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OBJECTIVES - To detail the greatest areas of unmet scientific and clinical needs in rheumatology.
METHODS - The 21st annual international Advances in Targeted Therapies meeting brought together more than 100 leading basic scientists and clinical researchers in rheumatology, immunology, epidemiology, molecular biology and other specialties. During the meeting, breakout sessions were convened, consisting of 5 disease-specific groups with 20-30 experts assigned to each group based on expertise. Specific groups included: rheumatoid arthritis, psoriatic arthritis, axial spondyloarthritis, systemic lupus erythematosus and other systemic autoimmune rheumatic diseases. In each group, experts were asked to identify unmet clinical and translational research needs in general and then to prioritise and detail the most important specific needs within each disease area.
RESULTS - Overarching themes across all disease states included the need to innovate clinical trial design with emphasis on studying patients with refractory disease, the development of trials that take into account disease endotypes and patients with overlapping inflammatory diseases, the need to better understand the prevalence and incidence of inflammatory diseases in developing regions of the world and ultimately to develop therapies that can cure inflammatory autoimmune diseases.
CONCLUSIONS - Unmet needs for new therapies and trial designs, particularly for those with treatment refractory disease, remain a top priority in rheumatology.
© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.
Antimalarials (AMs) reduce disease activity and improve survival in patients with systemic lupus erythematosus (SLE), but studies have reported low AM prescribing frequencies. Using a real-world electronic health record cohort, we examined if patient or provider characteristics impacted AM prescribing. We identified 977 SLE cases, 94% of whom were ever prescribed an AM. Older patients and patients with SLE nephritis were less likely to be on AMs. Current age (odds ratio = 0.97, < 0.01) and nephritis (odds ratio = 0.16, < 0.01) were both significantly associated with ever AM use after adjustment for sex and race. Of the 244 SLE nephritis cases, only 63% were currently on AMs. SLE nephritis subjects who were currently prescribed AMs were more likely to be followed by a rheumatologist than a nephrologist and less likely to have undergone dialysis or renal transplant (both < 0.001). Non-current versus current SLE nephritis AM users had higher serum creatinine ( < 0.001), higher urine protein ( = 0.05), and lower hemoglobin levels ( < 0.01). As AMs reduce disease damage and improve survival in patients with SLE, our results demonstrate an opportunity to target future efforts to improve prescribing rates among multi-specialty providers.
OBJECTIVE - To utilize electronic health records (EHRs) to study SLE, algorithms are needed to accurately identify these patients. We used machine learning to generate data-driven SLE EHR algorithms and assessed performance of existing rule-based algorithms.
METHODS - We randomly selected subjects with ≥ 1 SLE ICD-9/10 codes from our EHR and identified gold standard definite and probable SLE cases by chart review, based on 1997 ACR or 2012 SLICC Classification Criteria. From a training set, we extracted coded and narrative concepts using natural language processing and generated algorithms using penalized logistic regression to classify definite or definite/probable SLE. We assessed predictive characteristics in internal and external cohort validations. We also tested performance characteristics of published rule-based algorithms with pre-specified permutations of ICD-9 codes, laboratory tests and medications in our EHR.
RESULTS - At a specificity of 97%, our machine learning coded algorithm for definite SLE had 90% positive predictive value (PPV) and 64% sensitivity and for definite/probable SLE, 92% PPV and 47% sensitivity. In the external validation, at 97% specificity, the definite/probable algorithm had 94% PPV and 60% sensitivity. Adding NLP concepts did not improve performance metrics. The PPVs of published rule-based algorithms ranged from 45-79% in our EHR.
CONCLUSION - Our machine learning SLE algorithms performed well in internal and external validation. Rule-based SLE algorithms did not transport as well to our EHR. Unique EHR characteristics, clinical practices and research goals regarding the desired sensitivity and specificity of the case definition must be considered when applying algorithms to identify SLE patients.
Copyright © 2019 Elsevier Inc. All rights reserved.
In systemic lupus erythematosus (SLE), dsDNA antibodies are associated with renal disease. Less is known about comorbidities in patients without dsDNA or other autoantibodies. Using an electronic health record (EHR) SLE cohort, we employed a phenome-wide association study (PheWAS) that scans across billing codes to compare comorbidities in SLE patients with and without autoantibodies. We used our validated algorithm to identify SLE subjects. Autoantibody status was defined as ever positive for dsDNA, RNP, Smith, SSA and SSB. PheWAS was performed in antibody positive vs. negative SLE patients adjusting for age and race and using a false discovery rate of 0.05. We identified 1097 SLE subjects. In the PheWAS of dsDNA positive vs. negative subjects, dsDNA positive subjects were more likely to have nephritis ( p = 2.33 × 10) and renal failure ( p = 1.85 × 10). After adjusting for sex, race, age and other autoantibodies, dsDNA was independently associated with nephritis and chronic kidney disease. Those patients negative for dsDNA, RNP, SSA and SSB negative subjects were all more likely to have billing codes for sleep, pain and mood disorders. PheWAS uncovered a hierarchy within SLE-specific autoantibodies with dsDNA having the greatest impact on major organ involvement.
