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Patients with systemic sclerosis and systemic lupus erythematosus serologies present a unique challenge to the clinician when hypertension is detected in the outpatient setting. Treatment choices for non-renal crisis hypertension are different for systemic sclerosis versus systemic lupus erythematosus. Urgent laboratory studies and, in the presence of certain symptoms, imaging assessment are indicated in systemic sclerosis and systemic lupus erythematosus overlap patients with systemic hypertension. Long-term assessment of systemic hypertension may be enhanced by advances in non-contrast imaging that serve as valuable biomarkers for progressive vasculopathy. In this review, the diagnostic approach to systemic sclerosis and systemic lupus erythematosus overlap patients presenting with hypertension is discussed.

Objective Disparities in pregnancy outcomes among women with SLE remain understudied, with few available racially diverse datasets. We sought to identify disparities between Black and White women in pregnancy outcomes within academic institutions in the United States. Methods Using the Common Data Model electronic medical record (EMR)-based datasets within the Carolinas Collaborative, we identified women with pregnancy delivery data (2014–2019) and ≥1 SLE International Classification of Diseases 9 or 10 code (ICD9/10) code. From this dataset, we identified four cohorts of SLE pregnancies, three based on EMR-based algorithms and one confirmed with chart review. We compared the pregnancy outcomes identified in each of these cohorts for Black and White women. Results Of 172 pregnancies in women with ≥1 SLE ICD9/10 code, 49% had confirmed SLE. Adverse pregnancy outcomes occurred in 40% of pregnancies in women with ≥1 ICD9/10 SLE code and 52% of pregnancies with confirmed SLE. SLE was frequently over-diagnosed in women who were White, resulting in 40–75% lower rates of adverse pregnancy outcomes in EMR-derived vs confirmed SLE cohorts. Over-diagnosis was less common for Black women with pregnancy outcomes 12–20% lower in EMR-derived vs confirmed SLE cohorts. Black women had higher rates of adverse pregnancy outcomes than White women in the EMR-derived, but not the confirmed cohorts. Conclusion EMR-derived cohorts of pregnancies in women who are Black, but not White, provided accurate estimations of pregnancy outcomes. The data from the confirmed SLE pregnancies suggest that all women with SLE, regardless of race, referred to academic centres remain at very high risk for adverse pregnancy outcome.

Electronic health records (EHRs) represent powerful tools to study rare diseases. Our objective was to develop and validate EHR algorithms to identify systemic lupus erythematosus (SLE) births across centers.We developed algorithms in a training set using an EHR with over 3 million subjects and validated the algorithms at 2 other centers. Subjects at all 3 centers were selected using ≥1 code for SLE International Classification of Diseases, Ninth Revision (ICD-9) or SLE International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, Clinical Modification (ICD-10-CM) and ≥1 ICD-9 or ICD-10-CM delivery code. A subject was a case if diagnosed with SLE by a rheumatologist and had a birth documented. We tested algorithms using SLE ICD-9 or ICD-10-CM codes, antimalarial use, a positive antinuclear antibody ≥1:160, and ever checked double-stranded DNA or complement, using both rule-based and machine learning methods. Positive predictive values (PPVs) and sensitivities were calculated. We assessed the impact of case definition, coding provider, and subject race on algorithm performance.Algorithms performed similarly across all 3 centers. Increasing the number of SLE codes, adding clinical data, and having a rheumatologist use the SLE code all increased the likelihood of identifying true SLE patients. All the algorithms had higher PPVs in African American versus White SLE births. Using machine learning methods, the total number of SLE codes and an SLE code from a rheumatologist were the most important variables in the model for SLE case status.We developed and validated algorithms that use multiple types of data to identify SLE births in the EHR. Algorithms performed better in African American mothers than in White mothers.

