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ObjectiveCharacterise the relationship between hydroxychloroquine (HCQ) blood levels and the number of missed doses, accounting for dosage, dose timing and the large variability in pharmacokinetics (PK) between patients.MethodsWe externally validated a published PK model and then conducted dosing simulations. We developed a virtual population of 1000 patients for each dosage across a range of body weights and PK variability. Using the model, 10 Monte Carlo simulations for each patient were conducted to derive predicted whole blood concentrations every hour over 24 hours (240 000 HCQ levels at steady state). To determine the impact of missed doses on levels, we randomly deleted a fixed proportion of doses.ResultsFor patients receiving HCQ 400 mg daily, simulated random blood levels <200 ng/mL were exceedingly uncommon in fully adherent patients (<0.1%). In comparison, with 80% of doses missed, approximately 60% of concentrations were <200 ng/mL. However, this cut-off was highly insensitive and would miss many instances of severe non-adherence. Average levels quickly dropped to <200 ng/mL after 2–4 days of missed doses. Additionally, mean levels decreased by 29.9% between peak and trough measurements.ConclusionsWe propose an algorithm to optimally interpret HCQ blood levels and approximate the number of missed doses, incorporating the impact of dosage, dose timing and pharmacokinetic variability. No single cut-off has adequate combinations of both sensitivity and specificity, and cut-offs are dependent on the degree of targeted non-adherence. Future studies should measure trough concentrations to better identify target HCQ levels for non-adherence and efficacy.

ObjectiveWe have developed a new conceptual model to characterise the signs and symptoms of SLE: the Type 1 and 2 SLE Model. Within the original model, Type 1 SLE consists of inflammatory manifestations like arthritis, nephritis and rashes; Type 2 SLE includes symptoms of fatigue, myalgia, mood disturbance and cognitive dysfunction. Through in-depth interviews, we explored how the Type 1 and 2 SLE Model fits within the lived experience of patients with SLE, with a focus on the connection between Type 1 and Type 2 SLE symptoms.MethodsSemistructured in-depth interviews were conducted among adult participants meeting 1997 American College of Rheumatology or Systemic Lupus International Collaborating Clinics criteria for SLE. Participants were purposefully selected for age, race, sex and nephritis history. All interviews were audio-recorded and transcribed. Data were analysed through episode profile and thematic analysis.ResultsThrough interviews with 42 patients with SLE, two patterns of Type 2 SLE emerged: Intermittent (n=18) and Persistent (n=24). Participants with Intermittent Type 2 SLE described feeling generally well when Type 1 is inactive; these participants were younger and had more internal SLE manifestations. Participants with Persistent Type 2 described always experiencing Type 2 symptoms despite inactive Type 1, although the severity may fluctuate. Participants with Persistent Type 2 SLE experienced traditional lupus symptoms of joint pain, hair loss and rash, but less often had severe organ system involvement.ConclusionsBy listening to the stories of our patients, we found two underlying patterns of Type 2 SLE: Intermittent Type 2 symptoms that resolve in synchrony with Type 1 inflammatory symptoms, and Persistent Type 2 symptoms that continue despite remission of Type 1 symptoms.

