Accuracy of an algorithm to identify rheumatoid arthritis in the Longitudinal Ageing Study Amsterdam population: a validation study. (4th July 2021)

• Record Type: Journal Article
• Authors: ter Wee, MM; Raterman, HG; van Schoor, NM; Deeg, DJH; Lems, WF; Nurmohamed, MT; Simsek, S
• Is Part Of: Scandinavian journal of rheumatology. Volume 50:Number 4(2021)
• Page Start: 290
• Page End: 294
• Publication Date: 2021-07-04
• Subjects: Rheumatology -- Periodicals; Arthritis; Rheumatic Diseases; 616.72005
• DOI: 10.1080/03009742.2020.1852442
• Languages: English
• ISSNs: 0300-9742

Abstract

Abstract : Objective : In view of global ageing and the scarcity of knowledge about disease determinants in older individuals with rheumatoid arthritis (RA), an algorithm with optimal diagnostic accuracy was developed to identify RA patients in the Longitudinal Ageing Study Amsterdam (LASA). Method : Four case ascertainment algorithms were constructed and assessed for validity in LASA, an ongoing cohort study (≥ 55 years) representing the general older population of the Netherlands. Data sources used to identify the diagnosis RA were: self-reported morbidity, specialist diagnosis, and medication. A validation subsample of LASA participants was taken to verify RA diagnosis by a standard procedure using a checklist. Results : Data from 272/300 (91%) participants were verified. Four algorithms were developed: 'treatment', 'diagnosis', 'treatment or diagnosis', and 'treatment and diagnosis'. The algorithm 'treatment and diagnosis' showed the best measurement properties: specificity 100%, positive predictive value 100%, and area under the receiver operating characteristics curve 0.72. Applying this algorithm in the LASA sample (mean age 71 years) revealed a prevalence of RA of 1.0% (19/1908 participants). Conclusion : An algorithm for RA identification in the LASA population was developed, with high diagnostic accuracy. It provides an accurate tool to identify older adults with RA in LASA and, after validation, may be applicable in other large population-based studies.