POS1382 INCIDENCE AND PREVALENCE OF CONNECTIVE TISSUE DISEASES WITH INTERSTITIAL LUNG DISEASE IN THE UNITED STATES
Bibliographic record
Abstract
Background: Despite the high disease burden and reduced quality of life for patients with interstitial lung disease associated with connective tissue diseases (CTD-ILD), there are limited data on the incidence and prevalence of CTD-ILD, particularly by CTD subtypes: rheumatoid arthritis (RA), systemic sclerosis (SSc), systemic lupus erythematosus (SLE), idiopathic inflammatory myositis (IIM), mixed connective tissue disease (MCTD) and primary Sjögren's syndrome (pSS). Objectives: To describe incidence and prevalence of CTD-ILD in the US, overall and by CTD subtypes. Methods: This retrospective cohort study (GSK Studies 223907, 224023) used two large US databases (Optum® de-identified Electronic Health Record data set [Optum® EHR] and MarketScan® Commercial and Medicare Databases) to identify adults (≥18 years of age) with CTD-ILD subgroups of interest between January 2018 and December 2023. CTD-ILD cases were identified using published International Classification of Diseases 10th Revision (ICD-10) diagnosis codes and algorithms in a two-step process: 1) identifying individuals with CTD, and 2) identifying ILD within 365 days prior to, on, or after the CTD diagnosis. Index date was the later date of either the first CTD diagnosis or first ILD diagnosis meeting the inclusion definition. Inclusion definitions with ICD-10 codes by CTD subtype were: ILD (J84.1X, J84.89, J84.9; ≥2 inpatient or outpatient claims ≥30 days apart), RA (M05.X, M06.X, M08.X; ≥2 inpatient or outpatient claims >7 days apart), SSc (M34.X; ≥2 inpatient or outpatient claims), SLE (M32.X; ≥1 inpatient or ≥2 outpatient claims >30 days apart), IIM (M33.X, M36.0, M60.1X, M60.8X, M60.9, G72.4; ≥1 inpatient or ≥2 outpatient claims >30 days apart), MCTD (M35.1; ≥1 inpatient or ≥2 outpatient claims), pSS (M35.0X; ≥1 inpatient or ≥2 outpatient claims). Any identified CTD-ILD case was classified as prevalent. From this cohort, incident cases required 1-year of enrolment prior to their index date and no ILD claim in this 1-year window (‘washout-period'). Crude rates and age- and sex-adjusted rates were presented per 100,000 person-years (PY) for incidence and per 100,000 persons for prevalence. Crude rates were also stratified by age (18─64 or 65+) and sex. A sensitivity analysis was performed using a broader list of ILD and disease-specific ICD-10 diagnosis codes. Results: Among all patients with CTD-ILD identified between 2018 and 2023 (N=20,276 Optum® EHR and N=9342 MarketScan data), most were female (74.1% in Optum® EHR and 76.0% in MarketScan data) and had RA-ILD (57.4% in Optum® EHR and 53.2% in MarketScan data). Median (interquartile range) age was 65 (56, 73) years in Optum® EHR and 56 (49, 63) in MarketScan data. Race of patients with CTD-ILD in Optum® EHR was 73.2% Caucasian, 16.8% Black, 2.6% Asian and 7.4% missing. Race was not captured in MarketScan data. The proportion of patients with CTD who had ILD was highest in SSc (21%) and MCTD (10–13%), compared with other CTD subtypes (range: 2–8%). Age- and sex-adjusted incidence rates for CTD-ILD were 9.4 (Optum® EHR) and 10.7 (MarketScan data) per 100,000 PY (Table 1). Adjusted prevalence rates were 45.1 (Optum® EHR) and 44.5 (MarketScan data) per 100,000 persons (Table 2). Adjusted incidence rates per PY varied by CTD subtypes in Optum® EHR (0.7 [MCTD-ILD] to 5.6 [RA-ILD]) and MarketScan data (0.7 [MCTD-ILD] to 6.6 [RA-ILD]). This variation also existed for adjusted prevalence rates per 100,000 persons in Optum® EHR (3.5 [MCTD-ILD] to 26.1 [RA-ILD]) and MarketScan data (2.9 [MCTD-ILD] to 27.0 [RA-ILD]). Crude rates were approximately double in females compared with males and 3- to 4-times higher in ages 65 and older compared with 18–64 years; this pattern occurred across all CTD-ILD subtypes (Tables 1 and 2). Incidence and prevalence rates were found to increase over the study period. Findings using the broader ILD definition were similar, although slightly higher, compared with the primary definition. Conclusion: These findings highlight the importance of investigating ILD across all patients with CTD, and investigating CTD across all patients with ILD, to facilitate diagnosis and intervention, particularly in older individuals and females, where incidence and prevalence rates of CTD-ILD are highest. The proportion of CTD patients with ILD was found to differ by CTD subtypes, which was notably higher in patients with SSc and MCTD compared with other CTD subtypes. Overall, CTD-ILD incidence and prevalence rates were highest for RA-ILD, corresponding to the higher rates of RA compared with other CTD subtypes. There are limited US data to compare these CTD-ILD prevalence rates; however, SSc-ILD rates are comparable to those previously published [1, 2]. Importantly, findings for age- and sex-adjusted rates were similar between these two large US databases capturing different US patient populations. With the 2023 US adult population estimated at 262.1 million [3], these adjusted prevalence rates indicate that approximately 116,000–118,000 individuals in the US may be living with CTD-ILD. These novel data highlight a substantial healthcare burden, especially in older patients and females. REFERENCES: [1] Fan Y et al. J Manag Care Spec Pharm 2020;26:1539–47. [2] Li Q et al. Rheumatol 2021;60:1915–25. [3] United States Census Bureau. https://www.census.gov/programs-surveys/international-programs/about/idb.html [Accessed Jan 2025]. Acknowledgements: This study (GSK studies 223907 and 224023) was funded by GSK. Editorial support was provided by Claire Barron, MSc, Fishawack Indicia Ltd, UK, part of Avalere Health, and was funded by GSK. Disclosure of Interests: Diana Martins Shares: GSK, Employee: GSK, George Mu Shares: GSK, Employee: GSK, Elaine Irving Shares: GSK, Employee: GSK, Roger A. Levy Shares: GSK, Employee: GSK, Nisha Bhatt Shares: GSK, Amgen, Employee: GSK, Keele Wurst Shares: GSK, Employee: GSK. © The Authors 2025. This abstract is an open access article published in Annals of Rheumatic Diseases under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Neither EULAR nor the publisher make any representation as to the accuracy of the content. The authors are solely responsible for the content in their abstract including accuracy of the facts, statements, results, conclusion, citing resources etc.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".