Identification and prediction of difficult-to-treat rheumatoid arthritis patients in structured and unstructured routine care data: results from a hackathon

A Review of Difficult-to-Treat Rheumatoid Arthritis: Definition, Clinical Presentation, and Management

PURPOSE OF REVIEW

A subset of patients with rheumatoid arthritis (RA) who fail multiple biologic therapies are deemed to have "difficult-to-treat" (D2T) RA. In 2021, a European Alliance of Associations for Rheumatology (EULAR) task force proposed a clinical definition of D2T RA. Here we review RA phenotypes and clinical assessment of RA, propose a different definition of D2T RA, discuss possible D2T RA risk factors, and summarize existing literature on the management of D2T RA.

RECENT FINDINGS

High disease activity at the time of diagnosis or prior to treatment with a biologic is associated with the development of D2T RA. Prolonged time from diagnosis to beginning treatment has been consistently associated with the development of D2T RA. Other clinical factors such as burden of disease, extraarticular disease, obesity, smoking, pain, fatigue, and psychological conditions have inconsistent associations with D2T RA according to current literature. D2T RA is a relatively new concept that represents an area of great need for research regarding the characterization of those with the disease as well as how best to treat the disease. With this gained knowledge, rheumatologists will be able to better identify patients at the time of diagnosis that are likely to develop D2T RA to help guide management.

Artificial intelligence in rheumatoid arthritis: potential applications and future implications

The widespread adoption of digital health records, coupled with the rise of advanced diagnostic testing, has resulted in an explosion of patient data, comparable in scope to genomic datasets. This vast information repository offers significant potential for improving patient outcomes and decision-making, provided one can extract meaningful insights from it. This is where artificial intelligence (AI) tools like machine learning (ML) and deep learning come into play, helping us leverage these enormous datasets to predict outcomes and make informed decisions. AI models can be trained to analyze and interpret patient data, including physician notes, laboratory testing, and imaging, to aid in the management of patients with rheumatic diseases. As one of the most common autoimmune diseases, rheumatoid arthritis (RA) has attracted considerable attention, particularly concerning the evolution of diagnostic techniques and therapeutic interventions. Our aim is to underscore those areas where AI, according to recent research, demonstrates promising potential to enhance the management of patients with RA.

Refractory inflammatory arthritis definition and model generated through patient and multi-disciplinary professional modified Delphi process

OBJECTIVE

Various definitions have been proposed for Refractory Disease in people with Rheumatoid Arthritis; however, none were generated for Polyarticular Juvenile Idiopathic Arthritis or involving adult and paediatric multidisciplinary healthcare professionals and patients. The study aim is to redefine Refractory Disease, using Delphi methodology.

METHODS

Three rounds of surveys (one nominal group and two online (2019-2020)) to achieve consensus using a predetermined cut-off were conducted voting on: a) name, b) treatment and inflammation, c) symptoms and impact domains, and d) rating of individual components within domains. Theoretical application of the definition was conducted through a scoping exercise.

RESULTS

Votes were collected across three rounds from Patients, Researchers and nine multi-disciplinary healthcare professional groups (n = 106). Refractory Inflammatory Arthritis was the most popular name. Regarding treatment and inflammation, these were voted to be kept broad rather than specifying numbers/cut-offs. From 10 domains identified to capture symptoms and disease impact, six domains reached consensus for inclusion: 1) Disease Activity, 2) Joint Involvement, 3) Pain, 4) Fatigue, 5) Functioning and Quality of Life, and 6) Disease-Modifying Anti-Rheumatic Drug Experiences. Within these domains, 18 components, from an initial pool (n = 73), were identified as related and important to capture multi-faceted presentation of Refractory Inflammatory Arthritis, specifically in Rheumatoid Arthritis and Polyarticular Juvenile Idiopathic Arthritis. Feasibility of the revised definition was established (2022-2023) with good utility as was applied to 82% of datasets (n = 61) incorporating 20 outcome measures, with two further measures added to increase its utility and coverage of Pain and Fatigue.

