Automated high-resolution computed tomography (HRCT) scoring may provide objective, reproducible assessment of lung images and can help physicians predict clinical outcomes in patients with rheumatoid arthritis-associated interstitial lung disease (RA-ILD), according to an editorial published in Rheumatology1.
Visual interpretation of HRCT imaging has limited use in diagnosing and managing RA-ILD due to interobserver disagreement, insensitivity, and inefficiency. Several new studies have shown that quantitative scoring allows for more accurate analysis of HRCT images and may be useful in diagnosing and managing patients with RA-ILD.
A retrospective study by Oh et al2 published in Rheumatology quantitatively scored HCRT images from 144 patients diagnosed with RA-ILD at a single center in Korea between 1999 and 2015. Regions of interest on each CT image were scored for specific patterns and abnormalities. A quantitative lung fibrosis (QLF) score was calculated as the sum of reticulation and traction bronchiectasis. The researchers found that patients with high QLF scores ( ³12% of lung volume scored as fibrotic) had significantly higher mortality (50.0% vs 17.4%; P <.001).
The findings from Oh et al support the results of previous studies. For example, a study published by Alevizos et al3in Rheumatology in 2001 used quantitative scoring to measure the percentage of lung parenchyma with high attenuation areas (%HAAs) in patients with RA-ILD. They found that %HAA was linked to several known risk factors for RA-ILD, including smoking, higher body mass index, and positive anti-CCP antibodies. This study suggested that quantitative assessment of %HAA could be used to diagnose and track progression of RA-ILD more effectively than visual interpretation of CT images.
Another study by Jacob et al4 used a scoring method called CALIPER. They found quantitative measures of ILD, fibrosis, reticulation, and honeycombing were associated with RA-ILD mortality.
Limitations of the study by Oh et al included an inability to assess the impact of RA disease activity on disease outcomes, a low proportion of patients on biologic disease-modifying antirheumatic drugs, and a narrow study population. Because there was no replication cohort, future studies are needed to validate the models in an independent RA-ILD population.
The authors concluded, “Future studies investigating the impact of treatments and disease activity on both QCT measures and RA-ILD mortality would have important clinical implications. Longitudinal studies assessing the utility of QCT for progression of RA-ILD are also needed. QCT may be a crucial tool to deconstruct the inherent clinical heterogeneity of RA-ILD that is currently composed of a spectrum of entities with inflammatory and fibrotic features on the background of lung injury from infection, inhalants such as smoking, and [medications].”
One study author declared affiliations with the pharmaceutical industry. Please see the original reference for a full list of the author’s disclosures.
1. McDermott G, Sparks JA. Quantitative chest imaging and prediction of mortality in rheumatoid arthritis-associated interstitial lung disease. Rheumatology (Oxford). Published online June 2, 2022. doi:10.1093/rheumatology/keac329
2.Oh JH, Hyun G, Cross G, et al. Automated quantification system predicts survival in rheumatoid arthritis-associated interstitial lung disease. Rheumatology (Oxford). Published online March 18, 2022. doi:10.1093/rheumatology/keac184
3.Alevizos MK, Danoff SK, Pappas DA, et al. Assessing predictors of rheumatoid arthritis-associated interstitial lung disease using quantitative lung densitometry. Rheumatology (Oxford). Published online November 8, 2021. doi:10.1093/rheumatology/keab828
4.Sparks JA, He X, Huang J, et al. Rheumatoid arthritis disease activity predicting incident clinically apparent rheumatoid arthritis–associated interstitial lung disease: a prospective cohort study. Arthritis Rheumatol. Published online August 4, 2019. doi:10.1002/art.40904
This article originally appeared on Rheumatology Advisor