Interstitial lung disease (ILD) represents a significant cause of morbidity and mortality in systemic sclerosis (SSc). The purpose of this study was to examine recirculating lymphocytes from SSc patients for potential biomarkers of interstitial lung disease (ILD). Peripheral blood mononuclear cells (PBMCs) were isolated from patients with SSc and healthy controls enrolled in the Vanderbilt University Myositis and Scleroderma Treatment Initiative Center cohort between 9/2017–6/2019. Clinical phenotyping was performed by chart abstraction. Immunophenotyping was performed using both mass cytometry and fluorescence cytometry combined with t-distributed stochastic neighbor embedding analysis and traditional biaxial gating. This study included 34 patients with SSc-ILD, 14 patients without SSc-ILD, and 25 healthy controls. CD21lo/neg cells are significantly increased in SSc-ILD but not in SSc without ILD (15.4 ± 13.3% vs. 5.8 ± 0.9%, p = 0.002) or healthy controls (5.0 ± 0.5%, p lo/neg B cells can be identified from a single biaxial gate, tSNE analysis reveals that the biaxial gate is comprised of multiple distinct subsets, all of which are increased in SSc-ILD. CD21lo/neg cells in both healthy controls and SSc-ILD are predominantly tBET positive and do not have intracellular CD21. Immunohistochemistry staining demonstrated that CD21lo/neg B cells diffusely infiltrate the lung parenchyma of an SSc-ILD patient. Additional work is needed to validate this biomarker in larger cohorts and longitudinal studies and to understand the role of these cells in SSc-ILD.

Using a large, de-identified electronic health record database with over 3.2 million patients, we aimed to identify trends of systemic lupus erythematosus (SLE) medication use during pregnancy and birth outcomes from 1989 to 2020.Using a previously validated algorithm for SLE deliveries, we identified 255 pregnancies in patients with SLE and 604 pregnancies in controls with no known autoimmune diseases. We examined demographics, medications, SLE comorbidities, and maternal and fetal outcomes in SLE and control deliveries.Compared with control deliveries, SLE deliveries were more likely to be complicated by preterm delivery (odds ratio [OR]: 6.71; 95% confidence interval [CI]: 4.31-10.55; P 0.001) and preeclampsia (OR: 3.22; 95% CI: 1.83-5.66; P 0.001) after adjusting for age at delivery, race, and parity. In a longitudinal analysis, medication use during SLE pregnancies remained relatively stable, with some increased use of hydroxychloroquine over time but no increase in aspirin use. For SLE deliveries, preterm delivery and preeclampsia rates remained stable.We observed rates of preeclampsia and preterm delivery in SLE that were five times higher than the general population and higher compared with other prospective SLE cohorts. Furthermore, we did not observe improved outcomes over time with preeclampsia and preterm delivery. Despite increasing evidence for universal use of hydroxychloroquine and aspirin, we did not observe substantially higher use of these medications over time, particularly for aspirin. Our results demonstrate the continued need to prioritize educational and implementation efforts to improve adverse pregnancy outcomes in SLE.

OBJECTIVE: No guidelines exist for Pneumocystis jirovecii pneumonia (PJP) prophylaxis in patients with systemic lupus erythematosus (SLE). Limited data are available on incidence of PJP infection and use of PJP prophylaxis. Using a real-world, electronic health record (EHR) cohort, we investigated the frequency of PJP infections as well as patient and provider factors that impacted use and type of PJP prophylaxis. METHODS: In a large, de-identified EHR, we identified possible SLE patients using a previously validated algorithm. PJP ICD-9 or ICD-10-CM billing codes and PJP keywords were used to identify possible PJP cases within this SLE cohort. We assessed for PJP prophylaxis prescribing in all SLE patients using keywords and reviewing medication lists for prophylactic agents. Chart review was used to confirm cases of SLE, PJP, and PJP prophylaxis and to obtain data on demographics, comorbidities, and immunosuppressants. RESULTS: Of 977 SLE patients, there were only four with confirmed PJP infection. Two of these patients had concurrent Acquired Immunodeficiency Syndrome, and none were on prophylaxis. Of 977 SLE patients, 132 (14%) were prescribed PJP prophylaxis. Of 617 SLE patients ever prescribed immunosuppressants, 128 (21%) were prescribed PJP prophylaxis. Sulfonamides were the most common prophylaxis prescribed (69%), and possible adverse events were documented in 22 out of 117 instances of being placed on a sulfonamide. Patients of younger age, Black race, nephritis, and renal transplant, and on chronic glucocorticoids were all more likely to have PJP prophylaxis prescribed. Patients who were on transplant induction medications, calcineurin/mTOR inhibitors, cyclophosphamide, and mycophenolate mofetil all were more likely to be prescribed PJP prophylaxis compared to other immunosuppressants. CONCLUSION: PJP is a rare diagnosis among SLE patients, and prior studies may even overestimate its prevalence. PJP prophylaxis was less common in our cohort than previously described. Adverse events related to sulfonamides used for PJP prophylaxis were relatively rare with lower rates than previously reported. Our study demonstrates real-world PJP prophylaxis prescribing patterns in a large cohort of SLE patients.