ObjectiveManifestations of SLE can be categorised as type 1 (classic signs and symptoms of SLE) or type 2 (fatigue, widespread pain and brain fog with an unclear relationship to inflammation). While measures of type 1 SLE activity exist, most current physician-reported measures do not encompass type 2 SLE manifestations. To better evaluate type 2 SLE symptoms, we developed and psychometrically evaluated a physician-reported measure of type 2 symptoms, the Type 2 Physician Global Assessment (‘Type 2 PGA’).Methods and analysisThe Type 2 PGA was developed and evaluated by six rheumatologists practising in the same academic lupus clinic. The study began with a roundtable discussion to establish consensus guidelines for scoring the Type 2 PGA. Following the roundtable, the Type 2 PGA was psychometrically evaluated using data prospectively collected from 263 patients with SLE enrolled in the Duke Lupus Registry.ResultsThere was strong intra-rater and inter-rater reliability (intraclass correlation coefficient=0.83), indicating the Type 2 PGA scores were consistent within a rheumatologist and across rheumatologists. The Type 2 PGA was correlated with patient-reported symptoms of polysymptomatic distress (r=0.76), fatigue (r=0.68), cognitive dysfunction (r=0.63), waking unrefreshed (r=0.62) and forgetfulness (r=0.60), and weakly correlated with the Type 1 PGA and the Systemic Lupus Erythematosus Disease Activity Index.ConclusionThe Type 2 PGA performed well as a physician-reported measure of type 2 SLE symptoms. The incorporation of the Type 2 PGA into a routine rheumatology visit may improve patient care by bringing the provider’s attention to certain symptoms not well represented in conventional measures of disease activity.

IntroductionIn the type 1 and 2 SLE model, inflammation mediates type 1 manifestations, but its role in type 2 manifestations (eg, fatigue, myalgias, mood disturbance, cognitive dysfunction) is less clear. Therapeutic hydroxychloroquine (HCQ) levels reduce type 1 activity, but their relationship with type 2 activity is unknown. Exploring this relationship may illuminate type 2 SLE pathophysiology.MethodsWe measured whole blood HCQ levels using liquid chromatography–mass spectrometry, categorising them as underexposure (<200 ng/mL), subtherapeutic (200 to <750 ng/mL) or therapeutic (≥750 ng/mL). We measured type 1 SLE activity using the type 1 Physician Global Assessment (PGA) and Systemic Lupus Erythematosus Disease Activity Index and type 2 SLE activity using the type 2 PGA and patient-reported polysymptomatic distress scores. Patients were categorised into minimal (low type 1 and type 2), type 1 (high type 1 and low type 2), type 2 (low type 1 and high type 2) and mixed activity (high type 1 and type 2) groups. We analysed relationships between HCQ levels and type 1 and type 2 SLE activities.ResultsAmong 154 patients (median age 43, 90% women, 63% Black race, 7% Hispanic ethnicity) across 297 visits, HCQ levels were underexposed at 41 (14%) visits, subtherapeutic at 76 (26%) and therapeutic at 180 (61%) visits. Patients had minimal activity at 102 visits (34%), type 1 activity at 33 (11%), type 2 activity at 85 (29%) and mixed activity at 77 (26%) visits.Underexposed HCQ levels were independently associated with higher type 1 (OR 2.33, 95% CI 1.23 to 4.44) and type 2 activities (OR 1.80, 95% CI 1.07 to 3.04). Mixed activity most strongly associated with Underexposed HCQ levels (OR 3.4–10.3, p<0.05).ConclusionsLow HCQ levels are associated with increased type 1 and type 2 SLE activities, particularly for the mixed activity group, suggesting that immunologic activity may contribute to type 2 symptoms in some patients.

Objective To account for heterogeneity in systemic lupus erythematosus (SLE) and bridge discrepancies between patient- and physician-perceived SLE activity, we developed the Type 1 and 2 SLE model. We examined PROMIS-29 scores, a composite patient-reported outcome (PRO) measure, through the lens of the model. Methods Patients completed PROMIS-29 and the polysymptomatic distress scale (PSD). Rheumatologists completed the SLE disease activity index (SLEDAI), and physician’s global assessments (PGAs) for Type 1 and 2 SLE. We defined Type 1 SLE using SLEDAI, Type 1 PGA, and active nephritis, and Type 2 SLE using PSD and Type 2 PGA. We compared PROMIS-29 T-scores among Type 1 and 2 SLE groups and explored whether PROMIS-29 can predict Type 1 and 2 SLE activity. Results Compared to the general population, patients with isolated Type 1 SLE reported greater pain and physical dysfunction but less depression and improved social functions; patients with high Type 2 SLE (irrespective of Type 1 activity) reported high levels of pain, fatigue, and social and physical limitations. Patients with minimal Type 1 and 2 SLE had less depression and greater physical functioning with other domains similar to national norms. PROMIS-29 predicted Type 2 but not Type 1 SLE activity. Conclusion PROMIS-29 similarities in patients with high Type 2 SLE, with and without active Type 1 SLE, demonstrate the challenges of using PROs to assess SLE inflammation. In conjunction with the Type 1 and 2 SLE model, however, PROMIS-29 identified distinct symptom patterns, suggesting that the model may help clinicians interpret PROs.

