Herpes Simplex Virus Encephalitis in Patients With Autoimmune Conditions or Exposure to Immunomodulatory Medications.

BACKGROUND AND OBJECTIVES: Among infectious etiologies of encephalitis, herpes simplex virus type 1 (HSV-1) is most common, accounting for ∼15%-40% of adult encephalitis diagnoses. We aim to investigate the association between immune status and HSV encephalitis (HSVE). Using a US Medicaid database of 75.6 million persons, we evaluated the association between HSVE and autoimmune conditions, exposure to immunosuppressive and immunomodulatory medications, and other medical comorbidities. METHODS: We used the US Medicaid Analytic eXtract data between 2007 and 2010 from the 29 most populated American states. We first examined the crude incidence of HSVE in the population. We then age and sex-matched adult cases of HSVE with a sufficient enrollment period (12 months before HSVE diagnosis) to a larger control population without HSVE. In a case-control analysis, we examined the association between HSVE and exposure to both autoimmune disease and immunosuppressive/immunomodulatory medications. Analyses were conducted with conditional logistic regression progressively adjusting for sociodemographic factors, Charlson Comorbidity Index, and non-autoimmune comorbidities. RESULTS: Incidence of HSVE was ∼3.01 per 105 person-years among adults. A total of 951 HSVE cases and 95,100 age and sex-matched controls were compared. The HSVE population had higher rates of medical comorbidities than the control population. The association of HSVE and autoimmune conditions was strong (adjusted odds ratio (OR) 2.6; 95% CI 2.2-3.2). The association of HSVE and immunomodulating medications had an OR of 2.2 (CI 1.9-2.6), also after covariate adjustment. When both exposures were included in regression models, the associations remained robust: OR 2.3 (CI 1.9-2.7) for autoimmune disease and 2.0 (CI 1.7-2.3) for immunosuppressive and immunomodulatory medications. DISCUSSION: In a large, national population, HSVE is strongly associated with preexisting autoimmune disease and exposure to immunosuppressive and immunomodulatory medications. The role of antecedent immune-related dysregulation may have been underestimated to date.

Personalizing cardiovascular risk prediction for patients with systemic lupus erythematosus.

OBJECTIVE: Cardiovascular disease (CVD) risk is increased in SLE and underestimated by general population prediction algorithms. We aimed to develop a novel SLE-specific prediction tool, SLECRISK, to provide a more accurate estimate of CVD risk in SLE. METHODS: We studied patients in the Brigham and Women's Hospital SLE cohort. We collected one-year baseline data including the presence of traditional CVD factors and SLE-related features at cohort enrollment. Ten-year follow-up for the first major adverse cardiovascular event (MACE; myocardial infarction (MI), stroke, or cardiac death) began at day +1 following the baseline period (index date). ICD-9/10 codes identified MACE were adjudicated by board-certified cardiologists. Least absolute shrinkage and selection operator regression selected SLE-related variables to add to the American College of Cardiology/American Heart Association (ACC/AHA) Pooled Cohort Risk Equations 10-year risk Cox regression model. Model fit statistics and performance (sensitivity, specificity, positive/negative predictive value, c-statistic) for predicting moderate/high 10-year risk (≥7.5 %) of MACE were assessed and compared to ACC/AHA, Framingham risk score (FRS), and modified FRS (mFRS). Optimism adjustment internal validation was performed using bootstrapping. RESULTS: We included 1,243 patients with 90 MACEs (46 MIs, 36 strokes, 19 cardiac deaths) over 8946.5 person-years of follow-up. SLE variables selected for the new prediction algorithm (SLECRISK) were SLE activity (remission/mild vs. moderate/severe), disease duration (years), creatinine (mg/dL), anti-dsDNA, anti-RNP, lupus anticoagulant, anti-Ro positivity, and low C4. The sensitivity for detecting moderate/high-risk (≥7.5 %) of MACE using SLECRISK was 0.74 (95 %CI: 0.65, 0.83), which was better than the sensitivity of the ACC/AHA model (0.38 (95 %CI: 0.28, 0.48)). It also identified 3.4-fold more moderate/high-risk patients than the ACC/AHA. Patients who were moderate/high-risk according to SLECRISK but not ACC/AHA, were more likely to be young women with severe SLE and few other traditional CVD risk factors. Model performance between SLECRISK, FRS, and mFRS were similar. CONCLUSION: The novel SLECRISK tool is more sensitive than the ACC/AHA for predicting moderate/high 10-year risk for MACE and may be particularly useful in predicting risk for young females with severe SLE. Future external validation studies utilizing cohorts with more severe SLE are needed.

