Greater need but reduced access: a population study of planned and elective surgery rates in adult mental health service users

AIMS

Timely access to surgery is an essential part of healthcare. People living with mental health (MH) conditions may have higher rates of chronic illness requiring surgical care but also face barriers to care. There is limited evidence about whether unequal surgical access contributes to health inequalities in this group.

METHODS

We examined 1.22 million surgical procedures in public and private hospitals in New South Wales (NSW), Australia, in 2019. In a cross-sectional study of 76,320 MH service users aged 18 and over, surgical procedure rates per 1,000 population were compared to rates for 6.23 million other NSW residents after direct standardisation for age, sex and socio-economic disadvantage. Rates were calculated for planned and emergency surgery, for major specialty groups, for the top 10 procedure blocks in each specialty group and for 13 access-sensitive procedures. Subgroup analyses were conducted for hospital and insurance type and for people with severe or persistent MH conditions.

RESULTS

MH service users had higher rates of surgical procedures (adjusted incidence rate ratio [aIRR]: 1.53, 95% CI: 1.51-1.56), due to slightly higher planned procedure rates (aIRR: 1.22, 95% CI: 1.19-1.24) and substantially higher emergency procedure rates (aIRR: 3.60, 95% CI: 3.51-3.70). Emergency procedure rates were increased in all block groups with sufficient numbers for standardisation. MH service users had very high rates (aIRR > 4.5) of emergency cardiovascular, skin and plastics and respiratory procedures, higher rates of planned coronary artery bypass grafting, coronary angiography and cholecystectomy but lower rates of planned ophthalmic surgery, cataract repair, shoulder reconstruction, knee replacement and some plastic surgery procedures.

CONCLUSIONS

Higher rates of surgery in MH service users may reflect a higher prevalence of conditions requiring surgical care, including cardiac, metabolic, alcohol-related or smoking-related conditions. The striking increase in emergency surgery rates suggests that this need may not be being met, particularly for chronic and disabling conditions which are often treated by planned surgery in private hospital settings in the Australian health system. A higher proportion of emergency surgery may have serious personal and health system consequences.

Association of prior treatment with nitrogen-containing bisphosphonates on outcomes of COVID-19 positive patients

COVID-19 infection has resulted in significant morbidity and mortality globally, especially among older adults. Repurposed drugs have demonstrated activity in respiratory illnesses, including nitrogen-containing bisphosphonates. In this retrospective longitudinal study at 4 academic medical centers, we show no benefit of nitrogen-containing bisphosphonates regarding ICU admission, ventilator use, and mortality among older adults with COVID-19 infection. We specifically evaluated the intravenous bisphosphonate zoledronic acid and found no difference compared to oral bisphosphonates.

BACKGROUND

Widely used in osteoporosis treatment, nitrogen-containing bisphosphonates (N-BP) have been associated with reduced mortality and morbidity among older adults. Based on prior studies, we hypothesized that prior treatment with N-BP might reduce intensive care unit (ICU) admission, ventilator use, and death among older adults diagnosed with COVID-19.

METHODS

This retrospective analysis of the PCORnet Common Data Model across 4 academic medical centers through 1 September 2021 identified individuals age >50 years with a diagnosis of COVID-19. The composite outcome included ICU admission, ventilator use, or death within 15, 30, and 180 days of COVID-19 diagnosis. Use of N-BP was defined as a prescription within 3 years prior. ICU admission and ventilator use were determined using administrative codes. Death included both in-hospital and out-of-hospital events. Patients treated with N-BP were matched 1:1 by propensity score to patients without prior N-BP use. Secondary analysis compared outcomes among those prescribed zoledronic acid (ZOL) to those prescribed oral N-BPs.

RESULTS

Of 76,223 COVID-19 patients identified, 1,853 were previously prescribed N-BP, among whom 559 were prescribed ZOL. After propensity score matching, there were no significant differences in the composite outcome at 15 days (HR 1.22, 95% CI: 0.89-1.67), 30 days (HR 1.24, 95% CI: 0.93-1.66), or 180 days (HR 1.17, 95% CI: 0.93-1.48), comparing those prescribed and not prescribed N-BP. Compared to those prescribed oral N-BP, there were no significant differences in outcomes among those prescribed ZOL.

CONCLUSION

Among older COVID-19 patients, prior exposure to N-BP including ZOL was not associated with a reduction in ICU admission, ventilator use, or death.

Photo-induced high-temperature ferromagnetism in YTiO3

In quantum materials, degeneracies and frustrated interactions can have a profound impact on the emergence of long-range order, often driving strong fluctuations that suppress functionally relevant electronic or magnetic phases^1-7. Engineering the atomic structure in the bulk or at heterointerfaces has been an important research strategy to lift these degeneracies, but these equilibrium methods are limited by thermodynamic, elastic and chemical constraints⁸. Here we show that all-optical, mode-selective manipulation of the crystal lattice can be used to enhance and stabilize high-temperature ferromagnetism in YTiO₃, a material that shows only partial orbital polarization, an unsaturated low-temperature magnetic moment and a suppressed Curie temperature, T_c = 27 K (refs. ^9-13). The enhancement is largest when exciting a 9 THz oxygen rotation mode, for which complete magnetic saturation is achieved at low temperatures and transient ferromagnetism is realized up to T_neq > 80 K, nearly three times the thermodynamic transition temperature. We interpret these effects as a consequence of the light-induced dynamical changes to the quasi-degenerate Ti t_2g orbitals, which affect the magnetic phase competition and fluctuations found in the equilibrium state^14-20. Notably, the light-induced high-temperature ferromagnetism discovered in our work is metastable over many nanoseconds, underscoring the ability to dynamically engineer practically useful non-equilibrium functionalities.

Association of Metformin With the Development of Age-Related Macular Degeneration

Importance

Age-related macular degeneration (AMD) is a leading cause of blindness with no treatment available for early stages. Retrospective studies have shown an association between metformin and reduced risk of AMD.

Objective

To investigate the association between metformin use and age-related macular degeneration (AMD).

Design, Setting, and Participants

The Diabetes Prevention Program Outcomes Study is a cross-sectional follow-up phase of a large multicenter randomized clinical trial, Diabetes Prevention Program (1996-2001), to investigate the association of treatment with metformin or an intensive lifestyle modification vs placebo with preventing the onset of type 2 diabetes in a population at high risk for developing diabetes. Participants with retinal imaging at a follow-up visit 16 years posttrial (2017-2019) were included. Analysis took place between October 2019 and May 2022.

Interventions

Participants were randomly distributed between 3 interventional arms: lifestyle, metformin, and placebo.

Main Outcomes and Measures

Prevalence of AMD in the treatment arms.

Results

Of 1592 participants, 514 (32.3%) were in the lifestyle arm, 549 (34.5%) were in the metformin arm, and 529 (33.2%) were in the placebo arm. All 3 arms were balanced for baseline characteristics including age (mean [SD] age at randomization, 49 [9] years), sex (1128 [71%] male), race and ethnicity (784 [49%] White), smoking habits, body mass index, and education level. AMD was identified in 479 participants (30.1%); 229 (14.4%) had early AMD, 218 (13.7%) had intermediate AMD, and 32 (2.0%) had advanced AMD. There was no significant difference in the presence of AMD between the 3 groups: 152 (29.6%) in the lifestyle arm, 165 (30.2%) in the metformin arm, and 162 (30.7%) in the placebo arm. There was also no difference in the distribution of early, intermediate, and advanced AMD between the intervention groups. Mean duration of metformin use was similar for those with and without AMD (mean [SD], 8.0 [9.3] vs 8.5 [9.3] years; P = .69). In the multivariate models, history of smoking was associated with increased risks of AMD (odds ratio, 1.30; 95% CI, 1.05-1.61; P = .02).

Conclusions and Relevance

These data suggest neither metformin nor lifestyle changes initiated for diabetes prevention were associated with the risk of any AMD, with similar results for AMD severity. Duration of metformin use was also not associated with AMD. This analysis does not address the association of metformin with incidence or progression of AMD.

Watchman device migration and embolization: a report from the NCDR LAAO registry

Background

Incomplete anchoring of the Watchman left atrial appendage closure (LAAO) device can result in substantial device migration or device embolization requiring percutaneous or surgical retrieval.

Purpose

To report rates and characteristics of in-hospital and post-discharge Watchman device migration and embolization events in the United States.

Methods

We performed a retrospective analysis of Watchman procedures (January 2016 through March 2021) reported to the National Cardiovascular Data Registry LAAO Registry. We excluded patients with prior LAAO interventions, no device released, and missing device information. In-hospital events were assessed among all patients and post-discharge events were assessed among patients with 45-day follow-up.

Results

Of 120,278 Watchman procedures, device migration or embolization occurred in 0.07% of patients (n=84) during the index hospitalization and surgery was performed in 39 patients. The in-hospital mortality rate was 14% among patients with device migration or embolization and 20.5% among patients who underwent surgery. In-hospital migration or embolization was more common: at hospitals with a lower median annual procedure volume (24 vs. 41 procedures, p<0.0001), with first-generation Watchman versus next-generation Watchman FLX devices (0.08% vs. 0.04%, p=0.0048), with larger LAA ostia (median 23 mm vs. 21 mm, p=0.004), and with a smaller difference between device and LAA ostial size (median difference 4 mm vs. 5 mm, p=0.04). There were no differences by age, sex, hospital type, hospital size, or teaching versus non-teaching status. Of 98,147 patients with 45-day follow-up, device migration or embolization after discharge occurred in 0.06% (n=54) patients and cardiac surgery was performed in 7.4% (n=4) of cases. The 45-day mortality rate was 3.7% (n=2) among patients with post-discharge device migration or embolization. Post-discharge migration or embolization was more common among men (79.7% of events but 58.9% of all procedures, p=0.0019), taller patients (177.9 cm vs. 172 cm, p=0.0005), and those with greater body mass (99.9 kg vs. 85.5 kg, p=0.0055); in contrast to in-hospital events, there were no differences in hospital volume, device characteristics, or LAA characteristics.

Conclusions

Watchman device migration or embolization is rare but associated with high mortality (Figure 1) and frequently requires surgical retrieval. A substantial proportion of all device migration or embolization cases occur after discharge and different patient and procedure characteristics are associated with in-hospital versus post-discharge cases. Given the morbidity and mortality associated with device migration or embolization, risk mitigation strategies and on-site cardiac surgical back-up are of paramount importance.

Funding Acknowledgement

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): National Institutes of Health

AB0888 Guselkumab Provides Sustained Improvements in Work Productivity and Daily Activity in Patients With Active Psoriatic Arthritis Through 2 Years of DISCOVER-2

Background

Psoriatic arthritis (PsA) impacts patients’ (pts) work productivity (WP) and daily activity.1 DISCOVER-2 (D2), a Phase 3 trial of the selective interleukin-23 p19-subunit inhibitor guselkumab (GUS) in biologic-naïve pts with PsA,2 demonstrated significant improvements in pt-reported WP and daily activity following 1 year (Y) of GUS treatment.3

Objectives

Assess WP and daily activity impairment in D2 pts through 2Y. Estimate indirect savings associated with GUS treatment and assess changes in employment status.

Methods

Pts with active PsA received GUS 100 mg every 4 weeks (Q4W); GUS 100 mg at W0, W4, then Q8W; or placebo (PBO). At W24, PBO pts crossed over to GUS 100 mg Q4W. WPAI-PsA assesses PsA-related work time missed (absenteeism), impairment while working (presenteeism), and impaired overall WP (absenteeism + presenteeism) for pts employed at baseline (EBL) and daily activity for all pts, including those unemployed at baseline (UBL) during the previous week. Mean changes in WPAI-PsA domains were calculated for each multiple imputation (MI) dataset using an analysis of covariance (ANCOVA); the reported LS mean is the average of all MI datasets. Significance was defined as p<0.05. Among pts EBL, potential indirect savings from improved overall WP were estimated using 2020 European Union mean yearly wage estimate (all occupations) combined with LS mean change from BL in WPAI-PsA overall work impairment.4 A shift analysis evaluated proportions of pts employed vs unemployed by treatment group using observed data over time.

Results

Pts EBL comprised 64% of the analysis cohort. Significant improvements in WP in pts EBL and in daily activity among all pts were observed with GUS Q4W/Q8W vs PBO at W24;3 mean improvements in WP and daily activity increased with continued GUS through 2Y (Table 1). Potential annual indirect savings from improved overall WP in pts EBL were €10,826 GUS Q4W, €12,712 GUS Q8W, and €10,948 PBO→ GUS Q4W at 2Y. Shift analysis showed relatively stable employment in pts EBL with GUS up to 2Y (>83% continued to work). Among pts UBL (36% of cohort), the proportion of pts employed increased by >20% through 2Y of GUS (Figure 1).

Table 1.

Model-Based Estimates of Change From BL in WPAI-PsA Domains1

GUS 100mg Q4WGUS 100mg Q8WPBO (W0-24) → GUS 100 mg Q4W (W24-100)VisitW24W100W24W100W24W100Absenteeism, N145147147149162166  LS Mean (95% CI)-3.4 (-6.5, -0.3)-1.8 (-4.5, 0.9)-3.0 (-6.0, 0.1)-4.2 (-6.8,-1.5)-3.0 (-6.0, 0.04)-4.2 (-6.8,-1.6)  Diff vs. PBO-0.4 (-4.6, 3.8)--0.01 (-4.2, 4.2)---Presenteeism, N145147147149162166  LS Mean (95% CI)-20.1 (-23.7, -16.6)-26.3 (-30.1,-22.5)-19.6 (-23.2, -16.1)-28.0 (-31.8, -24.2)-10.5 (-13.9, -7.0)-24.2 (-27.9, -20.5)  Diff vs PBO-9.7* (-14.4, -5.0)--9.2* (-13.9, -4.5)---Work productivity, N145147147149162166  LS Mean (95% CI)-20.1 (-24.1, -16.1)-23.8 (-28.0, -19.6)-19.2 (-23.1, -15.2)-28.0 (-32.1, -23.8)-10.6 (-14.4, -6.8)-24.1 (-28.1, -20.1)  Diff vs PBO-9.5* (-14.8, -4.2)--8.6* (-13.9, -3.3)---Daily Activity, N242242246246245245  LS Mean (95% CI)-20.5 (-23.3, -17.7)-29.2 (-32.2, -26.1)-21.2 (-23.9, -18.4)-28.0 (-31.0, -24.9)-9.9 (-12.6, -7.1)-26.6 (-29.6, -23.6)  Diff vs PBO-10.6* (-14.4, -6.8)--11.3* (-15.1, -7.5)-

1Mean changes in WPAI-PsA domains were calculated for each MI dataset using an ANCOVA; reported LS mean (95% confidence interval [CI]) = average of all MI datasets.

*p<0.002

Conclusion

In GUS-treated bio-naïve PsA pts, robust improvements in WP and daily activity seen at W24 were maintained and increased through 2Y of GUS. Long-term improvements in WP achieved may result in substantial indirect cost savings for GUS-treated pts. Rates of employment remained stable in pts employed and increased in those unemployed at BL.

References

[1]Tillett W et al. Rheumatol (Oxford). 2012;51:275–83.

[2]Mease PJ, et al. Lancet. 2020;395:1126–36.

[3]Curtis JR et al. EULAR, June 2–5, 2021. POS1026.

[4]OECD (2020). Average wages (indicator). https://data.oecd.org/earnwage/average-wages.htm

Disclosure of Interests

Jeffrey Curtis Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, CorEvitas, Eli Lilly and Company, Janssen, Myriad, Novartis, Pfizer, Sanofi, and UCB, Grant/research support from: AbbVie, Amgen, Bristol-Myers Squibb, CorEvitas, Eli Lilly and Company, Janssen, Myriad, Novartis, Pfizer, Sanofi, and UCB, Iain McInnes Shareholder of: Causeway Therapeutics, and Evelo Compugen, Consultant of: Astra Zeneca, AbbVie, Bristol-Myers Squibb, Amgen, Eli Lilly and Company, Cabaletta, Compugen, GSK, Gilead, Janssen, Novartis, Pfizer, Sanofi, Roche, and UCB, Grant/research support from: Astra Zeneca, Bristol-Myers Squibb, Amgen, Eli Lilly and Company, GSK, Janssen, Novartis, Roche, and UCB, Proton Rahman Consultant of: AbbVie, Amgen, Bristol Myers Squibb, Celgene, Eli Lilly, Janssen, Merck, Novartis, Pfizer, and UCB, Grant/research support from: Janssen and Novartis, Dafna D Gladman Consultant of: Abbvie, Amgen, BMS, Eli Lilly, Galapagos, Gilead, janssen, Novartis, Pfizer and UCB., Grant/research support from: Abbvie, Amgen, Eli Lilly, Janssen, Pfizer, UCB, Feifei Yang Shareholder of: Johnson & Johnson, Employee of: Janssen Global Services, LLC, Steve Peterson Shareholder of: Johnson & Johnson, Employee of: Janssen Global Services, LLC, Alexa Kollmeier Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Natalie Shiff Shareholder of: Johnson & Johnson, Abbvie, Gilead, Employee of: Janssen Scientific Affairs, LLC, Chenglong Han Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, May Shawi Shareholder of: Johnson & Johnson, Employee of: Immunology Global Medical Affairs, Janssen Pharmaceutical Companies, William Tillett Speakers bureau: Abbvie, Amgen, Eli-Lilly, Janssen, MSD, Novartis, Pfizer, and UCB, Consultant of: Abbvie, Amgen, Eli-Lilly, Janssen, MSD, Novartis, Pfizer, and UCB, Grant/research support from: Abbvie, Amgen, Eli-Lilly, Janssen, and UCB, Philip J Mease Speakers bureau: AbbVie, Amgen, Eli Lilly, Janssen, Novartis, Pfizer, Sun Pharma, and UCB, Consultant of: AbbVie, Aclaris, Amgen, Boehringer Ingelheim, Bristol Myers Squibb, Eli Lilly, Galapagos, Gilead, GlaxoSmithKline, Inmagene, Janssen, Novartis, Pfizer, Sun Pharma, and UCB, Grant/research support from: AbbVie, Amgen, Bristol Myers Squibb, Eli Lilly, Galapagos, Gilead, Janssen, Novartis, Pfizer, Sun Pharma, and UCB

AB0881 Guselkumab Provides Sustained Improvements in Health-Related Quality of Life in Patients With Active Psoriatic Arthritis Through 2 Years of DISCOVER-2

Background

Psoriatic arthritis (PsA), a chronic inflammatory disease characterized by peripheral arthritis, axial inflammation, dactylitis, enthesitis, and skin/nail psoriasis, is associated with reduced health-related quality of life (HRQoL).

Objectives

To assess long-term effect of guselkumab (GUS), a human monoclonal antibody that selectively targets the interleukin (IL)-23p19 subunit, on HRQoL of bio-naïve PsA patients (pts) who participated in the Phase 3 2-year DISCOVER-2 trial.1

Methods

Pts with active PsA despite nonbiologic disease-modifying antirheumatic drugs (DMARDs) and/or nonsteroidal anti-inflammatory drugs (NSAIDs) received GUS 100 mg every 4 weeks (Q4W); GUS 100 mg at W0, W4, then Q8W; or placebo (PBO). At W24, PBO pts crossed over to GUS 100 mg Q4W. HRQoL was assessed using the pt-reported EuroQoL-5 Dimension-5 Level (EQ-5D-5L) questionnaire index and EuroQol Visual Analog Scale (EQ-VAS), widely used and complimentary tools that allow pts to provide a global assessment of their HRQoL. The EQ-5D-5L index assesses mobility, self-care, usual activities, pain/discomfort, and anxiety/depression; an index score is derived ranging from 0 (death) to 1 (perfect health).2 EQ-VAS assesses pt health state on a scale of 0-100, with higher scores indicating better health. Using mixed effects models for repeated measures (MMRM), least squares (LS) mean changes from baseline in the EQ-5D-5L index and EQ-VAS through W100 were assessed. Observed changes from baseline were evaluated; in pts who met treatment failure rules before W24 and in pts who discontinued with missing data after W24, changes from baseline were imputed as 0.

Results

GUS-treated pts achieved greater improvements in pt-reported health status than PBO at both W16 and W24 when evaluated using both the EQ-5D-5L index score and the EQ-VAS. The improvements by GUS in EQ-5D-5L index scores through W24 (0.12 for GUS Q4W/Q8W vs 0.05 for PBO; each nominal p<0.0001) were maintained with continued GUS through 2 years (0.15 for GUS Q4W/Q8W) (Table 1). PBO-treated pts who started GUS at W24 reported comparable improvements in their HRQoL by W52 (0.12), with maintenance though W100 (0.14). Similar results were observed with EQ-VAS (Figure 1). W24 improvements in EQ-VAS scores were greater following GUS treatment (18.2/18.4 GUS Q4W/Q8W) vs PBO (6.8; nominal p<0.0001). EQ-VAS scores continued to improve with GUS through 2 years (25.0/24.6 GUS Q4W/Q8W). Likewise, PBO-treated pts who crossed over to GUS at W24 experienced improvements in HRQoL by W52 (18.8), with maintenance through W100 (21.2).

Table 1.