BackgroundMedication nonadherence is common and is associated with increased disease activity, morbidity, and mortality in SLE. Medication nonadherence is more common among patients from racial and ethnic minority groups, which likely contributes to racial disparities in SLE outcomes. Effective patient-clinician communication can improve adherence through honest exchange of information and by strengthening trust and therapeutic alliance. However, patient-clinician discussions about nonadherence occur sporadically and at times may be potentially confrontational. Further, Black patients tend to experience poorer communication quality with clinicians and have less active participation in decision- making in clinic visits. Existing adherence interventions in SLE have not addressed patient-clinician communication nor focused on reducing racial disparities in SLE medication nonadherence. Our group developed an intervention that involves clinicians reviewing real-time pharmacy refill data during the visit and using effective communication techniques with patients to collaboratively overcome adherence barriers (figure 1). Prior pilot testing demonstrated intervention feasibility, acceptability, and preliminary effect on adherence. However, we found that the intervention did not work as well for patients who were Black, single, of younger age, and lower income. To enhance the intervention effects for patients with these characteristics, we conducted a follow up study to examine how the intervention is performed in practice and identify areas for improvement to inform future implementation.MethodsClinicians at a tertiary lupus clinic implemented the intervention during routine visits. We audio-recorded 4–5 encounters per clinician of nonadherent patients (90-day proportion of days covered (PDC) <80% for SLE medications). Recordings were coded and scored for intervention components performed, communication quality, and time spent discussing adherence. Clinician communication quality was rated using a 5-point Likert scale (with 5 being the best) on the following domains: level of engagement (attentiveness), how well clinicians addressed and anticipated patient concerns, flow of the conversation, and how much respect and warmth the clinician showed. We assessed active patient participatory behavior such as asking questions and making assertive statements. Following the intervention encounter, we also conducted audio-recorded semi-structured interviews with patients and clinicians about their experiences with the intervention and analyzed the data using applied thematic analysis. Lastly, we assessed change in 90-day PDC after the intervention visit and considered a 20% increase as major improvement.ResultsWe recorded and analyzed 25 patient encounters (median age 39, 100% female, 72% Black) among 6 clinicians. Clinicians performed most intervention components in most encounters, with the exception of asking open-ended questions which occurred in about half of visits (table 1). Global communication scores and rates of active patient participation were high, suggesting excellent communication. Adherence discussions took on average 3.8 minutes. Following the intervention visit, 44% of patients had a major improvement (>20% increase) in PDC.Nineteen patients and 5 clinicians completed in-depth interviews. Nearly all participants felt the time spent discussing adherence was just right and necessary. Many patients felt heard and valued and described being more honest about nonadherence and more motivated to take SLE medications. To improve the intervention for Black patients, patients emphasized patient-clinician communication and financial and logistical assistance. Some clinicians wanted additional resources and training to improve adherence conversations (table 2).ConclusionOur findings suggest that this intervention encouraged high quality communication and can be performed within an encounter. Both patients and clinicians described positive experiences with the intervention. Future work will focus on optimizing clinician training, exploring ways to increase the intervention’s efficiency and effectiveness, and testing the intervention in a larger controlled setting.Abstract 107 Figure 1Adherence