Repurposing is an increasingly attractive method within the field of drug development for its efficiency at identifying new therapeutic opportunities among approved drugs at greatly reduced cost and time of more traditional methods. Repurposing has generated significant interest in the realm of rare disease treatment as an innovative strategy for finding ways to manage these complex conditions. The selection of which agents should be tested in which conditions is currently informed by both human and machine discovery, yet the appropriate balance between these approaches, including the role of artificial intelligence (AI), remains a significant topic of discussion in drug discovery for rare diseases and other conditions. Our drug repurposing team at Vanderbilt University Medical Center synergizes machine learning techniques like phenome-wide association study—a powerful regression method for generating hypotheses about new indications for an approved drug—with the knowledge and creativity of scientific, legal, and clinical domain experts. While our computational approaches generate drug repurposing hits with a high probability of success in a clinical trial, human knowledge remains essential for the hypothesis creation, interpretation, “go-no go” decisions with which machines continue to struggle. Here, we reflect on our experience synergizing AI and human knowledge toward realizable patient outcomes, providing case studies from our portfolio that inform how we balance human knowledge and machine intelligence for drug repurposing in rare disease.

Objectives Patients with RA and SLE have an excess cardiovascular risk. We aimed to evaluate outcomes of acute cardiovascular events in these patients. Methods Using a nationwide database of Taiwan, we identified adult patients who experienced first-time acute myocardial infarction (n = 191 008), intracranial haemorrhage (n = 169 923) and ischaemic stroke (n = 486 890) over a 13-year period. Odds ratios (ORs) of in-hospital mortality and hazard ratios (HRs) of overall mortality and adverse outcomes during long-term follow-up in relation to RA and SLE were estimated with adjustment for potential confounders. Results In each cohort, 748, 410 and 1419 patients had established RA; 256, 292 and 622 patients had SLE. Among acute myocardial infarction patients, RA and SLE were associated with in-hospital mortality (RA: OR 1.61, 95% CI 1.33, 1.95; SLE: OR 2.31, 95% CI 1.62, 3.28) and overall mortality. Additionally, RA (HR 1.28, 95% CI 1.18, 1.38) and SLE (HR 1.46, 95% CI 1.27, 1.69) increased the risk of major adverse cardiac events. After intracranial haemorrhage, patients with RA and SLE had higher risks of in-hospital mortality (RA: OR 1.61, 95% CI 1.26, 2.06; SLE: OR 3.00, 95% CI 2.33, 3.86) and overall mortality. After ischaemic stroke, RA and SLE increased in-hospital mortality (RA: OR 1.45, 95% CI 1.15, 1.83; SLE: OR 2.18, 95% CI 1.57, 3.02), overall mortality and recurrent cerebrovascular events (RA: HR 1.10, 95% CI 1.002, 1.21; SLE: HR 1.31, 95% CI 1.14, 1.51), among which ischaemic stroke (HR 1.39, 95% CI 1.19, 1.62) was more likely to recur in SLE patients. Conclusion Both RA and SLE are consistently associated with adverse outcomes following acute cardiovascular events, highlighting the necessity of integrated care for affected patients.

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.

Background Blood pressure visit-to-visit variability is a novel risk factor for deleterious long-term cardiac and renal outcomes in the general population. We hypothesized that patients with systemic lupus erythematosus (SLE) have greater blood pressure visit-to-visit variability than control subjects and that blood pressure visit-to-visit variability is associated with a higher comorbidity burden. Methods We studied 899 patients with SLE and 4172 matched controls using de-identified electronic health records from an academic medical center. We compared blood pressure visit-to-visit variability measures in patients with SLE and control subjects and examined the association between blood pressure visit-to-visit variability and patients’ characteristics. Results Patients with SLE had higher systolic blood pressure visit-to-visit variability 9.7% (7.8–11.8%) than the control group 9.2% (7.4–11.2%), P Conclusion Patients with SLE had higher blood pressure visit-to-visit variability than controls, and this increased blood pressure visit-to-visit variability was associated with greater Charlson comorbidity scores, several clinical characteristics and immunosuppressant medications. In particular, hydroxychloroquine prescription was associated with lower blood pressure visit-to-visit variability.