LS mean change from baseline through W100 in EQ-5D-5L index

GUS 100mg Q4W(W0-100)GUS 100mg Q8W(W0-100)PBO → GUS 100 mg Q4WPBO(W0-24)GUS(W24-100)Week162410016241001624100N243244243247246248244244244LS mean change (95% CI)0.10 (0.09,0.12)0.12 (0.1,0.13)0.15 (0.13,0.16)0.11 (0.1,0.13)0.12 (0.1,0.13)0.15 (0.13,0.17)0.06 (0.04,0.07)0.05 (0.04,0.07)0.14 (0.12,0.16)  Diff vs. PBO0.04 (0.02,0.06)0.06 (0.04,0.09)--0.05 (0.03,0.07)0.06 (0.04,0.08)--------  Nominal p-value<0.0001<0.0001--<0.0001<0.0001--------

CI=Confidence interval; Diff=Difference

Conclusion

In bio-naïve pts with active PsA receiving GUS, earlier improvements (at the first timepoint assessed) in self-reported HRQoL measures were sustained through 2 years.

References

[1]Mease PJ, et al. Lancet. 2020;395:1126–36.

[2]EuroQol Group. 1990;16:199-208.

Disclosure of Interests

Jeffrey Curtis Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, CorEvitas, Eli Lilly, Janssen, Myriad, Novartis, Pfizer, Sanofi, and UCB, Grant/research support from: AbbVie, Amgen, Bristol-Myers Squibb, CorEvitas, Eli Lilly, Janssen, Myriad, Novartis, Pfizer, Sanofi, and UCB, Iain McInnes Shareholder of: Causeway Therapeutics, and Evelo Compugen, Consultant of: Astra Zeneca, AbbVie, Amgen, Bristol-Myers Squibb, Cabaletta, Compugen, Eli Lilly, Gilead, GSK, Janssen, Novartis, Pfizer, Roche, Sanofi, and UCB, Grant/research support from: Astra Zeneca, Amgen, Bristol-Myers Squibb, Eli Lilly, GSK, Janssen, Novartis, Roche, and UCB, Proton Rahman Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, Celgene, Eli Lilly, Janssen, Merck, Novartis, Pfizer, and UCB, Grant/research support from: Janssen and Novartis, Dafna D Gladman Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, Eli Lilly, Galapagos, Gilead, Janssen, Novartis, Pfizer, and UCB, Grant/research support from: AbbVie, Amgen, Eli Lilly, Janssen, Pfizer, and UCB, Feifei Yang Shareholder of: Johnson & Johnson, Employee of: Janssen Global Services, LLC (a wholly owned subsidiary of Johnson & Johnson), Steve Peterson Shareholder of: Johnson & Johnson, Employee of: Janssen Global Services, LLC (a wholly owned subsidiary of Johnson & Johnson), Alexa Kollmeier Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC (a wholly owned subsidiary of Johnson & Johnson), Natalie Shiff Shareholder of: AbbVie, Gilead, and Johnson & Johnson, Employee of: Janssen Scientific Affairs, LLC (a wholly owned subsidiary of Johnson & Johnson), Chenglong Han Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC (a wholly owned subsidiary of Johnson & Johnson), May Shawi Shareholder of: Johnson & Johnson, Employee of: Immunology Global Medical Affairs, Janssen Pharmaceutical Companies (a wholly owned subsidiary of Johnson & Johnson), William Tillett Speakers bureau: AbbVie, Amgen, Eli Lilly, Janssen, MSD, Novartis, Pfizer, and UCB, Consultant of: AbbVie, Amgen, Eli Lilly, Janssen, MSD, Novartis, Pfizer, and UCB, Grant/research support from: AbbVie, Amgen, Eli Lilly, Janssen, and UCB, Philip J Mease Speakers bureau: AbbVie, Amgen, Eli Lilly, Janssen, Novartis, Pfizer, SUN Pharma, and UCB, Consultant of: AbbVie, Aclaris, Amgen, Boehringer Ingelheim, Bristol-Myers Squibb, Eli Lilly, Galapagos, Gilead, GSK, Inmagene, Janssen, Novartis, Pfizer, SUN Pharma, and UCB, Grant/research support from: AbbVie, Amgen, Bristol-Myers Squibb, Eli Lilly, Galapagos, Gilead, Janssen, Novartis, Pfizer, SUN Pharma, and UCB

POS1434 USE OF BIOLOGIC OR TARGETED SYNTHETIC DISEASE MODIFYING ANTI-RHEUMATIC DRUGS AND THE RISK OF LYMPHOMA IN RHEUMATOID ARTHRITIS

Background

Epidemiologic studies suggest that disease duration and degree of inflammatory activity of rheumatoid arthritis (RA) contribute to lymphoma development (1). Whether the decrease in inflammatory burden seen with use of biologic or targeted synthetic disease modifying anti-rheumatic drugs (bDMARDs or tsDMARDs) translates into a lower risk of lymphoma in RA needs to be studied.

Objectives

The objective of our study was to examine the effect of administration of b/tsDMARDS on the incidence of lymphoma relative to conventional synthetic DMARDs (csDMARDs) in an inception cohort of Veterans with RA.

Methods

We identified patients >18 years of age diagnosed with RA in any US Veterans Affairs (VA) facility from 1/1/2002 and 12/31/2018 using the VA Corporate Data Warehouse (CDW). To be included, each patient was required to meet the following criteria: 2+ RA diagnostic codes at least 7 days apart but no more than 365 days apart; 2) a prescription for a csDMARD within 90 days of the first RA diagnosis; and 3) an inpatient or outpatient visit 30 days to 2 years preceding first RA diagnosis (indicating they are a regular user of the VA). The csDMARDs included in these analyses were: methotrexate, sulfasalazine, leflunomide, and hydroxychloroquine. The bDMARDs included were tumor necrosis factor inhibitors (TNFi) and non-TNFi biologics such as tocilizumab, rituximab, abatacept, and biosimilars; tsDMARD was tofacitinib. Patients with prevalent lymphoma were excluded. Lymphoma diagnoses were identified using International Classification of Diseases Version 9, 10 and Oncology (ICD9, ICD10, ICDO) codes.

We used marginal structural models as described by Hernan et al (2) and time-varying Cox models to control for confounding by indication while evaluating this association. We adjusted for baseline demographics (age, sex, race, ethnicity, year of cohort entry, rheumatology visits), and time-varying CRP and time-varying Rheumatoid Disease Comorbidity Index (RDCI) (3) to control for confounding.

Results

27,421 Veterans with RA met our eligibility criteria. Most of the Veterans (56%) were in the age range 61-80 years old; 89% male, 76% White, 14% African American. 8,225 (30%) patients were treated with a b-/tsDMARD. The crude incidence rates were 1.71 (95% CI 1.5-1.94) per 1000 person-years for those only on csDMARDs and 1.78 (95% CI 1.44-2.18) for patients during or following use of a b/tsDMARDs. After adjustment with both time-fixed and time-varying covariates using marginal structural models, the incidence of lymphoma was not different between patients who did and did not use a b/tsDMARD (hazard ratio=1.06, 95% CI= 0.82-1.37) (Table 1).

Table 1.

Estimates of Effect of bDMARD or tsDMARD use on Lymphoma relative to use of csDMARDs

Marginal Structural Models; adjusted for:@Demographics1.04(0.80, 1.34)#Demographics + CRP1.06(0.82, 1.37)

* per 1000 person-years

@Demographics = age, gender, race, ethnicity, rheumatology visits, and year of cohort entry

#Adjusts for CRP, baseline rheumatology visits (yes/no) and RDCI.

CRP = C-Reactive Protein, RDCI = Rhematic Disease Comorbidity Index, CI = Confidence Interval, b/tsDMARD = biologic or targeted synthetic DMARD, csDMARD = conventional synthetic DMARD

Conclusion

In this large study using the nationwide VA data, we did not observe an association between the use of b/ts DMARDs and an increased risk of lymphoma.

References

[1]Baecklund E, Iliadou A, Askling J, Ekbom A, Backlin C, Granath F, et al. Association of chronic inflammation, not its treatment, with increased lymphoma risk in rheumatoid arthritis. Arthritis Rheum. 2006;54(3):692-701.

[2]Robins JM, Hernan MA, Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiology. 2000;11(5):550-60.

[3]England BR, Sayles H, Mikuls TR, Johnson DS, Michaud K. Validation of the rheumatic disease comorbidity index. Arthritis care & research. 2015;67(6):865-72.

Disclosure of Interests

None declared

POS1564-PARE EDUCATION TOPICS AND SMARTPHONE APP FUNCTIONS PRIORITIZED BY PEOPLE WITH RHEUMATIC AND MUSCULOSKELETAL DISEASES

Background

Generating information that people living with a rheumatic and musculoskeletal disease (RMD) find useful while making decisions about their treatment requires identifying and understanding educational needs and interests directly expressed from people living with RMD.

Objectives

To identify what types of information US adults with RMD perceive as important to know about their disease and how they express and prioritize such information.

Methods

Using nominal group technique, focus groups of participants (pts) with RMD generated sets of rank-order educational items which were then aggregated across groups into themes. Based on nominal group results, a survey with the final 28 items was administered online, along with a question about desired functions of a smartphone app for RMD, to members of the ArthritisPower registry in January 2022.

Results

Six nominal groups (n=47) yielded 28 unique items for the online survey of educational priorities. To date, a total of 570 pts completed the survey, of whom 85.4% were female, 89.5% white, mean age of 59.6 (SD 11.2) years. Rheumatoid arthritis (52.5%), osteoarthritis (16.0%), psoriatic arthritis (12.5%), and axial spondyloarthritis (7.5%) were the most common RMDs. Knowing how to tell when a medication is not working, how RMD affects other medical conditions, understanding the results of tests used to monitor their RMD, available treatment options and possible side effects, and how life will change as an RMD progresses were each items that > 75% of pts considered extremely important (Table 1). Top functions pts listed as useful for a smartphone app included being able to participate in research, view lab results, record symptoms or flares, share how they are doing with their provider, and get educational information about their disease (Table 2).

Table 1.

Top Education Topics Adults with Rheumatic and Musculoskeletal Disease Consider Extremely Important (N=570).

Itemn (%)Knowing when the medication is not working505 (88.6)Knowing how a rheumatologic condition can affect your other health conditions or medical issues481 (84.4)Understanding the results of tests used to monitor your condition471 (82.6)Knowing the side effects of available drugs, and how the drugs interact with each other461 (80.9)Finding the right rheumatologist453 (79.5)Having realistic expectations of the effectiveness of the medications445 (78.1)Knowing how the disease will progress, even if the news is bad439 (77.0)Knowing the available medications and treatments for your rheumatologic condition437 (76.7)Knowing how long it takes drugs to work436 (76.5)Understanding how your life will change as your disease progresses434 (76.1)Table 2.

Desired Smartphone App Functions Rated By Adults with Rheumatic and Musculoskeletal Disease (N=570).

App Functionn (%)Participate in patient-centered research299 (52.5)View my lab results283 (49.7)Record my symptoms (e.g. pain, fatigue) or disease flares to track my health over time278 (48.8)Record my symptoms and share how I am doing with my rheumatology provider to know if I am meeting my treatment goals230 (40.4)Get educational information about my disease225 (39.5)Keep track of the medications prescribed by doctor200 (35.1)Schedule and keep track of my medical appointments, rheumatology and other199 (34.9)Track the vaccines I get (i.e. vaccination record)188 (33.0)Help me improve some of my health habits (e.g. sleep, diet, exercise)187 (32.8)Keep track of my use of over-the-counter, complementary or alternative therapies (herbs, tinctures, acupuncture, massage, stretching, etc.)174 (30.5)Get support for my disease from trained patients with my same health condition (i.e. ‘peer coaching’)144 (25.3)

Conclusion

People with RMD prioritized information about medications and prognosis in educational materials, providing guidance for the development of educational tools. A sizeable minority felt educational materials were an important component of a smartphone app, but also identified other important features such as participation in research.

Disclosure of Interests

W. Benjamin Nowell Grant/research support from: Research support from AbbVie, Amgen, Eli Lilly and Scipher, Kelly Gavigan: None declared, Kimberly Garza: None declared, Alexis Ogdie: None declared, Michael George: None declared, Jessica A. Walsh Consultant of: AbbVie, Amgen, Eli Lilly and Company, Janssen, Novartis, Pfizer, and UCB, Grant/research support from: AbbVie, Merck, and Pfizer, Maria Danila: None declared, Shilpa Venkatachalam: None declared, Laura Stradford: None declared, Jeffrey Curtis Consultant of: AbbVie, Amgen, BMS, Corrona, Eli Lilly and Company, Gilead, Janssen, Myriad, Novartis, Pfizer, Regeneron, Roche, and UCB, Grant/research support from: AbbVie, Amgen, BMS, Corrona, Eli Lilly and Company, Janssen, Myriad, Pfizer, Regeneron, Roche, and UCB

Implementation of a lifestyle and life-skills intervention to prevent weight-gain and cardiometabolic abnormalities in people with first-episode psychosis: the Keeping the Body in Mind program

Introduction

The development of obesity and metabolic abnormalities that seed future ill-health occur early with antipsychotic treatment. In 2013, the 12-week Keeping the Body in Mind (KBIM) pilot lifestyle intervention was delivered to a small sample of youth experiencing first-episode psychosis (FEP) with <4 weeks of antipsychotic exposure in a cluster-controlled design. The control group experienced significant increases in weight (mean 7.8kg) and waist circumference (mean 7.1cm) compared to non-significant increases (mean 1.8kg) in the KBIM group.

Objectives

To evaluate the effect of KBIM as routine care on anthropometry and metabolic biochemistry in a larger sample of youth with FEP across three mental health services.

Methods

This retrospective chart audit was conducted on youth with FEP, prescribed a therapeutic dose of antipsychotic medication, and who engaged with KBIM between 2015 and 2019. Primary outcomes were weight and waist circumference. Secondary outcomes were blood pressure, blood glucose and blood lipids. Outcomes were collected in a pre-post design. Implementation elements were also obtained from the participant’s medical file.

Results

One-hundred and eighty-two people met inclusion criteria. Follow-up data were available on up to 134 people for individual outcomes. Mean number of sessions attended was 11.1 (SD=7.3). Weight and waist changes were limited to 1.5kg (SD=5.3, t(133)=3.2, p=0.002) and 0.7cm (SD=5.8, t(109)=1.2, p=0.23). Nineteen percent experienced clinically significant weight gain. There were no changes to blood pressure or metabolic biochemistry.

Conclusions

The positive outcomes for weight and waist circumference found in the initial pilot study were maintained with implementation as routine care.

Disclosure

No significant relationships.

POS0296 INTEGRATED SAFETY ANALYSIS OF TOFACITINIB IN ANKYLOSING SPONDYLITIS CLINICAL TRIALS

Background

Tofacitinib is an oral JAK inhibitor for the treatment of adults with ankylosing spondylitis (AS).

Objectives

To describe the tofacitinib safety profile from an integrated analysis of randomised controlled trials (RCTs) in patients (pts) with active AS.

Methods

Pooled data from Phase (P)2 (NCT01786668) and P3 (NCT03502616) RCTs in pts with AS were analysed in 3 cohorts (Table 1): the 16-Week (Wk) placebo (PBO)-controlled cohort (pts receiving tofacitinib 5 mg twice daily [BID] or PBO from Wks 0–12 [P2 RCT] or Wks 0–16 [P3 RCT]), the 48-Wk all tofacitinib 5 mg BID cohort and the 48-Wk all tofacitinib cohort (pts receiving ≥1 dose of tofacitinib 2, 5 or 10 mg BID), including pts receiving tofacitinib from Wks 0–12 (P2 RCT) or Wks 0–48 (P3 RCT). Pts receiving tofacitinib 5 mg BID were included in the 16-Wk PBO-controlled cohort and both 48-Wk tofacitinib cohorts. Adverse event (AE)/AEs of special interest incidence rates (IRs; pts with events/100 pt-yrs) were reported based on a 28-day risk period (time of first to last study drug dose +28 days). Baseline (BL) cardiovascular (CV) risk was calculated post hoc by the atherosclerotic CV disease (ASCVD)-pooled cohort equations calculator for pts without history of coronary artery disease (48-Wk tofacitinib cohorts).

Results

At BL, most pts (>76%) in the 48-Wk tofacitinib cohorts had <5% (low) 10-yr ASCVD risk (Figure 1). The most common treatment-emergent AEs were nasopharyngitis/upper respiratory tract infection. Serious AE IRs were higher with tofacitinib 5 mg BID vs PBO in the 16-Wk PBO-controlled cohort, and similar in the 48-Wk tofacitinib cohorts (Table 1). Discontinuation due to AEs was similar between groups in the 16-Wk PBO-controlled cohort and between the 48-Wk tofacitinib cohorts (Table 1). One pt receiving tofacitinib 5 mg BID (included in the 16-Wk PBO-controlled and both 48-Wk tofacitinib cohorts) had a serious infection (SI; meningitis; Table 1). No SIs with PBO. Herpes zoster (HZ; all non-serious) occurred in the 48-Wk all tofacitinib 5 mg BID (5 pts [1.6%]) and 48-Wk all tofacitinib cohorts (7 pts [1.7%]; Table 1) only. Most cases involved a single dermatome, but 1 pt (tofacitinib 10 mg BID) had HZ involving 2 adjacent dermatomes. Across cohorts, there were no deaths or adjudicated opportunistic infections (OIs), OIs excluding tuberculosis (TB), TB, malignancies excluding non-melanoma skin cancer (NMSC), NMSC, major adverse CV events, thromboembolic events, gastrointestinal perforation or interstitial lung disease. Uveitis was reported in 1 (0.5%), 3 (1.6%), 4 (1.3%) and 6 (1.4%) pts in the tofacitinib 5 mg BID, PBO, 48-Wk all tofacitinib 5 mg BID and 48-Wk all tofacitinib groups, respectively; all but 1 pt (tofacitinib 2 mg BID) had history of uveitis. Psoriasis occurred in 1 (0.5%) pt (PBO) with history of psoriasis. There were no AEs of inflammatory bowel disease.

Table 1.

AEs and AEs of special interest

16-Wk PBO-controlled cohort48-Wk tofacitinib cohortsTofacitinib 5 mg BID N=185PBO N=18748-Wk all tofacitinib 5 mg BID N=31648-Wk all tofacitinib N=420AE, n (%), IR [95% CI per 100 pt-yrs]Serious AE3 (1.6) 5.28 [0.00, 11.25]2 (1.1) 3.56 [0.00, 8.49]8 (2.5) 3.49 [1.51, 6.87]9 (2.1) 3.45 [1.58, 6.55]Discontinuation due to AEs4 (2.2) 7.04 [0.14, 13.94]4 (2.1) 7.10 [0.14, 14.05]11 (3.5) 4.77 [2.38, 8.54]12 (2.9) 4.58 [2.37, 8.00]SI1 (0.5) 1.77 [0.00, 5.89]0 0.00 [0.00, 3.31]1 (0.3) 0.43 [0.01, 2.41]1 (0.2) 0.38 [0.01, 2.12]HZ0 0.00 [0.00, 3.28]0 0.00 [0.00, 3.31]5 (1.6) 2.18 [0.71, 5.08]7 (1.7) 2.68 [1.08, 5.53]All-cause mortality0 0.00 [0.00, 3.28]0 0.00 [0.00, 3.31]0 0.00 [0.00, 1.59]0 0.00 [0.00, 1.40]Malignancies excluding NMSC0 0.00 [0.00, 3.28]0 0.00 [0.00, 3.31]0 0.00 [0.00, 1.59]0 0.00 [0.00, 1.40]Major adverse CV event0 0.00 [0.00, 3.28]0 0.00 [0.00, 3.31]0 0.00 [0.00, 1.59]0 0.00 [0.00, 1.40]Venous thromboembolism0 0.00 [0.00, 3.28]0 0.00 [0.00, 3.31]0 0.00 [0.00, 1.59]0 0.00 [0.00, 1.40]

CI, confidence interval; n, number of pts with event within 28-day risk period

Conclusion

Tofacitinib 5 mg BID was well tolerated over 48 Wks in pts with AS, and safety was consistent with the established safety profile of tofacitinib.

Acknowledgements

Study sponsored by Pfizer Inc. Medical writing support was provided by Jennifer Arnold, CMC Connect, and funded by Pfizer Inc.