intervention stepsAbstract 107 Table 1Intervention components performed during 25 recordings of the adherence intervention Intervention components Definition N=25 (% yes) Reviewed refills Clinician reviewed pharmacy refill data with patient during the visit 88% Positive Reinforcement Clinician praised patient for good behavior observed related to adherence, e.g., refilling regularly, being honest about nonadherence 88% Validation Clinician made statement to normalize missing doses and difficulties patients face in taking medications consistently 84% Open-ended questions Clinician asked a question about adherence that cannot be answered with yes or no or a finite number of answers 52% Identified barriers Clinician and patients discussed reasons for missing refills 76% Addressed barriers Clinician made suggestions about how to improve adherence 68% Abstract 107 Table 2Quality of patient-clinician communication during the adherence intervention Global score category Definition Median score Attentive How engaged was the clinician 4.0/5.0 Concerns How well did clinician address and anticipate patient concerns 5.0/5.0 Flow Ease of flow of the conversation between clinician and patient 4.5/5.0 Respect How much respect clinician conveyed to patient 5.0/5.0 Warmth Warmth of the clinician 5.0/5.0 Active patient participatory behavior % of encounters, n/visit Asking questions Seeking information and clarification related to medical encounter 88%, 3/visit Assertive responses Offering opinions, making requests, introducing new topics, stating preferences, making recommendations, disagreeing, clarifying, or interruption 88%, 2/visit

DisclosuresAME, JLR, MEBC: Exagen, ImmunovantFundingDuke CTSA grant ( UL1TR002553 )BackgroundThe Type 1 & 2 SLE Model encompass symptoms classically attributed to inflammation, including arthritis, rash, serositis and nephritis (Type 1 SLE), and symptoms of fatigue, widespread pain, mood disturbance, and brain fog (Type 2 SLE). Our preliminary data suggest there are at least two distinct sub-types of Type 2 SLE, one related to active inflammation and another that exists regardless of inflammation. The objective of this study was to use longitudinal measures of Type 1 and Type 2 SLE activity to identify subgroups of Type 2 SLE.MethodsSLE patients meeting Systemic Lupus Collaborating Clinics (SLICC) criteria with ≥2 visits at a university rheumatology clinic over a 36-month period between February 2018 and August 2022 were included. At each visit, rheumatologists scored Type 1 and 2 SLE activity separately by Physician’s Global Assessments (PGA), visual analog scales of 0–3 (0=no activity, 3=severe activity). Growth mixture models derived classes of patients based on their Type 1 and Type 2 PGA trajectories, and posterior probabilities assigned patients to the class with the highest probability. Patients were then classified according to their classes of Type 1 and Type 2 PGA trajectories into different ‘groups’. Clinical and demographic characteristics were compared across groups.ResultsWe included 297 patients with 2,011 visits. The best model fit of trajectories for both the Type 1 and Type 2 PGA included three classes. When patients were grouped according to their Type 1 and Type 2 PGA classes, the majority (73%) fell into one of four groups: 29% had low Type 1 and Type 2 activity (Minimal); 19% had constant high Type 2 but low Type 1 activity (Type 2); 7% had constant high Type 1 but low Type 2 activity (Type 1); and 18% had constant high Type 1 and Type 2 activity (Mixed). The remaining 27% of patients had variable Type 1 and Type 2 changes over time that did not fit into a distinct group (figure 1).Patients in the Type 2 SLE group were older and more likely to be on disability (table 1). While the SLEDAI and Type 1 PGA scores were similar between the Type 1 and Mixed groups, the disease manifestations were somewhat different, with more nephritis in the Type 1 group and higher LFA-REAL Musculoskeletal PGA scores in the Mixed group. While overall Type 2 PGA scores were similar for those in the Type 2 and Mixed groups, there was more depression, pain, and symptom severity among those in the Mixed group.In a descriptive analysis to determine if Type 1 and