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Rheumatic disease and heart disease share common underpinnings involving inflammation. The high levels of inflammation that characterize rheumatic diseases provide a “natural experiment” to help elucidate the mechanisms by which inflammation accelerates heart disease. Rheumatoid arthritis (RA) is the most common of the rheumatic diseases and has the best studied relationships with heart disease. A review of current literature on heart disease and RA was conducted. Patients with RA have an increased risk of developing heart disease that is not fully explained by traditional cardiovascular risk factors. Therapies used to treat RA may also affect the development of heart disease; by suppressing inflammation, they may also reduce the risk of heart disease. However, their other effects, as in the case of steroids, may increase heart disease risk. Investigations of the innate and adaptive immune responses occurring in RA may delineate novel mechanisms in the pathogenesis of heart disease and help identify novel therapeutic targets for the prevention and treatment of heart disease.

With the worldwide digitalisation of medical records, electronic health records (EHRs) have become an increasingly important source of real-world data (RWD). RWD can complement traditional study designs because it captures almost the complete variety of patients, leading to more generalisable results. For rheumatology, these data are particularly interesting as our diseases are uncommon and often take years to develop. In this review, we discuss the following concepts related to the use of EHR for research and considerations for translation into clinical care: EHR data contain a broad collection of healthcare data covering the multitude of real-life patients and the healthcare processes related to their care. Machine learning (ML) is a powerful method that allows us to leverage a large amount of heterogeneous clinical data for clinical algorithms, but requires extensive training, testing, and validation. Patterns discovered in EHR data using ML are applicable to real life settings, however, are also prone to capturing the local EHR structure and limiting generalisability outside the EHR(s) from which they were developed. Population studies on EHR necessitates knowledge on the factors influencing the data available in the EHR to circumvent biases, for example, access to medical care, insurance status. In summary, EHR data represent a rapidly growing and key resource for real-world studies. However, transforming RWD EHR data for research and for real-world evidence using ML requires knowledge of the EHR system and their differences from existing observational data to ensure that studies incorporate rigorous methods that acknowledge or address factors such as access to care, noise in the data, missingness and indication bias.

Typically, algorithms to classify phenotypes using electronic medical record (EMR) data were developed to perform well in a specific patient population. There is increasing interest in analyses which can allow study of a specific outcome across different diseases. Such a study in the EMR would require an algorithm that can be applied across different patient populations. Our objectives were: (1) to develop an algorithm that would enable the study of coronary artery disease (CAD) across diverse patient populations; (2) to study the impact of adding narrative data extracted using natural language processing (NLP) in the algorithm. Additionally, we demonstrate how to implement CAD algorithm to compare risk across 3 chronic diseases in a preliminary study. We studied 3 established EMR based patient cohorts: diabetes mellitus (DM, n = 65,099), inflammatory bowel disease (IBD, n = 10,974), and rheumatoid arthritis (RA, n = 4,453) from two large academic centers. We developed a CAD algorithm using NLP in addition to structured data (e.g. ICD9 codes) in the RA cohort and validated it in the DM and IBD cohorts. The CAD algorithm using NLP in addition to structured data achieved specificity >95% with a positive predictive value (PPV) 90% in the training (RA) and validation sets (IBD and DM). The addition of NLP data improved the sensitivity for all cohorts, classifying an additional 17% of CAD subjects in IBD and 10% in DM while maintaining PPV of 90%. The algorithm classified 16,488 DM (26.1%), 457 IBD (4.2%), and 245 RA (5.0%) with CAD. In a cross-sectional analysis, CAD risk was 63% lower in RA and 68% lower in IBD compared to DM (p<0.0001) after adjusting for traditional cardiovascular risk factors. We developed and validated a CAD algorithm that performed well across diverse patient populations. The addition of NLP into the CAD algorithm improved the sensitivity of the algorithm, particularly in cohorts where the prevalence of CAD was low. Preliminary data suggest that CAD risk was significantly lower in RA and IBD compared to DM.