BackgroundRecognition of Anti tRNA synthetase (ARS) related interstitial lung disease (ILD) is key to ensuring patients have prompt access to immunosuppressive therapies. The purpose of this retrospective cohort study was to identify factors that may delay recognition of ARS-ILD.MethodsPatients seen at Vanderbilt University Medical Center (VUMC) between 9/17/2017-10/31/2018 were included in this observational cohort. Clinical and laboratory features were obtained via chart abstraction. Kruskal-Wallis ANOVA, Mann-Whitney U, and Fisher’s exact t tests were utilized to determine statistical significance.ResultsPatients with ARS were found to have ILD in 51.9% of cases, which was comparable to the frequency of ILD in systemic sclerosis (59.5%). The severity of FVC reduction in ARS (53.2%) was comparable to diffuse cutaneous systemic sclerosis (56.8%, p=0.48) and greater than dermatomyositis (66.9%, p=0.005) or limited cutaneous systemic sclerosis (lcSSc, 71.8%, p=0.005). Frank honeycombing was seen with ARS antibodies but not other myositis autoantibodies. ARS patients were more likely to first present to a pulmonary provider in a tertiary care setting (53.6%), likely due to fewer extrapulmonary manifestations. Only 33% of ARS-ILD were anti-nuclear antibody, rheumatoid factor, or anti-cyclic citrullinated peptide positive. Patients with ARS-ILD had a two-fold longer median time to diagnosis compared to other myositis-ILD patients (11.0 months, IQR 8.5 to 43 months vs. 5.0 months, IQR 3.0 to 9.0 months, p=0.003).ConclusionsARS patients without prominent extra-pulmonary manifestations are at high risk for not being recognized as having a connective tissue disease related ILD and miscategorized as UIP/IPF without comprehensive serologies.

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 × 10−9) and renal failure ( p = 1.85 × 10−5). 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.

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 × 10-3 ). 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.

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.

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, p

Background Studying births to women with systemic lupus erythematosus (SLE) is difficult given its rarity and the challenges of prospective cohort studies. While the electronic health record (EHR) is a powerful tool to capture coded diagnoses at a population level, accurately identifying SLE births is challenging. Our objective was to develop and externally validate algorithms for identifying births to SLE patients. Methods We used two EHR-based datasets: Vanderbilts Synthetic Derivative and Dukes Clarity. Potential cases had at least 1 SLE code (ICD-9: 710.0 or ICD-10:M32.1*, M32.8, M32.9) and at least 1 ICD-9 or ICD-10 code for pregnancy-related diagnoses. At Vanderbilt, 100 potential cases were randomly selected for chart review and each classified as a case if SLE was diagnosed by a rheumatologist, nephrologist, or dermatologist. Using this dataset, positive predictive values (PPVs) and sensitivity were calculated for combinations of counts of SLE ICD-9 or ICD-10 codes provided by any clinician and by a rheumatologist (rheumatology coded), antimalarial use, positive ANA, and checked lupus labs (dsDNA, C3 or C4). F-score measured the performance of each algorithm. At Duke, potential cases were compared with the Duke Autoimmunity in Pregnancy Registry; cases outside of this registry underwent chart review. Vanderbilt served as a training set; Duke served as validation. Results From Vanderbilts 2.8 million subject records, we identified 433 potential cases. Of the 100 cases randomly selected for chart review, 39 had confirmed SLE and a history of a birth. Of Dukes 659 potential cases, 545 were included in a validation set of which 208 had confirmed SLE. In the training set, algorithms with ICD-10 codes had higher PPVs than algorithms with ICD-9 codes (table 1). The algorithm with the highest F-score of 88% was 4 counts of ICD-9 or ICD-10 codes and checked lupus labs. Algorithms validated well in the Duke dataset. In the validation set, 1 ICD-9 or ICD-10 code (by a rheumatologist) performed best (F-score: 82%). Conclusions We have developed and validated algorithms to detect SLE patients with births in the EHR. The highest performing algorithms use SLE ICD-9 or ICD-10 codes and clinical parameters or ICD-10 codes alone. Algorithms using more SLE coded visits have greater PPVs at a cost to sensitivity. While the PPV and sensitivity nears 90%, EHR cohorts remain complementary to prospective cohorts. However, in the era of big data, developing methods to identify SLE births accurately is critical to examine adverse outcomes such as preterm births. Funding Source(s): None Research reported in this abstract was supported by NIH/NICHD 5K12HD043483-12 (Barnado), NIH/NIAMS 1 K08 AR072757-01 (Barnado), and NIH/NCATSNational 1UL1TR002553 (Duke). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Supplemental Material for Real-world electronic health record identifies antimalarial underprescribing in patients with lupus nephritis by W W Xiong, J B Boone, L Wheless, C P Chung, L J Crofford and A Barnado in Lupus