Type 2 PGA changed concordantly, 39% of patients had Type 1 and Type 2 SLE PGAs that consistently changed together.ConclusionWe identified four main longitudinal subgroups of patient trajectories. Supporting our prior qualitative work, we found changes in Type 1 and Type 2 occurred together in almost 40% of patients. Future work is needed to understand the underlying etiology of each subgroup, allowing us to target these groups with appropriate medical and non-medical treatments.Abstract 605 Figure 1Spaghetti plots showing the individual trajectories of Type 1 and 2 PGAs for patients in each group.Abstract 605 Table 1Cohort characteristics Minimal Type 2 SLE Type 1 SLE Mixed SLE Not classified p- value n=87 n=55 n=21 n=56 n=78 Age 45.8 (13.9) 48.0 (12.8) 37.8 (13.3) 40.1 (13.4) 40.3 (12.2) 0.0005 Female 78 (89.7%) 54 (98.2%) 18 (85.7%) 53 (94.6%) 72 (92.3%) 0.2486 Black 49 (56.3%) 27 (49.1%) 13 (61.9%) 34 (60.7%) 44 (57.1%) 0.1820 Hispanic Ethnicity 1/87 (1.1%) 3/54 (5.6%) 4/21 (19.0%) 1/56 (1.8%) 4/76 (5.3%) 0.0071 Disability 13/84 (15.5%) 23/51 (45.1%) 3/19 (15.8%) 20/50 (40.0%) 22/71 (31.0%) 0.0010 Medicare/Medicaid 30/83 (36.1%) 29/51 (56.9%) 7/19 (36.8%) 29/53 (54.7%) 28/72 (38.9%) 0.0583 Depression 12/76 (15.8%) 18/46 (39.1%) 4/18 (22.2%) 28/45 (62.2%) 29/61 (47.5%) <.0001 Total areas of widespread pain across visits 1.6 (1.7) 4.4 (3.1) 1.7 (1.7) 6.0 (3.6) 3.8 (3.1) <.0001 Symptom severity score across visits 2.5 (1.7) 6.4 (2.2) 2.6 (1.7) 7.3 (2.2) 5.7 (3.0) <.0001 Total FSS (also known as PSD) across visits 4.3 (3.1) 11.2 (4.7) 4.5 (2.9) 14.5 (5.4) 10.2 (6.0) <.0001 Active lupus nephritis during study period 8/87 (9.2%) 10/55 (18.2%) 13/21 (61.9%) 19/56 (33.9%) 29/78 (37.2%) <.0001 Clinical SLEDAI across visits 0.3 (0.8) 0.5 (1.1) 1.5 (2.2) 2.3 (2.4) 3.5 (2.8) <.0001 SELENA-SLEDAI across visits 1.2 (1.4) 1.4 (1.6) 4.7 (2.1) 4.7 (2.8) 3.6 (2.5) <.0001 Musculoskeletal PGA across visits 0.0 (0.1) 0.2 (0.2) 0.2 (0.3) 0.6 (0.4) 0.3 (0.3) <.0001 Mucocutaneous PGA across visits 0.1 (0.1) 0.1 (0.1) 0.5 (0.5) 0.4 (0.5) 0.3 (0.4) <.0001 Type 1 PGA across visits 0.1 (0.1) 0.3 (0.2) 0.9 (0.3) 1.0 (0.4) 0.8 (0.3) <.0001 Type 2 PGA across visits 0.2 (0.2) 1.1 (0.4) 0.3 (0.2) 1.2 (0.4) 0.9 (0.5) <.0001 Numbers are presented as n (%) or mean (SD)

Objective Medication nonadherence is more common in African Americans compared with Caucasians. We examined the racial adherence gaps among patients with systemic lupus erythematosus (SLE) and explored factors associated with nonadherence. Methods Cross‐sectional data were obtained from consecutive patients prescribed SLE medications seen at an academic lupus clinic between August 2018 and February 2019. Adherence was measured using both self‐report and pharmacy refill data. High composite adherence was defined as having both high self‐reported adherence and high refill rates. Covariates were patient‐provider interaction, patient‐reported health status, and clinical factors. We compared adherence rates by race and used race‐stratified analyses to identify factors associated with low composite adherence. Results Among 121 patients (37% Caucasian, 63% African American), the median age was 44 years (range 22‐72), 95% were female, 51% had a college education or more, 46% had private insurance, and 38% had high composite adherence. Those with low composite adherence had higher damage scores, patient‐reported disease activity scores, and more acute care visits. High composite adherence rate was lower among African Americans compared with Caucasians (30% vs 51%, P = 0.02), and the gap was largest for those taking mycophenolate (26% vs 75%, P = 0.01). Among African Americans, low composite adherence was associated with perceiving fewer “Compassionate respectful” interactions with providers and worse anxiety and negative affect. In contrast, among Caucasians, low composite adherence was only associated with higher SLE medication regimen burden and fibromyalgia pain score. Conclusion Significant racial disparities exist in SLE medication adherence, which likely contributes to racial disparities in SLE outcomes. Interventions may be more effective if tailored by race, such as improving patient‐provider interaction and mental health among African Americans.

