Machine learning predicts response to TNF inhibitors in rheumatoid arthritis: results on the ESPOIR and ABIRISK cohorts

Advancing precision rheumatology: applications of machine learning for rheumatoid arthritis management

Rheumatoid arthritis (RA) is an autoimmune disease causing progressive joint damage. Early diagnosis and treatment is critical, but remains challenging due to RA complexity and heterogeneity. Machine learning (ML) techniques may enhance RA management by identifying patterns within multidimensional biomedical data to improve classification, diagnosis, and treatment predictions. In this review, we summarize the applications of ML for RA management. Emerging studies or applications have developed diagnostic and predictive models for RA that utilize a variety of data modalities, including electronic health records, imaging, and multi-omics data. High-performance supervised learning models have demonstrated an Area Under the Curve (AUC) exceeding 0.85, which is used for identifying RA patients and predicting treatment responses. Unsupervised learning has revealed potential RA subtypes. Ongoing research is integrating multimodal data with deep learning to further improve performance. However, key challenges remain regarding model overfitting, generalizability, validation in clinical settings, and interpretability. Small sample sizes and lack of diverse population testing risks overestimating model performance. Prospective studies evaluating real-world clinical utility are lacking. Enhancing model interpretability is critical for clinician acceptance. In summary, while ML shows promise for transforming RA management through earlier diagnosis and optimized treatment, larger scale multisite data, prospective clinical validation of interpretable models, and testing across diverse populations is still needed. As these gaps are addressed, ML may pave the way towards precision medicine in RA.

Machine learning-based prediction of rheumatoid arthritis with development of ACPA autoantibodies in the presence of non-HLA genes polymorphisms

Machine learning (ML) algorithms can handle complex genomic data and identify predictive patterns that may not be apparent through traditional statistical methods. They become popular tools for medical applications including prediction, diagnosis or treatment of complex diseases like rheumatoid arthritis (RA). RA is an autoimmune disease in which genetic factors play a major role. Among the most important genetic factors predisposing to the development of this disease and serving as genetic markers are HLA-DRB and non-HLA genes single nucleotide polymorphisms (SNPs). Another marker of RA is the presence of anticitrullinated peptide antibodies (ACPA) which is correlated with severity of RA. We use genetic data of SNPs in four non-HLA genes (PTPN22, STAT4, TRAF1, CD40 and PADI4) to predict the occurrence of ACPA positive RA in the Polish population. This work is a comprehensive comparative analysis, wherein we assess and juxtapose various ML classifiers. Our evaluation encompasses a range of models, including logistic regression, k-nearest neighbors, naïve Bayes, decision tree, boosted trees, multilayer perceptron, and support vector machines. The top-performing models demonstrated closely matched levels of accuracy, each distinguished by its particular strengths. Among these, we highly recommend the use of a decision tree as the foremost choice, given its exceptional performance and interpretability. The sensitivity and specificity of the ML models is about 70% that are satisfying. In addition, we introduce a novel feature importance estimation method characterized by its transparent interpretability and global optimality. This method allows us to thoroughly explore all conceivable combinations of polymorphisms, enabling us to pinpoint those possessing the highest predictive power. Taken together, these findings suggest that non-HLA SNPs allow to determine the group of individuals more prone to develop RA rheumatoid arthritis and further implement more precise preventive approach.

Machine learning application in autoimmune diseases: State of art and future prospectives

Autoimmune diseases are a group of disorders resulting from an alteration of immune tolerance, characterized by the formation of autoantibodies and the consequent development of heterogeneous clinical manifestations. Diagnosing autoimmune diseases is often complicated, and the available prognostic tools are limited. Machine learning allows us to analyze large amounts of data and carry out complex calculations quickly and with minimal effort. In this work, we examine the literature focusing on the use of machine learning in the field of the main systemic (systemic lupus erythematosus and rheumatoid arthritis) and organ-specific autoimmune diseases (type 1 diabetes mellitus, autoimmune thyroid, gastrointestinal, and skin diseases). From our analysis, interesting applications of machine learning emerged for developing algorithms useful in the early diagnosis of disease or prognostic models (risk of complications, therapeutic response). Subsequent studies and the creation of increasingly rich databases to be supplied to the algorithms will eventually guide the clinician in the diagnosis, allowing intervention when the pathology is still in an early stage and immediately directing towards a correct therapeutic approach.