BACKGROUND - African Americans with systemic lupus erythematosus (SLE) have increased renal disease compared to Caucasians, but differences in other comorbidities have not been well-described. We used an electronic health record (EHR) technique to test for differences in comorbidities in African Americans compared to Caucasians with SLE.
METHODS - We used a de-identified EHR with 2.8 million subjects to identify SLE cases using a validated algorithm. We performed phenome-wide association studies (PheWAS) comparing African American to Caucasian SLE cases and African American SLE cases to matched non-SLE controls. Controls were age, sex, and race matched to SLE cases. For multiple testing, a false discovery rate (FDR) p value of 0.05 was used.
RESULTS - We identified 270 African Americans and 715 Caucasians with SLE and 1425 matched African American controls. Compared to Caucasians with SLE adjusting for age and sex, African Americans with SLE had more comorbidities in every organ system. The most striking included hypertension odds ratio (OR) = 4.25, FDR p = 5.49 × 10; renal dialysis OR = 10.90, FDR p = 8.75 × 10; and pneumonia OR = 3.57, FDR p = 2.32 × 10. Compared to the African American matched controls without SLE, African Americans with SLE were more likely to have comorbidities in every organ system. The most significant codes were renal and cardiac, and included renal failure (OR = 9.55, FDR p = 2.26 × 10) and hypertensive heart and renal disease (OR = 8.08, FDR p = 1.78 × 10). Adjusting for race, age, and sex in a model including both African American and Caucasian SLE cases and controls, SLE was independently associated with renal, cardiovascular, and infectious diseases (all p < 0.01).
CONCLUSIONS - African Americans with SLE have an increased comorbidity burden compared to Caucasians with SLE and matched controls. This increase in comorbidities in African Americans with SLE highlights the need to monitor for cardiovascular and infectious complications.
OBJECTIVE - Phenome-wide association studies (PheWAS) scan across billing codes in the electronic health record (EHR) and re-purpose clinical EHR data for research. In this study, we examined whether PheWAS could function as an EHR-based discovery tool for systemic lupus erythematosus (SLE) and identified novel clinical associations in male versus female patients with SLE.
METHODS - We used a de-identified version of the Vanderbilt University Medical Center EHR, which includes more than 2.8 million subjects. We performed EHR-based PheWAS to compare SLE patients with age-, sex-, and race-matched control subjects and to compare male SLE patients with female SLE patients, controlling for multiple testing using a false discovery rate (FDR) P value of 0.05.
RESULTS - We identified 1,097 patients with SLE and 5,735 matched control subjects. In a comparison of patients with SLE and matched controls, SLE patients were shown to be more likely to have International Classification of Diseases, Ninth Revision codes related to the SLE disease criteria. In the PheWAS of male versus female SLE patients, with adjustment for age and race, male patients were shown to be more likely to have atrial fibrillation (odds ratio 4.50, false discovery rate P = 3.23 × 10 ). Chart review confirmed atrial fibrillation, with the majority of patients developing atrial fibrillation after the SLE diagnosis and having multiple risk factors for atrial fibrillation. After adjustment for age, sex, race, and coronary artery disease, SLE disease status was shown to be significantly associated with atrial fibrillation (P = 0.002).
CONCLUSION - Using PheWAS to compare male and female patients with SLE, we identified a novel association of an increased incidence of atrial fibrillation in male patients. SLE disease status was shown to be independently associated with atrial fibrillation, even after adjustment for age, sex, race, and coronary artery disease. These results demonstrate the utility of PheWAS as an EHR-based discovery tool for SLE.
© 2018, American College of Rheumatology.
Differences in quality of care may contribute to health disparities in systemic lupus erythematosus (SLE). Studies show low physician adherence rates to the SLE quality indicators but do not assess physician perception of SLE quality indicators or quality improvement. Using a cross-sectional survey of rheumatologists in the southeastern USA, we assessed the perception and involvement of rheumatologists in quality improvement and the SLE quality indicators. Using electronic mail, an online survey of 32 questions was delivered to 568 rheumatologists. With a response rate of 19% (n = 106), the majority of participants were male, Caucasian, with over 20 years of experience, and seeing adult patients in an academic setting. Participants had a positive perception toward quality improvement (81%) with a majority responding that the SLE quality indicators would significantly impact quality of care (54%). While 66% of respondents were familiar with the SLE quality indicators, only 18% of respondents reported using them in everyday practice. The most commonly reported barrier to involvement in quality improvement and the SLE quality indicators was time. Rheumatologists had a positive perception of the SLE quality indicators and agreed that use of the quality indicators could improve quality of care in SLE; however, they identified time as a barrier to implementation. Future studies should investigate methods to increase use of the SLE quality indicators.