Posttraumatic stress disorder symptoms in systemic autoimmune rheumatic disease patients during the early COVID-19 pandemic.

AIM: Given reports of increased prevalence of PTSD symptoms at COVID-19 pandemic onset, we aimed to assess the prevalence of posttraumatic stress disorder (PTSD) symptoms at pandemic onset in individuals with and without systemic autoimmune rheumatic disease (SARD). METHODS: In May 2020, we invited 6678 patients to complete the Brief Trauma Questionnaire and the Posttraumatic Stress Disorder Checklist (PCL-5), validated PTSD symptom screenings. We compared responses from patients with and without SARD using multivariable logistic regression. RESULTS: We received 1473 responses (22% response rate) from 5/2020 to 9/2021 (63 with prior PTSD diagnoses, 138 with SARD history). The SARD population was more female (p .0001) and had a higher baseline prevalence of stress disorders (56% vs. 43%, p .004). SARD subjects reported more experiences with life-threatening illness, 60%, versus 53% among those without SARD (p .13), and more antidepressant or anxiolytic medication use pre-pandemic (78% vs. 59%, p .0001). Adjusting for pre-pandemic PTSD diagnosis, younger age and history of stress disorder were the most significant predictors of PCL-5 positivity. There were no significant differences in PCL-5 score or positivity among those with or without SARD. CONCLUSION: In this population, patients with SARD had a higher pre-COVID-19 prevalence of stress-related conditions, but it was not the case that they had an increased risk of PTSD symptoms in the early pandemic. Younger individuals, those with baseline depression, anxiety, or adjustment disorders, and those taking antidepressant or anxiolytic medications were more likely to have PTSD symptoms in the first waves of the COVID-19 pandemic.

Assessing clusters of comorbidities in rheumatoid arthritis: a machine learning approach.

BACKGROUND: Comorbid conditions are very common in rheumatoid arthritis (RA) and several prior studies have clustered them using machine learning (ML). We applied various ML algorithms to compare the clusters of comorbidities derived and to assess the value of the clusters for predicting future clinical outcomes. METHODS: A large US-based RA registry, CorEvitas, was used to identify patients for the analysis. We assessed the presence of 24 comorbidities, and ML was used to derive clusters of patients with given comorbidities. K-mode, K-mean, regression-based, and hierarchical clustering were used. To assess the value of these clusters, we compared clusters across different ML algorithms in clinical outcome models predicting clinical disease activity index (CDAI) and health assessment questionnaire (HAQ-DI). We used data from the first 3 years of the 6-year study period to derive clusters and assess time-averaged values for CDAI and HAQ-DI during the latter 3 years. Model fit was assessed via adjusted R2 and root mean square error for a series of models that included clusters from ML clustering and each of the 24 comorbidities separately. RESULTS: 11,883 patients with RA were included who had longitudinal data over 6 years. At baseline, patients were on average 59 (SD 12) years of age, 77% were women, CDAI was 11.3 (SD 11.9, moderate disease activity), HAQ-DI was 0.32 (SD 0.42), and disease duration was 10.8 (SD 9.9) years. During the 6 years of follow-up, the percentage of patients with various comorbidities increased. Using five clusters produced by each of the ML algorithms, multivariable regression models with time-averaged CDAI as an outcome found that the ML-derived comorbidity clusters produced similarly strong models as models with each of the 24 separate comorbidities entered individually. The same patterns were observed for HAQ-DI. CONCLUSIONS: Clustering comorbidities using ML algorithms is not computationally complex but often results in clusters that are difficult to interpret from a clinical standpoint. While ML clustering is useful for modeling multi-omics, using clusters to predict clinical outcomes produces models with a similar fit as those with individual comorbidities.

Posttraumatic Stress Disorder and Risk of Systemic Lupus Erythematosus Among Medicaid Recipients.

