Machine learning-based prediction of relapse in rheumatoid arthritis patients using data on ultrasound examination and blood test

Recent effective therapies enable most rheumatoid arthritis (RA) patients to achieve remission; however, some patients experience relapse. We aimed to predict relapse in RA patients through machine learning (ML) using data on ultrasound (US) examination and blood test. Overall, 210 patients with RA in remission at baseline were dichotomized into remission (n = 150) and relapse (n = 60) based on the disease activity at 2-year follow-up. Three ML classifiers [Logistic Regression, Random Forest, and extreme gradient boosting (XGBoost)] and data on 73 features (14 US examination data, 54 blood test data, and five data on patient information) at baseline were used for predicting relapse. The best performance was obtained using the XGBoost classifier (area under the receiver operator characteristic curve (AUC) = 0.747), compared with Random Forest and Logistic Regression (AUC = 0.719 and 0.701, respectively). In the XGBoost classifier prediction, ten important features, including wrist/metatarsophalangeal superb microvascular imaging scores, were selected using the recursive feature elimination method. The performance was superior to that predicted by researcher-selected features, which are conventional prognostic markers. These results suggest that ML can provide an accurate prediction of relapse in RA patients, and the use of predictive algorithms may facilitate personalized treatment options

多職種連携と患者特性に配慮したケアを行った高度肥満症の一例

A ４８-year-old man who weighed ２１６ kg was significantly overweight with a body mass index （BMI）of ７５．６kg/m２, and was unable to walk due to disuse syndrome. Because of the psychological and social problems in the background, a psychological examination was performed and the staff took time to build a trusting relationship with the patient, taking into account his characteristics. With diet and rehabilitation, he was able to lose weight to １２４kg and BMI ４３．９kg/m２ over ６００ days, and was able to walk with assistive devices and defecate by himself. The patient was discharged from our hospital after a series of multidisciplinary meetings with medical, nursing, welfare, and governmental agencies to create an environment for home recuperation. The reasons for the improvement to enable him to be discharged from the hospital were due to the multi-disciplinary meetings among the staff inside and outside the hospital, information sharing and advanced coordination, and smooth communication with the patient by taking into account his characteristics from a psychological standpoint