Search

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.

OBJECTIVE: Limited data exists on the effect of rheumatoid arthritis (RA) on maternal postpartum outcomes. Using a real-world, electronic health record (EHR) cohort, we assessed maternal postpartum outcomes in RA. METHODS: In a large, de-identified EHR, we identified possible RA deliveries using ≥1 delivery ICD-9 or ICD-10-CM codes and a validated RA algorithm. RA cases were required to be diagnosed by a rheumatologist on chart review. Maternal postpartum outcomes included rates of blood transfusion, rates of infection up to 6 weeks postpartum defined by a clinician, and length of hospital stay. We also identified deliveries to women without autoimmune diseases. RESULTS: We identified 202 deliveries occurring after RA diagnosis and 596 deliveries to controls without autoimmune diseases. Postpartum infection rates were similar among RA patients and controls (8% vs. 4%, p = 0.10), as were red blood cell transfusion rates (2% vs. 2%, p = 1.00). RA case status was not significantly associated with postpartum infection (OR = 2.10, 95% CI 0.88 – 4.98, p = 0.09) but was significantly associated with preterm birth (OR = 2.11, 95% CI 1.38 – 3.23, p = 0.001). Corticosteroid use during pregnancy was common at 41%, while tumor necrosis factor inhibitor use was 13%. After adjusting for age at delivery and race, corticosteroid use at delivery was not associated with postpartum maternal infections but was associated with a significantly lower birthweight in RA cases. CONCLUSION: Women with RA have an increased risk of adverse pregnancy outcomes, particularly preterm birth. Our study highlights, however, that maternal postpartum outcomes such as postpartum infection and blood transfusion are not significantly increased in RA patients.

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.

Background Systemic sclerosis (SSc) is a rare disease with studies limited by small sample sizes. Electronic health records (EHRs) represent a powerful tool to study patients with rare diseases such as SSc, but validated methods are needed. We developed and validated EHR-based algorithms that incorporate billing codes and clinical data to identify SSc patients in the EHR. Methods We used a de-identified EHR with over 3 million subjects and identified 1899 potential SSc subjects with at least 1 count of the SSc ICD-9 (710.1) or ICD-10-CM (M34*) codes. We randomly selected 200 as a training set for chart review. A subject was a case if diagnosed with SSc by a rheumatologist, dermatologist, or pulmonologist. We selected the following algorithm components based on clinical knowledge and available data: SSc ICD-9 and ICD-10-CM codes, positive antinuclear antibody (ANA) (titer ≥ 1:80), and a keyword of Raynaud’s phenomenon (RP). We performed both rule-based and machine learning techniques for algorithm development. Positive predictive values (PPVs), sensitivities, and F-scores (which account for PPVs and sensitivities) were calculated for the algorithms. Results PPVs were low for algorithms using only 1 count of the SSc ICD-9 code. As code counts increased, the PPVs increased. PPVs were higher for algorithms using ICD-10-CM codes versus the ICD-9 code. Adding a positive ANA and RP keyword increased the PPVs of algorithms only using ICD billing codes. Algorithms using ≥ 3 or ≥ 4 counts of the SSc ICD-9 or ICD-10-CM codes and ANA positivity had the highest PPV at 100% but a low sensitivity at 50%. The algorithm with the highest F-score of 91% was ≥ 4 counts of the ICD-9 or ICD-10-CM codes with an internally validated PPV of 90%. A machine learning method using random forests yielded an algorithm with a PPV of 84%, sensitivity of 92%, and F-score of 88%. The most important feature was RP keyword. Conclusions Algorithms using only ICD-9 codes did not perform well to identify SSc patients. The highest performing algorithms incorporated clinical data with billing codes. EHR-based algorithms can identify SSc patients across a healthcare system, enabling researchers to examine important outcomes.

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.

Patients with autoimmune disorders (AD) have altered cancer risks compared to the general population. Systemic lupus erythematosus and multiple sclerosis lead to a heightened risk for hematological malignancies and decreased risk for breast, ovarian, and prostate malignancies. Often patients with autoimmune disease have dysregulated antiviral immune responses, including against oncogenic viruses. To uncover the relationship between viral incidence and cancer risk in the context of autoimmune disease, we extracted electronic health records (EHR) from Vanderbilt University. ICD-9/10 codes and laboratory values were collected for hematological, lung, anal-vaginal, thyroid, hepatobiliary, bladder, prostate, and breast cancers; and viruses including Epstein Barr virus (EBV), Human papilloma virus (HPV), and Hepatitis A/B/C (Hep). Only viral infections that led to a physician visit or laboratory test were entered into the EMR; therefore, only clinically relevant cases were noted and considered positive in this study. The relationship between virus infection and cancer in an SLE cohort (SLE-cases n = 2,313, and SLE-controls n = 5,702) and an MS cohort (MS-case n = 7,277, MS-control n = 7,277) was examined by multilinear logistic regression. Viral infection was strongly associated with increased risk for cancer overall. SLE and MS patients were more susceptible to all viral infections. MS patients trended toward increased risk for cancers overall, while decreased risk for hormone-based cancers in SLE patients non-significantly reduced their risk for overall cancer. Both SLE and MS patients had increased clinically relevant EBV infection, which was associated with risk for hematological cancers. Preventing viral infections by vaccination may be especially helpful in controlling risk for cancer in SLE and MS patients.

Summary Patients with systemic lupus erythematosus (SLE) have an increased risk of developing osteoporosis and fractures due to systemic inflammation and glucocorticoids (GCs). Professional organizations recommend bone mineral density (BMD) testing in SLE patients on GCs, especially within 6 months of initiation. Using a validated algorithm, we identified SLE patients in an electronic health record cohort with long-term GC exposure (≥90 days). Our primary outcome was ever BMD testing. We assessed the impact of patient and provider factors on testing. We identified 693 SLE cases with long-term GC exposure, 41% of whom had BMD testing performed. Only 18% of patients had BMD testing within 6 months of GC initiation. In a logistic regression model for BMD testing, male sex (OR = 0.49, 95% CI 0.27 – 0.87, p = 0.01) was associated with being less likely to have BMD testing after adjusting for race and ethnicity. In contrast, older age (OR = 1.04, p