Disclosure of Interests

Atul Deodhar Consultant of: AbbVie, Amgen, Aurinia, Boehringer Ingelheim, Bristol-Myers Squibb, Celegene, Eli Lilly, GlaxoSmithKline, Janssen, MoonLake, Novartis, Pfizer Inc and UCB, Grant/research support from: AbbVie, Eli Lilly, GlaxoSmithKline, Novartis, Pfizer Inc and UCB, Servet Akar Speakers bureau: AbbVie, Amgen, Eli Lilly, MSD, Novartis, Pfizer Inc and UCB, Consultant of: AbbVie, Amgen, Eli Lilly, MSD, Novartis, Pfizer Inc and UCB, Grant/research support from: Pfizer Inc, Jeffrey Curtis Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, CorEvitas, LLC (formerly Corrona, LLC), Eli Lilly, Janssen, Myriad, Pfizer Inc, Radius, Roche and UCB, Grant/research support from: AbbVie, Amgen, Bristol-Myers Squibb, CorEvitas, LLC (formerly Corrona, LLC), Eli Lilly, Janssen, Myriad, Pfizer Inc, Radius, Roche and UCB, Bassel Zorkany Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, Eva, Eli Lilly, Hekma, Janssen, MSD, New Bridge, Novartis, Pfizer Inc, Roche, Sanofi-Aventis and Servier, Grant/research support from: AbbVie, Amgen, Bristol-Myers Squibb, Eva, Eli Lilly, Hekma, Janssen, MSD, New Bridge, Novartis, Pfizer Inc, Roche, Sanofi-Aventis and Servier, Marina Magrey Consultant of: AbbVie, Eli Lilly, Novartis, Pfizer Inc and UCB, Grant/research support from: AbbVie and UCB, Cunshan Wang Shareholder of: Pfizer Inc, Employee of: Pfizer Inc, Joseph Wu Shareholder of: Pfizer Inc, Employee of: Pfizer Inc, Solomon B Makgoeng Shareholder of: Pfizer Inc, Employee of: Pfizer Inc, Ivana Vranic Shareholder of: Pfizer Inc, Employee of: Pfizer Ltd, Sujatha Menon Shareholder of: Pfizer Inc, Employee of: Pfizer Inc, Dona Fleishaker Shareholder of: Pfizer Inc, Employee of: Pfizer Inc, Annette Diehl Shareholder of: Pfizer Inc, Employee of: Pfizer Inc, Lara Fallon Shareholder of: Pfizer Inc, Employee of: Pfizer Inc, Arne Yndestad Shareholder of: Pfizer Inc, Employee of: Pfizer Inc, Robert B.M. Landewé Consultant of: AbbVie, AstraZeneca, Bristol-Myers Squibb, Eli Lilly, Galapagos NV, Novartis, Pfizer Inc and UCB

Exploring the Motivational Drivers of Young Adults with Diabetes for Participation in Kidney Research

Understanding motivational drivers and barriers to patient participation in diabetes research are important to ensure research is relevant and valuable. Young adults with type 1 diabetes (T1D) completed a 31-question qualitative survey evaluating participant experience, understanding, and motivators and barriers to research involvement. A total of 35 participants, 19–28 years of age, 60% female, completed the survey. Motivating factors included personal benefit, relationship with the study team, curiosity, financial compensation, altruism, and nostalgia. Older participants (>22 years) reported higher levels of trust in the study team (p = 0.02) and their relationship with the study team positively influenced their decision to participate (p = 0.03). Financial compensation was a strong motivator for participants with higher education (p = 0.02). Age, sex, education level, and trust in the study team influenced participants’ understanding. Barriers included logistics and lack of familial support. Important motivational drivers and barriers to participation in research by young adults with T1D must be considered to increase research engagement and facilitate the discovery of new knowledge.

National radiology on-call survey: a cross-sectional survey investigating diagnostic radiology on-call provision by trainees out of hours

AIMS

To investigate how diagnostic radiology on-call work is conducted by trainees out of hours, and to explore how this on-call experience may be improved from a trainee perspective.

MATERIALS AND METHODS

A nationwide online questionnaire was distributed to each radiology training scheme. A trainee on the diagnostic on-call rota completed the questionnaire on behalf of the scheme. Twenty-six questions spanning four domains were assessed exploring how radiology service provision is performed by trainees out of hours, and ways to improve it.

RESULTS

Forty schemes responded, representing the entire population size. Twenty-eight (70%) schemes formally assessed trainees prior to joining the on-call rota. Almost half (46%) of trainees start verifying reports independently at ST2. The most common combinations of imaging performed out of hours accounting for 32% each were: (1) computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, and radiography; and (2) CT, ultrasound and radiography. A majority of schemes (54%) had a fixed number of trainees across all shift types.

CONCLUSION

Radiology on-call provision by trainees varies considerably. Common factors between schemes include all trainees providing an on-call service on weekend day shifts. The most sought-after recommendation to improve the on-call experience was to introduce a collaborative reporting on-call hub set-up where trainees cross-cover multiple sites remotely as a team. Further analytical studies are needed to assess if any relationships between on-call set-up and trainee satisfaction exist.

POS0607 PROMIS ASSESSMENT OF RESPONSE TO TREATMENT WITH GOLIMUMAB IV OR INFLIXIMAB IN RHEUMATOID ARTHRITIS PATIENTS: RESULTS FROM THE PHASE-4 AWARE STUDY

Background:

AWARE is a phase-4 observational study designed to provide real-world assessment of Golimumab (GLM) IV & infliximab (IFX) in patients (pts) with rheumatoid arthritis (RA).

Objectives:

To assess patient-reported aspects of social, mental, & physical health through the 8th infusion (≈1 year of treatment) using Patient Reported Outcomes Measurement Information System (PROMIS), a validated, disease-agnostic set of health assessment instruments.

Methods:

AWARE enrolled 1270 RA pts initiating treatment with GLM/IFX. The 52 week analysis set included pts with ≥1-year treatment or those discontinued and, while enrolled, completed PROMIS-29 or PROMIS short form (SF) questionnaires. PROMIS instruments were administered at baseline & prior to infusions 2, 5, & 8. The raw score was converted into a standardized T-score with a mean of 50 and SD of 10.

Results:

At baseline, treatment groups were balanced on demographics & medical characteristics. Most pts were white (87.0% GLM, 86.2% IFX) & female (83.4% GLM, 82.4% IFX). Mean ages were 58.5 ±12.96 years for GLM & 59.6 ±13.24 years for IFX. Overall, 35.3% GLM & 42.9% IFX pts were bio-naïve. The proportion of GLM & IFX pts with prior exposure to 1 or 2 biologics was similar; however, 20.1% GLM pts vs 10.8% IFX pts had exposure to ≥3 biologics. Methotrexate use was similar between GLM (76.4%) & IFX pts (75.0%). Based on mean PROMIS T-scores at baseline (Table 1), Fatigue, Pain Interference, & Physical Function domains approached or exceeded 1 SD worse than those of general US population. Through the 8th infusion, GLM- & IFX-treated pts achieved meaningful improvement based on mean changes from baseline in most PROMIS-29 domains & respective SFs with no significant difference between GLM and IFX. The percentage of GLM or IFX pts with improvements of ≥3, ≥5, or ≥10 units change in T-scores increased from infusion 2 through infusion 8.

Conclusion:

RA pts treated with GLM or IFX achieved comparable improvements across PROMIS-assessed social, mental, & physical health. PROMIS-29 was able to detect change to subsequent anti-tumor necrosis factor-α therapies.

Table 1.

Mean (SD) Change from Baseline PROMIS-29 Domain and Short Form T-Scores: 52 Week Analysis Set

GLMIFXLSM difference (95% CI)*Anxiety (4-item)N=6N=570Baseline53.4 (10.13)54.6 (10.53)Change from baseline at infusion 8N=223 -2.6 (8.10)N=286-3.7 (7.86)-0.29 (-1.54, 0.97)Depression (4-item)BaselineN=67451.9 (9.83)N=57452.5 (10.21)Change from baseline at infusion 8N=225-2.1 (7.56)N=287-2.3 (7.89)0.49 (-0.72, 1.70)Fatigue (4-item)BaselineN=67158.4 (9.91)N=57459.4 (9.99)Change from baseline at infusion 8N=225-3.4 (8.72)N=281-3.1 (7.77)0.69 (-0.64, 2.03)Short form Fatigue 7aBaselineN=68159.1 (8.51)N=57659.7 (8.25)Change from baseline at infusion 8N=228-3.2 (7.40)N=287-2.4 (6.35)1.01 (-0.11, 2.14)Pain interference (4-item)BaselineN=67963.0 (7.56)N=57463.9 (7.80)Change from baseline at infusion 8N=227-4.2 (8.23)N=284-3.1 (7.77)1.84 (0.55, 3.13)Short form Pain interference 6bBaselineN=68061.9 (7.45)N=57662.8 (7.54)Change from baseline at infusion 8N=228-3.8 (7.88)N=287-3.2 (6.67)1.31 (0.15, 2.48)Physical function (4-item)BaselineN=67838.2 (6.79)N=57138.0 (6.90)Change from baseline at infusion 8N=2242.2 (5.64)N=2831.9 (5.85)-0.76 (-1.73, 0.21)Sleep disturbance (4-item)BaselineN=67154.6 (8.72)N=569N=55.5 (8.61)Change from baseline at infusion 8N=221-1.4 (7.45)N=281-1.7 (7.61)0.23 (-0.96, 1.42)Social participation (4-item)BaselineN=67343.7 (8.40)N=57442.9 (8.77)Change from baseline at infusion 8N=2253.2 (8.15)N=2833.4 (7.48)-0.10 (-1.36, 1.16)

*Least squares mean (LSM) difference & confidence interval (CI) are based on analysis of covariance controlling for baseline PROMIS score using inverse probability of treatment weighted propensity score.

Disclosure of Interests:

Clifton Bingham Consultant of: AbbVie, BMS, Eli Lilly, Gilead, Janssen, Pfizer, Regeneron/Sanofi, Grant/research support from: Bristol-Myers Squibb, Shelly Kafka Employee of: Janssen Research & Development, LLC, Shawn Black Employee of: Janssen Research & Development, LLC, Stephen Xu Employee of: Janssen Research & Development, LLC, Wayne Langholff Employee of: Janssen Research & Development, LLC, Jeffrey Curtis Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, UCB, Grant/research support from: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, UCB

POS1028 PATIENT CHARACTERISTICS & CLINICAL FEATURES ASSOCIATE WITH HEALTH-RELATED QUALITY OF LIFE IN BIO-NAÏVE PATIENTS WITH ACTIVE PSORIATIC ARTHRITIS THROUGH WEEK 24 OF THE DISCOVER-2 STUDY

Background:

Psoriatic arthritis (PsA) is a chronic inflammatory disease characterized by peripheral arthritis, axial inflammation, dactylitis, enthesitis, & skin/nail psoriasis. Patients (pts) with PsA often experience reduced health-related quality of life (HRQoL) due to these features.

Objectives:

Using EuroQoL-5 dimension-5 level (EQ-5D-5L) questionnaire index & visual analog scale (EQ-VAS) scores, we assessed HRQoL in pts with PsA & its association with pt characteristics & clinical features of PsA, including fatigue.

Methods:

The Phase 3 DISCOVER-2 trial evaluated guselkumab (GUS), a human monoclonal antibody targeting the IL-23p19-subunit, in bio-naïve adults with active PsA (swollen joint count [SJC] ≥5, tender joint count [TJC] ≥5, C-reactive protein [CRP] ≥0.6 mg/dL) despite standard therapies.1 Pts were randomized 1:1:1 to GUS 100 mg every 4 weeks (Q4W); GUS 100 mg at Week 0 (W0), W4, then Q8W; or placebo (PBO). EQ-5D-5L index assesses mobility, self-care, usual activities, pain/discomfort, & anxiety/depression. EQ-VAS assesses pt health state. Spearman correlation testing was used to evaluate relationships between baseline (BL) pt characteristics & PsA clinical features & BL EQ-5D-5L index & EQ-VAS scores (Figure 1). Employing absolute observed scores at both W0 & W24, univariate linear regression was used to assess the association between EQ-5D-5L index & EQ-VAS scores & pt characteristics/PsA clinical features. Variables with p<0.20 in the univariate analysis were included in a multivariate analysis employing mixed-effect model for repeated measures (MMRM), controlling for all other variables; resulting p values <0.05 were considered statistically significant. Least-squares (LS) mean changes in EQ-5D-5L index & EQ-VAS were assessed at W24 using MMRM.

Results:

Among 738 pts, BL EQ-5D-5L index & EQ-VAS scores were moderately to strongly correlated (ie, ≥0.4) with BL pt-reported pain (0-10 VAS), physical function (Health Assessment Questionnaire-Disability Index [HAQ-DI]), fatigue (Functional Assessment of Chronic Illness Therapy-Fatigue [FACIT-F] scale), & 36-item Short Form Health Survey (SF-36) physical & mental component summary (PCS & MCS) scores & weakly correlated with other variables (Figure 1). Based on univariate analyses (p<0.20) & evaluation of collinearity between variables, attributes at W0 & W24 included in the multivariate models were age, sex, CRP, FACIT-F, pain, psoriasis area & severity index (PASI) score, TJC, SJC, enthesitis, & dactylitis. In the final model, CRP, FACIT-F, pain, PASI score, & the presence of dactylitis were significantly associated with EQ-5D-5L index & EQ-VAS scores. A higher TJC was significantly associated with a worse EQ-5D-5L index score. A higher SJC was significantly associated with a worse EQ-VAS score (Table 1). For reference, in the GUS Q4W (N=244), GUS Q8W (N=246), & PBO (N=244) groups, the LS mean changes from baseline at W24 were 0.12, 0.12, & 0.05, respectively, for EQ-5D-5L index & 18.1, 18.4, & 6.8, respectively, for EQ-VAS.

Conclusion:

Joint & skin symptoms, dactylitis, fatigue, pain, & elevated levels of CRP were significantly associated with reduced HRQoL (measured by EQ-5D-5L index & EQ-VAS) in bio-naïve pts with active PsA. Treatment of multiple PsA domains may help optimize HRQoL. Improvement across clinical domains1 & in HRQoL has been observed in GUS-treated pts with PsA.

References:

[1]Mease P, et al. Lancet 2020;395:1126-36.

Table 1.

Multivariate analysis of pt characteristics/clinical features & EQ-5D-5L index & EQ-VAS scores at W0 & W24

ParameterEQ-5D-5L IndexEQ-VASEstimatep valueEstimatep valueAge (y)-0.00010.690.060.12Female-0.0030.531.110.20CRP (mg/dL)-0.005<0.001-0.510.007FACIT-F (0-52)0.007<0.0010.57<0.001Pain (0-10)-0.02<0.001-3.47<0.001PASI (0-72)-0.0010.03-0.17<0.001SJC (0-66)-0.0010.21-0.170.02TJC (0-68)-0.0010.04-0.040.41Dactylitis (Y/N)0.010.021.740.49Enthesitis (Y/N)-0.0040.33-0.980.22

Disclosure of Interests:

Jeffrey Curtis Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Eli Lilly, Janssen, Myriad, Pfizer, Regeneron, Roche, and UCB, Grant/research support from: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Eli Lilly, Janssen, Myriad, Pfizer, Regeneron, Roche, and UCB, Iain McInnes Consultant of: AbbVie, Bristol Myers Squibb, Celgene, Eli Lilly, Gilead, Janssen, Novartis, Pfizer, and UCB, Grant/research support from: Bristol Myers Squibb, Celgene, Eli Lilly, Janssen, and UCB, Dafna D Gladman Consultant of: Abbvie, Amgen, Bristol Myers Squibb, Eli Lilly, Galapagos, Gilead, Janssen, Novartis, Pfizer and UCB, Grant/research support from: Abbvie, Amgen, Bristol Myers Squibb, Eli Lilly, Galapagos, Gilead, Janssen, Novartis, Pfizer and UCB, Feifei Yang Shareholder of: Johnson & Johnson, Employee of: Janssen Global Services, LLC, Steve Peterson Shareholder of: Johnson & Johnson, Employee of: Janssen Global Services, LLC, Prasheen Agarwal Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Alexa Kollmeier Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Elizabeth C Hsia Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Chenglong Han Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, May Shawi Shareholder of: Johnson & Johnson, Employee of: Janssen Global Services, LLC, William Tillett Speakers bureau: AbbVie, Amgen, Celgene, Eli Lilly, Janssen, Novartis, Pfizer, and UCB, Consultant of: AbbVie, Amgen, Celgene, Eli Lilly, Janssen, Novartis, MSD, Pfizer, and UCB, Grant/research support from: AbbVie, Celgene, Eli Lilly, Janssen, Novartis, Philip J Mease Consultant of: AbbVie, Amgen, Boehringer Ingelheim, Bristol Myers Squibb, Eli Lilly, Galapagos, Gilead, GlaxoSmithKline, Janssen, Novartis, Pfizer, SUN, and UCB, Grant/research support from: AbbVie, Amgen, Bristol Myers Squibb, Eli Lilly, Galapagos, Gilead, Janssen, Novartis, Pfizer, SUN, and UCB, Proton Rahman Speakers bureau: AbbVie, Eli Lilly, Janssen, Novartis, Pfizer, and UCB, Consultant of: AbbVie, Amgen, Bristol Myers Squibb, Celgene, Eli Lilly, Janssen, Novartis, Pfizer, Roche, and UCB, Grant/research support from: Janssen and Novartis.

POS0200 CLINICAL CHARACTERISTICS & OUTCOMES ASSOCIATE WITH WORK PRODUCTIVITY IN BIO-NAÏVE PATIENTS WITH ACTIVE PSORIATIC ARTHRITIS THROUGH WEEK 24 OF THE DISCOVER-2 STUDY

Background:

Psoriatic arthritis (PsA), a chronic inflammatory disease characterized by peripheral arthritis, axial inflammation, dactylitis, enthesitis & skin/nail psoriasis, causes impaired physical function, disability & loss of work productivity.

Objectives:

Evaluate associations between PsA clinical characteristics & outcomes including fatigue & work productivity using Work Productivity & Activity Impairment Questionnaire: PsA (WPAI-PsA).

Methods:

The Phase 3 DISCOVER-2 trial assessed guselkumab (GUS), an anti-IL-23p19 subunit monoclonal antibody, in bio-naïve adults with active PsA (swollen joint count [SJC] ≥5 & tender joint count [TJC] ≥5, C-reactive protein [CRP] ≥0.6 mg/dL) despite standard therapies.1 Patients (Pts) were randomized 1:1:1 to GUS 100 mg Q4W; GUS 100 mg at W0, W4, then Q8W; or placebo (PBO). WPAI-PsA assesses PsA-related work time missed (absenteeism), impairment while working (presenteeism), productivity loss (absenteeism+presenteeism), & daily activity during the previous week. Spearman correlation testing evaluated relationships between pt demographics & disease characteristics of PsA & WPAI domain scores based on observed values at baseline. Univariate linear regression assessed associations between WPAI & these variables based on observed data at W0 & at W24. Variables with p<0.10 were included in a multivariate analysis employing a mixed-effects model for repeated measures, controlling for all other variables; resulting p-values <0.05 were considered statistically significant.

Results:

As reported elsewhere,2 least-squares mean % changes from baseline at W24 were -3.8/-19.5/-20.0/-20.5 for GUS Q4W, -3.1/-19.4/-19.7/-21.5 for GUS Q8W, & -3.5/-10.2/-10.9/-10.3 for PBO for absenteeism, presenteeism, absenteeism+presenteeism, & daily activity impairment, respectively. Among 738 pts, WPAI domain scores were moderately to strongly correlated (ie, ≥0.4) with pt-reported pain (0-10 visual analog scale), physical function (Health Assessment Questionnaire Disability Index [HAQ-DI]), fatigue (Functional Assessment of Chronic Illness Therapy-Fatigue [FACIT-F] scale) & 36-Item Short Form Health Survey (SF-36) Physical Component Summary (PCS) score, but weakly correlated with other variables (Figure 1). Based on univariate analyses & evaluation of collinearity between variables, attributes included in multivariate models were age, body mass index (BMI), gender, CRP, FACIT-F, pain, Psoriasis Area Severity Index (PASI), TJC, SJC, enthesitis & dactylitis. In final model, CRP, FACIT-F, & pain were statistically significantly associated with all WPAI domains (Table 1). Presence of enthesitis & higher PASI score were significantly associated with higher loss of work productivity & activity outside work.

Conclusion:

In PsA pts, extra-articular symptoms, fatigue, pain & elevated CRP were significantly associated with WPAI-assessed work & activity impairment. Treating all major clinical manifestations of PsA is needed to help pts improve work & activity impairment. GUS effectively treats all major clinical manifestations1 & improves work & activity impairment in PsA.2

References:

[1]Mease P. Lancet 2020;395:1126-36.

[2]Curtis J. ACR 2020; Poster 0332.

Table 1.

Multivariate analysis of clinical characteristics/outcomes & WPAI domains at W0 & W24

ParameterAbsenteeismaPresenteeismaProductivity LossaActivity ImpairmentbEstimatep-valueEstimatep-valueEstimatep-valueEstimatep-valueAge-0.050.42-0.27<0.001-0.28<0.001-0.060.17Female0.910.46-1.540.22-1.740.202.380.02CRP0.730.040.970.011.010.010.89<0.001FACIT-F-0.31<0.001-0.67<0.001-0.73<0.001-0.75<0.001Pain1.03<0.0014.15<0.0014.25<0.0014.02<0.001PASI0.060.360.160.020.140.050.150.003SJC0.080.48-0.050.61-0.050.660.030.75TJC-0.100.130.110.090.090.190.100.04Dactylitis (Y/N)-1.100.392.470.052.580.050.540.57Enthesitis (Y/N)1.520.202.380.042.990.012.400.01

aPts working at baseline

bAll pts in study

Disclosure of Interests:

Jeffrey Curtis Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Eli Lilly, Myriad, Pfizer, Regeneron, Roche, and UCB, Grant/research support from: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Eli Lilly, Myriad, Pfizer, Regeneron, Roche, and UCB, Iain McInnes Consultant of: AbbVie, Bristol-Myers Squibb, Celgene, Eli Lilly, Gilead, Janssen, Novartis, Pfizer, and UCB, Grant/research support from: Bristol-Myers Squibb, Celgene, Eli Lilly, Janssen, and UCB, Dafna D Gladman Consultant of: AbbVie, Amgen, Bristol Myers Squibb, Eli Lilly, Galapagos, Gilead, Janssen, Novartis, Pfizer and UCB, Grant/research support from: AbbVie, Amgen, Bristol Myers Squibb, Eli Lilly, Galapagos, Gilead, Janssen, Novartis, Pfizer and UCB, Feifei Yang Shareholder of: Janssen, Employee of: Janssen, Steve Peterson Shareholder of: Janssen, Employee of: Janssen, Prasheen Agarwal Shareholder of: Janssen, Employee of: Janssen, Alexa Kollmeier Shareholder of: Janssen, Employee of: Janssen, Elizabeth C Hsia Shareholder of: Janssen, Employee of: Janssen, Chenglong Han Shareholder of: Janssen, Employee of: Janssen, May Shawi Shareholder of: Janssen, Employee of: Janssen, William Tillett Speakers bureau: AbbVie, Amgen, Celgene, Eli Lilly, Janssen, Novartis, Pfizer, and UCB, Consultant of: AbbVie, Amgen, Celgene, Eli Lilly, Janssen, Novartis, MSD, Pfizer, and UCB, Grant/research support from: AbbVie, Celgene, Eli Lilly, Janssen, and Novartis, Philip J Mease Consultant of: AbbVie, Amgen, Boehringer Ingelheim, Bristol Myers Squibb, Eli Lilly, Galapagos, Gilead, GlaxoSmithKline, Janssen, Novartis, Pfizer, SUN, and UCB, Grant/research support from: AbbVie, Amgen, Bristol Myers Squibb, Eli Lilly, Galapagos, Gilead, Janssen, Novartis, Pfizer, SUN, and UCB, Proton Rahman Speakers bureau: AbbVie, Amgen, Bristol Myers Squibb, Celgene, Eli Lilly, Janssen, Novartis, Pfizer, Roche, and UCB, Consultant of: AbbVie, Amgen, Bristol Myers Squibb, Celgene, Eli Lilly, Janssen, Novartis, Pfizer, Roche, and UCB, Grant/research support from: Janssen and Novartis

OP0118 EFFECT OF WITHDRAWING ETANERCEPT OR METHOTREXATE ON PATIENT-REPORTED OUTCOMES IN RHEUMATOID ARTHRITIS PATIENTS IN REMISSION ON COMBINATION THERAPY: RESULTS FROM THE SEAM-RA TRIAL

Background:

Limited studies have assessed the effect of withdrawal of either methotrexate (MTX) or etanercept (ETN) on patient-reported outcomes (PROs) in rheumatoid arthritis (RA).

