METHODS: In this longitudinal study, patients with RA who started a biological disease-modifying antirheumatic drug (bDMARD) in a tertiary care center were analyzed. Demographic and clinical characteristics were collected at treatment baseline, 12-month, and 24-month follow-up. A wrapper feature selection algorithm was used to determine an attribute core set. Four different ML algorithms, namely, LR, random forest, K-nearest neighbors, and extreme gradient boosting, were then trained and validated with 10-fold cross-validation to predict 24-month sustained DAS28 (Disease Activity Score on 28 joints) remission. The performances of the algorithms were then compared assessing accuracy, precision, and recall. RESULTS: Our analysis included 367 patients (female 323/367, 88%) with mean age ± SD of 53.7 ± 12.5 years at bDMARD baseline. Sustained DAS28 remission was achieved by 175 (47.2%) of 367 patients. The attribute core set used to train algorithms included acute phase reactant levels, Clinical Disease Activity Index, Health Assessment Questionnaire-Disability Index, as well as several clinical characteristics. Extreme gradient boosting showed the best performance (accuracy, 72.7%; precision, 73.2%; recall, 68.1%), outperforming random forest (accuracy, 65.9%; precision, 65.6%; recall, 59.3%), LR (accuracy, 64.9%; precision, 62.6%; recall, 61.9%), and K-nearest neighbors (accuracy, 63%; precision, 61.5%; recall, 54.8%). CONCLUSIONS: We showed that ML models can be used to predict sustained remission in RA patients on bDMARDs. Furthermore, our method only relies on a few easy-to-collect patient attributes. Our results are promising but need to be tested on longitudinal cohort studies.
METHODS: In this longitudinal study, patients with RA who started a biological disease-modifying antirheumatic drug (bDMARD) in a tertiary care center were analyzed. Demographic and clinical characteristics were collected at treatment baseline, 12-month, and 24-month follow-up. A wrapper feature selection algorithm was used to determine an attribute core set. Four different ML algorithms, namely, LR, random forest, K-nearest neighbors, and extreme gradient boosting, were then trained and validated with 10-fold cross-validation to predict 24-month sustained DAS28 (Disease Activity Score on 28 joints) remission. The performances of the algorithms were then compared assessing accuracy, precision, and recall. RESULTS: Our analysis included 367 patients (female 323/367, 88%) with mean age ± SD of 53.7 ± 12.5 years at bDMARD baseline. Sustained DAS28 remission was achieved by 175 (47.2%) of 367 patients. The attribute core set used to train algorithms included acute phase reactant levels, Clinical Disease Activity Index, Health Assessment Questionnaire-Disability Index, as well as several clinical characteristics. Extreme gradient boosting showed the best performance (accuracy, 72.7%; precision, 73.2%; recall, 68.1%), outperforming random forest (accuracy, 65.9%; precision, 65.6%; recall, 59.3%), LR (accuracy, 64.9%; precision, 62.6%; recall, 61.9%), and K-nearest neighbors (accuracy, 63%; precision, 61.5%; recall, 54.8%). CONCLUSIONS: We showed that ML models can be used to predict sustained remission in RA patients on bDMARDs. Furthermore, our method only relies on a few easy-to-collect patient attributes. Our results are promising but need to be tested on longitudinal cohort studies.
Authors: Lukas Folle; David Simon; Koray Tascilar; Gerhard Krönke; Anna-Maria Liphardt; Andreas Maier; Georg Schett; Arnd Kleyer Journal: Front Med (Lausanne) Date: 2022-03-10