OBJECTIVE: We studied posttraumatic stress disorder (PTSD), a severe trauma-related mental disorder, and systemic lupus erythematosus (SLE) risk in a large, diverse population enrolled in Medicaid, a US government-sponsored health insurance program for low-income individuals. METHODS: We identified SLE cases and controls among patients ages 18-65 years enrolled in Medicaid for ≥12 months in the 29 most populated US states from 2007 to 2010. SLE and PTSD case statuses were defined based on validated patterns of International Classification of Diseases, Ninth Revision codes. Index date was the date of the first SLE code. Controls had no SLE codes but had another inpatient or outpatient code on the index date and were matched 1:10 to cases by age, sex, and race. Conditional logistic regressions calculated odds ratios (ORs) and 95% confidence intervals (95% CIs) for the association of PTSD with incident SLE, adjusting for smoking, obesity, oral contraceptive use, and other covariates. RESULTS: A total of 10,942 incident SLE cases were matched to 109,420 controls. The prevalence of PTSD was higher in SLE cases, at 10.74 cases of PTSD per 1,000 person-years (95% CI 9.37-12.31) versus 7.83 cases (95% CI 7.42-8.27) in controls. The multivariable-adjusted OR for SLE among those with PTSD was 2.00 (95% CI 1.64-2.46). CONCLUSION: In this large, racially and sociodemographically diverse US population, we found patients with a prior PTSD diagnosis had twice the odds of a subsequent diagnosis of SLE. Studies are necessary to clarify the mechanisms driving the observed association and to inform possible interventions.

Examining the potential direct cardiovascular benefit of tumor-necrosis factor inhibitor in rheumatoid arthritis: Natural and controlled direct effect analyses.

BACKGROUND/PURPOSE: Tumor necrosis factor inhibitors (TNFi) may have a direct benefit on cardiovascular (CV) disease beyond reducing rheumatoid arthritis (RA) disease activity measured by the Clinical Disease Activity Index (CDAI). METHODS: We compared TNFi initiators and methotrexate (MTX) monotherapy initiators from the CorEvitas RA registry. Two approaches to the "direct effect" of TNFi beyond CDAI were used. In the natural direct effect (NDE) analysis, the potential CV benefit of TNFi was partitioned into NDE and the natural indirect effect (NIE) mediated by CDAI during the first 6 months. We also estimated the controlled direct effects (CDE), corresponding to the direct benefit of TNFi when CDAI trajectories were hypothetically equalized between the initiators of TNFi and MTX monotherapy at a constant value. Estimates were given on the hazard ratio scale. RESULTS: We identified 5764 initiators of TNFi and 3588 initiators of MTX monotherapy. TNFi initiators were younger (58 vs. 64 years) with a shorter disease duration. Our total effect estimates (TNFi vs. MTX [reference]) were protective in direction (0.76-0.91). The NDE estimate was 0.76 [95% confidence interval (CI) 0.59, 0.98], whereas the NIE estimate was 1.00 [95%CI 1.00, 1.00]. In the CDE analyses accounting for longitudinal CDAI, the CDE estimates was 1.27 [95%CI 0.60, 2.69]. CONCLUSIONS: We could not convincingly demonstrate a direct benefit of TNFi outside its impact on CDAI. At present, the emphasis should be on the stringent control of RA disease activity, a known important CV risk factor, regardless of medication choice.

Prevalence of ECG testing and characteristics among new hydroxychloroquine and chloroquine users within a multi-center tertiary care center.

COVID-19 raised concern regarding cardiotoxicity and QTc prolongation of hydroxychloroquine (HCQ) and chloroquine (CQ). We examined the frequency and patient factors associated with ECG testing and the detection of prolonged QTc among new HCQ/CQ users in a large academic medical system. 10,248 subjects with a first HCQ/CQ prescription (1/2015-3/2020) were included. We assessed baseline (1 year prior to and including day of initiation of HCQ/CQ through 2 months after initial HCQ/CQ prescription) and follow-up (10 months after the baseline period) patient characteristics and ECGs obtained from electronic health records. Among 8384 female HCQ/CQ new users, ECGs were obtained for 22.3%, 14.3%, and 7.6%, at baseline, follow, and both periods, respectively. Among 1864 male HCQ/CQ new users, ECGs were obtained more frequently at baseline (29.7%), follow-up (18.0%), and both periods (11.3%). Female HCQ/CQ users with a normal QTc at baseline but prolonged QTc (> 470 ms) at follow-up (13.1%) were older at HCQ/CQ initiation [mean 64.7 (SD 16.5) vs. 58.7 (SD 16.9) years, p = 0.004] and more likely to have history of myocardial infarction (41.0% vs. 21.6%, p = 0.0003) compared to those who had normal baseline and follow-up QTc. The frequency of prolonged QTc development was similar (12.4%) among male HCQ/CQ new users (> 450 ms). Prior to COVID-19, ECG testing before and after HCQ/CQ prescription was infrequent, particularly for females who are disproportionately affected by rheumatic diseases and were just as likely to develop prolonged QTc (> 1/10 new users). Prospective studies are needed to guide future management of HCQ/CQ therapy in rheumatic populations.