CONCLUSION

Refractory Inflammatory Arthritis has been found to be broader than not achieving low disease activity, with wider biopsychosocial components and factors incorporating Persistent Inflammation or Symptoms identified as important. This definition needs further refinement to assess utility as a classification tool to identify patients with unmet needs.

Effectiveness and safety of intravenous golimumab with and without concomitant methotrexate in patients with rheumatoid arthritis in the prospective, noninterventional AWARE study

BACKGROUND

Biologic therapies are often prescribed for patients with rheumatoid arthritis (RA) who have inadequate responses to or are intolerant of methotrexate (MTX) and patients with poor prognostic indicators. This post hoc analysis evaluated effectiveness and safety of intravenous golimumab + MTX vs golimumab without MTX in RA patients.

METHODS

AWARE, a real-world, prospective and pragmatic, Phase 4 study, compared effectiveness and safety of golimumab and infliximab in biologic-naïve and biologic-experienced patients. All treatment decisions were at the discretion of the treating rheumatologist. Effectiveness was evaluated by mean change in CDAI scores at Months 6 and 12. Safety was monitored through approximately 1 year.

RESULTS

Among 685 golimumab-treated patients, 420 (61%) received concomitant MTX during the study and 265 (39%) did not receive MTX after enrollment; 63% and 72%, respectively, discontinued the study. Relative to golimumab without MTX, golimumab + MTX patients had shorter mean disease duration (8.7 vs 10.0 years) and a lower proportion received prior biologics (60% vs 72%); mean ± standard deviation (SD) baseline CDAI scores were similar (30.8 ± 15.1 and 32.6 ± 15.4). Mean ± SD changes from baseline in CDAI scores at Months 6 and 12, respectively, were similar with golimumab + MTX (- 10.2 ± 14.2 and - 10.8 ± 13.8) and golimumab without MTX (- 9.6 ± 12.9 and - 9.9 ± 13.1). The incidence of adverse events/100 patient-years (PY) (95% confidence interval [CI]) was 155.6 (145.6, 166.1) for golimumab + MTX and 191.2 (176.2, 207.1) for golimumab without MTX; infections were the most common type. The incidence of infusion reactions/100PY (95% CI) was 2.1 (1.1, 3.6) for golimumab + MTX versus 5.1 (2.9, 8.3) for golimumab without MTX; none were considered serious. For golimumab + MTX versus golimumab without MTX, rates/100PY (95% CI) of serious infections, opportunistic infections, and malignancies were 2.6 (1.5, 4.3) versus 7.0 (4.4, 10.6), 0.9 (0.3, 2.0) versus 2.6 (1.1, 5.0), and 3.0 (1.7, 4.7) versus 1.0 (0.2, 2.8), respectively.

CONCLUSIONS

Mean change in CDAI score in the  golimumab without MTX group was generally similar to that of the golimumab + MTX group through 1 year, regardless of prior biologic therapy. Adverse events were consistent with the known IV golimumab safety profile. These results provide real world evidential data that may assist healthcare providers and patients with RA in making informed treatment decisions.

TRIAL REGISTRATION

clinicaltrials.gov NCT02728934 05/04/2016.

Difficult-to-treat rheumatoid arthritis (D2T RA): clinical issues at early stages of disease

OBJECTIVES

Most studies on difficult-to-treat rheumatoid arthritis (D2T RA) have focused on established RA. Here, we analyse whether disease activity in the early stages of RA could influence progression to a D2T RA under real-life conditions. Other clinical and treatment-related factors were also analysed.

METHODS

A longitudinal multicentre study of patients with RA was conducted from 2009 to 2018. Patients were followed up until January 2021. D2T RA was defined based on EULAR criteria (treatment failure, signs suggestive of currently active/progressive disease and management being perceived as problematic by the rheumatologist and/or patient). The main variable was disease activity in the early stages. The covariates were sociodemographic, clinical and treatment-related factors. We ran a multivariable logistic regression analysis to investigate risk factors associated with progression to D2T RA.

