Conventional disease-modifying antirheumatic drugs therapy may not slow spinal radiographic progression in ankylosing spondylitis: results from an 18-year longitudinal dataset

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

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.

Association between changes in serum alkaline phosphatase levels and radiographic progression in ankylosing spondylitis

This retrospective study evaluated the electronic medical records of patients with ankylosing spondylitis (AS) (January 2001-December 2018) to determine the relationship between serum alkaline phosphatase (ALP) levels and radiographic changes over time. Longitudinal data, including serum ALP levels, were imputed by linear interpolation at 3-month intervals. Among the serum ALP levels calculated for 8 years prior to modified Stoke Ankylosing Spondylitis Spinal Score (mSASSS) measurement, those having the highest beta coefficient with the mSASSS were selected in the correlation between ALP and longitudinal mSASSS. Linear mixed models with the selected serum ALP levels, mSASSS, and clinical variables were investigated. We included 1122 patients (mean follow-up, 8.20 [standard deviation: 2.85] years). The serum ALP level from 5 years and 3 months prior showed the highest beta coefficient with the mSASSS. In the linear mixed model, the serum ALP level at 5 years and 3 months before radiographic changes was significantly associated with the mSASSS (β = 0.021, 95% confidence interval: 0.017-0.025, p < 0.001). Serum ALP levels measured approximately 5 years before may be a surrogate marker for predicting spinal radiographic changes. Long-term prospective clinical and experimental studies of > 5 years are required for biomarker discovery or therapeutic research on AS radiographic progression.

Prediction of radiographic progression pattern in patients with ankylosing spondylitis using group-based trajectory modeling and decision trees

OBJECTIVE

This study aimed to identify trajectories of radiographic progression of the spine over time and use them, along with associated clinical factors, to develop a prediction model for patients with ankylosing spondylitis (AS).

METHODS

Data from the medical records of patients diagnosed with AS in a single center were extracted between 2001 and 2018. Modified Stoke Ankylosing Spondylitis Spinal Scores (mSASSS) were estimated from cervical and lumbar radiographs. Group-based trajectory modeling classified patients into trajectory subgroups using longitudinal mSASSS data. In multivariate analysis, significant clinical factors associated with trajectories were selected and used to develop a decision tree for prediction of radiographic progression. The most appropriate group for each patient was then predicted using decision tree analysis.

RESULTS

We identified three trajectory classes: class 1 had a uniformly increasing slope of mSASSS, class 2 showed sustained low mSASSS, and class 3 showed little change in the slope of mSASSS but highest mSASSS from time of diagnosis to after progression. In multivariate analysis for predictive factors, female sex, younger age at diagnosis, lack of eye involvement, presence of peripheral joint involvement, and low baseline erythrocyte sedimentation rate (log) were significantly associated with class 2. Class 3 was significantly associated with male sex, older age at diagnosis, presence of ocular involvement, and lack of peripheral joint involvement when compared with class 1. Six clinical factors from multivariate analysis were used for the decision tree for classifying patients into three trajectories of radiographic progression.

CONCLUSION

We identified three patterns of radiographic progression over time and developed a decision tree based on clinical factors to classify patients according to their trajectories of radiographic progression. Clinically, this model holds promise for predicting prognosis in patients with AS.

The Epidemiology and Treatment of Ankylosing Spondylitis in Korea

Ankylosing spondylitis is a chronic inflammatory disorder characterized by inflammation of the axial skeleton and sacroiliac joints and to a lesser extent by peripheral arthritis and the involvement of some extra-articular organs. It is paramount for the provision of effective health care delivery to be familiar with the epidemiologic studies on prevalence, mortality, and disability. Furthermore, there is no systematic arrangement of studies related to the treatment of ankylosing spondylitis in Korea. In this review, we addressed Korean ankylosing spondylitis epidemiological studies related to prevalence, genetic factor especially human leucocyte antigen-B27, extra-articular manifestations, infections, mortality, radiologic progression, child-birth, and quality of life. Furthermore, we reviewed Korean ankylosing spondylitis treatment researches about treatment trend, patients' registration program called The KOrean College of Rheumatology BIOlogics and targeted therapy (KOBIO) registry project, biologics and biosimiliars, complications especially infections, and issues about bony progression. There would be value to further studying the epidemiology and treatment of Korean ankylosing spondylitis.

A pilot study on deep learning-based grading of corners of vertebral bodies for assessment of radiographic progression in patients with ankylosing spondylitis

Background:

Radiographs are widely used to evaluate radiographic progression with modified stoke ankylosing spondylitis spinal score (mSASSS).

Objective:

This pilot study aimed to develop a deep learning model for grading the corners of the cervical and lumbar vertebral bodies for computer-aided detection of mSASSS in patients with ankylosing spondylitis (AS).

Methods:

Digital radiographic examination of the spine was performed using Discovery XR656 (GE Healthcare) and Digital Diagnost (Philips). The disk points were detected between the bodies using a key-point detection deep learning model from the image obtained in DICOM (digital imaging and communications in medicine) format from the cervical and lumbar spinal radiographs. After cropping the vertebral regions around the disk point, the lower and upper corners of the vertebral bodies were classified as grade 3 (total bony bridges) or grades 0, 1, or 2 (non-bridges). We trained a convolutional neural network model to predict the grades in the lower and upper corners of the vertebral bodies. The performance of the model was evaluated in a validation set, which was separate from the training set.