Objective The Type 1 and 2 systemic lupus erythematosus (SLE) Model was developed to encapsulate all signs and symptoms that patients with SLE experience. Our previous qualitative work demonstrated the model accurately reflects the lived experience of people living with SLE. The objective of this study was to present the Type 1 and 2 SLE Model to rheumatologists to understand how the model fits with their experiences treating patients with SLE. Methods We conducted a qualitative descriptive study using semistructured interviews with rheumatologists. Rheumatologists were asked about their general impression of the Type 1 and 2 SLE Model, how the model does or does not fit within their approach to treating patients with SLE, the utility of the model in clinical practice, and any suggested changes. Applied thematic analysis identified salient themes. Results We interviewed 13 rheumatologists. The majority of rheumatologists approved of the model and found it useful to guide therapy and clinical decision‐making. Several rheumatologists thought the model was helpful for patient education to manage expectations about differences between Type 1 and Type 2 symptoms and treatments. A few rheumatologists expressed concern that the model could lead to an overdiagnosis of SLE. Conclusion The Type 1 and 2 SLE Model was accepted by most rheumatologists interviewed and welcomed as a useful approach to identifying and treating symptoms in patients with SLE. Future studies will determine how implementing the Type 1 and 2 SLE Model affects patient understanding, the physician–patient relationship, and clinical outcomes.

IntroductionCell-bound complement activation products (CB-CAPs) in a multi-analyte assay with algorithm (MAP) is a valuable biomarker for the diagnosis of SLE. Erythrocyte-bound complement activation products have been associated with SLE disease activity. The clinical and serologic phenotype of longitudinal MAP positive patients has not been well described. Herein, we evaluated the relationship between longitudinal MAP results with clinical and laboratory variables.MethodsThis was a longitudinal study of adult SLE patients (2012 SLICC or 2019 ACR/EULAR criteria with a range of disease activity) with ≥2 routine lupus clinic visits from June 2020 to July 2022. Patients completed the polysymptomatic distress scale. The treating rheumatologist scored the PGA and SLEDAI scores at the time of the visit. Autoantibodies including ANA and anti-RNA-binding proteins were measured by ELISA. Anti-dsDNA was determined by immunofluorescence using the Crithidia luciliea assays. CB-CAPs were analyzed by flow cytometry. The multi-analyte assay panel (MAP) was determined using a 2-tier algorithm. Chi- square and ANOVA tests were used to analyze differences in demographic and disease history between persistently MAP positive, MAP negative, and patients with changing MAP positivity. Serologies and clinical variables