Supplemental Material for Increased blood pressure visit-to-visit variability in patients with systemic lupus erythematosus: association with inflammation and comorbidity burden by T Reese, A L Dickson, M M Shuey, J S Gandelman, A Barnado, K A Barker, J E Neal, O A Khan, W D Dupont, C M Stein and C P Chung in Lupus

Sodium and potassium intake are modifiable determinants of hypertension in the general population but have not been studied in patients with systemic lupus erythematosus (SLE). We examined the relationship between urinary excretion of sodium and potassium, as an estimate of intake, and blood pressure in patients with SLE. We studied 178 SLE patients and 86 controls, matched for age, sex, and race. Urine sodium (Na+) and potassium (K+) were measured by flame photometry. Blood pressure was the average of two resting measurements. The associations between systolic (SBP) and diastolic blood pressures (DBP) and estimated 24-hour urinary Na+, K+, and Na+:K+ ratio were tested. The estimated mean 24-hour urinary K+ excretion was lower, and the Na+:K+ ratio was higher in patients with SLE than controls. There were no significant differences in the estimated 24-hour urinary Na+. In patients with SLE, a higher urinary Na+:K+ ratio was associated with higher SBP (β coefficient = 4.01, p = 0.023) and DBP (β coefficient = 4.41, p = 0.002) after adjusting for age, sex, and race. SLE patients had significantly lower estimated 24-hour urinary K+ and higher estimated 24-hour urinary Na+: K+ ratio than controls. The urinary Na+:K+ ratio was significantly associated with SBP and DBP.

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.

Background Systemic lupus erythematosus (SLE) is a heterogeneous disease with diverse presentations. dsDNA antibodies associate with renal disease. Less is known about comorbidities in SLE patients without dsDNA antibodies. Using a large electronic health record (EHR) cohort, we sought to identify comorbidities that associate with dsDNA status. We used a technique that scans across EHR billing codes called phenome-wide association study (PheWAS) to compare SLE patients with and without dsDNA antibodies. We also evaluated the relative importance of SLE specific autoantibodies on SLE criteria. Methods We used our validated SLE algorithm of ≥4 counts of the SLE ICD-9 code and ANA positive ≥1:160 while excluding dermatomyositis and systemic sclerosis ICD-9 codes with a positive predictive value of 94% and a sensitivity of 86%. We identified SLE cases in a de-identified EHR that contains over 2.8 million subjects. Autoantibody status was defined as ever positive for dsDNA, RNP, Smith, SSA, and SSB. PheWAS was performed in dsDNA positive vs negative SLE patients using logistic regression adjusting for current age and race. For multiple testing, a false discovery rate (FDR) p of 0.05 was used. We also performed logistic regression to assess the impact of autoantibodies, age, sex, and race on SLE criteria. Results We identified 1097 SLE subjects. As expected, dsDNA positive subjects, compared to dsDNA negative, were more likely to have renal codes including nephritis (OR=4.60, FDR p=2.33 × 10-9), renal failure (OR=2.30, FDR p=1.85 × 10-5), and end stage renal disease (OR=2.63, FDR p=0.01). After adjusting for sex, race, age, and other autoantibodies, dsDNA was independently associated with nephritis (p=1.98 × 10-6) and chronic kidney disease (p=0.001) and also associated, along with SSB, with serositis (p=0.01) and hematologic criteria (p=0.001) (figure 1). dsDNA negative subjects were more likely to have codes for sleep, pain, and mood disorders. Conclusion Using PheWAS, we uncovered a hierarchy within SLE specific autoantibodies where dsDNA, compared to other autoantibodies, was a powerful predictor of major organ involvement in SLE.

OBJECTIVE To compare the risk of resistant hypertension (RHTN) in patients with systemic lupus erythematosus (SLE) and in controls without SLE, and to define factors associated with RHTN in patients with SLE. METHODS We studied 1,044 patients with SLE and 5,241 control subjects using de-identified electronic health records from a tertiary care center. SLE was defined as ≥4 International Classification of Diseases, Ninth Revision codes for SLE and antinuclear antibody titer ≥1:160. RHTN was defined as uncontrolled blood pressure on 3 antihypertensive medications or requiring 4 or more antihypertensives to attain control. First, we compared the risk of RHTN between groups. Second, we examined the association between RHTN and all-cause mortality in patients with SLE. RESULTS RHTN was nearly twice as prevalent in patients with SLE compared to control subjects (10.2% and 5.3%, respectively), with an incidence rate of 10.2 versus 6.1 cases per 1,000 person-years of observation (hazard ratio [HR] 1.72 [95% confidence interval 1.28-2.30]; P < 0.001, adjusted for age, sex, race, baseline end-stage renal disease [ESRD], creatinine, and calendar year). In patients with SLE, we found associations between RHTN and black race, lower renal function, hypercholesterolemia, and increased inflammatory markers. RHTN was associated with a significantly higher mortality risk (HR 2.91, P = 0.0005) after adjustment for age, sex, race, calendar year, creatinine, baseline ESRD, and number of visits. CONCLUSION Patients with SLE have a higher risk of RHTN compared to frequency-matched controls, independent of multiple covariates. RHTN is an important comorbidity for clinicians to recognize in SLE, because it is associated with a higher risk of mortality.