Time-Varying Association of Rheumatoid Arthritis Disease Activity to Subsequent Cardiovascular Risk.

OBJECTIVE: It is unknown how the relationship between disease activity in rheumatoid arthritis (RA) and cardiovascular (CV) events may change over time. We examined the potentially time-varying association of RA disease activity to CV events. METHODS: We used the CorEvitas prevalent RA registry. The Clinical Disease Activity Index (CDAI) score category, averaged within each 6-month window since enrollment, was the exposure, and the outcome was major adverse CV events (MACEs). We used marginal structural models to estimate the hazard ratio (HR), comparing each CDAI score category with remission, allowing for differential association over time. We predicted MACE-free survival under several CDAI score scenarios. RESULTS: We found 44,816 eligible patients (77% female; mean age 58 years) with a crude event rate of 5.3/1000 person-years (median follow-up 3.4 years). The strongest association between higher CDAI score and MACEs was observed during the first 6 months of enrollment (HR for CDAI score low 2.29 [95% CI: 1.21-4.36], moderate 2.81 [95% CI: 1.46-5.43], and high 2.99 [95% CI: 1.48-6.02]). These estimates gradually diminished; by year 5, the HRs were 1.00 (95% CI: 0.49-2.05) for low, 1.18 (95% CI: 0.51-2.71) for moderate, and 1.04 (95% CI: 0.45-2.40) for high CDAI score. Predicted MACE-free survival suggested a potential decrease in MACEs with a hypothetical earlier transition to remission. CONCLUSION: The association of higher disease activity with CV events may be stronger earlier in the disease course of RA. Interventional studies may be warranted to precisely determine the effect of disease activity suppression on CV events in RA.

Predicting Response to Tocilizumab Monotherapy in Rheumatoid Arthritis: A Real-world Data Analysis Using Machine Learning.

OBJECTIVE: Tocilizumab (TCZ) has shown similar efficacy when used as monotherapy as in combination with other treatments for rheumatoid arthritis (RA) in randomized controlled trials (RCTs). We derived a remission prediction score for TCZ monotherapy (TCZm) using RCT data and performed an external validation of the prediction score using real-world data (RWD). METHODS: We identified patients in the Corrona RA registry who used TCZm (n = 452), and matched the design and patients from 4 RCTs used in previous work (n = 853). Patients were followed to determine remission status at 24 weeks. We compared the performance of remission prediction models in RWD, first based on variables determined in our prior work in RCTs, and then using an extended variable set, comparing logistic regression and random forest models. We included patients on other biologic disease-modifying antirheumatic drug monotherapies (bDMARDm) to improve prediction. RESULTS: The fraction of patients observed reaching remission on TCZm by their follow-up visit was 12% (n = 53) in RWD vs 15% (n = 127) in RCTs. Discrimination was good in RWD for the risk score developed in RCTs, with area under the receiver-operating characteristic curve (AUROC) of 0.69 (95% CI 0.62-0.75). Fitting the same logistic regression model to all bDMARDm patients in the RWD improved the AUROC on held-out TCZm patients to 0.72 (95% CI 0.63-0.81). Extending the variable set and adding regularization further increased it to 0.76 (95% CI 0.67-0.84). CONCLUSION: The remission prediction scores, derived in RCTs, discriminated patients in RWD about as well as in RCTs. Discrimination was further improved by retraining models on RWD.

Comparative risks of cardiovascular disease events among SLE patients receiving immunosuppressive medications.

