Faculty Bibliography

BACKGROUND:Lupus patients are at risk for pregnancy loss, and it has been generally accepted that women with SLE should have low disease activity prior to conception. However, there are conflicting results regarding the effect of pregnancy on SLE flares. This study aims to identify predictors of flares during and after pregnancy in SLE patients with inactive or stable disease activity during the first trimester and to characterize and estimate the frequency of post-partum flares in these patients. METHODS:SLE patients in the multicenter, prospective PROMISSE (Predictors of Pregnancy Outcome: Biomarkers in Antiphospholipid Antibody Syndrome and Systemic Lupus Erythematosus) study were evaluated for flares during and after pregnancy using the SELENA-SLEDAI Flare Index. Flares during pregnancy were assessed in all 384 patients and post-partum flares in 234 patients with study visits 2-6 months post-partum. Logistic regression models were fit to the data to identify independent risk factors for flare. RESULTS:During pregnancy, 20.8% of patients had mild/moderate flares and 6.25% had severe. Post-partum, 27.7% of patients had mild/moderate flares and 1.7% had severe. The mild flares rarely required treatment. Younger age, low C4 and higher PGA at baseline were independently associated with higher risk of having at least one mild/moderate or severe flare during pregnancy. Older patients were at decreased risk of flare, as well as those with quiescent disease at baseline. No variables evaluated at baseline or the visit most proximal to delivery was significantly associated with risk of flare post-partum. Medications were not associated with flare during or after pregnancy. CONCLUSION/CONCLUSIONS:In patients with inactive or stable mild disease activity at the time of conception, lupus disease flares during and after pregnancy are typically mild and occur at similar rates. Flares during pregnancy are predicted by the patients' age and clinical and serological activity at baseline.

Background/UNASSIGNED:The clinical and pathologic diversity of systemic lupus erythematosus (SLE) hinders diagnosis, management, and treatment development. This study addresses heterogeneity in SLE through comprehensive molecular phenotyping and machine learning clustering. Methods/UNASSIGNED: = 13) were assessed by multiplex bead-based assays and ELISAs. Patient clusters were identified by machine learning combining K-means clustering and random forest analysis of co-expression module scores and soluble mediators. Findings/UNASSIGNED:SLEDAI scores correlated with interferon, plasma cell, and select cell cycle modules, and with circulating IFN-α, IP10, and IL-1α levels. Co-expression modules and soluble mediators differentiated seven clusters of SLE patients with unique molecular phenotypes. Inflammation and interferon modules were elevated in Clusters 1 (moderately) and 4 (strongly), with decreased T cell modules in Cluster 4. Monocyte, neutrophil, plasmablast, B cell, and T cell modules distinguished the remaining clusters. Active clinical features were similar across clusters. Clinical SLEDAI trended highest in Clusters 3 and 4, though Cluster 3 lacked strong interferon and inflammation signatures. Renal activity was more frequent in Cluster 4, and rare in Clusters 2, 5, and 7. Serology findings were lowest in Clusters 2 and 5. Musculoskeletal and mucocutaneous activity were common in all clusters. Interpretation/UNASSIGNED:Molecular profiles distinguish SLE subsets that are not apparent from clinical information. Prospective longitudinal studies of these profiles may help improve prognostic evaluation, clinical trial design, and precision medicine approaches. Funding/UNASSIGNED:US National Institutes of Health.

OBJECTIVES/OBJECTIVE:Using a reversible multistate model, we prospectively examined neuropsychiatric (NP) events for attribution, outcome and association with health-related quality of life (HRQoL), in an international, inception cohort of systemic lupus erythematosus (SLE) patients. METHODS:Annual assessments for 19 NP events attributed to SLE and non-SLE causes, physician determination of outcome and patient HRQoL (short-form (SF)-36 scores) were measured. Time-to-event analysis and multistate modelling examined the onset, recurrence and transition between NP states. RESULTS:NP events occurred in 955/1827 (52.3%) patients and 592/1910 (31.0%) unique events were attributed to SLE. In the first 2 years of follow-up the relative risk (95% CI) for SLE NP events was 6.16 (4.96, 7.66) and non-SLE events was 4.66 (4.01, 5.43) compared with thereafter. Patients without SLE NP events at initial assessment had a 74% probability of being event free at 10 years. For non-SLE NP events the estimate was 48%. The majority of NP events resolved over 10 years but mortality was higher in patients with NP events attributed to SLE (16%) versus patients with no NPSLE events (6%) while the rate was comparable in patients with non-SLE NP events (7%) compared with patients with no non-SLE events (6%). Patients with NP events had lower SF-36 summary scores compared with those without NP events and resolved NP states (p<0.001). CONCLUSIONS:NP events occur most frequently around the diagnosis of SLE. Although the majority of events resolve they are associated with reduced HRQoL and excess mortality. Multistate modelling is well suited for the assessment of NP events in SLE.