Objectives:

To evaluate the baseline and change in PROs following withdrawal of MTX or ETN in RA patients with sustained remission receiving combination ETN+MTX.

Methods:

Adult patients with RA on ETN+MTX and in remission (SDAI ≤3.3) for ≥12 months (including a 24-week, open-label, run-in period) were randomized to a 48-week double-blind period to receive ETN 50 mg weekly (N=101), oral MTX 10-25 mg weekly (N=101) or continue ETN+MTX (N=51). The primary endpoint was maintenance of SDAI remission without disease worsening (DW) at week 48 between ETN and MTX groups. Patients who experienced SDAI >11 at any time after randomization, or SDAI >3.3 and ≤11 during 2 consecutive or on 3 non-consecutive visits were considered to have DW and resumed ETN+MTX. PROs assessed were patient global assessment of disease activity (PtGA, 0-100 mm), patient joint pain (PtJP, 0-100 mm), Health Assessment Questionnaire-Disability Index (HAQ-DI), and the 36-item short-form health survey (SF-36) component and domain scores. A 2-sample t-test was used to compare the treatment differences between groups. A subgroup analysis for patients with DW was also performed (DW analysis set) and compared PROs between ETN vs MTX arms (ETN+MTX not shown given the small sample size).

Results:

Of the 253 patients randomized, 121 (47.8%) experienced DW and were included in the DW analysis set. Baseline demographics were generally balanced between the 3 treatment groups. Most patients were women (76.3%), White (87.0%), and with a mean age of 55.6 years. The mean (SD) MTX dose was 16.3 (4.69) mg and the mean (SD) duration of RA was 10.3 (7.8) years. At week 48, a significantly greater proportion of patients on ETN vs MTX monotherapy maintained SDAI remission (49.5% vs 28.7%; P=0.004) after therapy withdrawal. In the overall population, PtGA and PtJP scores were very low at baseline (PtGA–MTX: 4.4, ETN: 4.5, ETN+MTX: 3.5; PtJP–MTX: 4.9, ETN: 5.5, ETN+MTX: 3.5) and showed some worsening over the study period in all treatment groups, with a mean change at week 48 ranging from 5.0 to 10.0 units for PtGA and 3.7 to 8.1 units for PtJP. Patients on ETN had less worsening, with a nominally significant treatment difference observed between ETN and MTX monotherapy groups for PtGA at almost all timepoints, and for PtJP at weeks 12 and 36 (Figure). Mean HAQ-DI (MTX: 0.32; ETN: 0.26; ETN+MTX: 0.28) and SF-36 scores (physical component [PCS]–MTX: 52.1, ETN: 52.7, ETN+MTX: 52.3; mental component [MCS]–MTX: 55.5, ETN: 55.8, ETN+MTX: 57.1) at baseline show that patients had low disability and excellent health-related quality of life compared with normative values for the general non-RA population. HAQ-DI scores were well maintained at weeks 24 and 48 (change from baseline at week 48–MTX: 0.14; ETN: 0.15; ETN+MTX: 0.21). The SF-36 PCS, MCS, and domain scores decreased minimally from baseline with treatment differences that were not nominally significant between groups. Among patients with DW during the study, those on ETN showed less PtGA and PtJP worsening from baseline than those on MTX at weeks 12, 36, and 48 (Figure). Other PROs (HAQ-DI [change from baseline at week 24–ETN: 0.34; MTX: 0.21; at week 48–ETN: 0.15; MTX: 0.15], SF-36 PCS, MCS, and domain scores) showed a similar degree of worsening in both the MTX and ETN arms.

Conclusion:

In patients with sustained SDAI remission on ETN+MTX, mental and physical health as measured by SF-36 was comparable with that of the non-RA population. Withdrawal of ETN (MTX monotherapy) resulted in a greater worsening of PtGA and PtJP than withdrawal of MTX (ETN monotherapy), and patients on ETN monotherapy restored these scores close to baseline towards the end of the treatment period. These findings demonstrate that ETN monotherapy has a greater effect on maintaining overall patient assessment of disease and joint pain compared with MTX monotherapy.

Disclosure of Interests:

Jeffrey Curtis Speakers bureau: AbbVie, BMS, Gilead, Lilly, Novartis, Sanofi, Scipher, Amgen, Corrona, Janssen, Myriad, and Pfizer, Consultant of: AbbVie, BMS, Gilead, Lilly, Novartis, Sanofi, Scipher, Amgen, Corrona, Janssen, Myriad, and Pfizer, Grant/research support from: AbbVie, BMS, Gilead, Lilly, Novartis, Sanofi, Scipher, Amgen, Corrona, Janssen, Myriad, and Pfizer, Elaine Karis Shareholder of: Amgen Inc., Employee of: Amgen Inc., Vivian Bykerk Speakers bureau: Amgen, BMS, Gilead, Pfizer, Sanofi-Genzyme/Regeneron, Scipher Medicine, and UCB., Consultant of: Amgen, BMS, Gilead, Pfizer, Sanofi-Genzyme/Regeneron, Scipher Medicine, and UCB., Grant/research support from: Amgen and Novartis, Greg Kricorian Shareholder of: Amgen Inc., Employee of: Amgen Inc., Priscilla Yen Shareholder of: Amgen Inc., Employee of: Amgen Inc., Paul Emery Speakers bureau: AbbVie, BMS, Celltrion, Gilead, Lilly, MSD, Novartis, Pfizer, Roche, Samsung, Sandoz, and UCB., Consultant of: AbbVie, BMS, Celltrion, Gilead, Lilly, MSD, Novartis, Pfizer, Roche, Samsung, Sandoz, and UCB., Paul Haraoui Speakers bureau: AbbVie, Celgene, Janssen, Pfizer, and UCB., Consultant of: AbbVie, Amgen, BMS, Celgene, Eli Lilly, Janssen, Merck, Pfizer, Roche, Sandoz, Sanofi-Genzyme, and UCB., Grant/research support from: Roche, AbbVie, Amgen, Merck, and Pfizer, David Collier Shareholder of: Amgen Inc., Employee of: Amgen Inc., Brad Stolshek Shareholder of: Amgen Inc., Employee of: Amgen Inc.

POS0594 MEANINGFUL IMPROVEMENT AND WORSENING IN PATIENTS WHO DO NOT ACHIEVE LDA AND SWITCH THERAPY TO A NEW BIOLOGIC OR TARGETED THERAPY: RESULTS FROM THE CORRONA REGISTRY

Background:

Guidelines recommend adjusting therapy in patients with rheumatoid arthritis (RA) who fail to reach and sustain low disease activity (LDA) or remission (disease control). Many factors can affect the decision to change therapy, including the potential for improvement as well as the fear of potential worsening or loss of improvement already achieved. Although data exist on response to treatment in patients who switch therapy, data addressing the likelihood of worsening are limited.

Objectives:

The aim of this analysis was to describe the demographic, clinical characteristics, and change in clinical outcomes in patients on biologic/targeted synthetic disease-modifying anti-rheumatic drugs (b/tsDMARDs) who had some improvement in clinical disease activity index (CDAI) but did not achieve LDA after ~ 6-12 months of treatment and then switched to a different b/tsDMARD.

Methods:

This study included adult inadequately responding RA patients from the CORRONA registry who: (1) started a biologic or Janus kinase inhibitor (JAKi) between January 2010 to November 2020 (V1), (2) had any CDAI improvement (i.e., decrease ≥1 unit) but were not in LDA or remission at a subsequent visit (baseline [BL]) occurring 3 to 15 months after V1; (3) had a third visit (follow-up [F/U]) 6 (±3) months after BL with a valid CDAI measure; (4) switched therapy at the BL or between BL and F/U, with the switch occurring at least 3 months prior to the F/U. CDAI >10 and ≤22 was defined as moderate disease activity (MDA) and CDAI >22 was defined as high disease activity (HDA). Two thresholds of change in CDAI (≥6 and ≥12 units) were used to define meaningful improvement and meaningful worsening after the switch. If there was no meaningful improvement or meaningful worsening, this was considered as no meaningful change (-5 to +5 for 6 units change and -11 to +11 for 12 units change). These thresholds for meaningful change were set for all switchers regardless of their pre-switch CDAI value. Descriptive statistics were generated for demographic and clinical characteristics for the switchers at BL, and the change of clinical outcomes was evaluated from BL to F/U.

Results:

Of the 1,224 patients fulfilling the inclusion criteria, 93 (7.6%) switched therapy and 1,131 (92.4%) did not switch therampy after not achieving an adequate response on the initial b/tsDMARD. At BL, 42.5% and 70.0% of patients had no meaningful improvement to their prior therapy based on ≥6 and ≥12-unit change, respectively; mean (SD) age was 53.1 (14.0) years; duration of RA 10.7 (10.4) years; CDAI 22.2 (10.8); 81.7% were female; 64.5% had MDA, 35.5% had HDA; 21.5 % reported being disabled, 24.7% were current smokers, and 50% were obese. In terms of prior biologic use 57.0%, 22.6%, and 20.4% had been on 1, 2, and 3+, respectively. From BL to F/U, meaningful worsening occurred in 30.1% and 12.9% using a threshold of 6 and 12, respectively, with the remaining patients experiencing meaningful improvement or no meaningful change (Figure 1).

Figure 1.

Meaningful Worsening, Meaningful Improvement, and No Meaningful Change Based on CDAI Change Thresholds of ≥6 and ≥12 From BL to F/U (N=93)

Conclusion:

In our analysis, a large proportion of patients who initiated a biologic/JAKi and experienced some improvement but failed to attain LDA or remission, did not switch therapy within approximately a year. This analysis consisted of many patients who did not have a meaningful response to their prior biologic/JAKi, patients who had received multiple prior biologics, and a large portion of patients with poor prognostic factors. Despite this, the proportion of patients with meaningful worsening was low compared with most patients who had either meaningful improvement or no meaningful change. Additional research is warranted to understand the reasons for not switching and whether the likelihood of a meaningful change correlates with prior response, poor prognosis, or other factors.

Acknowledgements:

Amy Praestgaard (Sanofi) contributed to the statistical analysis for this abstract. Medical writing support for this abstract was provided by Krishna Kammari (Sanofi).

Disclosure of Interests:

Jeffrey Curtis Grant/research support from: and personal fees from AbbVie, Amgen, BMS, CORRONA, Eli Lily, Janssen, Myriad, Pfizer, Roche, Regeneron, Radius, UCB, outside the submitted work, Stefano Fiore Shareholder of: Sanofi, Employee of: Sanofi. In addition, he has a patent EP 19306553.9; USPTO #s 62/799,698; 62/851,474; 62/935,395 issued, Kerri Ford Shareholder of: Sanofi, Employee of: Sanofi, Judson Janak: None declared, Hong Chang: None declared, Dimitrios A Pappas Employee of: CORRONA LLC. He has previously acted as a consultant for Sanofi, Abbvie, Gtech Roche Hellas, and Novartis. He has an equity interest in CORRONA LLC. and is on the Board of directors of the CORRONA research foundation, Taylor Blachley: None declared, Kelechi Emeanuru: None declared, Vivian Bykerk Grant/research support from: reports grants from Amgen, BMS, UCB, and Novartis were given to institution, that grants from the NIH, PCORI, and CIHR were given to institutions which whom she is affiliated, and that she has received personal fees from Amgen, Gilead, BMS, Pfizer, Sanofi Aventis, Roche, UCB and Regeneron, outside the submitted work.

POS0638 DISEASE SEVERITY AND OUTCOMES AMONG PATIENTS WITH RHEUMATOID ARTHRITIS WHO RECEIVE A NEWLY APPROVED BIOLOGIC: REAL-WORLD US EXPERIENCE WITH SARILUMAB FROM THE ACR RISE REGISTRY

Background:

Patients with rheumatoid arthritis (RA) who have received multiple biologics or targeted therapies over time tend to have more refractory and more severe disease, which may lead to worse clinical response to treatment.

Objectives:

We used data from the ACR RISE registry to assess whether disease severity was greater in those who received sarilumab shortly after its FDA approval (May 2017) than in subsequent time periods and to evaluate the effectiveness of sarilumab in populations with various degrees of disease severity.

Methods:

Patients with RA who initiated sarilumab treatment in the period 2017-2020 were identified in the ACR RISE registry and divided into Cohort 1 (2017, year of the FDA approval) and the calendar year-based Cohorts 2-4 (2018-2020). Patient demographics, RA-related features, and comorbidities were determined using data prior to sarilumab initiation. The cohorts were compared using chi-square test (categorical variables) and a nonparametric test (continuous variables). Sarilumab effectiveness was assessed using 3 cohorts assembled based on progressively restrictive criteria: Active Disease cohort (Clinical Disease Activity Index [CDAI] >10 or Routine Assessment of Patient Index Data 3 [RAPID3] >6, and C-reactive protein, if measured, ≥8 mg/L), TARGET Eligibility cohort (patients who satisfied enrolment criteria for TARGET,1 a Phase 3 sarilumab trial in patients with RA and an inadequate response to TNF inhibitors), and TARGET Baseline cohort (patients from TARGET Eligibility cohort with characteristics weighted to match those from the TARGET trial baseline,1 using the matching-adjusted indirect comparison method2). In all 3 effectiveness cohorts, mean changes in CDAI and RAPID3 at 6 and 12 months post-initiation of sarilumab were evaluated using a model adjusted for baseline score, age, sex, race, calendar year, and seropositivity.

Results:

A total of 2949 patients, treated by 585 rheumatologists, initiated sarilumab treatment in the period 2017–2020. The 4 yearly cohorts were relatively similar in terms of patients’ age, sex, race, and most clinical characteristics. However, patients receiving sarilumab shortly after FDA approval (Cohort 1) had more ambulatory visits, a greater number of previously used non-TNFi biologics (particularly tocilizumab), and a higher comorbidity burden, and were more likely to be current users of glucocorticoids or opioids than sarilumab initiators in the subsequent 3 years. In the 3 cohorts used to assess sarilumab effectiveness, the greatest improvement was observed in the TARGET Baseline cohort, which also had the greatest mean baseline CDAI score (43), compared with the other two (24 both).

Conclusion:

In this real-world cohort, we observed modest evidence for channeling of patients with greater RA severity and greater prior exposure to non-TNFi biologics to sarilumab shortly after its FDA approval. This cohort effect did not diminish the effectiveness of sarilumab. All cohorts showed improvement, with the greatest clinical improvement observed in the cohort with the highest baseline CDAI score who most closely resembled those enrolled in a phase 3 trial of patients with an inadequate response to TNF inhibitors.

References:

[1]Fleischmann R, et al. Arthritis Rheumatol 2017;69:277-290.

[2]Signorovitch JE et al. Value Health 2012;15:940-7.

Figure 1.

Adjusted improvements in CDAI and RAPID3

Acknowledgements:

This study was sponsored by Sanofi. Medical writing support was provided by Vojislav Pejović, PhD (Eloquent Medical Affairs, division of Envision Pharma Group) and funded by Sanofi.

Disclosure of Interests:

Stefano Fiore Employee of: Sanofi, Lang Chen: None declared, Cassie Clinton Consultant of: Information available in profile, Huifeng Yun Grant/research support from: Research support for Pfizer, Amy Praestgaard Employee of: Sanofi, Kerri Ford Employee of: Sanofi, Jeffrey Curtis Consultant of: Received consulting and research grants from AbbVie, Amgen, BMS, Lilly, Gilead, GSK, Janssen, Myriad, Pfizer, Roche, Samsung, Sandoz, Sanofi, UCB, Grant/research support from: Received consulting and research grants from AbbVie, Amgen, BMS, Lilly, Gilead, GSK, Janssen, Myriad, Pfizer, Roche, Samsung, Sandoz, Sanofi, UCB

POS1026 GUSELKUMAB PROVIDES SUSTAINED IMPROVEMENTS IN WORK PRODUCTIVITY AND NON-WORK ACTIVITY IN PATIENTS WITH PSORIATIC ARTHRITIS: RESULTS THROUGH 1 YEAR OF A PHASE 3 TRIAL

Background:

DISCOVER-2 was a Phase 3 trial of the first-in-class anti-IL-23-specific mAb guselkumab (GUS) in patients (pts) with psoriatic arthritis (PsA). PsA impacts patients’ productivity at work and in daily activity.1

Objectives:

To evaluate the effect of GUS on work productivity and daily activity in DISCOVER-2 through 1 year using the Work Productivity and Activity Impairment Questionnaire: PsA (WPAI- PsA).

Methods:

Bio-naïve adults with active PsA despite nonbiologic DMARDs &/or NSAIDs received subcutaneous GUS 100 mg every 4 weeks (Q4W); GUS 100 mg W0, W4, then Q8W; or placebo (PBO). At W24, PBO pts crossed over to GUS 100 mg Q4W. WPAI-PsA assesses PsA-related work time missed (absenteeism), impairment while working (presenteeism), impaired overall work productivity (absenteeism + presenteeism), and daily activity during the previous week. A shift analysis evaluated proportions of pts employed vs unemployed (regardless of desire to work) over time. Among pts working at baseline, least-squares (LS) mean changes from baseline in WPAI-PsA domains were determined using a mixed-effects model for repeated measures analysis, whereby mean changes in WPAI-PsA domains were calculated for each multiple imputation (MI) dataset using an analysis of covariance (ANCOVA); the reported LSmean is the average of all MI datasets. Also, among pts employed at baseline, indirect savings from improved overall work productivity were estimated using 2020 EU mean yearly wage estimate (all occupations).2

Results:

In pts working at baseline, significant improvement in work productivity and non-work activity vs PBO was observed at W24. Productivity gains seen with GUS at W24 continued to improve through 1 year (Table 1). Shift analysis showed relatively stable employment in pts employed at baseline (62% of shift analysis cohort) through 1 year of GUS (>91% continued to work when assessed at W16, W24, and W52 [data not shown]). For those unemployed at baseline (38% of cohort), the proportion of pts working increased by ~10% following 1 year of GUS (Figure 1). Potential yearly indirect savings from improved overall work productivity were: €7409 GUS Q4W and €7039 GUS Q8W vs €4075 PBO at W24 and were €8520 GUS Q4W, €9632 GUS Q8W, and €6668 PBO→GUS Q4W at W52.

Conclusion:

Improvement in work productivity and non-work activity was greater with GUS vs PBO among pts with active PsA through W52. Improvements demonstrated may result in reduction in PsA costs associated with work productivity.

References:

[1]Tillett W et al. Rheumatol (Oxford). 2012;51:275–83.

[2]OECD (2020). Average wages (indicator). https://data.oecd.org/earnwage/average-wages.htm

Table 1.

Model-based estimates of LSmean changea (95% CI) from baseline in WPAI-PsA domains among pts working at baseline and with an observed change through W24 (N=474) and W52 (N=475)

Change from baselineGUS 100mg Q4WGUS 100mg Q8WPBO(W0-24)PBO → GUS 100 mg Q4W (W24-52)VisitW24W52W24W52W24W52Absenteeism, N145145147147162163LSmean-3.4 (-6.5,-0.3)-4.1 (-6.8,-1.5)-3.0 (-6.0,0.1)-4.0 (-6.6,-1.3)-3.0 (-6.0, 0.04)-3.0 (-5.5,-0.4)Diff vs. PBO-0.4 (-4.6,3.8)-0.01 (-4.2, 4.2)Presenteeism, N145145147147162163LSmean-20.1 (-23.7,-16.6)-22.4 (-26.3,-18.6)-19.6 (-23.2,-16.1)-25.7 (-29.5,-21.8)-10.5 (-13.9,-7.0)-18.5 (-22.2,-14.7)Diff vs PBO-9.7* (-14.4,-5.0)-9.2* (-13.9,-4.5)Work productivity, N145145147147162163LSmean-20.1 (-24.1,-16.1)-22.6 (-26.8,-18.3)-19.2 (-23.1,-15.2)-25.9 (-30.0,-21.7)-10.6 (-14.4,-6.8)-17.6 (-21.7,-13.6)Diff vs PBO-9.5* (-14.8,-4.2)-8.6* (-13.9,-3.3)Non-work Activity, N242242246246245245LSmean-20.5 (-23.3,-17.7)-25.7 (-28.6,-22.7)-21.2 (-23.9,-18.4)-25.4 (-28.4,-22.5)-9.9 (-12.6,-7.1)-22.3 (-25.3,-19.4)Diff vs PBO-10.6* (-14.4,-6.8)-11.3* (-15.1,-7.5)

CI=Confidence interval

a. LSmean for each MI dataset is calculated based on an ANCOVA model for the change from baseline at W24/W52. The combined LSmean, which is the average of the LSmean, taken over all the MI datasets, is presented.