RESULTS

The study population comprised 631 patients and 35 (5.87%) developed D2T RA. At the time of diagnosis, the D2T RA group were younger, with a higher disability, 28-joint Disease Activity Score (DAS28) score, tender joint count and pain scores. In our final model, DAS28 was not statistically significantly associated with D2T RA. No differences were found between groups for therapy. Disability was independently associated with D2T RA (OR: 1.89; p=0.01).

CONCLUSIONS

In this cohort of patients newly diagnosed with RA, our results do not allow us to prove the influence of active disease according to DAS28. However, we did find that younger patients and those with elevated initial disability scores are more likely to develop D2T RA regardless of other factors.

Tight control in patients with rheumatoid arthritis treated with targeted therapies across the COVID-19 pandemic era

OBJECTIVES

To analyze the impact of different patterns of healthcare delivery on remission of rheumatoid arthritis (RA) patients treated with targeted therapies during the first wave (2020) and second/third waves (2021) of the pandemic compared to the pre-pandemic period (2019).

METHODS

In this observational real-life study, data from RA patients treated with biologic or targeted synthetic drugs were extracted from a longitudinal registry. Clinical Disease Activity Index (CDAI) was analyzed in the same period from the 22nd of February to the 18th of May for three consecutive years. These three periods were characterized by different patterns of healthcare delivery: (1) before the pandemic (2019) only in-person visits, (2) during the first wave (2020) both in-person visits and telehealth, and (3) during the second/third waves (2021) only in-person visits. A generalized linear model with the binomial error was fitted to evaluate the difference in the proportion of patients in CDAI remission. Quantile regression was used to compare the median of CDAI in difficult-to-treat (D2T) patients.

RESULTS

In the three periods, we included 407, 450, and 540 RA patients respectively. The percentages of patients in CDAI remission were similar in the three periods (prevalence ratio 1.07, p value 0.423 between 2020 and 2019, and 1.01, p-value 0.934 between 2021 and 2019). The CDAI remission rate was 40.55% (N = 163), 43.18% (N = 155) and 40.82% (N = 220) in 2019, 2020 and 2021, respectively. Among our cohort of D2T patients, CDAI remission was similar across the three periods (N = 30, 22.22%; N = 27, 23.68%; and N = 34, 21.52% respectively).

CONCLUSION

Although the pandemic has imposed changes in our healthcare delivery, these different strategies seem to be effective in ensuring satisfactory management of RA treated with targeted therapies. The approaches modulated in the context of the different periods have been a feasible compensation for ensuring disease control even in D2T patients.

Difficult-to-treat rheumatoid arthritis: Current concept and unsolved problems

Over the past several decades, the treatment of rheumatoid arthritis (RA) has advanced significantly, and clinical, structural, and functional remission are achievable therapeutic goals. However, a substantial number of patients show resistance to multiple drugs. In particular, patients whose disease activity cannot be controlled despite the use of two or more biological disease-modifying antirheumatic drugs (DMARDs) or targeted synthetic DMARDs (tsDMARDs) with different mechanisms of action (MOA) have recently been referred to as having difficult-to-treat RA (D2T RA). D2T RA is a heterogeneous and multifactorial disease state, and the major problems are uncontrolled disease activity and decreased quality of life, as well as the economic burden due to frequent healthcare utilization and multiple admissions. Since the concept of D2T RA is relatively new and publication regarding D2T RA is limited, the mechanism underlying DMARD inefficacy and which factors form a "difficult-to-treat" state in such patients are not yet fully understood. It is also possible that factors contributing to D2T RA may differ by patient, sex, country, and race. The present Mini Review introduces the current concept and unsolved problems of D2T RA, including the definition, prevalence, and factors contributing to D2T RA. We then discuss the management and therapeutic strategies for D2T RA. Finally, we explore a clinical approach to prevent patients from developing D2T RA.