Results:

Among 1280 patients with AS for whom mSASSS data were available, 5,083 cervical and 5245 lumbar lateral radiographs were reviewed. The total number of corners where mSASSS was measured in the cervical and lumbar vertebrae, including the upper and lower corners, was 119,414. Among them, the number of corners in the training and validation sets was 110,088 and 9326, respectively. The mean accuracy, sensitivity, and specificity for mSASSS scoring in one corner of the vertebral body were 0.91604, 0.80288, and 0.94244, respectively.

Conclusion:

A high-performance deep learning model for grading the corners of the vertebral bodies was developed for the first time. This model must be improved and further validated to develop a computer-aided tool for assessing mSASSS in the future.

Age-stratified trends in the progression of spinal radiographic damage in patients with ankylosing spondylitis: a longitudinal study

Objective:

The objective of this study was to investigate spinal radiographic progression in specific age ranges of ankylosing spondylitis (AS) patients.

Methods:

Longitudinal data for 1125 AS patients at a single hospital from 2000 to 2018 were retrospectively reviewed. Radiographic intervals were obtained from patients with consecutive spinal radiographs. The radiographic progression rate was defined as the modified Stoke Ankylosing Spondylitis Spinal Score (mSASSS) change per year within each interval. Using generalized estimating equations (GEEs), estimated marginal means were calculated for the mSASSS progression rate across age groups after adjusting for potential confounders.

Results:

We obtained 4016 radiographic intervals and stratified them into five groups based on patient age at the interval start: <20 (n = 122); 20–29 (n = 1124); 30–39 (n = 1690); 40–49 (n = 794); and ⩾50 years (n = 286). The mean (SD) mSASSS progression rate for all the intervals was 0.8 (1.9). The GEE-estimated mean mSASSS progression rate increased with age, peaking in the 30–39 age group with a value of 1.15 [95% confidence interval (CI) 1.03, 1.27], and decreased slightly thereafter. In the presence of risk factors, rapid progression occurred at earlier ages: the GEE-estimated mean mSASSS progression rate in those with elevated C-reactive protein levels and preexisting syndesmophytes was 2.82 (95% CI 1.93, 3.71) in the 20–29 age group.

Conclusion:

Spinal structural damage in AS seems to progress most rapidly when patients are age 30–39 years. An awareness of the trends in radiographic progression with advancing age could improve understanding of the natural course of AS.

2022 French Society for Rheumatology (SFR) recommendations on the everyday management of patients with spondyloarthritis, including psoriatic arthritis

OBJECTIVE

Update the French Society for Rheumatology (SFR) recommendations on the everyday management of patients with spondyloarthritis, including psoriatic arthritis.

METHODS

Following standardized procedures, a systematic literature review was done by four supervised rheumatology residents based on questions defined by a task force of 16 attending rheumatologists. The findings were reviewed during three working meetings that culminated in each recommendation receiving a grade and the level of agreement among experts being determined.

RESULTS

Five general principles and 15 recommendations were developed. They take into account pharmacological and non-pharmacological measures along with treatment methods based on the dominant phenotype present (axial, articular, enthesitis/dactylitis) and the extra-articular manifestations (psoriasis, inflammatory bowel disease, uveitis). NSAIDs are the first-line pharmacological treatment in the various presentations. Conventional synthetic disease modifying antirheumatic drugs (csDMARDs) are not indicated in the axial and isolated entheseal forms. If the response to conventional treatment is not adequate, targeted therapies (biologics, synthetics) should be considered; the indications depend on the clinical phenotype and presence of extra-articular manifestations.

CONCLUSION

This update incorporates recent data (published since the prior update in 2018) and the predominant clinical phenotype concept. It aims to help physicians with the everyday management of patients affected by spondyloarthritis, including psoriatic arthritis.

Early control of C-reactive protein levels with non-biologics is associated with slow radiographic progression in radiographic axial spondyloarthritis

AIM

Predicting radiographic progression is vital for assessing the prognosis of patients with radiographic axial spondyloarthritis, and C-reactive protein (CRP) may be a valuable biomarker for this purpose. This study aimed to investigate the relationship between changes in the CRP level and spinal radiographic progression in patients with radiographic axial spondyloarthritis who were initially treated with non-biologics.

METHODS

Patients with radiographic axial spondyloarthritis who were followed up for 18 years at a single center and initially treated with nonsteroidal anti-inflammatory drugs and/or conventional disease-modifying antirheumatic drugs for 3 months were included. Patients with a CRP level of <0.8 mg/dL or 50% of the baseline CRP at 3 months were assigned to the controlled CRP group (n = 351), and the remaining patients were assigned to the uncontrolled CRP group (n = 452). A generalized estimating equation was used to analyze the differences in the modified Stoke Ankylosing Spondylitis Spinal Score (mSASSS) between the 2 groups.

RESULTS

The increase in the mSASSS was slower in the controlled CRP group than in the uncontrolled CRP group (interaction term β = -.499, 95% confidence interval -0.699 to -0.300).

CONCLUSION

Controlled CRP achieved in response to initial treatment with non-biologic agents for 3 months was significantly associated with a slower rate of spinal radiographic change in patients with radiographic axial spondyloarthritis. The CRP level at 3 months after initial non-biologic treatment is a good predictor of radiographic progression.