*p<0.05

Disclosure of Interests:

Jeffrey Curtis Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, and UCB, Grant/research support from: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, and UCB, Iain McInnes Consultant of: AbbVie, Bristol-Myers Squibb, Celgene, Eli Lilly and Company, Gilead, Janssen, Novartis, Pfizer, and UCB, Grant/research support from: Bristol-Myers Squibb, Celgene, Eli Lilly and Company, Janssen, and UCB, Steve Peterson Shareholder of: Johnson & Johnson, Employee of: Janssen Global Services, LLC, Prasheen Agarwal Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Feifei Yang Shareholder of: Johnson & Johnson, Employee of: Janssen Global Services, LLC, Alexa Kollmeier Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Elizabeth C Hsia Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Chenglong Han Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, William Tillett Speakers bureau: AbbVie, Amgen, Celgene, Lilly, Janssen, Novartis, Pfizer Inc, and UCB, Consultant of: AbbVie, Amgen, Celgene, Lilly, Janssen, Novartis, MSD, Pfizer Inc, and UCB, Grant/research support from: AbbVie, Celgene, Eli Lilly, Janssen, Novartis, Pfizer Inc, and UCB, Philip J Mease Speakers bureau: Boehringer Ingelheim and GlaxoSmithKline, Grant/research support from: AbbVie, Amgen, Bristol Myers Squibb, Eli Lilly, Galapagos, Gilead, Janssen, Novartis, Pfizer, SUN, and UCB, Proton Rahman Speakers bureau: AbbVie, Eli Lilly, Janssen, Novartis, Pfizer, and UCB, Consultant of: AbbVie, Amgen, Bristol Myers Squibb, Celgene, Eli Lilly, Janssen, Novartis, Pfizer, Roche, and UCB, Grant/research support from: Janssen and Novartis.

POS1499-PARE PATIENT PERSPECTIVES OF BIOLOGIC TREATMENTS FOR AXIAL SPONDYLOARTHRITIS: SATISFACTION, WEAR-OFF BETWEEN DOSES, AND USE OF SUPPLEMENTAL MEDICATIONS

Background:

Biologic disease-modifying antirheumatic drug (bDMARD) therapy has been shown to be effective in the treatment of axial spondyloarthritis (axSpA).1,2 Little is understood about patients’ experience of axSpA treatment from their own perspective.

Objectives:

To characterize patient experiences and perspectives with bDMARD treatments for axSpA, including satisfaction, and use of supplementary treatments when wear-off between doses is perceived among those currently treated with bDMARD therapy.

Methods:

Adult US participants (pts) within the ArthritisPower registry with physician-diagnosed axSpA were invited to complete electronic PRO measures, such as the BASDAI (0-10 scale, score ≥4 indicates suboptimal disease control), and an online survey about their perspectives of treatment. Analysis compared pt characteristics and treatment satisfaction by whether or not pt reported wear-off between bDMARD doses.

Results:

128 pts with axSpA and on bDMARD therapy met inclusion criteria of whom 82.8% were female, with mean age of 47 years. Mean BASDAI scores indicated poor disease control (6.4, SD 1.8), worse for those perceiving wear-off between doses compared with those who did not [6.8 (1.6) vs. 5.9 (2.0), p=0.01]. A majority of pts on a bDMARD reported being somewhat (57.8%) or very satisfied (26.6%) with their current axSpA treatment, and about 53.1% were satisfied with how well it controls axSpA-related pain. However, 60.9% (n=78) of pts reported that their current bDMARD typically wears off before the next dose. Treatment satisfaction was lower for pts experiencing wear-off compared to pts without wear-off (highly satisfied: 21.8% vs. 34%; somewhat satisfied: 60.3% vs. 54%; dissatisfied: 17.9% vs. 12%). 82.1% (n=64) of pts reporting wear-off used additional medications or supplements when that happened, chiefly NSAIDs (68.8%, n=44), muscle relaxers (42.2%, n=27) and/or opioids (37.5%, n=24). Among the 20 pts not satisfied with current axSpA treatment, side effects (6/20, 30.0%), or worry about risk of side effects (2/20, 10%) were the main reasons.

Conclusion:

In a predominantly female sample of bDMARD-treated axSpA patients with high disease activity, most expressed satisfaction with treatment. However, most experienced wear-off between doses and took supplementary medications, including opioids, to manage.

References:

[1]Dubash S, et al. Ther Adv Chronic Dis. 2018;9(3):77–8.

[2]Van Der Heijde D, et al. Ann Rheum Dis. 2017;76(6):978–91.

Table 1.

Demographic and clinical characteristics by wear-off between bDMARD doses (n=128)

Pts currently on bDMARD(N=128)Wear-off between bDMARD oses(N=78)No wear-off / Not sure(N=50)p-valueNumber or mean (% or SD)Age46.9 (10.3)46.1 (9.2)48.2 (11.8)0.25Female106 (82.8)69 (88.5)37(74.0)0.03White115 (89.8)70 (89.7)45 (90.0)0.96Body Mass Index30.9 (7.8)31.2 (8.5)30.4 (6.6)0.57Current Medications, addition to bDMARDConventional Synthetic DMARD (e.g. methotrexate, sulfasalazine)17 (13.3)15 (19.2)2 (4.0)0.01Prescription NSAID59 (46.1)39 (50.0)20 (40.0)0.27Other prescription medication¥70 (54.7)44 (56.4)26 (52.0)0.62Noticed improvement in symptoms related to axSpA since starting current bDMARD80 (62.5)51 (65.4)29 (58.0)0.40Noticed improvement in symptoms NOT related to axSpA since starting current bDMARD40 (31.3)22 (28.2)18 (36.0)0.35BASDAI‡6.4 (1.8)6.8 (1.6)5.9 (2.0)0.01PROMIS Pain Interference ł65.3 (5.7)66.0 (5.1)64.3 (6.4)0.09PROMIS Physical Function ł36.7 (5.6)36.1 (5.3)37.7 (5.8)0.11PROMIS Sleep Disturbance ł59.8 (8.5)61.2 (7.7)57.6 (9.3)0.02

* Statistical significance between groups of pts who experienced wear-off between bDMARD or not, p < 0.05

¥ Other prescription medications: muscle relaxers, nerve pain medications or anti-depressants, and opioids

‡ BASDAI is scored on a 0-10 scale with score ≥4 indicating suboptimal control of disease

ł PROMIS measures use T-score metric in which 50 is mean, 10 is standard deviation (SD), of US population; higher T-score = more of concept measured

Acknowledgements:

This study was sponsored by Eli Lilly and Company. We thank the patients who participated in this study.

Disclosure of Interests:

W. Benjamin Nowell Grant/research support from: Full-time employee of Global Healthy Living Foundation, an independent nonprofit research organization, which received funding pursuant to a contract from Eli Lilly to conduct the study that is the subject of this abstract; Principal Investigator for studies with grant support from AbbVie, Amgen and Eli Lilly, Kelly Gavigan Grant/research support from: Full-time employee of Global Healthy Living Foundation, an independent nonprofit research organization, which received funding pursuant to a contract from Eli Lilly to conduct the study that is the subject of this abstract, Theresa Hunter Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, William Malatestinic Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Rebecca Bolce Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Jeffrey Lisse Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Carol Himelein Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Jeffrey Curtis Consultant of: AbbVie, Amgen, BMS, Corrona, Eli Lilly, Janssen, Myriad, Pfizer, Roche, Regeneron, Radius, UCB, Grant/research support from: AbbVie, Amgen, BMS, Corrona, Eli Lilly, Janssen, Myriad, Pfizer, Roche, Regeneron, Radius, UCB, Jessica A. Walsh Consultant of: AbbVie, Amgen, Eli Lilly and Company, Janssen, Merck, Novartis, Pfizer, UCB, Grant/research support from: AbbVie, Merck, Pfizer

POS0456 EXTERNAL VALIDATION OF A MULTI-BIOMARKER-BASED CARDIOVASCULAR DISEASE RISK PREDICTION SCORE FOR RHEUMATOID ARTHRITIS PATIENTS

Background:

A novel score for predicting 3-year risk for CVD events in RA patients combines age, four traditional CVD risk factors (diabetes, hypertension, smoking, history of high-risk CVD event), a personalized assessment of RA-related inflammation based on the multi-biomarker disease activity (MBDA) score and, individually, 3 of its 12 biomarkers, TNF-R1, MMP-3 and leptin (log-transformed). This score was developed and internally validated using patient data from the Medicare database.

Objectives:

The purpose of this analysis was to externally validate the MBDA-based CVD risk prediction score in a younger cohort from the Symphony claims database.

Methods:

A cohort of patients greater than or equal to 18 years old with RA diagnosis from a rheumatologist and evidence of an RA-specific treatment, excluding patients with malignancy, past myocardial infarction (MI) or stroke, was created by a third party (Symphony) by matching medical and pharmaceutical claims and linking them to MBDA scores from a database of tests done for routine care. Medicare patients were excluded to avoid overlap with the internal validation cohort. Only the first MBDA test was used for each patient. The study endpoint was time from MBDA testing to first CVD event within a 3-year time horizon. CVD event was defined as MI or stroke, based on ICD-9 or ICD-10 diagnosis codes in hospital claims. Analyses focused on relative risk, not absolute risk, because CVD event data in Symphony may be incomplete. A univariate Cox proportional hazards regression model was fit with the MBDA-based CVD risk score as the sole predictor of time to CVD event to obtain a hazard ratio (HR) estimate (95% CI) and p-values from a likelihood ratio test (LRT). Sensitivity analyses determined HR for patient subgroups, with p-values determined for the interaction between subgroups and the MBDA-based CVD risk score. Using a multivariate Cox proportional hazard regression model, the MBDA-based CVD risk score was compared to a simpler model that included only age, sex, diabetes, hypertension, history of other CVD, smoking and CRP (log-transformed) for ability to predict time to a CVD event.

Results:

48,868 patients with 337 CVD events met eligibility criteria and had linked biomarker data. Mean age was 54.4 years. 81.7% were female (Table 1). Mean follow-up was 24.4 months. The MBDA-based CVD risk score (mean 3.3, IQR 2.8–3.8) was highly significant in univariate analysis, with HR = 3.99 (95% CI: 3.52-4.51, p = 4.4×10-95); i.e., for every 1-unit increase in risk score, the CVD event rate in this cohort was ~4 times as high. Similar results were seen in the subset of 44,379 patients <65 years old, with HR=4.26 (95% CI: 3.53-5.14, p = 1.2×10-47). In sensitivity analyses, after adjusting for multiple comparisons, there were no significant differences between HR of complementary subgroups (Figure 1). The MBDA-based CVD risk score added significant prognostic information to a simpler, clinical model (HR=2.28 [95% CI: 1.69-3.08, p = 1.6×10-7] after accounting for all other factors).

Conclusion:

The MBDA-based CVD risk prediction score has been externally validated in a cohort that is younger than and independent of the Medicare cohort used previously for test development and internal validation.

Table 1.

Cohort characteristics of RA patients with linked biomarker data and at risk for CVD events.

VariableMedian (IQR) or N (%)Total patients48,868()Age, years54 (46-60)Sex, male8,940 (18.3%)Diabetes7,974 (16.3%)Hypertension19,132 (39.2%)History of high-risk CVD event6,713 (13.7%)Smoking7,487 (15.3%)CRP, mg/L4.1 (1.4-11.5)Leptin, ng/mL24.3 (10.6-47.1)MMP-3, ng/mL21.1 (14.3-36.2)TNF-RI, ng/mL1.4 (1.1-1.7)MBDA score40 (31-48)MBDA-based CVD risk score3.3 (2.8-3.8)

Disclosure of Interests:

Jeffrey Curtis Grant/research support from: Abbvie, Amgen, BMS, Corrona, Eli Lilly, Jannsen, Myriad Genetics, Inc., Pfizer, Regeneron, Roche, and UCB., Eric Sasso Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Autoimmune, Elena Hitraya Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Autoimmune, Cheryl Chin Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Autoimmune, Richard Bamford Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Autoimmune, Rotem Ben-Shachar Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Alexander Gutin Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Darl Flake Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Brent Mabey Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Jerry Lanchbury Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc.

POS0287 USE OF BIOLOGIC DISEASE MODIFYING ANTI-RHEUMATIC DRUGS IN RELATION TO THE RISK OF LYMPHOMA: A COHORT STUDY OF US VETERANS WITH RHEUMATOID ARTHRITIS

Background:

Epidemiologic studies suggest that disease duration and degree of inflammatory activity of rheumatoid arthritis (RA) contribute to lymphoma development. However, the association of the use of biologic disease modifying anti-rheumatic drugs (bDMARDs) in patients with RA on lymphoma risk needs further evaluation.

Objectives:

Examine the effect of administration of bDMARDS on the incidence of lymphoma in an inception cohort of RA.

Methods:

We identified patients diagnosed with RA in any US Veterans Affairs (VA) facility from 1/1/2002 and 12/31/2018 using the Veteran’s Health Administration (VHA) databases. To be included, each patient was required to meet the following criteria: 1) 2+ RA diagnostic codes at least 7 days apart but no more than 365 days apart 2) a prescription for a conventional synthetic DMARD (csDMARD) within 90 days of the first RA diagnosis 3) One inpatient or outpatient visit 30 days to 2 years preceding first RA diagnosis (indicating they are a regular user of the VHA). We excluded patients for any of the following if they preceded the first RA diagnosis: 1) a prior single RA diagnostic code 2) a prescription for any DMARD medication 3) a concomitant diagnosis of another inflammatory arthritis (e.g. psoriatic arthropathy) 4) a diagnosis of lymphoma. Index date for the study is the date of the first qualifying RA diagnosis. Lymphoma diagnoses were identified through VHA records using the International Classification of Diseases-Oncology codes.

Results:

We identified 27,536 veterans with RA in the study period meeting the inclusion and exclusion criteria. Of these, 53% (n=14,705) were in the age range 60 to 80 years. The cohort was 89% male, 75.5% White, 13.7% African American. Over the study period, 1.2% (n=332) of the study population developed a lymphoma.

Conclusion:

Using the nationwide VHA we have identified a large inception cohort of patients with RA of whom 1.2% developed lymphoma over study follow-up. This data will be used in future analyses to produce estimates of the effect of biologic medications on lymphoma risk, adjusting for confounding by indication and other variables.

Table 1.

Baseline characteristics of the cohort based on bDMARD exposure status

CharacteristicbDMARD-naive (n= 19,095)bDMARD-exposed (n=8,441)Overall Lymphomas Age (years)171161 18-4046 40-606378 60-8010074 >8043 Males17,206 (90%)7,270 (86%)Race White14,150 (74%)6,627 (76%) Black2,674 (14%)1,090 (13%) Asian96 (0.5%)46 (0.5%) Native American or Pacific Islander371 (2%)187 (2.2%) Missing1,804 (9%)491 (6%)

Acknowledgements:

The work in this abstract is supported by Investigator Award from the Rheumatology Research Foundation to Dr Singh.

Disclosure of Interests:

None declared

POS0092 HERPES ZOSTER IN THE FILGOTINIB RHEUMATOID ARTHRITIS PROGRAM

Background:

The once daily, oral Janus kinase (JAK)-1 preferential inhibitor filgotinib (FIL) improved signs and symptoms of rheumatoid arthritis (RA) in phase (P)3 trials.1-3 Patients (pts) with RA have increased herpes zoster (HZ) reactivation risk vs the general population. JAK inhibition is associated with increased infection incidence, including HZ.4

Objectives:

To assess long-term safety of FIL across the global clinical program with respect to HZ.

Methods:

Pts meeting 2010 ACR/EULAR RA criteria in a pooled analysis of P2 DARWIN 1–2 (D1–2), P3 FINCH 1–3 (F1–3), and long-term extension studies (D3, F4) were included. Placebo (PBO)-controlled as-randomised analysis included pts receiving FIL 100 mg (FIL100), FIL 200 mg (FIL200), or PBO up to week (W)12 (D1–2, F1–2); active-controlled as-randomised analysis included pts receiving FIL100, FIL200, adalimumab (ADA), or methotrexate (MTX) up to W52 (F1, F3). Long-term as-treated analysis included pts in all 7 studies receiving FIL100, FIL200, ADA, MTX, or PBO; data after re-randomisation were included and contributed to treatment received. Exposure-adjusted incidence rates (EAIR)/100 patient-years, calculated up to the last follow-up time or day, and differences with 95% confidence intervals (CIs) were calculated from the Poisson model. Logistic regression model was used for treatment-emergent (TE) HZ risk factor analysis and odds ratio (95% CI) and P value were provided.

Results:

Table 1 shows TE HZ EAIRs in a pooled analysis. Rates of HZ were lower for FIL200 vs PBO during the 12W PBO-controlled period. At 52W, HZ rates were higher for FIL200/100 vs active control. Long-term HZ rates increased for FIL200 vs FIL100.

Table 1.

EAIR of treatment-emergent herpes zoster

NPatient-years exposureEAIR(95% CI)EAIR diff(95% CI vs PBO/active control)12W PBO-controlled FIL200777179.80.6 (0.1, 3.9)−0.56 (−2.5, 1.3) FIL100788181.61.1 (0.3, 4.4)−0.02 (−2.2, 2.2) PBO781178.41.1 (0.3, 4.5)Active-controlled, as-randomiseda FIL200475439.71.4 (0.6, 3.0)0.69 (−0.7, 2.1) FIL100480443.40.9 (0.3, 2.4)0.23 (−1.1, 1.5) ADA325297.60.7 (0.2, 2.7)Active-controlled, as-randomiseda FIL200626578.01.7 (0.9, 3.2)0.65 (−0.8, 2.2) FIL100207195.01.5 (0.5, 4.8)0.46 (−1.6, 2.5) MTX416372.21.1 (0.4, 2.9)Long-term as-treatedb FIL20022674047.71.8 (1.4, 2.3)NC FIL10016472032.91.1 (0.8, 1.7)NC

aup to W52. bdata cut for LTE FINCH 4, Sept 19, 2019; DARWIN 3, April 26 2019.

ADA, adalimumab; CI, confidence interval; EAIR, exposure-adjusted incidence rate; FIL, filgotinib; MTX, methotrexate; NC, not calculated; PBO, placebo; W week.

Figure 1 shows multivariate logistic regression model of TE risk factors.

Of 104 pts with TE HZ in long-term as-treated analysis set, 5 receiving FIL200 had history of HZ; EAIR (95% CI) was 8.7 (3.6–21.0). Of 8 pts with multiple events, 3 had events of differing severity for the same HZ episode.

EAIRs (95% CI) of TE HZ in Asia were: 3.7 (1.7–8.1) FIL200, n=197; 2.8 (1.3–6.3) FIL100, n=158; 0 ADA, n=40; 2.8 (0.4–19.6) MTX, n=43; and 3.4 (0.5–23.8) PBO, n=77 in long-term as-treated population. EAIRs (95% CI) in rest of the world were: 1.6 (1.2–2.1) FIL200, n=2070; 0.9 (0.6–1.5) FIL100, n=1489; 0.8 (0.2–3.1) ADA, n=285; 0.9 (0.3–2.9) MTX, n=373; and 0.7 (0.2–2.9) PBO, n=704 for all pts as-treated.

Most TE HZ infections were mild to moderate and non-serious; 6 were serious; 2 were recurrences. No visceral TE HZ occurred across the FIL RA program; there was 1 case each of genital, disseminated, and ophthalmic HZ. The disseminated HZ occurred in a pt with prior HZ history. Lymphopenia was not associated with HZ during the PBO-controlled W12 period.

Conclusion:

HZ was more common in both FIL groups vs ADA or MTX up to 52 weeks but comparable vs PBO during the 12-week placebo-controlled period. In multivariate analyses, prior history of HZ, Asian region, and age ≥50 years were associated with increased HZ risk.

References:

[1]Genovese et al. JAMA. 2019;322:315–25.

[2]Westhovens et al. Ann Rheum Dis. 2021; online first.

[3]Combe et al. Ann Rheum Dis. 2021; online first.

[4]Higarashi and Honda. Drugs. 2020;80:1183–201.

