The chronic nature of rheumatoid arthritis (RA) and its global prevalence underscores the importance of a comprehensive understanding that extends beyond the joints. RA, which affects hundreds of thousands of people worldwide, with prevalence estimates of approximately 0.5% [1], carries a heavy burden due to its association with an extensive array of comorbidities. This correlation is particularly relevant as the emergence of comorbidities leads to unfavorable health outcomes, including diminished functionality, worsening quality of life, and elevated rates of morbidity and mortality [2–4]. The chronic nature of RA generates a wealth of patient data over time, and leveraging this information can enhance the understanding of RA comorbidities. Nonetheless, clinical trials and traditional cohort studies present difficulties including broad samples of patients during long follow-up periods. Thus, the holistic approach that RA management requires may be facilitated by new research methodologies, including the use of big databases from clinical practice, that can help deepen our understanding of the disease and thereby tailor patient care. These approaches not only allow for a more nuanced understanding of disease progression and comorbidities but also enable the use of data from different sources for the formulation of predictive models for patient outcomes. While these advancements offer the potential to fundamentally reshape RA management landscape, they are not without challenges and limitations that warrant attention. This perspective article aims to analyze the existing evidence on these methodologies, specifically in relation to RA comorbidities, with the goal of providing a roadmap to navigate and address these challenges effectively.

The spectrum of comorbidities that accompany RA extends across multiple health domains, further complicating patient management. This was clearly evident in the international, population-based study COMOrbidities in RA (COMORA), which examined 3,920 patients across 17 countries, shedding light on the most observed comorbidities associated with RA [5]. Depression, reported in 15% of RA patients, was the most common, followed by asthma (7%), cardiovascular (CV) events (6%), and solid-organ malignancies (5%). It has consistently been shown that patients with RA face a heightened risk of CV disease compared to healthy controls [6]. Correspondingly, CV risk factors, such as hypertension, diabetes mellitus, and hyperlipidemia, exhibit a high prevalence among patients with RA [7, 8]. Susceptibility to infections is also notably higher in the RA population, with a reported two-fold rate of hospitalizations due to infections in patients with RA compared to age and sex-matched controls [9]. Similarly, the risk of lymphoma in RA patients is more than double that of the general population [10].

The presence of comorbidities significantly influences therapeutic decisions. For example, comorbidities impact initial treatment choices in patients with early RA [11]. Specifically, the presence of at least one comorbidity has been linked to the sole use of methotrexate (MTX) versus other treatment combinations, potentially affecting treatment efficacy and disease progression. A systematic review showed the impact of gastrointestinal and liver comorbidities on the choice of pain treatment in patients with RA, among other forms of inflammatory arthritis [12]. In fact, the link between an increased risk of infections in patients with RA comorbidities and treatment choices has been evaluated [13]. Accurate identification and management of these comorbidities are paramount not only for enhancing RA outcomes but also for optimizing disease control and prognosis. A study examining difficult-to-treat RA revealed that comorbidities, among other factors, can restrict treatment options and amplify disease burden [14]. Thus, investigating comorbidities in RA patients is vital for optimizing treatment decisions and enhancing patient care through tailored approaches.

Building upon the numerous previously mentioned comorbidities linked to RA, it has become clear that an accurate and comprehensive evaluation of these associations is vital to understanding the global impact of the disease. Nonetheless, this endeavor is fraught with intricate challenges, reflected in the disparities of reporting frequencies across studies [15]. Existing methodologies, such as randomized clinical trials (RCTs), post-commercialization surveillance, and clinical registries, each possess inherent advantages and disadvantages. For example, RCTs provide rigorous scientific data, but often fall short in representing the wider, real-world demographics of RA patients, limiting their external validity. Post-commercialization surveillance, which hinges upon spontaneous adverse event reports, is hampered by under-reporting biases, as it depends upon active participation from healthcare practitioners and patients. While traditional registry-based cohort studies somewhat address these limitations, their data collection typically involves a limited number of participants and variables over relatively brief study periods. This limits their ability to yield in-depth insights into the long-term and diverse nature of RA.

In light of these challenges, there is rising interest in harnessing research that routinely collects data from a variety of sources, including patient experiences and electronic health records (EHRs), collectively referred to as real-world data (RWD) studies [16]. Among these, it is worth noting the value of structured databases, which include the use of diagnostic codes to encode information in a predefined format, as well as unstructured information, gathered as free text in EHRs or from patient images [17]. These innovative approaches aspire to provide a broader and more representative depiction of the comorbidities landscape in RA (Table 1). In fact, building on these innovative approaches, the European Medicines Agency has significantly expanded the utilization of RWD for regulatory decision-making and underscored the need for wider access to data sources [18]. A multi-faceted approach, utilizing both structured and unstructured data, would provide a more representative view of the diseases, thereby enhancing patient outcomes.

The integration of RWD into routine practice may significantly enhance the clinical management of RA. Databases from real-world practice can help to monitor the effectiveness of drugs, thereby offering clinicians a dynamic overview beyond the data gleaned from randomized trials. Moreover, insights into healthcare resource utilization can also be extracted from these studies. By identifying inefficiencies in current treatment pathways or locating bottlenecks in healthcare delivery, resources for long-term disease management could potentially be optimized, leading to more cost-effective services. Additionally, the combination of structured databases and NLP-based data provides a large amount of data from different sources that allows the development of predictive models, helping to forecast disease courses and associated comorbidity risks. These models enable early intervention strategies, potentially altering disease progression and improving patient outcomes. Furthermore, these predictive models can be embedded into clinical decision support systems. This ensures that evidence-based, personalized recommendations are readily available to clinicians at the point of care. Such an approach not only augments the clinical decision-making process but also minimizes the risk of oversight, enhancing the overall quality of patient management in RA.

Despite all the potential shown by RWD studies, the deployment of these new research methods involves intrinsic limitations. EHRs, which are devised with clinical or billing objectives in mind rather than meeting the precise research criteria, may lead to some uncertainty as to whether relevant covariates are being consistently recorded across time and patients. Potential selection bias may emerge from factors like missing clinical information or patient disenrollment during follow-up. Moreover, the absence of various data streams may not be randomly distributed; data often focuses on attributes that are directly relevant to management, a variable that can introduce statistical bias into the findings. On top of this, extracting data from unstructured sources requires high precision to achieve proper validity. One instructive instance in rheumatology can be gleaned from a study that scrutinized the efficacy of the Phenotype KnowledgeBase (PheKB) algorithm in RA patient identification [40]. Despite good specificity (95%), the algorithm’s sensitivity was significantly lower (72%), which underlines the challenges facing algorithm design. Hence, addressing these challenges requires a rethinking of methodologies and a shift towards new frameworks that consider the complex nature of RWD. The analysis must decompose the data’s origins into manageable components, reflecting the idiosyncrasies of the EHR environment. Strategies for optimizing variable detection and managing missing data are of the utmost importance to fully realize the potential of RWD for clinical applications.

The inherent chronicity of RA results in the accumulation of substantial volumes of patient data over time in EHRs. This wealth of information has the potential to unlock valuable insights into comorbidities that affect patients. Pooling this data from expanding databases significantly enhances our research capabilities, providing crucial information towards bettering understanding of disease characteristics. Tools such as NLP are pivotal to achieving this aim, facilitating automated access to a vast reservoir of information. Harnessing these technological innovations may catalyze significant improvements in the characterization and understanding of the disease, promising unprecedented improvements in our pursuit of better health outcomes.