Search

Background Medicare measures readmissions within 30 days of hospitalization as a marker of care quality across several conditions, although beyond hospital quality, neighborhood disadvantage is a known predictor of readmission. While not a reporting condition, a 2010 study noting 27% lupus readmissions led to our interest in examining readmission in lupus versus general Medicare including the impact of neighborhood disadvantage. Methods Using a 20% US national Medicare sample, we identified all patients with inpatient hospitalizations 2013–14 to compare odds of 30 day readmission among patients with SLE to the general Medicare population. Inclusion required age >18 y.o., 12 mos. Medicare Part A/B (30d. post discharge), and being alive at discharge. Covariates included age, gender, race, ethnicity, prior Medicaid, comorbidity, prior hospitalization, length of stay, hospital volume, medical school affiliation, rural urban commuter area, and area deprivation index (ADI)a measure of neighborhood disadvantage derived from patients census block group (www.neighborhoodatlas.medicine.wisc.edu). Analysis used multivariable logistic regression clustered by patient. Results Nearly 1.4 million acute Medicare hospitalizations were analyzed including 10 868 lupus hospitalizations. Patients with lupus were younger (55% of hospitalizations in 18- Lupus readmissions ranged from 20% within lower disadvantage to 27% within the greatest decile of ADI neighborhood disadvantage, compared to 17%–21% in general Medicare regardless of ADI decile (Figure 1). Before adjustment, patients with SLE were 60% more likely to be readmitted than non-lupus Medicare patients (Unadj. OR 1.61 (1.51, 1.71)). Lupus remained a readmission risk in adjusted models (Adj. OR 1.28 (1.20, 1.36)). SLE patients with 30 day readmissions were more likely to be Conclusions Among nationally representative Medicare hospitalizations, having lupus was a major readmission risk factor. Hospitalized patients with lupus who lived in a neighborhood with greatest disadvantage had higher 30 day readmissions, impacting nearly 1 in 3 patients. Future studies are needed to examine readmission causes and interventions to systematically reduce readmissions among vulnerable hospitalized patients with lupus. Funding Source(s): None

Background Patients of color are more likely to have systemic lupus erythematosus (SLE) and a smoking history. Prior literature notes that both smoking and race impact odds of cutaneous manifestations. Therefore, we sought to examine the impact of cumulative smoking and race on cutaneous manifestations of SLE. Methods Our cohort study included 631 consecutive SLE patients at a single academic center. Adults with at least one ambulatory rheumatology encounter with an SLE ICD- 9 or 10 code from 2008–16 were identified. Electronic health records were manually abstracted to include patients meeting ACR 1997 or SLICC 2012 classification criteria. The primary outcomes were ACR or SLICC cutaneous criteria and SLICC Damage Index (DI) cutaneous criteria. The primary explanatory variable was smoking exposure defined as low ( 10 pack-years), compared to nonsmokers. Covariates included age category at diagnosis (early onset 50 years), sex, and race. Analysis was performed using multivariate logistic regression to calculate odds ratios and 95% confidence intervals (OR, (95% CI)). Results Among 631 SLE patients, mean age was 42, 91% female, 82% white, and 40% ever smokers. Patients with low smoking exposure were nine times more likely to develop any mucocutaneous manifestations (OR 9.0, (1.2, 67.7)), four times more likely to meet any SLICC cutaneous criteria (OR 3.7, (OR 1.3, 10.6)), and twice as likely to meet ACR cutaneous criteria (OR 2.0 (1.0, 3.8)) compared to non-smokers (table 1). Patients with medium smoking exposure were twice as likely to meet acute cutaneous SLICC criteria (OR 2.3, (1.1, 5.1)), whereas those with high smoking exposure had two-fold higher odds of discoid lupus (OR 2.1, (1.1, 4.1) data not shown). Chronic cutaneous SLICC criteria and DI cutaneous criteria showed linear pack-year trends that met significance with high smoking exposure (OR 2.2, (1.2, 4.2); OR 4.2, (0.9, 9.2)). Patients of color had increased risk for alopecia, discoid lupus, chronic cutaneous lupus, and DI skin damage. Limitations included sample size and just 18% patients of color. Conclusions Any smoking exposure was an independent risk factor for nearly all cutaneous SLE manifestations whereas high smoking exposure and patients of color had significantly increased risk of chronic cutaneous manifestations and skin damage. Findings suggest a dose relationship between smoking exposure and cutaneous manifestations/damage, making cessation an important strategy to potentially reduce disparities and improve cutaneous outcomes in SLE. Funding Source(s): Support: Rheumatology Research Foundation

Background Despite the pivotal role hydroxychloroquine (HCQ) plays in treating SLE, less than 50% of patients take HCQ as prescribed. Non-adherence versus lack of effect to HCQ are difficult to distinguish, underscoring the importance of measuring HCQ blood levels to assess adherence. Despite this, information and consensus on the clinical impact of incorporating routine testing of HCQ blood levels is lacking. Therefore, we systematically reviewed publications examining the correlation between 1) HCQ levels and adherence, and 2) HCQ levels and SLEDAI scores, in SLE patients. We hypothesized that low HCQ levels would correlate with non-adherence and higher SLEDAI scores. Methods A comprehensive search was performed using MeSH heading and keywords in Medline, Embase, CINHL and Web of Science databases. We selected observational and interventional studies that measured HCQ levels and assessed adherence and/or SLEDAI scores in adults with SLE. Newcastle Ottawa Scale and Cochrane Collaboration Risk Assessment tools were used to rate the quality of observational and intervention studies, respectively. We used Forest plots to compare pooled estimates (95% CI) of correlations between HCQ levels and patient or physician reported nonadherence and SLEDAI scores. Heterogeneity was assessed using I2. Results From 306 manually reviewed studies, four studies analyzing correlation between HCQ levels and adherence, and five studies examining the correlation between SLEDAI and HCQ blood levels, met inclusion criteria. The odds of nonadherence measured by physician or reported by the patient was 3 times higher in patients with below threshold HCQ levels, compared to those with higher HCQ levels (OR 2.95, 95% CI 1.63, 5.35, p Conclusions We found good correlation between HCQ levels and non-adherence. Low HCQ levels showed higher mean SLEDAI scores but due to the limited number of studies and heterogeneity we did not find significant correlation. Findings support the clinical utility of measuring HCQ levels in SLE patients. Future larger studies should investigate standards for HCQ level interpretation and target levels needed to minimize SLEDAI scores. Funding Source(s): n/a