Disclosure of Interests:

Kevin Winthrop Consultant of: AbbVie, Bristol-Myers Squibb, Eli Lilly and Co., Galapagos NV, Gilead Sciences, GlaxoSmithKline, Pfizer, Roche, and UCB, Grant/research support from: AbbVie, Bristol-Myers Squibb, and Pfizer, Maya H Buch Speakers bureau: AbbVie; Eli Lilly and Company; Gilead Sciences, Inc.; Merck-Serono; Pfizer; Roche; Sandoz; Sanofi; and UCB, Consultant of: AbbVie; Eli Lilly and Company; Gilead Sciences, Inc.; Merck-Serono; Pfizer; Roche; Sandoz; Sanofi; and UCB, Grant/research support from: AbbVie; Eli Lilly and Company; Gilead Sciences, Inc.; Merck-Serono; Pfizer; Roche; Sandoz; Sanofi; and UCB, Jeffrey Curtis Grant/research support from: AbbVie, Amgen, BMS, Corrona, Eli Lilly, Janssen, Myriad, Pfizer, Regeneron, Roche, and UCB, Gerd Rüdiger Burmester Speakers bureau: AbbVie; Eli Lilly; Pfizer; and Gilead Sciences, Inc., Consultant of: AbbVie; Eli Lilly; Pfizer; and Gilead Sciences, Inc., Daniel Aletaha Speakers bureau: AbbVie, Amgen, Bristol-Myers Squibb, Celgene, Eli Lilly, Medac, Merck, Merck Sharp & Dohme, Novartis, Pfizer, Roche, Sandoz, Sanofi/Genzyme, and UCB, Consultant of: AbbVie, Amgen, Celgene, Eli Lilly, Janssen, Medac, Merck, Novartis, Pfizer, Roche, Sandoz, and Sanofi/Genzyme, Grant/research support from: AbbVie, Merck Sharp & Dohme, Novartis, and Roche, Koichi Amano Speakers bureau: AbbVie GK, Astellas, Chugai Pharmaceutical Co. Ltd., Eli Lilly, GlaxoSmithKline KK, Pfizer Japan, Mitsubishi-Tanabe Pharma, Grant/research support from: Asahi Kasei Pharma, Alena Pechonkina Shareholder of: Gilead Sciences, Inc., Employee of: Gilead Sciences, Inc., Iyabode Tiamiyu Shareholder of: Gilead Sciences, Inc., Employee of: Gilead Sciences, Inc., Cianna Leatherwood Shareholder of: Gilead Sciences, Inc., Employee of: Gilead Sciences, Inc., Lei Ye Shareholder of: Gilead Sciences, Inc., Employee of: Gilead Sciences, Inc., Qi Gong Shareholder of: Gilead Sciences, Inc., Employee of: Gilead Sciences, Inc., Robin Besuyen Shareholder of: Galapagos, BV, Employee of: Galapagos, BV, James Galloway Speakers bureau: Pfizer, Bristol-Myers Squibb, UCB and Celgene

830 Why We Should Be Looking for A Diagonal Ear Lobe Crease In ENT. A Meta-Analysis of Diagonal Ear Lobe Crease and Coronary Artery Disease

Introduction

An association between a diagonal ear lobe crease (DELC) and cardiovascular disease, was first suggested by Sanders T. Frank in 1973(1). Since then, there have been numerous further studies that have investigated the association of ‘Frank’s Sign’ with carotid disease, cerebral vascular disease, and diabetic retinopathy. This review looks to see if there is a significant association between the presence of a DELC and coronary artery disease (CAD).

Method

Meta-analysis of selected studies, published between 1974 and 2017, using the PRISMA checklist(2).

Results

We included 12 studies in the pooled analysis, which included 2415 cases and 2545 controls. Our study found that patients with DELC, have an increased likelihood of having CAD (OR 4.61). Also, despite some previous studies suggesting that DELC was simply a result of age, all ten of the included studies that looked at this found that the relationship between DELC and CAD was independent of both age and other known cardiovascular risk factors.

Conclusions

We found that DELC is associated with CAD independently of other known cardiovascular risk factors, including age. Patients with DELC appear to have a substantially increased risk of CAD, and this may be higher for patients with bilateral DELC.

Potentially preventable hospitalisations for physical health conditions in community mental health service users: a population-wide linkage study

AIMS

Mental health (MH) service users have increased prevalence of chronic physical conditions such as cardio-respiratory diseases and diabetes. Potentially Preventable Hospitalisations (PPH) for physical health conditions are an indicator of health service access, integration and effectiveness, and are elevated in long term studies of people with MH conditions. We aimed to examine whether PPH rates were elevated in MH service users over a 12-month follow-up period more suitable for routine health indicator reporting. We also examined whether MH service users had increased PPH rates at a younger age, potentially reflecting the younger onset of chronic physical conditions.

METHODS

A population-wide data linkage in New South Wales (NSW), Australia, population 7.8 million. PPH rates in 178 009 people using community MH services in 2016-2017 were compared to population rates. Primary outcomes were crude and age- and disadvantage-standardised annual PPH episode rate (episodes per 100 000 population), PPH day rate (hospital days per 100 000) and adjusted incidence rate ratios (AIRR).

RESULTS

MH service users had higher rates of PPH admission (AIRR 3.6, 95% CI 3.5-3.6) and a larger number of hospital days (AIRR 5.2, 95% CI 5.2-5.3) than other NSW residents due to increased likelihood of admission, more admissions per person and longer length of stay. Increases were greatest for vaccine-preventable conditions (AIRR 4.7, 95% CI 4.5-5.0), and chronic conditions (AIRR 3.7, 95% CI 3.6-3.7). The highest number of admissions and relative risks were for respiratory and metabolic conditions, including chronic obstructive airways disease (AIRR 5.8, 95% CI 5.5-6.0) and diabetic complications (AIRR 5.4, 95% CI 5.1-5.8). One-quarter of excess potentially preventable bed days in MH service users were due to vaccine-related conditions, including vaccine-preventable respiratory illness. Age-related increases in risk occurred earlier in MH service users, particularly for chronic and vaccine-preventable conditions. PPH rates in MH service users aged 20-29 were similar to population rates of people aged 60 and over. These substantial differences were not explained by socio-economic disadvantage.

CONCLUSIONS

PPHs for physical health conditions are substantially increased in people with MH conditions. Short term (12-month) PPH rates may be a useful lead indicator of increased physical morbidity and less accessible, integrated or effective health care. High hospitalisation rates for vaccine-preventable respiratory infections and hepatitis underline the importance of vaccination in MH service users and suggests potential benefits of prioritising this group for COVID-19 vaccination.

Hypofractionated adjuvant surgical cavity radiotherapy following resection of limited brain metastasis

BACKGROUND

Following surgical resection of oligometastatic disease to the brain there is a high rate of local relapse which is reduced by the addition of focal radiation therapy, often delivered as single fraction stereotactic radiosurgery (SRS) to the surgical cavity. This study audited the outcomes of an alternative approach using hypofractionated radiation therapy (HFRT) to the surgical resection cavity.

METHODS AND MATERIALS

Seventy-nine patients who received surgical resection and focal radiation therapy to the surgical cavity using HFRT with intensity modulated radiation therapy with or without stereotactic radiotherapy were identified. Doses were delivered in five fractions every second day for 10 days. Follow-up involved MRI surveillance with three-monthly MRI scans post resection. The major endpoints were local control at the surgical cavity site, and presence of radiation necrosis at the treated site.

RESULTS

Seventy-nine patients were included for the analysis with a median follow-up of 10.8 months. Of the cohort, 56% experienced intracranial progression, with all patients progressing distant to the resection cavity, and 7% progressing locally in addition. The one-year local control rate was 89.8%. The median progression-free survival was 10.0 months and median overall survival was 14.3 months. There was one CTCAE grade 3 toxicity of symptomatic radiation necrosis with no grade 4-5 toxicities seen.

CONCLUSIONS

The rate of local relapse following HFRT to the surgical cavity is low with minimal risk of radiation necrosis. HFRT can be considered as an alternative to SRS for focal radiotherapy after brain metastasis resection.

Mortality and readmission in non-ischemic compared with ischemic cardiomyopathies after implantable cardioverter-defibrillator implantation

Background/Introduction

Uncertainty remains regarding the benefit of primary prevention ICDs overall in contemporary practice, and particularly in those with NICM compared with ICM.

Purpose

To evaluate the contemporary risk of death and readmission following following implantable cardioverter-defibrillator (ICD) implantation in patients with non-ischemic cardiomyopathies (NICM) compared with ischemic cardiomyopathies (ICM) in a large nationally representative cohort in the United States.

Methods

We used data from the American College of Cardiology (ACC) National Cardiovascular Data Registry (NCDR) ICD Registry linked with Medicare claims from April 1, 2010 to December 31, 2013 to establish a cohort of NICM and ICM patients with a left ventricular ejection fraction ≤35% who received a de novo, primary prevention ICD. We compared mortality, all-cause readmission, and heart failure readmission using Kaplan-Meier curves and Cox proportional hazard regressions models. We also evaluated temporal trends in mortality.

Results

Among 31,044 NICM and 68,458 ICM patients with a median follow up of 2.4 years, one-year mortality was significantly higher in ICM patients (12.3%) compared with NICM (7.9%, p&lt;0.001). The higher mortality in ICM patients remained significant after adjustment for covariates (hazard ratio (HR) 1.40; 95% confidence interval (CI) 1.36 to 1.45), and was consistent in subgroup analyses. These findings were consistent across the duration of the study. ICM patients were also significantly more likely to be readmitted for all causes (adjusted HR 1.15, CI 1.12 to 1.18) and for heart failure (adjusted HR 1.25, CI 1.21 to 1.31).

Conclusions

The risks of mortality and hospital readmission after primary prevention ICD implantation were significantly higher in patients with ICM compared with NICM, and these findings were consistent across all patient subgroups tested and over the duration of the study.

Funding Acknowledgement

Type of funding source: None

Perioperative intravenous contrast administration and the incidence of acute kidney injury after major gastrointestinal surgery: prospective, multicentre cohort study

BACKGROUND

This study aimed to determine the impact of preoperative exposure to intravenous contrast for CT and the risk of developing postoperative acute kidney injury (AKI) in patients undergoing major gastrointestinal surgery.

METHODS

This prospective, multicentre cohort study included adults undergoing gastrointestinal resection, stoma reversal or liver resection. Both elective and emergency procedures were included. Preoperative exposure to intravenous contrast was defined as exposure to contrast administered for the purposes of CT up to 7 days before surgery. The primary endpoint was the rate of AKI within 7 days. Propensity score-matched models were adjusted for patient, disease and operative variables. In a sensitivity analysis, a propensity score-matched model explored the association between preoperative exposure to contrast and AKI in the first 48 h after surgery.

RESULTS

A total of 5378 patients were included across 173 centres. Overall, 1249 patients (23·2 per cent) received intravenous contrast. The overall rate of AKI within 7 days of surgery was 13·4 per cent (718 of 5378). In the propensity score-matched model, preoperative exposure to contrast was not associated with AKI within 7 days (odds ratio (OR) 0·95, 95 per cent c.i. 0·73 to 1·21; P = 0·669). The sensitivity analysis showed no association between preoperative contrast administration and AKI within 48 h after operation (OR 1·09, 0·84 to 1·41; P = 0·498).

CONCLUSION

There was no association between preoperative intravenous contrast administered for CT up to 7 days before surgery and postoperative AKI. Risk of contrast-induced nephropathy should not be used as a reason to avoid contrast-enhanced CT.

FRI0567 CONSTRUCT VALIDATION OF PROMIS SHORT FORM AND PROFILE-29 T-SCORES WITH SF-36 IN RHEUMATOID ARTHRITIS PATIENTS TREATED FOR 1 YEAR: RESULTS FROM A REAL‑WORLD EVIDENCE-BASED STUDY IN THE UNITED STATES

Background:

Use of patient-reported outcomes (PROs) to assess health-related quality of life in clinical practice, research studies, and clinical trials in rheumatoid arthritis (RA) remains an ongoing area of research. SF-36 is commonly used in RA trials but is not feasible for routine use in clinical practice settings. ThePatientReportedOutcomesMeasurementInformationSystem (PROMIS) may address this gap but has not been widely assessed in RA patients starting therapy in a real-world comparative effectiveness study, nor examined in that setting in relation to the SF36 and Clinical Disease Activity Index (CDAI).

Objectives:

To assess validity of PROMIS based on Comparative and Pragmatic Study of Golimumab Intravenous (IV) Versus Infliximab in Rheumatoid Arthritis (AWARE), an ongoing Phase 4 study providing real-world assessment of IV tumor necrosis factor inhibitor (TNFi) medications in RA patients.

Methods:

AWARE is a prospective, non-interventional, 3-year study conducted at 88 US sites. RA patients were enrolled when initiating TNFi treatment. Treatment decisions were made by treating rheumatologists. We report baseline PROMIS-29 (7 domains and pain intensity), PROMIS Pain Interference (PI) Short Form (SF) 6b (PI6b) and PROMIS Fatigue (F) Short Form 7a (F7a), domain T-Scores, and SF-36 subdomain and Component Scores (CS) in AWARE patients. Here we report baseline data obtained from the final 1-year AWARE dataset. Correlations between PROMIS measures and comparable SF-36 component scores were calculated using Pearson correlations. Data is shown as mean ± standard deviation (SD).

Results:

At baseline, mean CDAI of all patients (n=1262) was 32.3±15.6, with 70.4% in high disease activity (HDA, CDAI>22), 22.8% in moderate disease activity (MDA, CDAI: >10 and ≤22), 6.1% in low disease activity (LDA, CDAI: >2.8 and ≤10), and 0.7% in remission (CDAI ≤2.8). Mean PROMIS scores were >0.5 SD worse than population means for Physical Function (PF, 38.1±6.84), PI (63.4±7.68), F (58.8±9.95), Sleep Disturbance (55.1±8.68); and Ability to Participate in Social Roles/Activities (PSRA, 43.4±8.58). Baseline Depression and Anxiety were within 0.5 SD of population T-scores. PI6b, F7a, and P29 domain T-scores correlated with the comparable SF-36 subdomain and component scores (r’s >0.58), except sleep for which no comparable SF-36 element was applicable. Examples include: P6b (r=-0.80) and P29-PI (0.81) with SF-36 Bodily Pain; F7a (-0.77) and P29-F (-0.77) with SF-36 Vitality; P29-PF with SF-36 PF (0.77), Role-Physical (0.69), and Physical CS (0.73); P29 Anxiety with SF-36 Mental Health (-0.72), Role-Emotional (-0.56), Mental CS (-0.70); and P29-PRSA with SF-36-Social Functioning (0.71). Mean PROMIS-29 T-scores (except Anxiety and Sleep Disturbance) among patients with HDA were significantly different from patients with MDA, LDA or remission (p < 0.001 for all). Further, mean PROMIS T-scores of PF, F, PSRA, PI, Pain Intensity, PI6b and P7a among patients with MDA were significantly different from patients with more or less active RA (by CDAI category).

Conclusion:

Analysis of baseline results from a large cohort of RA patients indicates high correlations between individual P29 domain T-scores and SF-36 component scores, as well as categorical CDAI, providing strong evidence of PROMIS construct validity in a real-world population of RA patients.

Disclosure of Interests:

Clifton Bingham Grant/research support from: Bristol-Myers Squibb, Consultant of: Bristol-Myers Squibb, Shelly Kafka Employee of: Janssen Scientific Affairs, LLC, Shawn Black Employee of: Janssen Research & Development, LLC, Janssen Scientific Affairs, LLC, Stephen Xu Employee of: Janssen Research & Development, LLC, Wayne Langholff Employee of: Janssen Research & Development, LLC, Jeffrey Curtis Grant/research support from: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, UCB, Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, UCB

FRI0553 DEVELOPMENT AND VALIDATION OF A BIOMARKER-BASED CARDIOVASCULAR RISK PREDICTION SCORE IN RHEUMATOID ARTHRITIS

Background:

Rheumatoid arthritis (RA) patients are at elevated risk for cardiovascular (CV) events, but risk stratification based on CV prediction models is not part of routine rheumatology practice.

Objectives:

To develop and validate a biomarker-based CV risk prediction model and compare it to alternative risk prediction models.

Methods:

We constructed a cohort of RA patients - age ≥40 with ≥1 RA diagnosis from a rheumatologist, excluding patients with malignancy, past myocardial infarction (MI) or stroke - by linking Medicare administrative data from 2006-2016 to multi-biomarker disease activity (MBDA) test results obtained as part of routine care. The cohort was split 2:1 to create training and internal validation datasets. The composite CV outcome was MI, stroke or CV death occurring within 3 years. Clinical predictors examined were: age, sex, race, traditional CV risk factors (e.g. diabetes, hypertension, hyperlipidemia, high-risk CV conditions [e.g. angina]), RA-related factors (e.g. glucocorticoid use, MTX, number of prior biologics), adjusted MBDA score1and its 12 biomarkers, log-transformed. Backward elimination was used to remove predictors with p ≥0.05. The resulting CV risk score was compared to four prediction models (age+sex; age+sex+CRP; age+sex+diabetes+hypertension+ smoking+high risk CV [±CRP]) in the validation dataset. We evaluated: 1) incremental improvement in the likelihood ratio test (LRT) statistic, 2) discrimination (AUROC), and 3) goodness-of-fit (predicted vs. observed, based on Kaplan-Meier estimates). Validation analyses were prespecified.

Results:

30,751 RA patients with 904 CV events were linked to MBDA test results and eligible for analysis. Patient characteristics were mean (SD) age 68.7 (9.5) years; 23.4% age <65; 82% women. Comorbidities included diabetes (39%), hypertension (78%), smoking (24%) and history of high-risk CV condition (37%). RA-related features included use of glucocorticoids (58%), MTX (60%), TNFi (33%) and other biologics (16%). Mean (SD) MBDA score was 41 (14). The final covariates included in the MBDA-based CV risk score were age, diabetes, hypertension, smoking, history of high-risk CV conditions, adjusted MBDA score, leptin, TNFRI and MMP-3. Median (IQR) of the predicted 3-year CV risk was 3.4% (2.1%, 5.6%). Based on extrapolation to 10-year risk, 9.4% of patients would be considered low, 10.2% borderline, 52.2% intermediate, and 28.2% high risk per 2019 ACC/AHA guidelines.

Compared to four simpler CV prediction models, significant improvement in the LRT statistic was observed with the addition of the biomarker-based CV risk score (Figure 1). Model fit was good across deciles (Figure 2). The AUROC was 0.70. The MBDA-based model reclassified 28.5% of patients vs. the model based on age+sex+diabetes+hypertension +smoking+high risk CV+CRP.

Figure 1.

Incremental Improvement of MBDA-based CV Risk Score Compared to Other CV Risk Prediction Models

Figure 2.

MBDA-Based CV Risk Score Calibration for Composite CV Outcome at 3 Years

Conclusion:

A biomarker-based prediction score incorporating a few clinical risk factors appears to have good accuracy to predict CV risk in RA. Additional validation in independent cohorts will help verify its performance characteristics.

References:

[1] Curtis et al.,Rheumatology2018;58:874.

Disclosure of Interests:

Jeffrey Curtis Grant/research support from: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, UCB, Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, UCB, Fenglong Xie: None declared, Cynthia S. Crowson Grant/research support from: Pfizer research grant, Brent Mabey Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Darl Flake Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Richard Bamford Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Cheryl Chin Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Eric Sasso Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Elena Hitraya Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Rotem Ben-Shachar Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Alexander Gutin Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Jerry Lanchbury Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc.

AB0354 STEPPING UP FOR INFLAMMATORY ARTHRITIS: A PILOT TRIAL TO TEST BEHAVIORAL ECONOMICS STRATEGY TO INCREASE PHYSICAL ACTIVITY IN INFLAMMATORY ARTHRITIS

Background:

Regular physical activity may have benefits for patients with rheumatoid arthritis (RA) and psoriatic arthritis (PsA), but patients with active disease are often reluctant to increase activity. Principles from behavioral economics (BE), a field combining psychology and economics, have been applied to motivate increased physical activity in non-arthritis patients.1No published studies have examined the application of BE concepts in rheumatology to promote exercise.

Objectives:

To assess the feasibiility and efficacy of a loss aversion financial incentive for increasing step counts and improving disease symptoms in RA and PsA patients with active disease.

Methods:

A randomized controlled pilot trial was performed among patients with RA and PsA. Participants were required to have active disease defined by having at least one swollen joint and a Routine Assessment of Patient Index Data-3 (RAPID3) score>3 (range 0-30 with <3 indicating remission). The trial included two visits (baseline and 14-week) and weekly check-ins via virtual trial platforms, Way to Health and the ArthritisPower app. Patients were given a Fitbit Alta at baseline and completed a two-week run-in period to assess average step count. Patients were then prompted to select a step count goal and complete a commitment contract. After selection of a goal, participants randomized to the intervention arm received a financial loss aversion incentive (each month, patients started with $75 in their account and lost $2.50 for each day they did not reach their goal). Patients were blinded to the other study arm and investigators were blinded to assignment. All patients received weekly text message prompts providing feedback about their performance over the previous week, completed weekly PROs, and had the opportunity to report adverse events including flares of joint pain. After 12 weeks of the intervention (at week 14), the incentive was removed and patients were followed to 26 weeks to determine how long the effect persisted.

Results:

In the pilot trial, 71 patients were verbally consented for screening, 34 underwent screening (of these, two were ineligible), 27 were randomized, and 22 patients completed the 14-week study visit. Mean age of participants was 50 (SD 13), 85% were female, 17(63%) had PsA, mean BMI was 30.6 and mean swollen (0-66) and tender (0-68) joint counts were 6.2 (5.6) and 8.1 (9.1), respectively. Baseline RAPID3 was 10.5 (SD 4.6) and the mean step count at baseline was 5,962. By 28 days, 65% of patients increased their step count. Participants receiving the incentive had an average of 714 more steps per day over the first 14 weeks and a greater probability of reaching 10,000 steps per day during follow-up (30% v. 21%, p=0.41). Among patients who achieved their step count goals more than 50% of days, we observed more improvement in sleep quality, fatigue, and overall well-being (p<0.05) (Figure 1). After adjusting for baseline RAPID3, the 14-week RAPID3 scores were lower in the group that achieved their step goals 50% of the time [B: -3.91 (-11.8, 3.99); a difference that approximates the minimal clinically important difference (MCID) for the RAPID3 (3.6).