OBJECTIVE - Inconclusive findings about infection risks, importantly the use of immunosuppressive medications in patients who have undergone large-joint total joint arthroplasty, challenge efforts to provide evidence-based perioperative total joint arthroplasty recommendations to improve surgical outcomes. Thus, the aim of this study was to describe risk factors for developing a post-operative infection in patients undergoing TJA of a large joint (total hip arthroplasty, total knee arthroplasty, or total shoulder arthroplasty) by identifying clinical and demographic factors, including the use of high-risk medications (i.e., prednisone and immunosuppressive medications) and diagnoses [i.e., rheumatoid arthritis (RA), osteoarthritis (OA), gout, obesity, and diabetes mellitus] that are linked to infection status, controlling for length of follow-up.
METHODS - A retrospective, case-control study (N = 2212) using de-identified patient health claims information from a commercially insured, U.S. dataset representing 15 million patients annually (from January 1, 2007 to December 31, 2009) was conducted. Descriptive statistics, t-test, chi-square test, Fisher's exact test, and multivariate logistic regression were used.
RESULTS - Male gender (OR = 1.42, p < 0.001), diagnosis of RA (OR = 1.47, p = 0.031), diabetes mellitus (OR = 1.38, p = 0.001), obesity (OR = 1.66, p < 0.001) or gout (OR = 1.95, p = 0.001), and a prescription for prednisone (OR = 1.59, p < 0.001) predicted a post-operative infection following total joint arthroplasty. Persons with post-operative joint infections were significantly more likely to be prescribed allopurinol (p = 0.002) and colchicine (p = 0.006); no significant difference was found for the use of specific disease-modifying anti-rheumatic drugs and TNF-α inhibitors.
CONCLUSION - High-risk, post-operative joint infection groups were identified allowing for precautionary clinical measures to be taken.
Copyright © 2017 Elsevier Inc. All rights reserved.
OBJECTIVE - To study systemic lupus erythematosus (SLE) in the electronic health record (EHR), we must accurately identify patients with SLE. Our objective was to develop and validate novel EHR algorithms that use International Classification of Diseases, Ninth Revision (ICD-9), Clinical Modification codes, laboratory testing, and medications to identify SLE patients.
METHODS - We used Vanderbilt's Synthetic Derivative, a de-identified version of the EHR, with 2.5 million subjects. We selected all individuals with at least 1 SLE ICD-9 code (710.0), yielding 5,959 individuals. To create a training set, 200 subjects were randomly selected for chart review. A subject was defined as a case if diagnosed with SLE by a rheumatologist, nephrologist, or dermatologist. Positive predictive values (PPVs) and sensitivity were calculated for combinations of code counts of the SLE ICD-9 code, a positive antinuclear antibody (ANA), ever use of medications, and a keyword of "lupus" in the problem list. The algorithms with the highest PPV were each internally validated using a random set of 100 individuals from the remaining 5,759 subjects.
RESULTS - The algorithm with the highest PPV at 95% in the training set and 91% in the validation set was 3 or more counts of the SLE ICD-9 code, ANA positive (≥1:40), and ever use of both disease-modifying antirheumatic drugs and steroids, while excluding individuals with systemic sclerosis and dermatomyositis ICD-9 codes.
CONCLUSION - We developed and validated the first EHR algorithm that incorporates laboratory values and medications with the SLE ICD-9 code to identify patients with SLE accurately.
© 2016, American College of Rheumatology.
Neutrophil extracellular traps are associated with a unique form of cell death distinct from apoptosis or necrosis, whereby invading microbes are trapped and killed. Neutrophil extracellular traps can contribute to autoimmunity by exposing autoantigens, inducing IFN-α production, and activating the complement system. The association of neutrophil extracellular traps with autoimmune diseases, particularly systemic lupus erythematosus, will be reviewed. Increased neutrophil extracellular trap formation is seen in psoriasis, antineutrophil cytoplasmic antibody-associated vasculitis, antiphospholipid antibody syndrome rheumatoid arthritis, and systemic lupus erythematosus. Neutrophil extracellular traps may promote thrombus formation in antineutrophil cytoplasmic antibody-associated vasculitis and antiphospholipid antibody syndrome. In systemic lupus erythematosus, increased neutrophil extracellular trap formation is associated with increased disease activity and renal disease, suggesting that neutrophil extracellular traps could be a disease activity marker. Neutrophil extracellular traps can damage and kill endothelial cells and promote inflammation in atherosclerotic plaques, which may contribute to accelerated atherosclerosis in systemic lupus erythematosus. As neutrophil extracellular traps induce IFN-α production, measuring neutrophil extracellular traps may estimate IFN-α levels and identify which systemic lupus erythematosus patients have elevated levels and may be more likely to respond to emerging anti-IFN-α therapies. In addition to anti-IFN-α therapies, other novel agents, such as N-acetyl-cysteine, DNase I, and peptidylarginine deiminase inhibitor 4, target neutrophil extracellular traps. Neutrophil extracellular traps offer insight into the pathogenesis of autoimmune diseases and provide promise in developing disease markers and novel therapeutic agents in systemic lupus erythematosus. Priority areas for basic research based on clinical research insights will be identified, specifically the potential role of neutrophil extracellular traps as a biomarker and therapeutic target in systemic lupus erythematosus.
© Society for Leukocyte Biology.