Veeken · 2022
Typereview
Languageen
FieldMedicine
TopicInflammatory Myopathies and Dermatomyositis
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineInterstitial lung diseaseMeta-analysisInternal medicinePublication biasOdds ratioMEDLINELung

Abstract

fetched live from OpenAlex

Abstract Background/Aims Interstitial Lung Disease (ILD) affects approximately 30% of patients with idiopathic inflammatory myopathies (IIM). It is a leading cause of morbidity and mortality in IIM patients. Natural history of ILD varies widely from rapidly-progressive to indolent with minimal symptoms. IIM describes a collection of related autoimmune, inflammatory disorders predominantly affecting combinations of muscles, skin and lungs. Improving understanding of ILD clinical course will aid prognostication and guide sub-categorisation to improve future research in the field. We performed a systematic review and meta-analysis of prognostic factors in IIM-ILD. Methods MEDLINE and EMBASE databases were interrogated on 18/03/2021 using a pre-defined search protocol (PROSPERO ID:CRD42021240206). Studies providing summary data relating to numbers of survivors vs non-survivors according to any baseline criteria were selected. Baseline characteristics reported in ≥ 5 papers were included for meta-analysis. Risk of bias was assessed by Newcastle-Ottawa score. Stata-16 software was used for meta-analyses using a random effects model to report difference as odds ratio for binary variables, and hedge’s g standardised mean difference for continuous variables. A continuity correction was applied for zero effect studies. Heterogeneity was measured by I2, and publication bias assessed with Egger’s test. Results The search returned 4211 articles. 722 were relevant for abstract review, with 454 requiring full text assessment. 78 studies were eligible for inclusion. Overall mortality was 26.1% (+/-0.14 SD). 62 studies were from Asia, two from Mexico and four from Europe, with mortality rates of 26.5%, 13.6% and 25.3% respectively. The strongest risk factor is anti-MDA-5 antibody (OR 6.03). Conversely anti-tRNA-synthetase antibodies (ARS) are protective. When ARS+ was compared to MDA-5-/ARS- patients only, this difference between the groups disappeared. Anti-MDA-5 titre showed a non-significant effect direction towards higher mortality. ANA, anti-SS-A/Ro52 did not significantly impact mortality. Clinical predictors of increased mortality were male gender, acute/sub-acute onset, clinically amyopathic disease (CADM), dyspnoea, ulceration, fever and increasing age. Additional investigations shown to positively predict mortality were CRP, ferritin, LDH, AaO2 gradient, ground glass opacity and fibrosis HRCT scores. Whereas higher SP-D, lymphocytes, %FVC and %DLCO were protective. Conclusion Whilst many factors were associated with increased mortality in IIM-ILD, heterogeneity between studies is high with all having moderate to high risk of bias. A majority of the data come from studies in Japanese and Chinese populations where anti-MDA5 disease appears to be particularly prevalent and associated with deleterious outcomes. Extrapolating to local populations may not be appropriate. The domination of studies by anti-MDA5 disease may be masking risk factors relevant to other IIM subgroups. Due to the rarity of IIMs, these subgroups are often researched together, however this meta-analysis highlights the need to better categorise IIM patients in clinical research. Disclosure J.R. Hannah: None. T. Gordon: None. M. Rooney: None. J. Galloway: None. P. Gordon: 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)0.009
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.017
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.025
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0170.033
Bibliometrics0.0080.009
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.043
GPT teacher head0.318
Teacher spread0.275 · 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 designMeta-analysis
Domainnot available
GenreReview

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
Published2022
Admission routes1
Has abstractyes

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