Figure 1.

Change in fatigue severity among those with greater adherence to step count goals.

Conclusion:

While financial incentives have worked well in patients without arthritis, the estimated effect of the financial incentive in this small study was more modest in patients with RA and PsA. Those that were able to increase their physical activity and meet their step goals had greater improvements in symptoms over the course of the study. These data support further study in this area to promote physical activity by leveraging concepts from behavioral economics.

References:

[1]Ogdie & Asch. Nat Rev Rheumatol. 2019

Disclosure of Interests:

Alexis Ogdie Grant/research support from: Pfizer, Novartis, Consultant of: Abbvie, Amgen, BMS, Celgene, Corrona, Janssen, Lilly, Pfizer, Novartis, Mitesh Patel Shareholder of: Owner, Catalyst Health LLC, Consultant of: Advisory Board Member for Healthmine Services, Life.io, Holistic Industries, Jeffrey Curtis Grant/research support from: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, UCB, Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, UCB, Kelly Gavigan: None declared, W. Benjamin Nowell: None declared, Joshua Baker: None declared

THU0564 PARTICIPANT ENGAGEMENT IN AN ARTHRITISPOWER REAL-WORLD STUDY TO CAPTURE SMARTWATCH AND PATIENT-REPORTED OUTCOME DATA AMONG RHEUMATOID ARTHRITIS PATIENTS

Background:

Clear characterization of how different types of patient-generated data reflect patient experience is needed to guide integration of electronic patient-reported outcome (ePRO) measures and biometrics in generating real-word evidence (RWE) related to rheumatoid arthritis (RA).

Objectives:

To characterize the level of participant (pt) engagement/adherence and data completeness in an ongoing study of 250 RA pts enrolled in the Digital Tracking of Arthritis Longitudinally (DIGITAL) study1of the ArthritisPower real-world registry.

Methods:

ArthritisPower pts with RA were invited to join a digital RWE study with 14-day lead-in and 12-week main study period. In the lead-in, pts were required to electronically complete: a) two daily single-item Pain and Fatigue numeric rating scales and b) longer weekly sets of ePROs. Successful completers of the lead-in were mailed a smartwatch (Fitbit Versa) and study materials. The smartwatch collected activity, heart rate, and sleep duration/quality biosensor data; a study-specific customization of the ArthritisPower mobile application collected ePROs. The main study period included automated and manual reminders/prompts about completing ePROs, wearing the smartwatch and regularly syncing it. Study coordinators monitored pt data and contacted pts via email, text and/or phone to resolve adherence issues during the conduct of the study based on pre-determined rules triggering pt contact. Rules were based chiefly on consecutive spans of missing data. Pts were considered adherent in giving complete data for each week if providing (1) daily ePROs for ≥5 of 7 days/week, (2) weekly ePROs and (3) ≥80% of synced activity data for ≥5 of 7 days/week. Composite adherence for the first month of the main study period required meeting >70% weekly adherence parameters during the first 30 days, ie completing daily ePROs for ≥5 of 7 days/week, weekly ePROs ≥3 of 4 weeks and ≥80% of synced activity data for ≥5 of 7 days/week.

Results:

As of December 2019, 170 ArthritisPower members enrolled and completed at least 30 days of the main study period; 92.9% female with mean (SD) age 52.5 (10.7) and 10.5 (10.4) years since diagnosis. The overall conversion rate from initial interest to successful completion of the lead-in period was 49.0%. Pts who advanced to the main study were significantly more likely than those who did not to be currently employed (52.9% vs. 41.8%, p=0.038) and be on biologic DMARD monotherapy (64.7% vs. 47.5%, p=0.001). Overall, daily ePRO data had the lowest adherence with 70.0% of pts providing >70% of the requested data consistently across the first 30 days of the main study period (Figure 1). Composite adherence was met by 66.5% of pts. The most common time of day to provide ePRO data was morning, in the hours around scheduled app and email notifications at 10 a.m. in pt’s local time zone. Activity data had the highest adherence and persistence, with 92.9% of pts providing 80% or more of activity data for each 24-hour period in the first 30 days (Figures 1 & 2). Observed weekly adherence did not decline over time. Of 5100 possible person days in the study at day 30, we observed 643 days (91.0% of actual to maximum possible total patient days) where activity data was provided for at least 80% of the 24-hour period.

Conclusion:

RWE studies involving passive data collection in RA require pt-centric implementation and design to minimize pt burden, promote longitudinal engagement and maximize adherence. Passive data capture via activity trackers such as smartwatches, along with regular contact such as automated reminders, may facilitate greater pt adherence in providing longitudinal data for clinical trials.

References:

[1]Nowell WB, et al. JMIR Res Protoc. 2019;8(9):e14665.

Disclosure of Interests:

W. Benjamin Nowell: None declared, Jeffrey Curtis Grant/research support from: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, UCB, Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, UCB, Fenglong Xie: None declared, Hong Zhao: None declared, David Curtis: None declared, Kelly Gavigan: None declared, Shilpa Venkatachalam: None declared, Laura Stradford: None declared, Jessica Boles: None declared, Justin Owensby: None declared, Cassie Clinton: None declared, Ilya Lipkovich Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Amy Calvin Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Virginia S. Haynes Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company

AB0756 GUSELKUMAB IMPROVED WORK PRODUCTIVITY AND DAILY ACTIVITY IN PATIENTS WITH PSORIATIC ARTHRITIS: RESULTS FROM A PHASE 3 TRIAL

Background:

DISCOVER 2 (DISC 2) is a Phase 3 trial of anti-IL-23-specific mAb guselkumab (GUS) in psoriatic arthritis (PsA) pts, who experience impaired physical function, resulting in disability, work productivity loss, and economic consequences.1

Objectives:

To evaluate the effect of GUS on impaired work productivity and daily activity in DISC 2 using the Work Productivity and Activity Impairment Questionnaire: Psoriatic Arthritis (WPAI-PsA).

Methods:

Bio-naïve adults with active PsA despite nonbiologic DMARDs &/or NSAIDs received subcutaneous GUS 100 mg every (q) 4 weeks (W); GUS 100 mg W0, W4, q8W; or placebo (PBO). WPAI-PsA assesses, due to PsA over the previous week, work time missed (absenteeism), impairment while working (presenteeism), and impaired overall work productivity (absenteeism + presenteeism) and daily activity. Percentage change from baseline was analyzed for WPAI-PsA domains using mixed-effect model repeated measure (MMRM). Indirect savings from improved overall work productivity were estimated with 2018 US mean yearly wage estimate (all occupations).2

Results:

At Week 24, impaired overall work productivity and daily activity were improved 20-22% in GUS-treated and 10-11% in PBO-treated pts (Table). Potential yearly indirect savings from improved overall work productivity was $10,242 with GUS q8W and $10,404 with GUS q4W vs $5,648 with PBO; $4,594 and $4,756 difference, respectively.

Conclusion:

Improvement in overall work productivity and daily activity was greater with GUS versus PBO among pts with moderate-to-severe PsA, resulting in potential annual incremental economic gains.

References:

[1]Tillett W et al. Rheumatol (Oxford). 2012;51:275–283.

[2]US Bureau of Labor Statistics. May 2018 National Occupational Employment and Wage Estimates United States.https://www.bls.gov/oes/current/oes_nat.htm#00-000

Table.

Model-based estimates of mean change from baseline in WPAI-PsA domains

% change from baselinePBOGUS 100 mg q8WGUS 100 mg q4WW16W24W16W24W16W24Work time missed (absenteeism), n155152141145145143LSMean-4.6 (-7.2,-1.9)-3.5 (-6.4,-0.6)-3.5 (-6.2,-0.7)-3.1 (-6.1,-0.1)-4.7 (-7.4,-2.0)-3.8 (-6.8,-0.8)LSMean diff1.1 (-2.6,-4.8)*0.4 (-3.7,4.5)*-0.2 (-3.9,3.5)*-0.3 (-4.4,3.8)*Impairment while working (presenteeism), n131130125129133130LSMean-10.3 (-13.9,-6.7)-10.2 (-13.7,-6.7)-16.1 (-19.7,-12.4)-19.4 (-22.9,-15.9)-15.1 (-18.7,-11.5)-19.5 (-23.0,-16.0)LSMean diff-5.8 (-10.8,-0.8)†-9.2 (-14.0,-4.4)‡-4.8 (-9.7,0.1)*-9.3 (-14.1,-4.5)‡Overall work productivity impairment (absenteeism + presenteeism), n131130125129133130LSMean-11.2 (-15.0,-7.5)-10.9 (-14.6,-7.1)-15.9 (-19.7,-12.2)-19.7 (-23.4,-16.0)-15.8 (-19.5,-12.1)-20.0 (-23.7,-16.3)LSMean diff-4.7 (-9.9,0.5)*-8.8 (-14.0,-3.7)‡-4.6 (-9.7,0.5)*-9.2 (-14.3,-4.0)‡Daily activity impairment, n244244247246243245LSMean-10.6 (-13.3,-7.9)-10.3 (-13.1,-7.6)-17.1 (-19.8,-14.4)-21.5 (-24.2,-18.7)-17.0 (-19.7,-14.3)-20.5 (-23.2,-17.7)LSMean diff-6.5 (-10.2,-2.8)‡-11.1 (-15.0,-7.4)‡-6.5 (-10.2,-2.7)‡-10.2 (-14.0,-6.4)‡

Data are % (95% CI)

*p>0.05, †p<0.05,‡p<0.001

LSmeans, p values based on MMRM

LSmean diffs, p values vs PBO

Acknowledgments:

None

Disclosure of Interests:

Jeffrey Curtis Grant/research support from: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, UCB, Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, UCB, Iain McInnes Grant/research support from: Bristol-Myers Squibb, Celgene, Eli Lilly and Company, Janssen, and UCB, Consultant of: AbbVie, Bristol-Myers Squibb, Celgene, Eli Lilly and Company, Gilead, Janssen, Novartis, Pfizer, and UCB, Proton Rahman Grant/research support from: Janssen and Novartis, Consultant of: Abbott, AbbVie, Amgen, BMS, Celgene, Lilly, Janssen, Novartis, and Pfizer., Speakers bureau: Abbott, AbbVie, Amgen, BMS, Celgene, Lilly, Janssen, Novartis, Pfizer, William Tillett Grant/research support from: AbbVie, Celgene, Eli Lilly, Janssen, Novartis, Pfizer Inc, UCB, Consultant of: AbbVie, Amgen, Celgene, Lilly, Janssen, Novartis, MSD, Pfizer Inc, UCB, Speakers bureau: AbbVie, Amgen, Celgene, Lilly, Janssen, Novartis, Pfizer Inc, UCB, Philip J Mease Grant/research support from: Abbott, Amgen, Biogen Idec, BMS, Celgene Corporation, Eli Lilly, Novartis, Pfizer, Sun Pharmaceutical, UCB – grant/research support, Consultant of: Abbott, Amgen, Biogen Idec, BMS, Celgene Corporation, Eli Lilly, Novartis, Pfizer, Sun Pharmaceutical, UCB – consultant, Speakers bureau: Abbott, Amgen, Biogen Idec, BMS, Eli Lilly, Genentech, Janssen, Pfizer, UCB – speakers bureau, Alexa Kollmeier Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Elizabeth C Hsia Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Bei Zhou Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Prasheen Agarwal Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Steve Peterson Employee of: Janssen Research & Development, LLC, Chenglong Han Employee of: Janssen Research & Development, LLC

SAT0150 CHANGES IN PATIENT-REPORTED OUTCOME (PRO) SCORES FOR NAUSEA AND FATIGUE FOLLOWING WEEKLY METHOTREXATE DOSE IN A REAL-WORLD SAMPLE OF RA AND PSA PATIENTS IN THE ARTHRITISPOWER REGISTRY

Background:

Methotrexate (MTX) is frequently used in patients with rheumatoid arthritis (RA) or psoriatic arthritis (PsA) because of its beneficial effects in both populations1-3. Despite the well-known benefits of MTX, it is associated with a number of potential side effects4-6These include nausea and fatigue, are often temporally related to the timing of weekly MTX administration, and can be severe. The combined patient-reported side effects, along with potential of long-term toxicity, may make use of MTX more burdensome. Currently, there is a gap in patient-centered studies that focus on patients’ experience with MTX.

Objectives:

Examine patient temporal experience of fatigue and nausea relating to oral MTX therapy for the treatment of RA and PsA.

Methods:

Adult US patients in the ArthritisPower registry with self-reported RA or PsA taking MTX for less than 10 years were invited to participate in the study via email invitation. Participants (pts) completed a screener and brief online survey. In an ancillary study to the ArthritisPower registry and using a self-controlled case series study design where pts serve as their own control to avoid between-person confounding, pts were asked to complete a set of up to 8 assessments within 6-36 hours (‘risk’) and 96-144 hours (‘control’) after taking their oral dose of MTX each week, for up to 4 weeks. Risk and control windows were selected based on the expected temporal relationship between MTX use and peak onset of these symptoms. Assessments included PROMIS short forms for same-day Fatigue, same-day Nausea/Vomiting, and Patient Global. Descriptive statistics were conducted using paired t-tests two-way comparisons. Within-person change in PROMIS scores between the risk (1-2 days after MTX) and control (4-6 days after MTX) windows were analyzed using mixed models for repeated measures, stratified on whether pts reported fatigue or nausea with MTX at baseline. Recruitment for this study is ongoing.

Results:

As of December 2019, 91 pts had participated, of whom 76.9% were living with RA and 28.6% with PsA, with mean baseline PROMIS Patient Global score (SD) of 39.5 (7.1). Mean age (SD) was 50.9 (12.0) years, 84.6% female, 92.3% White, with mean BMI 33.7 (8.8). Mean duration of MTX treatment among current users was 2.1 (2.8) years. Among pts, 41.8% were on a biologic DMARD and 58.2% on non-biologic DMARDs only. Among pts reporting baseline nausea (n=30, 33.0%) where paired within-week measures were observed (n=64 observations among 20 pts), the mean increase in the PROMIS Nausea score was 4.5 units (adjusted p=0.003). Among those reporting MTX-associated fatigue (n=39, 42.9%) as a side effect of MTX on their baseline survey where paired within-week measures were observed (n=96 observations among 28 pts), the mean increase in PROMIS Fatigue was 4.7 (adjusted p=0.004) units. In those pts, the proportion of pts with worsened nausea and fatigue with minimally important difference of >5 units7-8was 40.0% (nausea), and 60.7% (fatigue) [Figures 1 and 2].

Conclusion:

People taking MTX to manage RA or PsA commonly experience bothersome side effects, notably fatigue and nausea, that are temporally related to weekly MTX dosing. In this sample, one-third or more of pts were bothered by nausea or fatigue shortly after MTX dosing, many of them with clinically meaningful symptoms.

References:

[1]Singh JA, et al.Arthritis Rheumatol. 2016;68:1-26.

[2]Singh JA, et al.Arthritis Rheumatol. 2019;71:5-32.

[3]Mease P.Bull NYU Hosp Jt Dis. 2013;71.(suppl 1):S41.

[4]Wang W, et al.Eur J Med Chem. 2018;158:502-516.

[5]Wilsdon TD, et al.Cochrane Database Syst Rev. 2019;1:CD012722.

[6]Husted JA, et al.Ann Rheum Dis. 2009;68:1553-1558.

[7]Norman GR, et al.Med Care. 2003;41:582-92.

[8]Bingham CO, et al.J Patient Rep Outcomes. 2019;3:14.

Disclosure of Interests:

W. Benjamin Nowell: None declared, Elaine Karis Shareholder of: Amgen Inc., Employee of: Amgen Inc., Kelly Gavigan: None declared, Laura Stradford: None declared, Scott Stryker Shareholder of: Amgen Inc., Employee of: Amgen Inc., Huifeng Yun Grant/research support from: Bristol-Myers Squibb and Pfizer, Shilpa Venkatachalam: None declared, Greg Kricorian Shareholder of: Amgen Inc., Employee of: Amgen Inc., Lang Chen: None declared, Hong Zhao: None declared, Fenglong Xie: None declared, Jeffrey Curtis Grant/research support from: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, UCB, Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, UCB

PARE0026 WHICH PATIENT-REPORTED OUTCOMES DO RHEUMATOLOGY PATIENTS FIND IMPORTANT TO TRACK DIGITALLY? A REAL-WORLD LONGITUDINAL STUDY IN ARTHRITISPOWER

Background:

Development of a standardized approach to assess key elements of disease activity in rheumatology clinical trials has been the goal of Outcome Measures in Rheumatology Clinical Trials (OMERACT), American College of Rheumatology (ACR), and European League Against Rheumatism (EULAR).1,2,3The core sets of measures developed include assessments and composite indices incorporating use of patient-reported outcomes (PROs) and clinical measures and clinicians’ assessments to quantify disease activity over time.2PROs are important indicators of disease activity and variability, and they are increasingly used to evaluate treatment effectiveness. Little is known about PROs that patients with rheumatic conditions find most important to convey their experience with their condition and its treatment.

Objectives:

To examine PROs selected by patients with rheumatic conditions in the ArthritisPower registry to identify symptoms they found most important to track digitally.

Methods:

Adult US patients within the ArthritisPower registry with rheumatoid arthritis (RA), psoriatic arthritis (PsA), ankylosing spondylitis (AS), systemic lupus erythematosus (SLE), osteoporosis (OP), osteoarthritis (OA), and fibromyalgia syndrome (FMS) were invited via email to participate in this study. Enrolled participants (pts) were prompted to select ≤10 PRO symptom measures they felt were important to track for their condition at baseline via the ArthritisPower app. At 3 subsequent time points (Month [m] 1, m2, m3), pts were given the option to continue tracking their previously selected PRO measures or to add, remove and/or select different measures. At m3, pts completed an exit survey to prioritize ≤5 measures from all measures selected during study participation and to specify other symptoms not available that they would have wanted to track. Measures were rank-ordered based on number of pts rating the item as their 1st, 2nd, 3rd, 4th or 5th choice and weighted by multiplying the rank number by its inverse for a single, weighted summary score for each measure. Values were summed across all pts to produce a summary score for each measure.

Results:

Among pts who completed initial selection of PRO assessments at baseline (N=253), 184 pts confirmed or changed PRO selections across m1-3. Mean (SD) age of pts was 55.7 (9.2) yrs, 89.3% female, 91.3% White, mean disease duration of 11.6 (10.6) yrs. The majority (64.8%) self-reported OA, followed by RA (48.6%), FMS (40.3%), PsA (26.1%), OP (21.0%), AS (15.8%) and SLE (5.9%), not mutually exclusive, and were similar to the overall ArthritisPower population. The average number of instruments (SD) selected for baseline completion was 7.0 (2.5), 7.1 (2.4) at m1, 7.2 (2.4) at m2, and 7.0 (2.5) at m3. The top 5 PROs ranked by pts overall as most important (weighted summary score) for tracking were Fatigue (71), Physical Function (58), Pain Intensity (50), Pain Interference (49), Duration of Morning Joint Stiffness (41) (Figure 1). Fatigue, Physical Function, and Pain were consistently in the top 5 across diseases while Depression was more frequent among pts with OA, AS and FMS. Pts’ PRO selections showed stability over time except for the RA Flare measure which decreased from 70.5% of RA pts at baseline to 13.6% at m3.

Conclusion:

The symptoms prioritized by pts included fatigue, physical function, pain, and joint stiffness. Pts‘ choices were consistent over time. These findings provide insights into symptoms rheumatology patients find most important and will be useful to inform design of future patient-centric clinical trials and real-world evidence generation.

References:

[1]Boers M, et al. J Rheumatol Suppl. 1994;41:86–89.

[2]Felson DT, et al. Arthritis Rheum. 1993;36:729–740.

[3]Tugwell P, et al. J Rheumatol. 1993;20:555–556.

Disclosure of Interests:

W. Benjamin Nowell: None declared, Carol L. Kannowski Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Kelly Gavigan: None declared, Zhihong Cai Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Anabela Cardoso Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Theresa Hunter Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Shilpa Venkatachalam: None declared, Julie Birt Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Jennifer Workman Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Jeffrey Curtis Grant/research support from: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, UCB, Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, UCB

FRI0572 LEPTIN-ADJUSTMENT OF THE MULTI-BIOMARKER DISEASE ACTIVITY (MBDA) SCORE REDUCES THE INFLUENCE OF ADIPOSITY

Background:

Obesity and excess adiposity influence inflammatory markers and bias disease activity assessment, especially among women. A multi-biomarker disease activity (aMBDA) score has been developed to account for the effects of age, sex and adiposity (leptin) and improves prediction of radiographic damage progression.1

Objectives:

1) Determine if the adjusted measure demonstrates a reduced association with adiposity.

2) Assess the impact of the leptin-adjustment on the score over the range of adiposity.

3) Assess relationships between MBDA scores and clinical disease activity.

Methods:

Patients with rheumatoid arthritis (RA), ages 18-75 years, completed whole-body dual-energy x-ray absorptiometry to quantify fat mass indices (FMI, kg/m2). Age-, sex-, and race-specific Z-Scores were calculated based on the distributions in a healthy reference population. Disease activity was assessed with the CDAI and swollen joint count (SJC). Baseline van der Heijde-Sharpe (vdHS) scores were determined by a radiologist. MBDA assays were performed on stored serum samples. Descriptive statistics described relationships between the FMI Z-Score and the MBDA and the aMBDA. Clinical disease activity, SJC, and radiographic damage were also compared across MBDA score categories.

