Abstract
Background: Mortality prediction in interstitial lung disease (ILD) poses a significant challenge to clinicians due to heterogeneity across disease subtypes. Currently, forced vital capacity (FVC) and Gender, Age, and Physiology (GAP) score are the two most utilized metrics in prognostication. Recently, a machine learning classifier system, Fibresolve, designed to identify a variety of computed tomography (CT) patterns associated with idiopathic pulmonary fibrosis (IPF), was demonstrated to have a significant association with mortality across multiple subtypes of ILD. The purpose of this follow-up study was to retrospectively validate these findings in a large, external cohort of patients with ILD. Methods: In this multi-center validation study, Fibresolve was applied to chest CT scans of patients with confirmed ILD that had available follow-up data. Fibresolve scores categorized by tertile were analyzed using Cox regression analysis adjusted for tobacco use and modified GAP (mGAP) score. Results: Of 643 patients included, 446 (69.3%) died over a median follow-up time of 144 [1-821] weeks. The median [range] mGAP score was 5 [3–7]. In multivariable analysis, Fibresolve score categorized by tertile was significantly associated with mortality (Tertile 2 HR 1.47, 95% CI 0.82–2.37, p = 0.11; Tertile 3 HR 3.12, 95% CI 1.98–4.90, p < 0.001). Subgroup analyses revealed significant associations amongst those with non-IPF ILDs (Tertile 2 HR 1.95, 95% CI 1.28–2.97, Tertile 3 HR 4.66, 95% CI 2.94–7.38) and severe disease, defined by a FVC ≤ 75% (Tertile 2 HR 2.29, 95% CI 1.43–3.67, Tertile 3 HR 4.80, 95% CI 2.93–7.86). Conclusions: Fibresolve is independently associated with mortality in ILD, particularly amongst patients with non-IPF ILDs and in those with severe disease.
| Original language | English (US) |
|---|---|
| Article number | 254 |
| Journal | BMC Pulmonary Medicine |
| Volume | 24 |
| Issue number | 1 |
| DOIs | |
| State | Published - Dec 2024 |
Funding
Dr. Selvan is supported by the National Institutes of Health (NIH)/National Heart, Lung, and Blood (NHLBI) grant T32HL007605. Dr. Adegunsoye is supported by the NIH grant K23HL146942. This study leveraged data from a subset of patients included in a large international registry of approximately 3,000 patients with ILD collected between 2005 and 2020. The registry includes data from both public and nonpublic sources []. Public data sources included data provided by the Lung Tissue Research Consortium (LTRC) supported by the National Heart, Lung, and Blood Institute (NHLBI), and the Open Source Imaging Consortium (OSIC). Key metrics including patient demographic information, medical history, ILD diagnosis, and imaging were collected from the electronic medical record. Information on patient outcomes, including mortality and hospitalization, was also obtained. We consulted extensively with Argus Institutional Review Board (IRB) who determined that our study did not need ethical approval. An IRB official waiver of ethical approval was granted from Argus IRB. The data protocols are in accordance with the ethical standards of our institution and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all participants.
Keywords
- Interstitial lung disease
- Machine learning
- Mortality
ASJC Scopus subject areas
- Pulmonary and Respiratory Medicine
