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Abstract Number: 1263

Develop a Master Algorithm for Drug Withdraw Strategy in Reduction of Adverse Events – a Machine Learning Model from the Smart System of Disease Management (SSDM)

Yan Zhao1, Jing Yang2, Jianlin Huang3, Hua Wei4, Yongfu Wang5, Rong Mu6, Xiaoxia Zuo7, Hongzhi Wang8, Xinwang Duan9, Jing Xue10, Hongsheng Sun11, Bin Wu12, Lirong Kang5, Feng Wei13, Cundong Mi14, Yanping Zhao15, Yang Li16, Haiying Chen17, Zhenbin Li18, Qingliang Meng19, Yuhua Jia20, Hui Xiao20 and Fei Xiao20, 1Rheumatology, Peking Union Medical College Hospital, Beijing, China, 2Department of rheumatology, Central Hospital of MianYang, Sichuan, Mian Yang, China, 3Department of rheumatology, The Sixth Hospital Affiliated to Sun yat-sen University, Guangzhou, China, 4Northern Jiangsu People's Hospital, Yangzhou, China, 5The First Affiliated Hospital of BaoTou Medical College, Baotou, China, 6Department of Rheumatology and Immunology, Peking University People's Hospital, Beijing, China, 7Xiangya Hospital Central South University, Changsha, China, 8The First Hospital of Jiaxing, Jiaxing, China, 9Department of rheumatology, The Second Affiliated Hospital of Nanchang University, Nanchang, China, 1088 Jiefang Road, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China, 11Department of Rheumatology and Immunology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, China, 12Department of Rheumatology, Chongqing Hospital of Traditional Chinese Medicine, Chongqing, China, 13JIANGMEN CENTRAL HOSPITAL, AFFILIATED JIANGMEN HOSPITAL OF SUN YAT-SEN UNIVERSITY, Jiangmen, China, 14The Second Affiliated Hospital of Guangxi Medical University, Nanning, China, 15The First Affiliated Hospital of Harbin Medical University, Harbin, China, 16The Second Affiliated Hospital of Harbin Medical University, Harbin, China, 17The third hospital of hebei medical university, Shijiazhuang, China, 18Peace Hospital, Shijiazhuang, China, 19Henan Province Hospital of TCM, Zhengzhou, China, 20Shanghai Gothic Internet Technology Co., Ltd., Shanghai, China

Meeting: 2018 ACR/ARHP Annual Meeting

Keywords: Adverse events, medication, rheumatoid arthritis (RA) and safety

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Session Information

Date: Monday, October 22, 2018

Session Title: Measures and Measurement of Healthcare Quality Poster II

Session Type: ACR Poster Session B

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

Background/Purpose: Combination therapy with DMARDs for treating RA is considered as standard of care. However, certain rates of adverse events (AEs) are unavoidable. The stigma is which drug should be stopped first once AEs emerge and the following sequence if AEs persist for optimal risk reductions. The decisions made by clinicians are always empirically. The purpose of this study is to develop an algorithm for decision making on drug withdraw sequence in face of adverse events with combination therapy based on data mining and machine learning from the smart system of disease management (SSDM).

Methods: SSDM is an interactive mobile disease management tool, RA patients can input medical records (including medication and laboratory test results) and perform self-evaluation via applications (App). The data synchronizes to the mobiles of authorized rheumatologists through cloud and advices could be delivered.

In order to develop the master algorithm, abnormal white blood cell counts (WBC) and alanine aminotransferase (ALT) elevation were targeted. WBC, ALT and medication data was collected, extracted, validated, and then based on Bayesian networks, data mining, modeling, calculating, analyzing were performed. WBC under 4,000/ml is defined as leukocytopenia (LP), over 10,000/ml as infection predisposing (IP), and ALT > 40 U/L as ALT elevation.

Results: From June 2014 to June 2018, 32,130 RA patients from 587 centers registered in SSDM. 7,086 are male and 24,144 are female with mean age of 49.82 year. 129 different drugs and 479 types of combination therapies are identified. Lab test results showed LP happened in 311 and IP 217, ALT 316 in 554 mono or combinational treatment regiments. Among them we selected prednisone (Pred), leflunomide (LEF), MTX, HCQ as an example to develop a master algorithm based on Bayesian networks and learning model. Image 1 shows Bayesian network and data processing, in which, quartet are correlating with 15 different regiments. Drug withdraw sequence for LP is HCQ, then LEF and then Pre, and the risks of LP are reduced by 39%, 33% and 23%, respectively. For IP, withdraw sequence is Pred, then MTX and then HCQ, and the risks of IP are reduced by 47%, 51% and 15%, respectively, For ALT, withdraw sequence is MTX, then Pred and then HCQ, and the risks of ALT are reduced by 51%, 28% and 16%, respectively.

Conclusion: Big data system can be built using SSDM via empowering patient. Through data mining, networking, modeling, and Bayesian calculation, a master algorithm for drug withdraw strategy in reduction of adverse events with combination therapy is developed, which can be applied on the other AEs in SSDM and may replicated in other diseases. Following the continuing data inputs and machine leaning, an artificial intelligent system in assisting clinical decision making may be achieved.

Limitations: This study only focus on rate of AE without considering the efficacy, without stratifying dosing.


Disclosure: Y. Zhao, None; J. Yang, None; J. Huang, None; H. Wei, None; Y. Wang, None; R. Mu, None; X. Zuo, None; H. Wang, None; X. Duan, None; J. Xue, None; H. Sun, None; B. Wu, None; L. Kang, None; F. Wei, None; C. Mi, None; Y. Zhao, None; Y. Li, None; H. Chen, None; Z. Li, None; Q. Meng, None; Y. Jia, None; H. Xiao, None; F. Xiao, None.

To cite this abstract in AMA style:

Zhao Y, Yang J, Huang J, Wei H, Wang Y, Mu R, Zuo X, Wang H, Duan X, Xue J, Sun H, Wu B, Kang L, Wei F, Mi C, Zhao Y, Li Y, Chen H, Li Z, Meng Q, Jia Y, Xiao H, Xiao F. Develop a Master Algorithm for Drug Withdraw Strategy in Reduction of Adverse Events – a Machine Learning Model from the Smart System of Disease Management (SSDM) [abstract]. Arthritis Rheumatol. 2018; 70 (suppl 10). https://acrabstracts.org/abstract/develop-a-master-algorithm-for-drug-withdraw-strategy-in-reduction-of-adverse-events-a-machine-learning-model-from-the-smart-system-of-disease-management-ssdm/. Accessed January 16, 2021.
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