Prevalence and clinical characteristics of patients with rheumatoid arthritis with interstitial lung disease using unstructured healthcare data and machine learning.

OBJECTIVES: Real-world data regarding rheumatoid arthritis (RA) and its association with interstitial lung disease (ILD) is still scarce. This study aimed to estimate the prevalence of RA and ILD in patients with RA (RAILD) in Spain, and to compare clinical characteristics of patients with RA with and without ILD using natural language processing (NLP) on electronic health records (EHR). METHODS: Observational case-control, retrospective and multicentre study based on the secondary use of unstructured clinical data from patients with adult RA and RAILD from nine hospitals between 2014 and 2019. NLP was used to extract unstructured clinical information from EHR and standardise it into a SNOMED-CT terminology. Prevalence of RA and RAILD were calculated, and a descriptive analysis was performed. Characteristics between patients with RAILD and RA patients without ILD (RAnonILD) were compared. RESULTS: From a source population of 3 176 165 patients and 64 241 683 EHRs, 13 958 patients with RA were identified. Of those, 5.1% patients additionally had ILD (RAILD). The overall age-adjusted prevalence of RA and RAILD were 0.53% and 0.02%, respectively. The most common ILD subtype was usual interstitial pneumonia (29.3%). When comparing RAILD versus RAnonILD patients, RAILD patients were older and had more comorbidities, notably concerning infections (33.6% vs 16.5%, p<0.001), malignancies (15.9% vs 8.5%, p<0.001) and cardiovascular disease (25.8% vs 13.9%, p<0.001) than RAnonILD. RAILD patients also had higher inflammatory burden reflected in more pharmacological prescriptions and higher inflammatory parameters and presented a higher in-hospital mortality with a higher risk of death (HR 2.32; 95% CI 1.59 to 2.81, p<0.001). CONCLUSIONS: We found an estimated age-adjusted prevalence of RA and RAILD by analysing real-world data through NLP. RAILD patients were more vulnerable at the time of inclusion with higher comorbidity and inflammatory burden than RAnonILD, which correlated with higher mortality.

Use of prognostic factors of rheumatoid arthritis in clinical practice and perception of their predictive capacity before and after exposure to evidence.

The aim of the study is to benchmark the use and attributed importance of well-established prognostic factors in rheumatoid arthritis (RA) in daily clinical practice, and to contrast the use of factors with their ability to predict outcome. Medline was searched (inception-Sep. 2016) for systematic reviews on factors predicting death, disability, structural damage or remission in RA. All factors identified were compiled in a matrix of factors × outcomes, and scoping reviews for each cell were then performed. A survey to 42 rheumatologists randomly selected explored the use of the list of prognostic factors and inquired about the perceived strength of association with poor prognosis. In a second round, participants were exposed to evidence from the matrix and to responses from other participants. Change on perceived strength of association was evaluated. Rheumatologists report using prognostic factors in clinical practice on a daily basis. Very young onset, joint counts at diagnosis, rheumatoid factor, ACPA, and radiographic erosions are used frequently and correctly recognized as strong predictors. Comorbidities and other associated problems, such as obesity, low bone mineral density, cardiovascular disease, or extra-articular manifestations, are perceived as moderately associated to prognosis but, nevertheless, rheumatologists also use them profusely. Genetic and other biomarkers and osteitis by magnetic resonance are less accessible in daily practice and they obtained better results on second round (probably after knowing the strength of association with prognosis). Rheumatologists use widely most prognostic factors with a strong predictive value. However, factors with low evidence of prognostic value are also used and some factors are not used despite good evidence.