OBJECTIVES: SLE patients have elevated cardiovascular disease (CVD) risk, but it is unclear whether this risk is affected by choice of immunosuppressive drug. We compared CVD risks among SLE patients starting MMF, CYC or AZA. METHODS: Using Medicaid Analytic eXtract (2000-2012), adult SLE patients starting MMF, CYC or AZA were identified and propensity scores (PS) were estimated for receipt of MMF vs CYC and MMF vs AZA. We examined rates of first CVD event (primary outcome), all-cause mortality, and a composite of first CVD event and all-cause mortality (secondary outcomes). After 1:1 PS-matching, Fine-Gray regression models estimated subdistribution hazard ratios (HRs.d.) for risk of CVD events. Cox regression models estimated HRs for all-cause mortality. The primary analysis was as-treated; 6- and 12-month intention-to-treat (ITT) analyses were secondary. RESULTS: We studied 680 PS-matched pairs of patients with SLE initiating MMF vs CYC and 1871 pairs initiating MMF vs AZA. Risk of first CVD event was non-significantly reduced for MMF vs CYC [HRs.d 0.72 (95% CI: 0.37, 1.39)] and for MMF vs AZA [HRs.d 0.88 (95% CI: 0.59, 1.32)] groups. In the 12-month ITT, first CVD event risk was lower among MMF than AZA new users [HRs.d 0.68 (95% CI: 0.47, 0.98)]. CONCLUSION: In this head-to-head PS-matched analysis, CVD event risks among SLE patients starting MMF vs CYC or AZA were not statistically reduced except in one 12-month ITT analysis of MMF vs AZA, suggesting longer-term use may convey benefit. Further studies of potential cardioprotective benefit of MMF are necessary.

Interactions Between Genome-Wide Genetic Factors and Smoking Influencing Risk of Systemic Lupus Erythematosus.

OBJECTIVE: To identify interactions between genetic factors and current or recent smoking in relation to risk of developing systemic lupus erythematosus (SLE). METHODS: For the study, 673 patients with SLE (diagnosed according to the American College of Rheumatology 1997 updated classification criteria) were matched by age, sex, and race (first 3 genetic principal components) to 3,272 control subjects without a history of connective tissue disease. Smoking status was classified as current smoking/having recently quit smoking within 4 years before diagnosis (or matched index date for controls) versus distant past/never smoking. In total, 86 single-nucleotide polymorphisms and 10 classic HLA alleles previously associated with SLE were included in a weighted genetic risk score (wGRS), with scores dichotomized as either low or high based on the median value in control subjects (low wGRS being defined as less than or equal to the control median; high wGRS being defined as greater than the control median). Conditional logistic regression models were used to estimate both the risk of SLE and risk of anti-double-stranded DNA autoantibody-positive (dsDNA+) SLE. Additive interactions were assessed using the attributable proportion (AP) due to interaction, and multiplicative interactions were assessed using a chi-square test (with 1 degree of freedom) for the wGRS and for individual risk alleles. Separate repeated analyses were carried out among subjects of European ancestry only. RESULTS: The mean ± SD age of the SLE patients at the time of diagnosis was 36.4 ± 15.3 years. Among the 673 SLE patients included, 92.3% were female and 59.3% were dsDNA+. Ethnic distributions were as follows: 75.6% of European ancestry, 4.5% of Asian ancestry, 11.7% of African ancestry, and 8.2% classified as other ancestry. A high wGRS (odds ratio [OR] 2.0, P = 1.0 × 10-51 versus low wGRS) and a status of current/recent smoking (OR 1.5, P = 0.0003 versus distant past/never smoking) were strongly associated with SLE risk, with significant additive interaction (AP 0.33, P = 0.0012), and associations with the risk of anti-dsDNA+ SLE were even stronger. No significant multiplicative interactions with the total wGRS (P = 0.58) or with the HLA-only wGRS (P = 0.06) were found. Findings were similar in analyses restricted to only subjects of European ancestry. CONCLUSION: The strong additive interaction between an updated SLE genetic risk score and current/recent smoking suggests that smoking may influence specific genes in the pathogenesis of SLE.

Comparative Risks of Cardiovascular Disease in Patients With Systemic Lupus Erythematosus, Diabetes Mellitus, and in General Medicaid Recipients.

OBJECTIVE: Cardiovascular disease (CVD) risk is elevated in patients with systemic lupus erythematosus (SLE) and diabetes mellitus (DM), but whether risk of CVD in patients with SLE is as high as in those with DM is unknown. The present study was undertaken to compare CVD risks between patients with SLE and DM and general population US Medicaid recipients. METHODS: In a cohort study, we identified age- and sex-matched adults (1:2:4) with SLE or DM and those from the general population using Medicaid Analytic eXtract, 2007-2010. We collected data on baseline sociodemographic factors, comorbidities, and medications. We used Cox regression models to calculate hazard ratios (HRs) of hospitalized nonfatal CVD events (combined myocardial infarction [MI] and stroke) and MI and stroke separately, accounting for competing risk of death and adjusting for covariates. We compared risks in age-stratified models. RESULTS: We identified 40,212 SLE patients, 80,424 DM patients, and 160,848 general population patients; 92.5% were female, and the mean ± SD age was 40.3 ± 12.1 years. Nonfatal CVD incidence rate per 1,000 person-years was 8.99 for patients with SLE, 7.07 for those with DM, and 2.36 for the general population. Nonfatal CVD risk was higher in SLE compared to DM (HR 1.27 [95% confidence interval (95% CI) 1.15-1.40]), driven by excess risk at ages 18-39 years (HR 2.22 [95% CI 1.81-2.71]). Patients with SLE had higher risk of CVD compared to the general population (HR 2.67 [95% CI 2.38-2.99]). CONCLUSION: SLE patients had a 27% higher risk of nonfatal CVD events compared to age- and sex-matched patients with DM and more than twice the risk of the Medicaid general population. The highest relative risk occurred at ages 18-39 years. These high risks merit aggressive evaluation for modifiable factors and research to identify prevention strategies.

