Identifying Important Clinical Features for Predicting Remission in Patients with Rheumatoid Arthritis Treated with Biologics Using Machine Learning Model

Ahmad Alsaber¹, Adeeba AlHerz², Balqees Alawadhi³, Parul Setiya⁴, KHULOUD MOHAMMED⁵, Adel Al-Awadhi⁶, Waleed Al-Kandari⁷, Eman Hasan⁸, Khaled Mokaddem⁹, Aqeel Ghanem¹⁰, Youssef Bartella¹¹, Mohammed Hussain⁸, Naser Alhadhood¹², Yaser Ali¹³, Ebrahim Nahar¹³, Ali Aldei⁸, Ahmad Alenizi¹⁴, Sawsan Hayat¹⁵, Fatemah Abutiban¹⁶ and Fatemah Abutiban¹¹, ¹American University of Kuwait, Kuwait City, Kuwait, ²Al-Amiri Hospital, Al Asimah Governate, Kuwait, ³The Public Authority for Applied Education and Training, Kuwait City, Kuwait, ⁴GBPUAT, Department of Agrometerology, Pantnagar, India, ⁵Farwaniya Hospital, Kuwait, Kuwait, ⁶Kuwait University, Kuwait City, Kuwait, ⁷Farwaniya Hospital, Division of Rheumatology, Ministry of Health, Kuwait, Kuwait, ⁸Amiri Hospital, Division of Rheumatology, Kuwait City, Kuwait, ⁹Al-Amiri Hospital, Rheumatoid, Al Kuwayt, Kuwait City, Kuwait, ¹⁰Mubarak Al-Kabeer Hospital, Division of Rheumatology, Kuwait City, Kuwait, ¹¹Farwaniya Hospital, Department of Rheumatology, Kuwait, Kuwait, ¹²Farwaniya Hospital, Kuwait City, Kuwait, ¹³Mubarak Al-Kabeer Hospital, Department of Rheumatology, Jabriya, Kuwait, ¹⁴Jahra Hospital, Al Jahra, NY, Kuwait, ¹⁵Ministry of Health - Kuwait, Jabriya, Kuwait, ¹⁶Jahra Hospital, Department of Medicine, Al Jahra, Kuwait

Meeting: ACR Convergence 2023

Keywords: Anti-TNF Drugs, Biostatistics, Disease Activity

Session Information

Date: Monday, November 13, 2023

Title: (0965–0992) Epidemiology & Public Health Poster II

Session Type: Poster Session B

Session Time: 9:00AM-11:00AM

Background/Purpose: Biologic disease-modifying antirheumatic drugs (bDMARDs) offer promising results for rheumatoid arthritis (RA) patients in general, but a substantial percentage of patients do not respond to them. It is important to predict the response before the treatment so that unnecessary adversities for the patients and costs for the healthcare system can be avoided. We developed a model to predict remissions in patients treated with bDMARDs and to identify important clinical features associated with remission using several machine learning (ML) models system that works with readily-available demographic and clinical factors for prediction of response to DAS-28.

Methods: We gathered the follow-up data of 1,000 patients treated with bDMARDs (etanercept, adalimumab, golimumab, infliximab, abatacept, and tocilizumab) from KRRD. Patients were recruited from public hospitals in Kuwait between February 2013 to September 2022. Remission (DAS-28 less than 2.6) was predicted at 1-year follow-up using baseline clinical data obtained at the time of enrollment. Machine learning methods system (including: lasso, ridge, support vector machine, random forest, XGBoost, and Shapley additive explanation (SHAP)) were used for the predictions.

Results: The ranges for accuracy and area under the receiver operating characteristic of the newly developed machine learning model for predicting remission were 52.8–72.9% and 0.463–0.719, respectively. The Shapley plot in XAI showed that the impacts of the variables on predicting remission differed for each bDMARD. The most important features were age for adalimumab, rheumatoid factor for etanercept, erythrocyte sedimentation rate for infliximab and golimumab, disease duration for abatacept, and C-reactive protein for tocilizumab, with mean SHAP values of − 0.250, − 0.234, − 0.514, − 0.227, − 0.804, and 0.135, respectively.

Conclusion: Our proposed machine learning model system successfully identified clinical features that were predictive of remission in each of the bDMARDs. This approach may be useful for improving treatment outcomes by identifying clinical information related to remissions in patients with rheumatoid arthritis.

Table 1. KRRD RA Patients Demographics

Table 2. Comparison between DAS_28 levels with other rheumatoid factors

Figure 1. The SHAP results that emphasize the most clinical characteristics of RA patients using rituximab (RIT)

Disclosures: A. Alsaber: None; A. AlHerz: None; B. Alawadhi: None; P. Setiya: None; K. MOHAMMED: None; A. Al-Awadhi: None; W. Al-Kandari: None; E. Hasan: None; K. Mokaddem: None; A. Ghanem: None; Y. Bartella: None; M. Hussain: None; N. Alhadhood: None; Y. Ali: None; E. Nahar: None; A. Aldei: None; A. Alenizi: None; S. Hayat: None; F. Abutiban: None; F. Abutiban: None.

To cite this abstract in AMA style:

Alsaber A, AlHerz A, Alawadhi B, Setiya P, MOHAMMED K, Al-Awadhi A, Al-Kandari W, Hasan E, Mokaddem K, Ghanem A, Bartella Y, Hussain M, Alhadhood N, Ali Y, Nahar E, Aldei A, Alenizi A, Hayat S, Abutiban F, Abutiban F. Identifying Important Clinical Features for Predicting Remission in Patients with Rheumatoid Arthritis Treated with Biologics Using Machine Learning Model [abstract]. Arthritis Rheumatol. 2023; 75 (suppl 9). https://acrabstracts.org/abstract/identifying-important-clinical-features-for-predicting-remission-in-patients-with-rheumatoid-arthritis-treated-with-biologics-using-machine-learning-model/. Accessed .

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