Exploring the predictive power of antinuclear antibodies and Rheumatoid factor correlations in anticipating therapeutic outcomes for female patients with coexisting Sjögren's syndrome and Rheumatoid arthritis

Q1 Medicine

Journal of oral biology and craniofacial research Pub Date : 2025-02-11 DOI:10.1016/j.jobcr.2025.01.012

Anitha Krishnan Pandarathodiyil , Hema Shree K , Pratibha Ramani , Sivapathasundharam B. , Ramya Ramadoss

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引用次数: 0

Abstract

Background

Sjögren's Syndrome (SS) and Rheumatoid Arthritis (RA) are autoimmune conditions that often coexist in female patients. Biomarkers such as antinuclear antibodies (ANA) and rheumatoid factor (RF) are used for diagnosis, but their predictive power for treatment outcomes remains unclear. This study aims to investigate the correlation between age, ANA, RF, and treatment response in female patients with both SS and RA.

Objective

To evaluate the relationships between age, ANA, RF levels, RA (disease present), and treatment response using Pearson correlation analysis and a neural network model, to predict treatment outcomes in patients with coexisting SS and RA.

Methods

A cohort of 56 female patients aged 30–73 was analyzed. Descriptive statistics provided an overview of key variables, followed by Pearson correlation analysis to assess relationships between age, ANA, RF, RA, and treatment response. A neural network model was developed to predict treatment response based on age, ANA, and RF levels, using a training-to-testing split of 81.3 % and 18.8 %, respectively.

Results

The Pearson correlation analysis revealed a significant positive correlation between age and ANA levels (r = .541, p = 0.031), though no significant correlations were found between age, RF, RA, and treatment response. The neural network model achieved an accuracy of 92.3 % during training and 100 % accuracy during testing for most treatment categories. However, the model struggled to accurately distinguish between certain classes, particularly treatment categories 1 and 3.

Conclusion

Age showed a significant correlation with ANA levels, indicating that older patients may have elevated ANA. The neural network model demonstrated strong predictive power for treatment response, although further refinement is needed to improve its ability to distinguish between all response categories. These findings suggest that machine learning models could enhance personalized treatment strategies for patients with SS and RA, but additional validation with larger datasets is required.

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来源期刊

Journal of oral biology and craniofacial research Medicine-Otorhinolaryngology

CiteScore

4.90

自引率

0.00%

发文量

133

审稿时长

167 days

期刊介绍： Journal of Oral Biology and Craniofacial Research (JOBCR)is the official journal of the Craniofacial Research Foundation (CRF). The journal aims to provide a common platform for both clinical and translational research and to promote interdisciplinary sciences in craniofacial region. JOBCR publishes content that includes diseases, injuries and defects in the head, neck, face, jaws and the hard and soft tissues of the mouth and jaws and face region; diagnosis and medical management of diseases specific to the orofacial tissues and of oral manifestations of systemic diseases; studies on identifying populations at risk of oral disease or in need of specific care, and comparing regional, environmental, social, and access similarities and differences in dental care between populations; diseases of the mouth and related structures like salivary glands, temporomandibular joints, facial muscles and perioral skin; biomedical engineering, tissue engineering and stem cells. The journal publishes reviews, commentaries, peer-reviewed original research articles, short communication, and case reports.