Diurnal production of cortisol and prediction of treatment response in rheumatoid arthritis: a 6-month, real-life prospective cohort study

OBJECTIVES

A reduced adrenal reserve-associated cortisol production relative to the enhanced needs of chronic inflammation (disproportion principle) has been observed in rheumatoid arthritis (RA). We examined the possible clinical value of diurnal cortisol measurements in active RA on treatment response prediction.

METHODS

Diurnal cortisol production (measured at: 08-12:00/18:00-22:00) was assessed by electrochemiluminescence immunoassay in 28 consecutive patients with moderately/highly active RA, as well as 3 and 6 months after treatment initiation or/escalation. Twenty-eight COVID-19 patients and 28 age-matched healthy individuals (HC) served as controls.

RESULTS

Saliva diurnal cortisol production in patients with RA was similar to that of HC, despite 12-fold higher serum C reactive protein (CRP) levels, and lower than COVID-19 patients (area under the curve: RA: 87.0±37.6 vs COVID-19: 146.7±14.3, p<0.001), having similarly high CRP. Moreover, a disturbed circadian cortisol rhythm at baseline was evident in 15 of 28 of patients with RA vs 4 of 28 and 20 of 28 of HC and COVID-19 patients, respectively. Treatment-induced minimal disease activity (MDA) at 6 months was achieved by 16 of 28 patients. Despite comparable demographics and clinical characteristics at baseline, non-MDA patients had lower baseline morning cortisol and higher adrenocorticotropic hormone (ACTH) levels compared with patients on MDA (cortisol: 10.9±4.0 vs 18.4±8.2 nmol/L, respectively, p=0.005 and ACTH: 4.8±3.3 vs 2.4±0.4 pmol/L, respectively, p=0.047). Baseline morning cortisol <13.9 nmol/L predicted non-MDA at 6 months (75% sensitivity, 92% specificity, p=0.006). Prospective measurements revealed that individualised diurnal cortisol production remained largely unchanged from baseline to 3 and 6 months.

CONCLUSIONS

An impaired adrenal reserve is present in patients with RA. Further studies to confirm that assessment of diurnal cortisol production may be useful in guiding treatment decisions and/or predicting treatment response in RA are warranted.

TRIAL REGISTRATION NUMBER

NCT05671627.

ChatGPT: when artificial intelligence replaces the rheumatologist in medical writing

In this editorial we discuss the place of artificial intelligence (AI) in the writing of scientific articles and especially editorials. We asked chatGPT « to write an editorial for Annals of Rheumatic Diseases about how AI may replace the rheumatologist in editorial writing ». chatGPT's response is diplomatic and describes AI as a tool to help the rheumatologist but not replace him. AI is already used in medicine, especially in image analysis, but the domains are infinite and it is possible that AI could quickly help or replace rheumatologists in the writing of scientific articles. We discuss the ethical aspects and the future role of rheumatologists.

Machine learning identifies a profile of inadequate responder to methotrexate in rheumatoid arthritis

OBJECTIVES

Around 30% of patients with RA have an inadequate response to MTX. We aimed to use routine clinical and biological data to build machine learning models predicting EULAR inadequate response to MTX and to identify simple predictive biomarkers.

METHODS

Models were trained on RA patients fulfilling the 2010 ACR/EULAR criteria from the ESPOIR and Leiden EAC cohorts to predict the EULAR response at 9 months (± 6 months). Several models were compared on the training set using the AUROC. The best model was evaluated on an external validation cohort (tREACH). The model's predictions were explained using Shapley values to extract a biomarker of inadequate response.

RESULTS

We included 493 therapeutic sequences from ESPOIR, 239 from EAC and 138 from tREACH. The model selected DAS28, Lymphocytes, Creatininemia, Leucocytes, AST, ALT, swollen joint count and corticosteroid co-treatment as predictors. The model reached an AUROC of 0.72 [95% CI (0.63, 0.80)] on the external validation set, where 70% of patients were responders to MTX. Patients predicted as inadequate responders had only 38% [95% CI (20%, 58%)] chance to respond and using the algorithm to decide to initiate MTX would decrease inadequate-response rate from 30% to 23% [95% CI: (17%, 29%)]. A biomarker was identified in patients with moderate or high activity (DAS28 > 3.2): patients with a lymphocyte count superior to 2000 cells/mm3 are significantly less likely to respond.

CONCLUSION

Our study highlights the usefulness of machine learning in unveiling subgroups of inadequate responders to MTX to guide new therapeutic strategies. Further work is needed to validate this approach.