ArticleBMC pulmonary medicine2026
Factors associated with delayed diagnosis of fibrotic interstitial lung disease: a retrospective cohort study.
Article in BMC pulmonary medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTimely diagnosis and treatment of fibrotic interstitial lung disease (ILD) is crucial to preserve lung function and limit healthcare costs. However, diagnosis is challenging and thus often delayed. Factors associated with delayed diagnosis of fibrotic ILD are poorly understood. This study assessed time to diagnosis among patients with fibrotic ILD and identified factors associated with diagnostic delay.
methodsThis retrospective cohort study used previously developed algorithms to identify patients from a large integrated tertiary-care health system in the US Midwest with evidence of fibrotic ILD in 2011-2019. Adults identified by these algorithms had their ILD diagnosis and symptom onset date confirmed by chart review. Time from symptom onset to diagnosis was dichotomized as ≤ 6 and > 6 months to assign patients to timely and delayed diagnosis cohorts, respectively. Potential predictors of diagnostic delay (demographics, clinical characteristics, ILD-related symptoms and tests, healthcare use, and healthcare costs) were analyzed by stepwise backward logistic regression and by machine learning using gradient boosting, with the latter illustrated using SHAP (SHapley Additive exPlanations) plots.
results239 patients met all selection criteria; 138 (57.7%) experienced delayed diagnosis and 101 (42.3%) timely diagnosis. Mean time from symptom onset to diagnosis was 16.8 months for the overall sample, 27.5 months for patients with delayed diagnosis, and 2.2 months for patients with timely diagnosis. Compared to patients with timely diagnosis, greater proportions of those with delayed diagnosis were female (55.8% vs. 40.2%, p = 0.013) and had gastroesophageal reflux disease (GERD) (47.1% vs. 21.6%, p < 0.001) or rheumatic disease (21.0% vs. 10.9%, p = 0.038). In multivariate logistic regression, GERD (odds ratio [OR] 2.64, 95% confidence interval [CI] 1.39-5.04) and number of complaints of cough (OR 1.09, 95% CI 1.02-1.15) and shortness of breath (OR 1.06, 95% CI 1.02-1.09) were associated with increased odds of delayed diagnosis. SHAP plots yielded similar findings.
conclusionsDelays in diagnosing fibrotic ILD are common and on average postpone clinical identification by about 2 years from symptom onset. Factors associated with delayed diagnosis include GERD and repeated complaints of cough/shortness of breath. Further research should explore how delayed diagnosis affects clinical outcomes.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.