Background Resistant hypertension (RHTN) is characterised by blood pressure that remains ≥140/90 mmHg despite concurrent use of 3 antihypertensive drugs. In the general population, RHTN is associated with a 47% increased risk of cardiovascular events. 1 Patients with systemic lupus erythematosus (SLE) have increased cardiovascular risk; however, no research has addressed the incidence, prevalence, or risk factors associated with RHTN in patients with SLE. Objectives To compare the risk of RHTN in patients with SLE and frequency-matched controls without SLE; to define factors associated with RHTN in patients with SLE. Methods We used a validated algorithm (94% PPV) to identify patients with SLE from the electronic health records (EHR) at an academic medical centre. 2 We established a control cohort matched by age, race, and sex with a 5:1 control-case ratio. Follow-up began at first ICD9 code for SLE (cases) or first ICD9 code (controls) and continued until RHTN diagnosis or last note. RHTN diagnosis required either the simultaneous use of 3 antihypertensive drugs and a mean blood pressure ≥140/90 mm Hg in the following 6 months, or the use of ≥4 antihypertensive drugs simultaneously. We used logistic regression and Cox proportional hazards (CPH) models to compare risk of RHTN between groups, with CPH performed on incident cases only. Results We studied 1044 patients with SLE and 5241 controls (median age 42, [31–54] 90% female and 70% Caucasian). Of the total cohort, RHTN developed in 106 SLE patients (10%) and 278 controls (4%). The incidence rate of RHTN was 14.7 cases/1000 person-years in SLE patients compared to 7.4 in controls [HR 1.66, 95% CI, 1.25–2.21] (figure 1). In logistic regression models, RHTN was associated with older age, black race, male gender and end stage renal disease (ESRD). Patients with SLE had a higher risk of RHTN when adjusted for age, sex, race, calendar year, and ESRD [HR 1.53, 1.15–2.05]. In an analysis among SLE patients, RHTN was associated with mortality in an unadjusted model [HR 3.38, 2.20–5.18]. This association remained when age, sex and race were added to the model [HR 2.58, 1.65–4.03], but when ESRD, calendar year and creatinine were included, the association was no longer significant [HR 1.51, 0.91–2.51]. Log rank test: p=0.000391 Conclusions Patients with SLE have a higher risk of RHTN compared to frequency-matched controls. RHTN is an important comorbiditiy for clinicans to recognise in SLE, as it is associated with a 3.3-fold higher risk of mortality. References [1] Muntner P, et al. Hypertension2014;64(5):1012–1021. [2] Barnado A, et al. Arthritis Care Res2017;69(5):687–693. Acknowledgements VUMC’s Synthetic Derivative supported by institutional funding and by the CTSA grant ULTR000445 from NCATS/NIH. CTSA awaUL1TR000445 from NCATS, The Rheumatology Research Foundation, Lupus Research Alliance and K-23 award from the NIAMS. Disclosure of Interest None declared

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− 15; renal dialysis OR = 10.90, FDR p = 8.75 × 10− 14; and pneumonia OR = 3.57, FDR p = 2.32 × 10− 8. 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− 40) and hypertensive heart and renal disease (OR = 8.08, FDR p = 1.78 × 10− 22). 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

Supplemental Material for Phenome-wide association study identifies dsDNA as a driver of major organ involvement in systemic lupus erythematosus by A. Barnado, R.J. Carroll, C. Casey, L. Wheless, J.C. Denny and L.J. Crofford in Lupus

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.

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.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.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.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.