External Validation of a Risk Score for Major Toxicity Among Nonsteroidal Anti-Inflammatory Drug Users: Real-World Application.

OBJECTIVE: We previously derived and validated a risk score for major nonsteroidal anti-inflammatory drug (NSAID) toxicity over 1 year among NSAID users in a randomized controlled trial. This work was extended to examine the risk score's performance in an external population using real-world data. METHODS: Patients enrolled in the Corrona Rheumatoid Arthritis (RA) Registry were included if they initiated use of an NSAID. We defined the original risk factors previously identified in the risk score for major NSAID toxicity: age; male sex; history of cardiovascular disease, hypertension, and diabetes; tobacco use; statin use; elevated serum creatinine and hematocrit values; and RA. Additionally, we defined the occurrence of major toxicity, including major adverse cardiovascular events, acute kidney injury, significant gastrointestinal events, and mortality. The original risk factors were assessed in Cox regression examining discrimination and calibration. Low (less than 1%), intermediate (1%-4%), and high (more than 4%) risk categories for 1-year risk were applied to the population. RESULTS: A total of 5231 patients from Corrona who had a new NSAID exposure period were included. The original risk score model showed good discrimination (C-index 0.70). Not all of the original variables were statistically significant in real-world data. Using the original risk score weights, 1363 (26.1%) patients had predicted risk of less than 1%, 3571 (68.3%) had predicted risk of 1% to 4%, and 297 (5.7%) had predicted risk of more than 4%. CONCLUSION: The original NSAID major toxicity risk score demonstrated good model fit characteristics in this external real-world cohort. These results suggest that such a risk score is valid in typical practice and could be considered for clinical care.

Physician Prescribing Patterns and Risk of Future Long-Term Opioid Use Among Patients With Rheumatoid Arthritis: A Prospective Observational Cohort Study.

OBJECTIVE: To identify the extent to which opioid prescribing rates for patients with rheumatoid arthritis (RA) vary in the US and to determine the implications of baseline opioid prescribing rates on the probability of future long-term opioid use. METHODS: We identified patients with RA from physicians who contributed ≥10 patients within the first 12 months of participation in the Corrona RA Registry. The baseline opioid prescribing rate was calculated by dividing the number of patients with RA reporting opioid use during the first 12 months by the number of patients with RA providing data that year. To estimate odds ratios (ORs) for long-term opioid use, we used generalized linear mixed models. RESULTS: During the follow-up period, long-term opioid use was reported by 7.0% (163 of 2,322) of patients of physicians with a very low rate of opioid prescribing (referent) compared to 6.8% (153 of 2,254) of patients of physicians with a low prescribing rate, 12.5% (294 of 2,352) of patients of physicians with a moderate prescribing rate, and 12.7% (307 of 2,409) of patients of physicians with a high prescribing rate. The OR for long-term opioid use after the baseline period was 1.16 (95% confidence interval [95% CI] 0.79-1.70) for patients of low-intensity prescribing physicians, 1.89 (95% CI 1.27-2.82) for patients of moderate-intensity prescribing physicians, and 2.01 (95% CI 1.43-2.83) for patients of high-intensity prescribing physicians, compared to very low-intensity prescribing physicians. CONCLUSION: Rates of opioid prescriptions vary widely. Our findings indicate that baseline opioid prescribing rates are a strong predictor of whether a patient will become a long-term opioid user in the future, after controlling for patient characteristics.

Initial disease severity, cardiovascular events and all-cause mortality among patients with systemic lupus erythematosus.

