Comparison of the Disease Activity Score-28 Based on the Erythrocyte Sedimentation Rate and C-reactive Protein in Rheumatoid Arthritis

Choi, In Ah

doi:10.4078/jrd.2017.24.5.287

Cited by 8 publications

(5 citation statements)

References 20 publications

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“…The data for this study were retrieved from the Korean College of Rheumatology Biologics therapy (KOBIO) registry [ 18 ], a prospective nationwide biologic therapy registry for RA, AS, and psoriatic arthritis, which includes 45 hospitals in South Korea. This registry enrolled patients who started bDMARDs with baseline clinical data and followed up annually.…”

Section: Methodsmentioning

confidence: 99%

Machine learning-based prediction model for responses of bDMARDs in patients with rheumatoid arthritis and ankylosing spondylitis

et al. 2021

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Background Few studies on rheumatoid arthritis (RA) have generated machine learning models to predict biologic disease-modifying antirheumatic drugs (bDMARDs) responses; however, these studies included insufficient analysis on important features. Moreover, machine learning is yet to be used to predict bDMARD responses in ankylosing spondylitis (AS). Thus, in this study, machine learning was used to predict such responses in RA and AS patients. Methods Data were retrieved from the Korean College of Rheumatology Biologics therapy (KOBIO) registry. The number of RA and AS patients in the training dataset were 625 and 611, respectively. We prepared independent test datasets that did not participate in any process of generating machine learning models. Baseline clinical characteristics were used as input features. Responders were defined as those who met the ACR 20% improvement response criteria (ACR20) and ASAS 20% improvement response criteria (ASAS20) in RA and AS, respectively, at the first follow-up. Multiple machine learning methods, including random forest (RF-method), were used to generate models to predict bDMARD responses, and we compared them with the logistic regression model. Results The RF-method model had superior prediction performance to logistic regression model (accuracy: 0.726 [95% confidence interval (CI): 0.725–0.730] vs. 0.689 [0.606–0.717], area under curve (AUC) of the receiver operating characteristic curve (ROC) 0.638 [0.576–0.658] vs. 0.565 [0.493–0.605], F1 score 0.841 [0.837–0.843] vs. 0.803 [0.732–0.828], AUC of the precision-recall curve 0.808 [0.763–0.829] vs. 0.754 [0.714–0.789]) with independent test datasets in patients with RA. However, machine learning and logistic regression exhibited similar prediction performance in AS patients. Furthermore, the patient self-reporting scales, which are patient global assessment of disease activity (PtGA) in RA and Bath Ankylosing Spondylitis Functional Index (BASFI) in AS, were revealed as the most important features in both diseases. Conclusions RF-method exhibited superior prediction performance for responses of bDMARDs to a conventional statistical method, i.e., logistic regression, in RA patients. In contrast, despite the comparable size of the dataset, machine learning did not outperform in AS patients. The most important features of both diseases, according to feature importance analysis were patient self-reporting scales.

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Section: Methodsmentioning

confidence: 99%

Machine learning-based prediction model for responses of bDMARDs in patients with rheumatoid arthritis and ankylosing spondylitis

et al. 2021

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“…The Korean College of Rheumatology Biologics and Targeted Therapy (KOBIO) registry is a nationwide, multicenter, prospective, web-based observational cohort that was launched in 2012 [ 2 , 7 ]. For our study, we selected adults (>18 years) from the KOBIO registry’s RA group who met the 1987 American College of Rheumatology (ACR) or 2010 ACR/European League Against Rheumatism (EULAR) RA classification criteria that either had bDMARD or tsDMARD or switched to another drug [ 8 , 9 ].…”

Section: Methodsmentioning

confidence: 99%

Implications of Persistent Pain in Patients With Rheumatoid Arthritis Despite Remission Status: Data From the KOBIO Registry