Background Treatments for rheumatoid arthritis (RA) are associated with complex changes in lipids and lipoproteins that may impact cardiovascular (CV) risk. The objective of this study was to examine lipid and lipoprotein changes associated with two common RA treatment strategies, triple therapy or tumor necrosis factor inhibitor (TNFi), and association with CV risk. Methods In this secondary data analysis of the TARGET trial, methotrexate (MTX) inadequate responders with RA were randomized to either add sulfasalazine and hydroxychloroquine (triple therapy), or TNFi for 24-weeks. The primary trial outcome was the change in arterial inflammation measured in the carotid arteries or aorta by FDG-PET/CT at baseline and 24-weeks; this change was described as the target-to-background ratio (TBR) in the most diseased segment (MDS). Routine lipids and advanced lipoproteins were measured at baseline and 24-weeks; subjects on statin therapy at baseline were excluded. Comparisons between baseline and follow-up lipid measurements were performed within and across treatment arms, as well as change in lipids and change in MDS-TBR. Results We studied 122 participants, 61 in each treatment arm, with median age 57 years, 76% female, and 1.5 year median RA disease duration. When comparing treatment arms, triple therapy had on average a larger reduction in triglycerides (15.9 mg/dL, p  = 0.01), total cholesterol to HDL-C ratio (0.29, p-value = 0.01), and LDL particle number (111.2, p  = 0.02) compared to TNFi. TNFi had on average a larger increase in HDL particle number (1.6umol/L, p  = 0.006). We observed no correlation between change in lipid measurements and change in MDS-TBR within and across treatment arms. Conclusions Both treatment strategies were associated with improved lipid profiles via changes in different lipids and lipoproteins. These effects had no correlation with change in CV risk as measured by vascular inflammation by FDG-PET/CT. Trial registration ClinicalTrials.gov ID NCT02374021.

Background Many patients with rheumatoid arthritis (RA) require a trial of multiple biologic disease-modifying anti-rheumatic drugs (bDMARDs) to control their disease. With the availability of several bDMARD options, the history of bDMARDs may provide an alternative approach to understanding subphenotypes of RA. The objective of this study was to determine whether there exist distinct clusters of RA patients based on bDMARD prescription history to subphenotype RA. Methods We studied patients from a validated electronic health record-based RA cohort with data from January 1, 2008, through July 31, 2019; all subjects prescribed ≥ 1 bDMARD or targeted synthetic (ts) DMARD were included. To determine whether subjects had similar b/tsDMARD sequences, the sequences were considered as a Markov chain over the state-space of 5 classes of b/tsDMARDs. The maximum likelihood estimator (MLE)-based approach was used to estimate the Markov chain parameters to determine the clusters. The EHR data of study subjects were further linked with a registry containing prospectively collected data for RA disease activity, i.e., clinical disease activity index (CDAI). As a proof of concept, we tested whether the clusters derived from b/tsDMARD sequences correlated with clinical measures, specifically differing trajectories of CDAI. Results We studied 2172 RA subjects, mean age 52 years, RA duration 3.4 years, and 62% seropositive. We observed 550 unique b/tsDMARD sequences and identified 4 main clusters: (1) TNFi persisters (65.7%), (2) TNFi and abatacept therapy (8.0%), (3) on rituximab or multiple b/tsDMARDs (12.7%), (4) prescribed multiple therapies with tocilizumab predominant (13.6%). Compared to the other groups, TNFi persisters had the most favorable trajectory of CDAI over time. Conclusion We observed that RA subjects can be clustered based on the sequence of b/tsDMARD prescriptions over time and that the clusters were correlated with differing trajectories of disease activity over time. This study highlights an alternative approach to consider subphenotyping of patients with RA for studies aimed at understanding treatment response.

Managing chronic diseases requires ongoing monitoring of disease activity and therapeutic responses to optimize treatment plans. With the growing availability of disease-modifying therapies, it is crucial to investigate comparative effectiveness and long-term outcomes beyond those available from randomized clinical trials. We introduce a comprehensive pipeline for generating reproducible and generalizable real-world evidence on disease outcomes by leveraging electronic health record data. The pipeline first generates scalable disease outcomes by linking electronic health record data with registry data containing a small sample of labeled outcomes. It then applies causal analysis using these scalable outcomes to evaluate therapies for chronic diseases. The implementation of the pipeline is illustrated in a case study based on multiple sclerosis. Our approach addresses challenges in real-world evidence generation for disease activity of chronic conditions, specifically the lack of direct observations on key outcomes and biases arising from imperfect or incomplete data. We present advanced machine learning techniques such as semisupervised and ensemble methods to impute missing outcome data, further incorporating steps for calibrated causal analyses and bias correction.

Background Specific patient characteristics increase the risk of cancer, necessitating personalized healthcare approaches. For high-risk individuals, tailored clinical management ensures proactive monitoring and timely interventions. Electronic health record (EHR) data are crucial for supporting these personalized approaches, improving cancer prevention and early diagnosis. We leverage EHR data and build a prediction model for early detection of non-small cell lung cancer (NSCLC). Methods We utilize data from Mass General Brigham’s EHR and implement a three-stage ensemble learning approach. Initially, we generate risk scores using multivariate logistic regression in a self-control and case–control design to distinguish between cases and controls. Subsequently, these risk scores are integrated and calibrated using a prospective Cox model to develop the risk prediction model. Results We identified 127 EHR-derived features predictive for early detection of NSCLC. The highly predictive features include smoking, relevant lab test results, and chronic lung diseases. The predictive model reached area under the receiver operating characteristic (ROC) curve (area under the curve, AUC) of 0.801 (positive predictive value (PPV) 0.0173 with specificity 0.02) for predicting 1-year NSCLC risk in a population aged 18 and above, and AUC of 0.757 (PPV 0.0196 with specificity 0.02) in a population aged 40 and above. Conclusions This study identified EHR-derived features which are predictive of early NSCLC diagnosis. The developed risk prediction model exhibits superior performance for early detection of NSCLC compared to a baseline model that only relies on demographic and smoking information, demonstrating the potential of incorporating EHR-derived features for personalized cancer screening recommendations and early detection.

