Quantitative prediction of radiographic progression in patients with axial spondyloarthritis using neural network model in a real-world setting

Arthritis Research & Therapy

卷:25

Research

In-Woon Baek¹  Yune-Jung Park²  Seung Min Jung²  Ki-Jo Kim²  Kyung-Su Park² 

[1] Division of Rheumatology, Department of Internal Medicine, Ewha Womans University College of Medicine, Seoul, Republic of Korea;

[2] Division of Rheumatology, Department of Internal Medicine, St. Vincent’s Hospital, College of Medicine, The Catholic University of Korea, 93 Jungbu-Daero, Paldal-Gu, 16247, Suwon, Gyeonggi-Do, Republic of Korea;

关键词: Axial spondyloarthritis;    Radiographic progression;    Artificial neural network;    Quantitative prediction;    Real-world setting;   

DOI  :  10.1186/s13075-023-03050-6

 received in 2022-11-05, accepted in 2023-04-12,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundPredicting radiographic progression in axial spondyloarthritis (axSpA) remains limited because of the complex interaction between multiple associated factors and individual variability in real-world settings. Hence, we tested the feasibility of artificial neural network (ANN) models to predict radiographic progression in axSpA.MethodsIn total, 555 patients with axSpA were split into training and testing datasets at a 3:1 ratio. A generalized linear model (GLM) and ANN models were fitted based on the baseline clinical characteristics and treatment-dependent variables for the modified Stoke Ankylosing Spondylitis Spine Score (mSASSS) of the radiographs at follow-up time points. The mSASSS prediction was evaluated, and explainable machine learning methods were used to provide insights into the model outcome or prediction.ResultsThe R2 values of the fitted models were in the range of 0.90–0.95 and ANN with an input of mSASSS as the number of each score performed better (root mean squared error (RMSE) = 2.83) than GLM or input of mSASSS as a total score (RMSE = 2.99–3.57). The ANN also effectively captured complex interactions among variables and their contributions to the transition of mSASSS over time in the fitted models. Structural changes constituting the mSASSS scoring systems were the most important contributing factors, and no detectable structural abnormalities at baseline were the most significant factors suppressing mSASSS change.ConclusionsClinical and radiographic data-driven ANN allows precise mSASSS prediction in real-world settings. Correct evaluation and prediction of spinal structural changes could be beneficial for monitoring patients with axSpA and developing a treatment plan.

【 授权许可】

CC BY   
© The Author(s) 2023

【 图 表 】

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