2022

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Objective This study aimed to assess the prevalence of pain in patients with RA in clinical remission and analyze the demographic and clinical characteristics of those who experienced persistent pain despite remission status. Methods Data from 1,891 patients with RA registered on the Korean College of Rheumatology Biologics and Targeted Therapy registry were obtained. Remission was defined as a Disease Activity Score of 28 joints-erythrocyte sedimentation rate (ESR) <2.6. Pain intensity was classified as severe (pain visual analog scale [VAS] ≥7), moderate (4≤VAS<7), or mild (VAS <4). Results Our analysis showed that 52.6% of patients complained of severe pain at the start of or during switching biological disease-modifying anti-rheumatic drugs (bDMARDs) or targeted synthetic DMARDs (tsDMARDs). Despite having a 36.0% (n=680) remission rate after the use of bDMARDs or tsDMARDs at their 1-year follow-up, 21.5% (n=146) of these patients had moderate-to-severe pain, higher frequency of foot erosions, and comorbidities, such as mental illness, endocrine, renal, and neurological disorders, than patients with a milder degree of pain. The multivariable regression analysis showed that presence of foot erosions, neurological disorders, and use of corticosteroids were independently associated with moderate-to-severe pain in patients with RA despite being in remission. The level of ESR and use of Janus kinase inhibitors were inversely associated with moderate-to-severe pain. Conclusion Persistent pain and discomfort continue to be a problem for patients with RA in clinical remission. Continued research on insistent pain in patients with RA is warranted to better alleviate distress and improve the quality of life in patients.

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“…The Korean College of Rheumatology Biologics Registry (KOBIO) was designed as a nationwide multicenter, hospital-based observation registry by the Korean College of Rheumatology as previously described ( 14 – 16 ). KOBIO was established to evaluate the effectiveness and adverse events related to the use of biologic agents in Korean patients with RA, AS, and PsA.…”

Section: Methodsmentioning

confidence: 99%

Comparison of Retention Rates Between Tumor Necrosis Factor-α Inhibitors in Patients With Ankylosing Spondylitis: Data From the Korean College of Rheumatology Biologics Registry

Kim

Lee

et al. 2021

Front. Med.

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This study aimed to investigate drug retention rates for various TNF inhibitors (TNFis) commonly prescribed to Korean patients with ankylosing spondylitis (AS) in the Korean College of Rheumatology Biologics registry (KOBIO; December 2012–June 2016). Discontinuation was defined as switching or stopping the biologic agent. Kaplan–Meier curves and Cox's proportional hazard models were used for further analysis. The reasons for discontinuation of TNFis were also assessed. Univariate and multivariate analyses were used to identify possible predictors of discontinuation. Data from 1,005 patients with AS were analyzed with a median follow-up period of 14 months. Seventy-six percent of patients were first-line biologic users. Discontinuation of TNFis occurred in 24.2% (switching in 9.6%) of patients during follow-up. An estimate of the drug failure showed that the adjusted hazard ratio (HR) for golimumab compared to etanercept was 0.441 (95% confidence interval: 0.277–0.703, p < 0.001). Reasons for discontinuation included lack of efficacy (32.6%), adverse events (23.6%), clinical improvement (11.2%), and others (32.6%). Predictors of discontinuation using a multivariate analysis were a shorter disease duration (HR: 0.973, p = 0.044) and being negative for HLA-B27 (HR: 1.623, p = 0.0093). In conclusion, few Korean patients with AS switched to other TNFis during their treatment. The drug retention rate for golimumab was higher than for other agents.

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Comparison of the Disease Activity Score-28 Based on the Erythrocyte Sedimentation Rate and C-reactive Protein in Rheumatoid Arthritis

Cited by 8 publications

References 20 publications

Machine learning-based prediction model for responses of bDMARDs in patients with rheumatoid arthritis and ankylosing spondylitis

Machine learning-based prediction model for responses of bDMARDs in patients with rheumatoid arthritis and ankylosing spondylitis

Implications of Persistent Pain in Patients With Rheumatoid Arthritis Despite Remission Status: Data From the KOBIO Registry

Comparison of Retention Rates Between Tumor Necrosis Factor-α Inhibitors in Patients With Ankylosing Spondylitis: Data From the Korean College of Rheumatology Biologics Registry

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