OBJECTIVE: SLE is associated with high risks of cardiovascular disease (CVD) and mortality, and has a wide spectrum of presentations. We investigated whether SLE severity at diagnosis was associated with CVD or mortality risk. METHODS: Within Medicaid (2000-10), we identified patients 18-65 years of age with incident SLE. Initial SLE severity was classified-mild, moderate, or severe-during the baseline year prior to the start of follow-up (incident index date) using a published algorithm based on SLE-related medications and diagnoses. Patients were followed from the index date to the first CVD event or death, disenrollment, loss to follow-up or end of follow-up period. Cox and Fine-Gray regression models, adjusted for demographics and comorbidities accounting for the competing risk of death (for CVD), estimated CVD and mortality risks by baseline SLE severity. RESULTS: Of 15 120 incident SLE patients, 48.7% had mild initial SLE severity, 33.9% moderate and 17.4% severe. Mean (s.d.) follow-up was 3.3 (2.4) years. After multivariable adjustment, CVD subdistribution hazard ratios (HRSD) were higher for initially severe [HRSD 1.64 (95% CI 1.32, 2.04)] and moderate [HRSD 1.19 (95% CI 1.00, 1.41)] SLE vs mild SLE. Mortality HRs were also higher for initially severe [HR 3.11 (95% CI 2.49, 3.89)] and moderate [HR 1.61 (95% CI 1.29, 2.01)] SLE vs mild SLE. CONCLUSION: SLE patients with high initial severity had elevated mortality and CVD events risks compared with those who presented with milder disease. This has implications for clinical care and risk stratification of newly diagnosed SLE patients.

Comparison of an administrative algorithm for SLE disease severity to clinical SLE Disease Activity Index scores.

Systemic lupus erythematosus (SLE) severity, reflecting both disease intensity and duration, is heterogeneous making it challenging to study in administrative databases where severity may confound or mediate associations with outcomes. Garris et al. developed an administrative claims-based algorithm employing claims over a 1-year period to classify SLE severity as mild, moderate or severe. We sought to compare this administrative algorithm to a measure of SLE activity, the SLE Disease Activity Index-2000 (SLEDAI-2K) score at clinical visits. We identified 100 SLE patients followed in the Brigham and Women's Hospital (BWH) Lupus Center (in 2008-2010) with SLEDAI-2K scores at each visit over a 1-year period per person. We obtained data for the Garris algorithm for the same year per subject. We compared Garris SLE severity to the highest SLEDAI-2K in that year, with SLEDAI-2K categories of mild < 3, moderate 3-6, and severe > 6. We compared classification using weighted kappa statistics, and positive and negative predictive values (PPV, NPV). We also assessed the binary comparison of mild vs. moderate/severe. We calculated sensitivity, specificity, and McNemar's test. We analyzed 377 SLEDAI-2K assessments (mean 3.8 [SD 2.6] per subject/year). For classifying moderate/severe vs. mild SLE severity, the sensitivity was 85.7%, specificity 67.6%, PPV 81.8% and NPV 73.5%. The Garris algorithm for classifying SLE severity in administrative datasets had moderate agreement for classification of mild vs. moderate/severe SLE activity assessed by SLEDAI-2K assessments in an academic lupus center. It may be a useful tool for classifying SLE severity in administrative database studies.

Atrial Fibrillation/flutter Hospitalizations among US Medicaid Recipients with and without Systemic Lupus Erythematosus.

OBJECTIVE: Systemic lupus erythematosus (SLE) is a multisystem chronic inflammatory autoimmune disease with high prevalence of several risk factors for atrial fibrillation/flutter (AF). However, the incidence and risk of AF in SLE have not been well quantified. METHODS: We used the United States Medicaid Analytic eXtract from 2007 to 2010 to identify beneficiaries aged 18-65 years, with prevalent SLE, each matched by age and sex to 4 non-SLE general Medicaid recipients. We estimated the incidence rates (IR) per 1000 person-years (PY) for AF hospitalizations and used multivariable Cox regression to estimate the HR for AF hospitalization. RESULTS: We identified 46,876 US Medicaid recipients with SLE, and 187,504 age- and sex-matched non-SLE controls (93% female; mean age 41.5 ± 12.2 yrs). Known AF risk factors such as hypertension (HTN), cardiovascular disease (CVD), and kidney disease were more prevalent in patients with SLE. During a mean followup of 1.9 ± 1.1 years for SLE, and 1.8 ± 1.1 years for controls, the IR per 1000 PY for AF was 1.4 (95% CI 1.1-1.6) among patients with SLE and 0.7 (95% CI 0.6-0.8) among non-SLE controls. In age- and sex-matched and race-adjusted Cox models, the HR for AF was 1.79 (95% CI 1.43-2.24); after adjustment for baseline HTN and CVD, the adjusted HR was reduced to 1.17 (95% CI 0.92-1.48). CONCLUSION: SLE was associated with a doubled rate of hospitalization for AF compared to age- and sex-matched general Medicaid patients. In a race-adjusted model, the risk was 80% higher. However, the AF risk factors HTN and CVD were more prevalent among patients with SLE and accounted for the excess risk.