Artificial Intelligence in Rheumatoid Arthritis: Current Status and Future Perspectives: A State-of-the-Art Review

Investigation of the potential applications of artificial intelligence (AI), including machine learning (ML) and deep learning (DL) techniques, is an exponentially growing field in medicine and healthcare. These methods can be critical in providing high-quality care to patients with chronic rheumatological diseases lacking an optimal treatment, like rheumatoid arthritis (RA), which is the second most prevalent autoimmune disease. Herein, following reviewing the basic concepts of AI, we summarize the advances in its applications in RA clinical practice and research. We provide directions for future investigations in this field after reviewing the current knowledge gaps and technical and ethical challenges in applying AI. Automated models have been largely used to improve RA diagnosis since the early 2000s, and they have used a wide variety of techniques, e.g., support vector machine, random forest, and artificial neural networks. AI algorithms can facilitate screening and identification of susceptible groups, diagnosis using omics, imaging, clinical, and sensor data, patient detection within electronic health record (EHR), i.e., phenotyping, treatment response assessment, monitoring disease course, determining prognosis, novel drug discovery, and enhancing basic science research. They can also aid in risk assessment for incidence of comorbidities, e.g., cardiovascular diseases, in patients with RA. However, the proposed models may vary significantly in their performance and reliability. Despite the promising results achieved by AI models in enhancing early diagnosis and management of patients with RA, they are not fully ready to be incorporated into clinical practice. Future investigations are required to ensure development of reliable and generalizable algorithms while they carefully look for any potential source of bias or misconduct. We showed that a growing body of evidence supports the potential role of AI in revolutionizing screening, diagnosis, and management of patients with RA. However, multiple obstacles hinder clinical applications of AI models. Incorporating the machine and/or deep learning algorithms into real-world settings would be a key step in the progress of AI in medicine.

High Throughput Genetic Characterisation of Caucasian Patients Affected by Multi-Drug Resistant Rheumatoid or Psoriatic Arthritis

Rheumatoid and psoriatic arthritis (RA and PsA) are inflammatory rheumatic disorders characterised by a multifactorial etiology. To date, the genetic contributions to the disease onset, severity and drug response are not clearly defined, and despite the development of novel targeted therapies, ~10% of patients still display poor treatment responses. We characterised a selected cohort of eleven non-responder patients aiming to define the genetic contribution to drug resistance. An accurate clinical examination of the patients was coupled with several high-throughput genetic testing, including HLA typing, SNPs-array and Whole Exome Sequencing (WES). The analyses revealed that all the subjects carry very rare HLA phenotypes which contain HLA alleles associated with RA development (e.g., HLA-DRB1*04, DRB1*10:01 and DRB1*01). Additionally, six patients also carry PsA risk alleles (e.g., HLA-B*27:02 and B*38:01). WES analysis and SNPs-array revealed 23 damaging variants with 18 novel “drug-resistance” RA/PsA candidate genes. Eight patients carry likely pathogenic variants within common genes (CYP21A2, DVL1, PRKDC, ORAI1, UGT2B17, MSR1). Furthermore, “private” damaging variants were identified within 12 additional genes (WNT10A, ABCB7, SERPING1, GNRHR, NCAPD3, CLCF1, HACE1, NCAPD2, ESR1, SAMHD1, CYP27A1, CCDC88C). This multistep approach highlighted novel RA/PsA candidate genes and genotype-phenotype correlations potentially useful for clinicians in selecting the best therapeutic strategy.

Big data analyses and individual health profiling in the arena of rheumatic and musculoskeletal diseases (RMDs)

Health care processes are under constant development and will need to embrace advances in technology and health science aiming to provide optimal care. Considering the perspective of increasing treatment options for people with rheumatic and musculoskeletal diseases, but in many cases not reaching all treatment targets that matter to patients, care systems bare potential to improve on a holistic level. This review provides an overview of systems and technologies under evaluation over the past years that show potential to impact diagnosis and treatment of rheumatic diseases in about 10 years from now. We summarize initiatives and studies from the field of electronic health records, biobanking, remote monitoring, and artificial intelligence. The combination and implementation of these opportunities in daily clinical care will be key for a new era in care of our patients. This aims to inform rheumatologists and healthcare providers concerned with chronic inflammatory musculoskeletal conditions about current important and promising developments in science that might substantially impact the management processes of rheumatic diseases in the 2030s.