at follow-up visits were compared using generalized linear models.ResultsIn this longitudinal cohort of 113 patients with 175 follow-up visits (100% SLICC SLE, 90% female, 62% Black, mean age 45) 65% were consistently MAP positive, 20% were negative, and MAP positivity changed in 15%. Patients with persistent MAP positivity were younger and more often of Black race. There was no difference in MAP positivity based on SLE disease duration. Significantly more MAP positive patients met 2019 ACR/EULAR criteria and had higher total ACR/EULAR scores. Patients who remained MAP positive were more likely to have a history of acute cutaneous lupus but there was no difference in other historical manifestations of SLE between the three groups (table 1).When evaluating longitudinal associations, patients with persistent MAP positivity had higher total SLEDAI scores, but there was no difference in the clinical SLEDAI. Therapy was comparable across groups except for greater use of belimumab, rituximab, and cyclophosphamide in persistent MAP-positive patients. Patients who remained MAP positive reported higher rates of depression, but a similar amount of polysymptomatic distress and fatigue. A greater number of lupus-specific serologies were present in those with MAP positivity (table 2).ConclusionIdentifying endotypes of SLE is important to advancing personalized medicine. In this longitudinal cohort of SLE with a full spectrum of disease active, MAP results were static in most patients. MAP resulted changed between visits in a subset of patients; although there was not a distinct clinical, demographic or laboratory phenotype in those patients. Patients with consistent MAP positivity reported more depression and had a greater burden of disease activity as measured by the ACR/EULAR score and greater use of biologic and cytotoxic therapy. Combining longitudinal MAP scores with traditional assessment of SLE assessments activity may provide useful prognostic information and allow identification of a higher risk cohort of patients. Larger longitudinal studies are on-going to evaluate the relationship between individual CB-CAPs and markers of disease activity.Abstract 303 Table 1Demographics and disease history of the SLE patient cohort Overall Patients (N = 113) MAP remains positive (N = 73) MAP remains negative (N = 23) MAP changing positivity (N = 17) p-value Demographics Age, mean (SD) 44.6 (14.3) 42.1 (15.0) 47.1 (12.9) 52.0 (10.2) 0.0227 Female 89.4% (101) 87.7% (64) 95.7% (22) 88.2% (15) 0.5485 Black race 61.9% (70) 71.2% (52) 39.1% (9) 52.9% (9) 0.0584 Ethnicity Hispanic 5.4% (6) 4.1% (3) 13.0% (3) 0.0% (0) 0.1488 SLE Disease History Duration of disease, mean (SD) 12.7 (9.0) 13.2 (9.0) 12.5 (9.2) 11.0 (9.4) 0.6764 1997 ACR Criteria 92.0% (104) 94.5% (69) 87.0% (20) 88.2% (15) 0.4149 2011 SLICC Criteria 100.0% (113) 100.0% (73) 100.0% (23) 100.0% (17) na 2019 ACR/EULAR Criteria 94.7% (107) 98.6% (72) 91.3% (21) 