Predicting rheumatoid arthritis in the middle-aged and older population using patient-reported outcomes: insights from the SHARE cohort.

Abstract

In light of global population aging and the increasing prevalence of Rheumatoid Arthritis (RA) with age, strategies are needed to address this public health challenge. Machine learning (ML) may play a vital role in early identification of RA, allowing an early start of treatment, thereby reducing costs. This study aims first to identify potential variables related to RA, and second to explore and evaluate the potential of ML to identify RA patients in people over 50 years. We developed ML predictive models (lightGBM, logistic regression, k nearest neighbor, naive Bayes, random forrest, and XGBoost) using patient-reported outcomes collected from the SHARE database (7th and 9th wave). Difficulties in daily life such as stooping and pulling are risk factors for RA. Lifestyle activities participation is negatively associated with RA. ML models performed differently with the lightGBM model achieving the highest AUC (0.748, 95 % CI: 0.739-0.758), and logistic regression and lightGBM showing the highest accuracy at 0.902. The sensitivity of naive Bayes was highest at 0.442. Significant differences were observed in the Hosmer-Lemeshow test (P < 0.05). The predictive models based on patient-reported outcome measures achieved fair performance with limited potential to early identify RA patients. Lifestyle activities and difficulties in daily life were associated with risk of RA and should be considered in anamnesis.