The patient’s perspective of how their health affects their function is health-related quality of life (HRQOL). HRQOL is poorer in patients with systemic lupus erythematosus (SLE). Few HRQOL studies in SLE patients have focused on African Americans despite an increased disease burden compared with Caucasians. The African American Gullah population of South Carolina has a homogeneous genetic and environmental background and a high prevalence of multi-patient families with SLE. Demographics, medical history, and Short-Form 36 (SF-36) were measured within a cohort of Gullah SLE cases and related controls. Compared with related controls ( n = 37), cases ( n = 89) had a lower Physical Component Summary (PCS, 41.8 vs. 52.3, p

Microparticles (MPs) are membrane-bound vesicles with important physiologic effects. MPs exchange information intercellularly, with each kind of MP carrying antigens and receptors of the cells from which they originated. They are biologic effectors in inflammation, angiogenesis, vascular injury, and thrombosis. Thrombosis is generally caused by abnormalities in blood flow, blood composition, and/or properties of the vessel wall. Thrombosis is a well-described feature of cardiovascular disease and cerebrovascular disease. Accumulating evidence suggests that increased risk of thrombosis is also characteristic of autoimmune disorders and immune-mediated diseases affecting all age groups, although the older adults are most vulnerable. Current research has also implicated MPs as a source of autoantigenic nuclear material that can form immune complexes, activate the innate immune system, and may lead to autoimmunity. This review focuses on the contribution of MPs to both the pathogenesis of autoimmune diseases and, as the immune and coagulation systems are tightly linked, their role in hypercoagulability in the setting of autoimmunity in an aging population.

Objective In a study of Gullah African–Americans, we compared pregnancy outcomes before and after systemic lupus erythematosus (SLE) diagnosis to controls to test whether there is a predisease state that negativelyaffects pregnancy outcomes. Design Cases and controls reporting at least one pregnancy were included. Controls were all Gullah African-American females. We collected demographic, socioeconomic and pregnancy data. We modelled pregnancy outcome associations with case status using multiple logistic regression to calculate ORs. Results After adjustment for age, years of education, medical coverage and pregnancy number, compared with controls, cases were more likely to have any adverse outcome (OR 2.35, 95% CI 1.78 to 3.10), including stillbirth (OR 4.55, 95% CI 1.53 to 13.50), spontaneous abortion (OR 2.05, 95% CI 1.40 to 3.00), preterm birth (OR 2.58, 95% CI 1.58 to 4.20), low birth weight (OR 2.64, 95% CI 1.61 to 4.34) and preeclampsia (OR 1.80, 95% CI 1.08 to 3.01). The odds of adverse pregnancy outcomes all increased after SLE diagnosis compared with before diagnosis, even after adjustment for age, years of education, pregnancy number and medical coverage. Conclusion From a large cohort of African–American women, our findings suggest there may be a predisease state that predisposes to adverse pregnancy outcomes.

Background Sodium and potassium intake are modifiable determinants of blood pressure in the general population. Higher sodium and lower potassium urinary excretion, as well as a higher sodium:potassium ratio, are associated with elevated blood pressure. The prevalence of hypertension and cardiovascular disease are increased in systemic lupus erythematosus (SLE), but the contribution of sodium and potassium intake is not known. Objectives To examine the hypothesis that urinary excretion of sodium and potassium are related to blood pressure in SLE. Methods We studied 178 patients with SLE and 86 controls frequency-matched for age, sex, and race. First morning urine specimens were collected and urine sodium and potassium concentrations measured by flame photometry. The Kawasaki formula, a validated method that incorporates age, sex, height, weight, and urinary creatinine, was used to estimate 24 hour urine sodium and potassium excretion. Blood pressure was the average of two resting measurements. Fisher9s exact and Mann-Whitney U tests were used to compare categorical and continuous variables, respectively. The associations between systolic (SBP) and diastolic blood pressures (DBP) with estimated 24 hour urinary sodium, potassium, and sodium:potassium ratio were tested using Spearman correlation and then modeled using linear regression adjusting for age, sex, and race. Two-sided p values Results SLE patients and controls had a similar mean age (40.8±12.4 vs. 41.2±12.0, p=0.76), were predominantly female (88% vs. 86%, p=0.70) and Caucasian (68% vs. 72%, p=0.67). Compared to controls, SLE patients had significantly higher rates of hypertension (44% vs. 19%, p Conclusions SLE patients had significantly lower estimated 24 hour urinary potassium and higher estimated 24 hour urinary sodium:potassium ratio than control subjects. The estimated 24 hour urinary sodium:potassium ratio was significantly associated with SBP and DBP in SLE patients. Further studies are needed to evaluate the impact of diets with low sodium and high potassium content on blood pressure in SLE patients. Disclosure of Interest None declared