82.4% (14) 0.0190 2019 ACR/EULAR Total Score, median (25th - 75th pctls) 21 (16–29) 24 (18–30) 18 (13–21) 19 (16–26) 0.0117 h/o Renal 45.1% (51) 45.2% (33) 34.8% (8) 58.8% (10) 0.3195 h/o Arthritis 67.3% (76) 72.6% (53) 65.2% (15) 47.1% (8) 0.1262 h/o Acute cutaneous SLE 62.8% (71) 68.5% (50) 65.2% (15) 35.3% (6) 0.0373 h/o Discoid SLE 18.6% (21) 21.9% (16) 17.4% (4) 5.9% (1) 0.3057 h/o Serositis 23.9% (27) 26.0% (19) 21.7% (5) 17.6% (3) 0.7385 h/o Neuropsychiatric 5.3% (6) 8.2% (6) 0.0% (0) 0.0% (0) 0.1762 h/o Oral ulcers 54.0% (61) 52.1% (38) 65.2% (15) 47.1% (8) 0.4480 h/o Alopecia 56.6% (64) 56.2% (41) 43.5% (10) 76.5% (13) 0.1135 h/o Hematologic 61.1% (69) 65.8% (48) 39.1% (9) 70.6% (12) 0.0504 Abstract 303 Table 2Serologies, medications, disease activity and patient symptoms at the longitudinal follow-up visits All Follow-up Visits (N = 175) MAP remains positive (N = 111) MAP remains negative (N = 43) MAP positive to negative (N = 10) MAP negative to positive (N = 11) Serologies Anti-dsDNA positive 21.7% (38/175) 34.2% (38/111) 0.0% (0/43) 0.0% (0/10) 0.0% (0/11) <.0001 Low C3 or C4 14.4% (25/174) 17.3% (19/110) 11.6% (5/43) 0.0% (0/10) 9.1% (1/11) 0.2236 Anti-Sm positive 3.0% (2/66) 5.6% (2/36) 0.0% (0/17) 0.0% (0/7) 0.0% (0/6) 0.4795 Anti-Ro60 positive 46.2% (80/173) 49.5% (54/109) 39.5% (17/43) 50.0% (5/10) 36.4% (4/11) 0.6242 Anti-U1RNP positive 29.3% (49/167) 44.8% (47/105) 0.0% (0/42) 11.1% (1/9) 9.1% (1/11) <.0001 Anti-Cq1 positive 23.4% (41/175) 27.9% (31/111) 14.0% (6/43) 10.0% (1/10) 27.3% (3/11) 0.1785 Medications Hydroxychloroquine 83.3% (145/174) 84.5% (93/110) 81.4% (35/43) 70.0% (7/10) 90.9% (10/11) 0.6051 Mycophenolate 31.0% (54/174) 33.6% (37/110) 25.6% (11/43) 20.0% (2/10) 36.4% (4/11) 0.6322 Prednisone 18.4% (32/174) 22.7% (25/110) 9.3% (4/43) 10.0% (1/10) 18.2% (2/11) 0.2010 Belimumab, Rituximab, Cyclophosphamide 13.2% (23/174) 17.3% (19/110) 9.3% (4/43) 0.0% (0/10) 0.0% (0/11) 0.0451 Current Disease Activity PGA, mean (SD), N 0.5 (0.5), 175 0.5 (0.5), 111 0.4 (0.4), 43 0.4 (0.5), 10 0.4 (0.5), 11 0.7886 PGA >= 1.5 9.7% (17/175) 11.7% (13/111) 4.7% (2/43) 10.0% (1/10) 9.1% (1/11) 0.5701 SLEDAI, mean (SD), N 2.2 (2.5), 175 2.6 (2.7), 111 1.9 (2.2), 43 0.8 (1.7), 10 0.5 (0.9), 11 0.0120 Clinical SLEDAI, mean (SD), N 1.1 (1.8), 175 1.2 (1.7), 111 1.1 (2.1), 43 0.8 (1.7), 10 0.4 (0.8), 11 0.5125 SLEDAI Renal 7.4% (13/175) 8.1% (9/111) 9.3% (4/43) 0.0% (0/10) 0.0% (0/11) 0.3183 SLEDAI Arthritis 12.0% (21/175) 9.9% (11/111) 18.6% (8/43) 20.0% (2/10) 0.0% (0/11) 0.1466 SLEDAI Rash 17.1% (30/175) 21.6% (24/111) 9.3% (4/43) 0.0% (0/10) 18.2% (2/11) 0.0601 Patient reported symptoms Polysymptomatic distress score, mean (SD), N 9.3 (6.7), 159 8.8 (6.7), 99 10.8 (6.6), 40 9.1 (5.1), 10 7.8 (7.3), 10 0.3985 Fatigue (moderate/severe) 48.0% (72/150) 46.4% (45/97) 54.3% (19/35) 55.6% (5/9) 33.3% (3/9) 0.6473 Depression 36.5% (54/148) 45.7% (43/94) 21.6% (8/37) 11.1% (1/9) 25.0% (2/8) 0.0136