What question did this study set out to answer?

This study aims to compare antifibrotic uptake and IPF/PF-ILD diagnoses between academic and non-academic centers from 2010 to 2024.

May 20, 2026

C103-20 Institutional Patterns and Disparities in Diagnosis and Antifibrotic Uptake for IPF and PF-ILD in Academic and Non-academic Centers, 2010-2024

Key Points

This study aims to compare antifibrotic uptake and IPF/PF-ILD diagnoses between academic and non-academic centers from 2010 to 2024.
Analyzed data from 40,505,857 patients in the TriNetX U.S. Network
Identified cohorts with IPF and PF-ILD; 59,890 IPF and 2,358 PF-ILD cases included
Used regression analysis and z tests to compare incidence and prevalence of diagnoses and treatment uptake.
IPF diagnosis rates increased faster at non-academic centers compared to academic centers (2024 prevalence: 109 vs 89/100k, p<0.001)
Higher antifibrotic use in academic centers, with prevalence of 33.5% vs 20.1% in 2024 (p<0.001)
In PF-ILD, antifibrotic uptake was also higher at academic centers, indicating a significant treatment disparity.

Abstract

Abstract Rationale In 2014, nintedanib and pirfenidone became the first approved therapies to slow IPF progression. In 2019, nintedanib was approved for PF-ILD. While several real-world studies on antifibrotic (AF) use in IPF show low uptake, limited research examines AF use and IPF/PF-ILD diagnosis by care setting. We compared Academic (AC) vs Non-Academic (NonAC) centers, evaluating diagnoses, AF uptake, and treatment predictors. Methods Using the TriNetX U. S. Network, we created retrospective cohorts. From 40, 505, 857 patients across 71 HCOs, we identified adults with IPF (J84. 112; 2010-2024; n = 59, 890: AC 42, 536, NonAC 16, 470, unclassified 884) and PF-ILD (J84. 170; 2018-2024; n = 2, 358: AC 1, 716, NonAC 216, unclassified 426). We compared AC vs NonAC incidence, prevalence, and AF uptake overall and in post-trial windows (INPULSIS/ASCEND 2014-2017; INBUILD 2019-2022). Trends were modeled with linear regression (SPSS). Proportions were compared with z tests, means with t tests, and demographics with propensity-matched logistic and Cox models. Results In IPF, NonAC diagnosis rates started lower but finished higher, increasing faster than ACs (2024 prevalence 109 vs 89/100k; βₚrevalence 2010-2024 +7. 43 vs + 5. 50/100k/y; βᵢncidence +1. 45 vs + 0. 73/100k/y; all p. 001). AF use was higher and rose faster at ACs (2024 prevalence 33. 5% vs 20. 1%, z = 9. 66, p. 001; incidence 9. 3% vs 5. 4%, z = 9. 15, p. 001; βₚrevalence +2. 42 vs + 1. 37 pp/y; βᵢncidence +0. 71 vs + 0. 41 pp/y; all p. 001). Post-INPULSIS/ASCEND, uptake accelerated more at ACs (βᵢncidence +0. 97 vs + 0. 60 pp/y, p=. 020; βₚrevalence +2. 61 vs + 1. 16 pp/y, p. 001). Odds ratio for AF at AC vs NonAC was 1. 708 (1. 594-1. 83). In PF-ILD, prevalence was similar in 2024 (9. 0/100k; z = 0, p = 1. 0). AF uptake was higher and faster at ACs (2024 prevalence 17. 1% vs 9. 8%, z = 3. 59, p. 001; βₚrevalence +2. 79 vs + 1. 48 pp/y, p=. 027; incidence 7. 1% vs 3. 1%, z = 2. 99, p=. 003; βᵢncidence +1. 23 vs + 0. 44 pp/y, p. 001). Post-INBUILD, uptake accelerated more at ACs (βᵢncidence +2. 56 vs + 1. 47 pp/y, p=. 008). Odds ratio for AF treatment at AC vs NonAC was 1. 319 (0. 634-2. 745). In IPF, AF uptake was more likely in male, White, BMI18. 5 (OR 1. 49 1. 42-1. 56; 1. 60 1. 43-1. 79; 1. 78 1. 40-2. 27). Demographic differences did not reach significance in PF-ILD. Conclusion Overall AF treatment rates for IPF and PF-ILD remain low. Post-trial, AF use was higher at ACs, reflecting faster adoption of guidelines. In IPF, treatment disparities favored White, male, and BMI18. 5 patients; none were seen in PF-ILD. Persistent gaps highlight the need for interventions ensuring equitable, guideline-driven ILD care. As new antifibrotics emerge, proactive strategies are essential to close diagnostic and treatment disparities across settings. This abstract is funded by: None

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