We report a case of a 43-year-old female who presented with right ear fullness and otorrhea. She was initially diagnosed with mastoiditis that was not responsive to multiple courses of antibiotics and steroids. She was then diagnosed with refractory inflammatory pseudotumor, and subsequent treatments included several mastoidectomies, further steroids, and radiation therapy. The patient went on to develop mastoiditis on the contralateral side as well as central nervous system involvement with headaches and right-sided facial paresthesias. Reexamination of the mastoid tissue revealed a significantly increased number of IgG4-positive cells, suggesting a diagnosis of IgG4-related disease. The patient improved clinically and radiographically with rituximab and was able to taper off azathioprine and prednisone. IgG4-related disease should be considered in patients with otologic symptoms and be on the differential diagnosis in patients with inflammatory pseudotumor. Staining for IgG and IgG4 is essential to ensure a prompt diagnosis and treatment.

Background Studies in Caucasian cohorts have found no significant clinical or serologic differences in systemic lupus erythematosus (SLE) patients with a family history of SLE compared to those with no family history. Using a unique cohort of African Americans with SLE and a high prevalence of multipatient families, we examined whether having a family history of SLE would impact autoantibodies, age at SLE diagnosis, ACR criteria, and disease damage. Methods Utilizing data from a cohort of African Americans with SLE, familial SLE was defined as having a confirmed family history of SLE versus sporadic SLE defined as no known family history of SLE. SSA, SSB, ds-DNA, anticardiolipin, and lupus anticoagulant were tested. Cumulative damage was measured using the Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index (SDI). Chi-square testing was used for comparing categorical and Student’s t-test used for comparing continuous variables. The association between familial vs. sporadic SLE and SDI was modeled using Poisson regression adjusting for covariates. Two-sided p-values Results 400 African American females had SLE, 55 of who were familial SLE cases. Mean age of familial cases was 47.1 + 13.9 years vs. 43.3 + 14.7 for sporadic cases (p = 0.08). Mean age at diagnosis was 28.1 + 11.5 years for familial vs. 31.0 + 13.0 for sporadic (p = 0.13). Mean disease duration was 18.7 + 9.1 years for familial vs. 12.3 + 7.1 for sporadic (p Conclusion Consistent with Caucasian SLE cohorts, we found African American SLE familial and sporadic cases had similar serologic and clinical profiles. However, familial SLE cases had significantly higher SDI scores compared to sporadic cases, even after adjusting for age at diagnosis and disease duration. Further studies are underway to elucidate the causes of higher disease damage in familial versus sporadic SLE.

Summary Two cases are presented of Haemophilus endocarditis in patients with presumed normal valves. One case was due to H. influenzae presenting as a typical case of infective endocarditis and the other was due to H. para-influenzae presenting as an acute pyrexia of unknown origin. The difficulties in making the diagnosis and the optimal treatment are discussed.

Additional file 1: Table S1. Performance of ICD-9 billing code algorithms by date. Table S2. Performance of electronic health record algorithms for systemic sclerosis excluding codes for systemic lupus erythematosus.

Table S1. Significant codes from the PheWAS of African Americans vs. Caucasians with SLE. Figure S1. Selected SLE disease criteria codes in the PheWAS of African Americans and Cauasians with SLE. Table S2. Selected SLE criteria codes from the PheWAS of African Americans and Caucasians with SLE. Table S3. Selected codes related to renal, cardiovascular disease, and infection from the PheWAS of African American SLE cases compared to matched African American controls. Table S4. Selected codes related to SLE criteria from the PheWAS of African American SLE cases and matched African American controls. Table S5. Selected codes from the PheWAS of Caucasian SLE cases compared to matched Caucasian controls. Table S6. Conditional logistic regression models with SLE cases and matched controls. (DOCX 134 kb)

Additional file 1: Table S1. Performance of ICD-9 billing code algorithms by date. Table S2. Performance of electronic health record algorithms for systemic sclerosis excluding codes for systemic lupus erythematosus.

Table S1. Significant codes from the PheWAS of African Americans vs. Caucasians with SLE. Figure S1. Selected SLE disease criteria codes in the PheWAS of African Americans and Cauasians with SLE. Table S2. Selected SLE criteria codes from the PheWAS of African Americans and Caucasians with SLE. Table S3. Selected codes related to renal, cardiovascular disease, and infection from the PheWAS of African American SLE cases compared to matched African American controls. Table S4. Selected codes related to SLE criteria from the PheWAS of African American SLE cases and matched African American controls. Table S5. Selected codes from the PheWAS of Caucasian SLE cases compared to matched Caucasian controls. Table S6. Conditional logistic regression models with SLE cases and matched controls. (DOCX 134 kb)