OBJECTIVES/OBJECTIVE:In a multi-ethnic/racial, prospective SLE inception cohort, to determine the frequency, clinical characteristics, associations and outcomes in different types of peripheral nervous system (PNS) disease. METHODS:Patients were evaluated annually for 19 neuropsychiatric (NP) events including seven types of PNS disease. SLE disease activity, organ damage, autoantibodies, patient and physician assessment of outcome were measured. Time to event and linear regressions were used as appropriate. RESULTS:Of 1,827 SLE patients, 88.8% were female, 48.8% Caucasian. The mean±SD age was 35.1±13.3 years, disease duration at enrollment 5.6±4.2 months and follow-up 7.6±4.6 years. There were 161 PNS events in 139/1,827 (7.6%) patients. The predominant events were peripheral neuropathy [66/161 (41.0%)], mononeuropathy [44/161 (27.3%)] and cranial neuropathy [39/161 (24.2%)] and the majority were attributed to SLE. Multivariate Cox regressions suggested longer time to resolution in patients with prior history of neuropathy, older age at SLE diagnosis, higher SLEDAI-2K scores, and for peripheral neuropathy versus other neuropathies. Neuropathy was associated with significantly lower SF-36 physical and mental component summary scores versus patients without NP events. By physician assessment, the majority of neuropathies resolved or improved over time and this was associated with improvements in SF-36 summary scores for peripheral neuropathy and mononeuropathy. CONCLUSION/CONCLUSIONS:PNS disease is an important component of total NPSLE and has a significant negative impact on health related quality of life. The outcome is favourable for most patients, but we noted several factors associated with longer time to resolution. This article is protected by copyright. All rights reserved.

OBJECTIVE:To construct a frailty index (FI) as a measure of vulnerability to adverse outcomes among patients with SLE, using data from the Systemic Lupus International Collaborating Clinics (SLICC) inception cohort. METHODS:The SLICC inception cohort consists of recently diagnosed SLE patients followed annually with clinical and laboratory assessments. For this analysis, the baseline visit was defined as the first study visit at which sufficient information was available for construction of a frailty index. Following a standard procedure, variables from the SLICC database were evaluated as potential health deficits. Selected health deficits were then used to generate a SLICC frailty index (SLICC-FI). The prevalence of frailty in the baseline dataset was evaluated using established cut points for FI values. RESULTS:The 1683 SLE patients (92.1% of the overall cohort) eligible for inclusion in the baseline dataset were mostly female (89%) with mean (SD) age 35.7 (13.4) years and mean (SD) disease duration 18.8 (15.7) months at baseline. Of 222 variables, 48 met criteria for inclusion in the SLICC-FI. Mean (SD) SLICC-FI was 0.17 (0.08) with a range from 0 to 0.51. At baseline, 27.1% (95% CI 25.0%-29.2%) of patients were classified as frail, based on SLICC-FI values greater than 0.21. CONCLUSION/CONCLUSIONS:The SLICC inception cohort permits feasible construction of an FI for use in patients with SLE. Even in a relatively young cohort of SLE patients, frailty was common. The SLICC-FI may be a useful tool for identifying SLE patients who are most vulnerable to adverse outcomes but validation of this index is required prior to its use.

OBJECTIVE:The soluble urokinase plasminogen activator receptor (suPAR) has potential as a prognosis and severity biomarker in several inflammatory and infectious diseases. In a previous cross-sectional study, suPAR levels were shown to reflect damage accrual in cases of systemic lupus erythematosus (SLE). Herein, we evaluated suPAR as a predictor of future organ damage in recent-onset SLE. METHODS:Included were 344 patients from the Systemic Lupus International Collaborating Clinics (SLICC) Inception Cohort who met the 1997 American College of Rheumatology classification criteria with 5-years of follow-up data available. Baseline sera from patients and age- and sex-matched controls were assayed for suPAR. Organ damage was assessed annually using the SLICC/ACR damage index (SDI). RESULTS:The levels of suPAR were higher in patients who accrued damage, particularly those with SDI≥2 at 5 years (N = 32, 46.8% increase, p = 0.004), as compared to patients without damage. Logistic regression analysis revealed a significant impact of suPAR on SDI outcome (SDI≥2; OR = 1.14; 95% CI 1.03-1.26), also after adjustment for confounding factors. In an optimized logistic regression to predict damage, suPAR persisted as a predictor, together with baseline disease activity (SLEDAI-2K), age, and non-Caucasian ethnicity (model AUC = 0.77). Dissecting SDI into organ systems revealed higher suPAR levels in patients who developed musculoskeletal damage (SDI≥1; p = 0.007). CONCLUSION:Prognostic biomarkers identify patients who are at risk of acquiring early damage and therefore need careful observation and targeted treatment strategies. Overall, suPAR constitutes an interesting biomarker for patient stratification and for identifying SLE patients who are at risk of acquiring organ damage during the first 5 years of disease.

