On-line First

J Rheum Dis

Published online December 20, 2023

© Korean College of Rheumatology

Machine learning models with time-series clinical features to predict radiographic progression in patients with ankylosing spondylitis

Bon San Koo, M.D., Ph.D.1* , Miso Jang, M.D., Ph.D.2,3* , Ji Seon Oh, M.D., Ph.D.4 , Keewon Shin, Ph.D.2 , Seunghun Lee, M.D., Ph.D.5 , Kyung Bin Joo, M.D., Ph.D.5 , Namkug Kim, Ph.D.6,7† , Tae-Hwan Kim, M.D., Ph.D.8†

1Department of Internal Medicine, Inje University Ilsan Paik Hospital, Inje University College of Medicine, 2Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, 3Department of Medicine, Asan Medical Center, University of Ulsan College of Medicine, 4Department of Information Medicine, Big Data Research Center, Asan Medical Center, 5Department of Radiology, Hanyang University Hospital for Rheumatic Diseases, Departments of 6Radiology and 7Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, 8Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Seoul, Korea

Correspondence to : Namkug Kim, https://orcid.org/0000-0002-3438-2217
Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Korea. E-mail: namkugkim@gmail.com
Tae-Hwan Kim, https://orcid.org/0000-0002-3542-2276
Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, 222-1 Wangsimni-ro, Seongdong-gu, Seoul 04763, Korea. E-mail: thkim@hanyang.ac.kr

*These authors contributed equally to this work.

Received: September 4, 2023; Revised: October 15, 2023; Accepted: October 30, 2023

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Objective: Ankylosing spondylitis (AS) is chronic inflammatory arthritis causing structural damage and radiographic progression to the spine due to repeated and continuous inflammation over a long period. This study establishes the application of machine learning models to predict radiographic progression in AS patients using time-series data from electronic medical records (EMRs).
Methods: EMR data, including baseline characteristics, laboratory findings, drug administration, and modified Stoke AS Spine Score (mSASSS), were collected from 1,123 AS patients between January 2001 and December 2018 at a single center at the time of first (T1), second (T2), and third (T3) visits. The radiographic progression of the (n+1)th visit (Pn+1=(mSASSSn+1–mSASSSn)/(Tn+1–Tn)≥1 unit per year) was predicted using follow-up visit datasets from T1 to Tn. We used three machine learning methods (logistic regression with the least absolute shrinkage and selection operation, random forest, and extreme gradient boosting algorithms) with three-fold cross-validation.
Results: The random forest model using the T1 EMR dataset best predicted the radiographic progression P2 among the machine learning models tested with a mean accuracy and area under the curves of 73.73% and 0.79, respectively. Among the T1 variables, the most important variables for predicting radiographic progression were in the order of total mSASSS, age, and alkaline phosphatase.
Conclusion: Prognosis predictive models using time-series data showed reasonable performance with clinical features of the first visit dataset when predicting radiographic progression.

Keywords Ankylosing spondylitis, Machine learning, Disease progression

Article

On-line First

J Rheum Dis

Published online December 20, 2023

Copyright © Korean College of Rheumatology.

Machine learning models with time-series clinical features to predict radiographic progression in patients with ankylosing spondylitis

Bon San Koo, M.D., Ph.D.1* , Miso Jang, M.D., Ph.D.2,3* , Ji Seon Oh, M.D., Ph.D.4 , Keewon Shin, Ph.D.2 , Seunghun Lee, M.D., Ph.D.5 , Kyung Bin Joo, M.D., Ph.D.5 , Namkug Kim, Ph.D.6,7† , Tae-Hwan Kim, M.D., Ph.D.8†

1Department of Internal Medicine, Inje University Ilsan Paik Hospital, Inje University College of Medicine, 2Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, 3Department of Medicine, Asan Medical Center, University of Ulsan College of Medicine, 4Department of Information Medicine, Big Data Research Center, Asan Medical Center, 5Department of Radiology, Hanyang University Hospital for Rheumatic Diseases, Departments of 6Radiology and 7Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, 8Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Seoul, Korea

Correspondence to:Namkug Kim, https://orcid.org/0000-0002-3438-2217
Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Korea. E-mail: namkugkim@gmail.com
Tae-Hwan Kim, https://orcid.org/0000-0002-3542-2276
Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, 222-1 Wangsimni-ro, Seongdong-gu, Seoul 04763, Korea. E-mail: thkim@hanyang.ac.kr

*These authors contributed equally to this work.

Received: September 4, 2023; Revised: October 15, 2023; Accepted: October 30, 2023

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Objective: Ankylosing spondylitis (AS) is chronic inflammatory arthritis causing structural damage and radiographic progression to the spine due to repeated and continuous inflammation over a long period. This study establishes the application of machine learning models to predict radiographic progression in AS patients using time-series data from electronic medical records (EMRs).
Methods: EMR data, including baseline characteristics, laboratory findings, drug administration, and modified Stoke AS Spine Score (mSASSS), were collected from 1,123 AS patients between January 2001 and December 2018 at a single center at the time of first (T1), second (T2), and third (T3) visits. The radiographic progression of the (n+1)th visit (Pn+1=(mSASSSn+1–mSASSSn)/(Tn+1–Tn)≥1 unit per year) was predicted using follow-up visit datasets from T1 to Tn. We used three machine learning methods (logistic regression with the least absolute shrinkage and selection operation, random forest, and extreme gradient boosting algorithms) with three-fold cross-validation.
Results: The random forest model using the T1 EMR dataset best predicted the radiographic progression P2 among the machine learning models tested with a mean accuracy and area under the curves of 73.73% and 0.79, respectively. Among the T1 variables, the most important variables for predicting radiographic progression were in the order of total mSASSS, age, and alkaline phosphatase.
Conclusion: Prognosis predictive models using time-series data showed reasonable performance with clinical features of the first visit dataset when predicting radiographic progression.

Keywords: Ankylosing spondylitis, Machine learning, Disease progression

JRD
Jan 01, 2024 Vol.31 No.1, pp. 1~63
COVER PICTURE
Characteristic findings in interstitial lung disease (ILD) detected by lung ultrasound. Lung ultrasound can reveal characteristic findings in ILD. However, the definitive diagnosis of ILD typically requires a combination of clinical assessment, imaging studies (such as high-resuloution computed tomography [HRCT]), and sometimes a lung biopsy. (A) Traction bronchiectasis and parenchymal changes of upper lung in HRCT (arrows). (B) Corresponding changes of lung ultrasound presented by B-lines (arrows). (J Rheum Dis 2024;31:3-14)

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