Abstract Background The gut-selective nature of vedolizumab has raised questions regarding increased joint pain or arthralgia with its use in inflammatory bowel disease (IBD) patients. As arthralgias are seldom coded and thus difficult to study, few studies have examined the comparative risk of arthralgia between vedolizumab and tumor necrosis factor inhibitor (TNFi). Our objectives were to evaluate the application of natural language processing (NLP) to identify arthralgia in the clinical notes and to compare the risk of arthralgia between vedolizumab and TNFi in IBD. Methods We performed a retrospective study using a validated electronic medical record (EMR)-based IBD cohort from 2 large tertiary care centers. The index date was the first date of vedolizumab or TNFi prescription. Baseline covariates were assessed 1 year before the index date; patients were followed 1 year after the index date. The primary outcome was arthralgia, defined using NLP. Using inverse probability of treatment weight to balance the cohorts, we then constructed Cox regression models to calculate the hazard ratio (HR) for arthralgia in the vedolizumab and TNFi groups. Results We studied 367 IBD patients on vedolizumab and 1218 IBD patients on TNFi. Patients on vedolizumab were older (mean age, 41.2 vs 34.9 years) and had more prevalent use of immunomodulators (52.3% vs 31.9%) than TNFi users. Our data did not observe a significantly increased risk of arthralgia in the vedolizumab group compared with TNFi (HR, 1.20; 95% confidence interval, 0.97-1.49). Conclusions In this large observational study, we did not find a significantly increased risk of arthralgia associated with vedolizumab use compared with TNFi.

The association between chronic inflammation and increased risk of cardiovascular disease in rheumatoid arthritis (RA) is well established. In the general population, inflammation is an established independent risk factor for cardiovascular disease, and much interest is placed on controlling inflammation to reduce cardiovascular events. As inflammation encompasses numerous pathways, the development of targeted therapies in RA provides an opportunity to understand the downstream effect of inhibiting specific pathways on cardiovascular risk. Data from these studies can inform cardiovascular risk management in patients with RA, and in the general population. This Review focuses on pro-inflammatory pathways targeted by existing therapies in RA and with mechanistic data from the general population on cardiovascular risk. Specifically, the discussions include the IL-1, IL-6 and TNF pathways, as well as the Janus kinase (JAK)–signal transducer and activator of transcription (STAT) signalling pathway, and the role of these pathways in RA pathogenesis in the joint alongside the development of atherosclerotic cardiovascular disease. Overall, some robust data support inhibition of IL-1 and IL-6 in decreasing the risk of cardiovascular disease, with growing data supporting IL-6 inhibition in both patients with RA and the general population to reduce the risk of cardiovascular disease.Rheumatoid arthritis is associated with an excess risk of atherosclerotic cardiovascular disease. This Review summarizes shared inflammatory pathways between rheumatoid arthritis and atherosclerotic cardiovascular disease that are targeted by existing therapies, and lessons learnt from clinical trials of these drugs.

The availability of monoclonal antibodies to tumor necrosis factor α has revolutionized management of Crohn's disease (CD) and ulcerative colitis. However, limited data exist regarding comparative effectiveness of these agents to inform clinical practice.MethodsThis study consisted of patients with CD or ulcerative colitis initiation either infliximab (IFX) or adalimumab (ADA) between 1998 and 2010. A validated likelihood of nonresponse classification score using frequency of narrative mentions of relevant symptoms in the electronic health record was applied to assess comparative effectiveness at 1 year. Inflammatory bowel disease–related surgery, hospitalization, and use of steroids were determined during this period.ResultsOur final cohort included 1060 new initiations of IFX (68% for CD) and 391 of ADA (79% for CD). In CD, the likelihood of nonresponse was higher in ADA than IFX (odds ratio, 1.62 and 95% CI, 1.21–2.17). Similar differences favoring efficacy of IFX were observed for the individual symptoms of diarrhea, pain, bleeding, and fatigue. However, there was no difference in inflammatory bowel disease–related surgery, hospitalizations, or prednisone use within 1 year after initiation of IFX or ADA in CD. There was no difference in narrative or codified outcomes between the 2 agents in ulcerative colitis.ConclusionsWe identified a modestly higher likelihood of symptomatic nonresponse at 1 year for ADA compared with IFX in patients with CD. However, there were no differences in inflammatory bowel disease–related surgery or hospitalizations, suggesting these treatments are broadly comparable in effectiveness in routine clinical practice.