Objective:Although SLE disproportionately affects minority racial groups, they are significantly under-represented in clinical trials in the USA. This may lead to misleading conclusions in race-based subgroup analyses. We conducted focus groups to evaluate the perceptions of diverse patients with lupus about clinical trial participation. Methods:A qualitative research design employed three 90 min focus groups led by a trained moderator and guided by the Theory of Planned Behaviour. Open-ended questions about trial participation included advantages and disadvantages (behavioural beliefs), approving and disapproving significant others (normative beliefs), and participation enhancers and barriers (control beliefs). Discussions were recorded, transcribed and analysed to identify emerging themes. Results:Patients with SLE (n=23) aged 21-72, with increased proportion of minority groups (65%), participated. Reported advantages of trial participation included altruism and personal benefit. Disadvantages included uncertainties, disappointment, information burden, and life-health balance. Although some patients had discussed research participation with approving or disapproving family or friends, self-approval superseded external approval. Barriers included logistics and time, and facilitators included flexibility in scheduling, advance notice of studies, streamlined forms, and hope for SLE improvement. Conclusions:Knowledge about potential benefits of clinical trial participation was high. Minority patients demonstrated confidence in making their own informed decisions, but major barriers for all participants included burdensome forms, travel, childcare, and work. These suggest a major impact on minority and all recruitment from behavioural and control aspects, which should be considered in the logistics of trial design. This does not minimise the potential importance of improved access and education about clinical research.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Background/Purpose : Given the heterogeneity of systemic lupus erythematosus (SLE), the effect of any intervention is expected to vary. The ability to identify those most and least likely to benefit from a treatment would improve the interpretability of trial outcomes and advance medical care. Conventional subgroup analyses suffer from low power, can encompass only a few variables at a time, and require a priori specification of cut-points for continuous variables. We explored the utility of a machine learning-based algorithm for discovering in a SLE trial the subgroups in which adding experimental therapy to standard of care considerably enhances or diminishes response compared to placebo (PBO). Methods : A two-step virtual twin (VT) method was applied to combined data from the BLISS-52 (N=865) and BLISS-76 (N=819) trials. A random forest algorithm was first used to estimate for each patient, given baseline characteristics, the probabilities of SRI-4 response to belimumab and PBO. A regression tree was then constructed to partition the study population into distinct subgroups and identify those in which the estimated difference in these response probabilities is much greater or smaller than the treatment effect in the overall population. Two separate VT analyses were conducted of the 10 mg/kg and 1 mg/kg belimumab doses compared to PBO. Cross-validation was used to assess the method ' s performance. Results : In the combined BLISS trials, response rates to the primary endpoint (SRI-4) were 51% in those receiving 10 mg/kg belimumab, 46% (1 mg/kg), and 39% (PBO). VT analysis of 10 mg/kg vs. PBO found a 23% belimumab response advantage over PBO in patients with SLEDAI >= 7 & steroid dose >= 4 mg/d & low C4 & no BILAG A at baseline , vs 12% in the total population. In contrast, the estimated response difference in those entering with SLEDAI < 7 & normal C4 was 5% lower on 10 mg/kg than PBO. In analysis of 1 mg/kg vs. PBO, two subgroups showed enhanced belimumab effect: SLEDAI >= 8 & steroid dose >= 19 mg/d and SLEDAI >= 8 & steroid dose < 19 mg/d & BLyS >= 1.9 ng/mL ; average estimated between-treatment response differences were 18% and 14%, respectively, compared to 7% in the overall population. But in patients with SLEDAI < 8 & steroid dose < 16 mg/d & age < 43 , the 1 mg/kg belimumab response rate was estimated to be 7% lower. Cross-validation indicated the accuracy of the VT method to identify subgroups exceeded 70%. Conclusion : Enhanced belimumab response was associated with low C4 and higher disease activity, steroid dose, and BLyS levels, as in prior studies. However, the VT method identified alternative cutpoints for continuous variables and additional features predicting non-response. SLEDAI >= 7 or 8 was most predictive of response to treatment. Thus, lower response difference is identified in patients who are potentially too ill (BILAG A severity) or not ill enough (minimal disease criteria) to benefit from adding belimumab. The 1 mg/kg belimumab effect was enhanced only in those on high baseline steroid doses. The VT and other machine learning techniques are promising for subgroup discovery in SLE trials as more sophisticated biomarkers, especially potent but less common indicators, become available