Readmissions, revisions, and mortality after treatment for proximal humeral fractures in three large states.

BACKGROUND: Proximal humeral fractures can be treated non-operatively or operatively with open reduction and internal fixation (ORIF) and arthroplasty. Our objective was to assess practice patterns for operative and non-operative treatment of proximal humeral fractures. We also report on complications, readmissions, in-hospital mortality, and need for surgery after initial treatment of proximal humeral fractures in California, Florida, and New York. METHODS: The State Inpatient Databases and State Emergency Department Databases from the Healthcare Cost and Utilization Project, sponsored by the Agency for Healthcare Research and Quality, were used for the states of California (2005-2011), Florida (2005-2014), and New York (2008-2014). Data on patients with proximal humeral fractures was extracted. Patients underwent non-operative or operative (ORIF or arthroplasty) treatment at baseline and were followed for at least 4 years from the index presentation. If the patient needed subsequent surgery, time to event was calculated in days, and Kaplan-Meier survival curves were plotted. RESULTS: At the index visit, 90.3% of patients with proximal humeral fractures had non-operative treatment, 6.7% had ORIF, and 3.0% had arthroplasty. 7.6% of patients initially treated non-operatively, 6.6% initially treated with ORIF, and 7.2% initially treated with arthroplasty needed surgery during follow-up. Device complications were the primary reason for readmission in 5.3% of ORIF patients and 6.7% of arthroplasty patients (p < 0.0001). All-cause in-hospital mortality was 9.8% for patients managed non-operatively, 8.8% for ORIF, and 10.0% for arthroplasty (p = 0.003). CONCLUSIONS: A majority of patients with proximal humeral fractures underwent non-operative treatment. There was a relatively high all-cause in-hospital mortality irrespective of treatment. Given the recent debate on operative versus non-operative treatment for proximal humeral fractures, our study provides valuable information on the need for revision surgery after initial treatment. The differences in rates of revision surgery between patients treated non-operatively, with ORIF, and with arthroplasty were small in magnitude. At nine years of follow-up, ORIF had the lowest probability of needing follow-up surgery, and arthroplasty had the highest.

Heart failure risk in systemic lupus erythematosus compared to diabetes mellitus and general medicaid patients.

BACKGROUND: Patients with systemic lupus erythematosus (SLE) have a similar risk of myocardial infarction as those with diabetes mellitus (DM). Whether the risk of heart failure (HF) in SLE is similar to the elevated risk in DM is unknown. We sought to estimate the rates and risks for HF hospitalization among US Medicaid patients with SLE and to compare them to those for DM and the general Medicaid population. METHODS: Using U.S. Medicaid data from 2007-2010, we identified patients with SLE or DM, and a matched cohort from the general Medicaid population and calculated incidence rates (IR), incidence rate ratios (IRR) and adjusted hazard ratios (HR) of a first HF hospitalization. RESULTS: We identified 37,902 SLE (93% female, mean age 40.1 ±â€¯12.1), 76,657 DM (93% female, mean age 40.0 ±â€¯12.1), and 158,695 general Medicaid patients (93% female, mean age 40.2 ±â€¯12.1). The IR per 1000-person years was 6.9 (95% CI 6.3-7.5) for SLE, 6.6 (95% CI 6.2-7.0) for DM, and 1.6 (95% CI 1.5-1.8) for general Medicaid patients. The highest IRR compared to general Medicaid was seen among SLE patients in age group 18-39 (14.7, 95% CI 13.9-15.5). Multivariable-adjusted HRs for HF compared to general Medicaid population were similar for SLE (2.7, 95% CI 2.3-3.1) and DM (3.0, 95% CI 2.6-3.4). CONCLUSION: The incidence of HF among SLE patients was 2.7-fold higher than general Medicaid patients, and similar to DM. Further investigation into the biologic mechanism of HF among SLE compared to non-SLE and DM patients may shed light on the findings of this study.