Results:

Of 104 participants (50% female), the mean (SD) age was 56.1 (12.5) and mean BMI was 28.8 (6.9) (Table 1). The unadjusted MBDA score was strongly associated with BMI among women (Women: Rho=0.46 [p< 0.001]; Men: Rho=-0.12), while the aMBDA was not associated with BMI in women and was inversely correlated in men (Women: Rho=0.17; Men: Rho=-0.32 [p=0.02]). The unadjusted MBDA score was also strongly associated with FMI Z-Score among women (Figure; Women: Rho=0.42 [p=0.002]; Men: Rho=-0.10; p=0.01). The aMBDA was not significantly associated with FMI Z-Score (Female: Rho= 0.17; Male: Rho=-0.26). Leptin-adjustment reduced the MBDA score in the highest quartile of FMI in women but not men, and increased the MBDA score in the lowest FMI quartiles in both women and men; these patients in the lowest FMI quartile had the highest median SJC (p=0.05 for men, p=0.78 for women; Figure). The aMBDA reclassified 4 women (8%) and 9 men (17%) into higher disease activity categories and 2 women (4%) and 2 men (4%) into lower categories. CDAI, SJC, and radiographic scores were similar across activity categories for the unadjusted MBDA score and aMBDA (Table 2).

Table 1.

Baseline Characteristics.

MenWomenN5252Age (yrs)59.1 (11.5)53.0 (12.8)Black, N (%)13 (25%)19 (36%)BMI27.3 (5.4)30.3 (8.0)FMI Z-Score-0.28 (1.3)0.05 (1.1)DAS28(CRP)3.09 (1.13)3.21 (1.24)Disease Duration11.4 (10.9)11.6 (11.9)CRP, mg/dL0.8 (0.5, 1.2)0.8 (0.5, 1.4)CCP Positive, N (%)45 (87%)40 (78%)vdHS (N=93)13 (4, 73)10.5 (2, 47)HAQ0.71 (0.59)0.83, (0.67)MBDA40.0 (13.8)42.1 (16.6)aMBDA43.6 (13.4)42.1 (15.3)Leptin, ng/mL15.1 (21.5)48.9 (41.5)Table 2.

Clinical assessments across MBDA score categories.

CDAISJCvdHSMBDAaMBDAMBDAaMBDAMBDAaMBDAMBDA CategoryLow14.6 (10.9)13.9 (9.9)2 (1, 5)2 (1, 5)9 (1, 33.5)9 (3, 32)Moderate13.2 (10.0)14.4 (11.4)2 (0, 5)3 (0, 6)10 (4, 49)10 (2, 53)High18.4 (12.3)17.7 (11.8)4 (1, 8)5 (2, 7)20.5 (5, 70.5)18 (4, 73)

Conclusion:

Leptin-adjustment of the MBDA score reduced bias related to excess adiposity in women with RA. Adjustment results in lower MBDA scores in women with greater adiposity, and higher MBDA scores in women and men with lesser adiposity. The aMBDA may reduce misclassification due to excess adiposity and improve identification of active disease among patients with lower adiposity. High aMBDA scores among men with low adiposity may reflect severe disease or excess comorbidity in this group.

References:

[1] Curtis et al.Rheumatology (Oxford) 2018. PMID: 30590790

Figure.

Impact of Adjustment on MBDA Score by FMI Z-Score Quartile.

Disclosure of Interests:

Joshua Baker Grant/research support from: Myriad RBM, Consultant of: Bristol-Myers Squib, Burns-White LLC, Jeffrey Curtis Grant/research support from: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, UCB, Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, UCB, David Chernoff Employee of: Myriad, Michael George Grant/research support from: Bristol Myers Squibb, Consultant of: AbbVie

AB1243 TRAINING AND VALIDATION OF A MULTIVARIATE PREDICTOR OF RISK OF RADIOGRAPHIC PROGRESSION FOR PATIENTS WITH RHEUMATOID ARTHRITIS

Background:

The multi-biomarker disease activity (MBDA) score, adjusted for age, sex and adiposity (MBDAadj), has been shown to be better than several conventional disease activity measures for predicting risk for radiographic progression (RP) in patients with rheumatoid arthritis (RA).1Serologic status and other non-disease activity measures are also predictive of RP risk. Combining them with the MBDAadjshould result in a stronger prognostic test for RP than any one measure alone.

Objectives:

Develop a multivariate model for predicting risk for RP that includes the adjusted MBDA score and other known predictors of RP.

Methods:

Four RA cohorts were used, two for training (OPERA and BRASS, n=555) and two for validation (SWEFOT and Leiden, n=397). Each pair of cohorts was heterogeneous in disease duration and treatment history. BMI data were not available for one validation cohort, so a BMI surrogate was modeled using forward selection with the two training cohorts and 3 others (CERTAIN, InFoRM, RACER) (N=1411). An RP risk score was then trained using forward selection in a linear mixed-effects regression, considering disease-related and demographic variables as predictors of change in modified total Sharp score over one year (ΔmTSS), with a random effect on cohort. The RP risk score was validated as a predictor of RP with two cutoffs (ΔmTSS >3 and >5) using logistic mixed-effects regression. Odds ratios (OR) and 95% profile likelihood-based confidence intervals (CI) were calculated from the models and significance was assessed by likelihood ratio tests. Risk curves were generated to show probability of RP as a function of the RP risk score.

Results:

The BMI surrogate included leptin, sex, age and age2and correlated well with BMI (ρ = 0.76). In training, the most significant independent predictors of RP were MBDAadj(p = 0.00020), seropositivity (p = 9.3 x 10-5), BMI surrogate score (p = 0.013) and use of targeted therapy (p = 0.0026). The final model was: RP risk score = 0.024 x MBDAadj+ 0.093 if seropositive – 0.063 x BMI surrogate score – 0.61 if using a targeted therapy. In validation, the OR (95% CI) of the RP risk score for predicting ΔTSS >3 or >5 were 2.2 (1.6, 3.2) (p = 2.6 × 10-6) and 3.1 (2.0, 5.0) (p = 5.7 × 10-8), respectively (Figure 1). The odds of a patient having RP increases by 50% for each 21-unit or 15-unit increase in MBDAadj, for RP defined as ΔTSS >3 or >5, respectively.

Figure 1.

Conclusion:

A multivariate model containing adjusted MBDA score, seropositivity, a BMI surrogate and use of targeted therapy has been trained and validated as a prognostic test for radiographic progression in RA.

References:

[1]Curtis, et al.Rheumatology [Oxford].2018;58:874

Disclosure of Interests:

Thomas Huizinga Grant/research support from: Ablynx, Bristol-Myers Squibb, Roche, Sanofi, Consultant of: Ablynx, Bristol-Myers Squibb, Roche, Sanofi, Michael E. Weinblatt Grant/research support from: BMS, Amgen, Lilly, Crescendo and Sonofi-Regeneron, Consultant of: Horizon Therapeutics, Bristol-Myers Squibb, Amgen, Abbvie, Crescendo, Lilly, Pfizer, Roche, Gilead, Nancy Shadick Grant/research support from: Mallinckrodt, BMS, Lilly, Amgen, Crescendo Biosciences, and Sanofi-Regeneron, Consultant of: BMS, Cecilie Heegaard Brahe: None declared, Mikkel Ǿstergaard Grant/research support from: AbbVie, Bristol-Myers Squibb, Celgene, Merck, and Novartis, Consultant of: AbbVie, Bristol-Myers Squibb, Boehringer Ingelheim, Celgene, Eli Lilly, Hospira, Janssen, Merck, Novartis, Novo Nordisk, Orion, Pfizer, Regeneron, Roche, Sandoz, Sanofi, and UCB, Speakers bureau: AbbVie, Bristol-Myers Squibb, Boehringer Ingelheim, Celgene, Eli Lilly, Hospira, Janssen, Merck, Novartis, Novo Nordisk, Orion, Pfizer, Regeneron, Roche, Sandoz, Sanofi, and UCB, Merete L. Hetland Grant/research support from: BMS, MSD, AbbVie, Roche, Novartis, Biogen and Pfizer, Consultant of: Eli Lilly, Speakers bureau: Orion Pharma, Biogen, Pfizer, CellTrion, Merck and Samsung Bioepis, Saedis Saevarsdottir Employee of: Part-time at deCODE Genetics/Amgen Inc, working on genetic research unrelated to this project, Megan Horton Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Brent Mabey Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Darl Flake Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Rotem Ben-Shachar Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Eric Sasso Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Alexander Gutin Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Elena Hitraya Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Jerry Lanchbury Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Jeffrey Curtis Grant/research support from: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, UCB, Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, UCB

FRI0018 USING SELF-REPORTED OUTCOMES TO DETECT NEW-ONSET FLARE IN A REAL-WORLD STUDY OF PARTICIPANTS WITH RHEUMATOID ARTHRITIS - INTERIM RESULTS FROM THE DIGITAL TRACKING OF ARTHRITIS LONGITUDINALLY (DIGITAL) STUDY

Background:

Patients with rheumatoid arthritis (RA) experience fluctuating symptoms, increased pain, decreased function and variable quality of life; such changes often occur between visits to clinicians. Digital Tracking of Arthritis Longitudinally (DIGITAL) study2is evaluating the use of electronically captured patient-reported outcomes (ePRO) and passive data collection from a Fitbit device to identify disease worsening in a real-world study of participants (pts) with RA.

Objectives:

Evaluate agreement between self-reported new-onset flare and ePROs in an interim analysis from DIGITAL using a classification model.

Methods:

Members of the ArthritisPower registry with RA were invited to participate in DIGITAL. Pts who successfully completed a two-week Lead-in period entered the Main Study in which they wore a smartwatch and provided daily (pain and fatigue numeric rating scales (NRS)) and weekly ePROs, including the OMERACT RA Flare Questionnaire (FLARE) and PROMIS measures. This interim analysis is of ePRO data from pts who completed at least 30 days of the Main Study. A “Yes” response to the FLARE item, “Are you having a flare now?” identified flare. For modeling association between new-onset flare and ePRO, the dataset was split into training (the first 30 days of the Main Study) and test data (Day 31 and following). Within each dataset, repeated binary outcomes (Flare/No Flare) per pt were defined each week. To focus on new-onset flare, within each dataset, outcomes for patient weeks for which flare was present in the previous week were excluded.

Candidate variables for the model included baseline and current FLARE score (0-50 scale) and each of its 5 items, daily pain, daily fatigue, and several PROMIS weekly instruments and their lagged values (last week or last 6 days for daily). ‘Baseline’ was calculated in non-flare weeks. Training data was used for logistic regression model selection combining clinical expertise with backward elimination. Performance of the final model was evaluated using test data.

Results:

The training data was composed of outcomes from 128 pts who reported 388 weekly flare assessments as no flare or onset flare over 2800 days during the first month of the Main Study. Of pts in the training dataset, 92.2% were female, 87.5% white, with mean age (SD) 52.7 (11.0) and years since RA diagnosis 10.4 (10.3); 62.5% were on a biologic. Among those in the training dataset, 58 flare outcomes occurred in 50 (39.1%) unique pts.

The test data comprised outcomes from 123 pts who reported 442 weekly flare assessments as no flare or onset flare over 3366 days in which 64 flare outcomes occurred, and primarily included continued observations from pts who contributed to the training dataset.

The best-performing model to classify flare in training data included the current and baseline FLARE instrument activity question (i.e. “Considering how active your rheumatoid arthritis has been, how much difficulty have you had when taking part in activities such as work, family life, social events that are typical for you during the last week”), current daily pain, and baseline daily pain average and standard deviation. In test data, this model had an area under the receiver operator curve of 0.81 (Figure). At a cut point requiring specificity to be ≥0.80, sensitivity to detect flare was 0.62 and overall accuracy was 0.78.

Conclusion:

New-onset flare is common among RA patients, and the FLARE instrument and daily pain scores appear effective to classify it. Evaluation of passive data as a proxy for self-reported new-onset flare is ongoing.

References:

[1]Bartlett SJ, et al. JRheumatol, 2017;44:1536-43.

[2]Nowell WB, et al. JMIR Res Protoc, 2019;8:e14665.

Disclosure of Interests:

Virginia S. Haynes Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Jeffrey Curtis Grant/research support from: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, UCB, Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, UCB, Fenglong Xie: None declared, Ilya Lipkovich Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Hong Zhao: None declared, Carol L. Kannowski Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Jiat-Ling Poon Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Kelly Gavigan: None declared, David Curtis: None declared, Sandra K. Nolot Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, W. Benjamin Nowell: None declared

FRI0314 ANNUAL DIAGNOSTIC PREVALENCE OF ANKYLOSING SPONDYLITIS (AS) IN THE UNITED STATES USING MEDICARE AND MARKETSCAN DATA

Background:

Axial spondyloarthritis (axSpA) is a chronic inflammatory disease that affects the axial skeleton and sacroiliac joints, and can be classified as ankylosing spondylitis (AS) or non-radiographic (nr)-axSpA.1A 2016 analysis estimated the US diagnostic prevalence of axSpA to be 0.2% and AS to be 0.1%.2Previous studies use disparate populations and diagnostic definitions;3,4it is therefore unclear how AS prevalence has changed over time.

Objectives:

To investigate the annual diagnostic prevalence of AS in US healthcare insurance claims databases.

Methods:

A retrospective, observational cohort study was conducted using 2006–2014 data from US Medicare Fee-for-Service Claims (5% random sample of all enrolled patients [pts]) and Truven MarketScan®. Eligible pts were ≥20 years (yrs) and had ≥6 months of continuous medical and pharmacy enrolment prior to diagnosis. Diagnoses used relevant International Classification of Disease, 9thversion (ICD-9) diagnosis codes: ICD-9 720.x [x=any number] for “AS and other inflammatory spondylopathies [SpA]” or 720.0 for “AS”. Two diagnosis definitions were used: Definition 1, ≥1 relevant ICD-9 code from hospital discharge or ≥2 from rheumatologist visit; Definition 2, ≥1 relevant ICD-9 code from hospital discharge or rheumatologist visit. Annual diagnostic prevalence of SpA/AS was calculated as “number of enrolled pts who met the definition of SpA/AS within each calendar yr and had full insurance coverage (medical and pharmacy)”, divided by “total number of pts with full insurance coverage in the same yr”. A primary analysis of SpA prevalence rates used Definitions 1 and 2, followed by a sensitivity analysis for AS prevalence rates using only Definition 2. All prevalence rates are shown per 10,000 pts enrolled.

Results:

The annual diagnostic prevalence of SpA appeared to increase from 2006–2014 (Table). Similarly, the sensitivity analysis showed the annual diagnostic prevalence of AS appeared to increase during the period from 2006 (Medicare: 2.87/10,000 pts [n=501,031]; MarketScan: 1.37/10,000 pts [n=17,562,637]) to 2014 (Medicare: 4.77/10,000 pts [n=1,046,107]; MarketScan: 2.14/10,000 pts [n=34,553,135];Figure).

Conclusion:

The apparent increase in diagnostic prevalence of SpA and AS during the period from 2006–‍2014 may be a consequence of increased awareness and availability of effective treatments. Furthermore, the 2009 Assessment of SpondyloArthritis international Society development of the axSpA classification criteria to include pts with both established AS and nr-axSpA may have accelerated this increase.5

References:

[1]Strand V. Mayo Clin Proc 2017;92:555–64;

[2]Curtis J. Perm J 2016;20:15–151;

[3]Reveille J. Arthritis Care Res (Hoboken) 2012;64:905–10;

[4]Danve A. Clin Rheumatol 2019;38:625–34;

[5]Rudwaleit M. Ann Rheum Dis 2009;68:777–83.

Table.

Prevalence of SpA by calendar year and data source

Medicare (5% random sample)MarketScanCalendar yrTotal number of eligible ptsPrevalence/10,000 ptsTotal number of eligible ptsPrevalence/10,000 ptsDefinition 1Definition 2Definition 1Definition 22006501,0314.397.6217,562,6371.332.172007816,9705.258.7219,518,0661.472.372008825,4454.898.7828,603,5251.582.532009830,9675.229.2131,757,0691.903.092010844,5285.499.9031,126,1721.963.172011879,9966.3010.7138,295,1211.943.112012921,9946.1710.8840,320,4371.913.0420131,032,8276.7410.8233,826,0412.003.1920141,046,1076.5210.8534,553,1352.213.51

Medicare data included a 5% random sample of all enrolled pts age ≥20 yrs. pts: patients; SpA: ankylosing spondylitis and other inflammatory spondylopathies; yr: year.

Acknowledgments:

This study was funded by UCB Pharma. Editorial services were provided by Costello Medical.

Disclosure of Interests:

Jeffrey Curtis Grant/research support from: AbbVie, Amgen, Bristol-Myers Squibb, Corona, Crescendo, Genentech, Janssen, Pfizer, Roche and UCB Pharma, Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, Corona, Crescendo, Genentech, Janssen, Pfizer, Roche and UCB Pharma, Kevin Winthrop Grant/research support from: Bristol-Myers Squibb, Consultant of: AbbVie, Bristol-Myers Squibb, Eli Lilly, Galapagos, Gilead, GSK, Pfizer Inc, Roche, UCB, Benjamin Chan: None declared, Sarah Siegel: None declared, Jeffrey Stark Employee of: UCB Pharma, Robert Suruki Employee of: UCB Pharma, Rhonda Bohn Consultant of: UCB Pharma, Fenglong Xie: None declared, Huifeng Yun Grant/research support from: Bristol-Myers Squibb and Pfizer, Lang Chen: None declared, Atul Deodhar Grant/research support from: AbbVie, Eli Lilly, GSK, Novartis, Pfizer, UCB, Consultant of: AbbVie, Amgen, Boehringer Ingelheim, Bristol Myer Squibb (BMS), Eli Lilly, GSK, Janssen, Novartis, Pfizer, UCB, Speakers bureau: AbbVie, Amgen, Boehringer Ingelheim, Bristol Myer Squibb (BMS), Eli Lilly, GSK, Janssen, Novartis, Pfizer, UCB

FRI0269 CHARACTERIZATION OF PATIENTS WITH ANKYLOSING SPONDYLITIS WHO INITIATED SECUKINUMAB: ELECTRONIC HEALTH RECORDS DATA FROM THE COLUMBUS REPOSITORY

Background:

Secukinumab was the first anti-interleukin 17A monoclonal antibody treatment approved by the FDA for ankylosing spondylitis (AS). There is scarce information on the characteristics of secukinumab vs other biologic initiators with AS.

Objectives:

To describe real-world physician and patient characteristics, and treatment patterns of secukinumab and tumor necrosis factor inhibitor (TNFi) initiators.

Methods:

Electronic health records (EHR) data from adult patients with AS who initiated a biologic therapy between January 2018 and March 2019 (index date) were included from the Columbus Repository, a network capturing EHR data from 120 US rheumatology providers. Physician and patient characteristics, and treatment patterns were reported for patients who were prescribed secukinumab and TNFis (adalimumab, etanercept, certolizumab pegol, infliximab, infliximab-abda, and golimumab). Categorical variables were summarized using frequency counts and percentages and continuous variables were presented using means and standard deviations. Standardized mean differences andPvalues were used to compare treatment groups.

Results:

As of March 2019, AS treatment data were available for 82 secukinumab initiators and 160 TNFi initiators. Regarding overall practice size, 33% of practices had a single physician, and 65% of physicians were located in the South US region. Secukinumab initiators were younger than TNFi initiators (47.4 vs 49.8 years) and had a similar prevalence of HLA-B27 positivity (≈ 55%; Table 1). Comorbid psoriatic arthritis (PsA) was more commonly reported among secukinumab initiators vs TNFi initiators (17% vs 9%), while hypertension (5% vs 11%), obesity (2% vs 11%), and uveitis (2% vs 9%) were less common (Figure 1). Secukinumab initiators were more likely to have prior opioid use vs TNFi initiators but were less likely to have prior methotrexate use (Figure 2A); 67% of secukinumab initiators and 49% of TNFi initiators were biologic experienced, of whom 73% and 76%, respectively, used 1 prior biologic, 25% and 20% used 2 prior biologics, and 2% and 4% used ≥ 3 prior biologics (Figure 2B). The most common reasons for discontinuation of prior biologics among secukinumab and TNFi initiators were because the biologic was no longer required (47% vs 41%) and lack of efficacy (20% vs 24%) (Figure 2C).

Table 1.

Baseline Demographics and Disease Characteristics Among Patients With AS at the Index Date.

.CharacteristicSecukinumab(N = 82)TNFi(N = 160)SMD*PValueAge, mean (SD), years47.4 (12.8)49.8 (14.6)0.170.21Female, n (%)43 (52)90 (56)0.080.57Race/ethnicity, n (%)N = 65N = 1290.200.66White52 (80)106 (82)Hispanic8 (12)13 (10)Black3 (5)8 (6)Asian1 (2)2 (2)Other1 (2)0Geographic distribution, n (%)0.370.05South49 (60)113 (71)Midwest27 (33)28 (18)West5 (6)16 (10)Northeast1 (1)3 (2)Health insurance, n (%)N = 79N = 1560.410.39Commercial57 (72)94 (60)Medicare8 (10)33 (21)Medicaid2 (3)4 (3)Other12 (15)25 (16)HLA-B27 positivity, n (%) [N]14 (54) [N = 26]31 (56) [N = 55]0.051.00Body mass index, mean (SD), kg/m230.6 (6.1)30.9 (7.7)0.030.82

SMD, standardized mean difference.

* Comparisons with SMD > 0.1 were suggestive of clinically relevant differences.

Conclusion:

Secukinumab initiators with AS were younger and more opioid and biologic experienced, were more likely to have a PsA diagnosis, and were more likely to discontinue their previous biologic because the biologic was no longer required compared to patients who initiated TNFis.

Acknowledgments:

This study was funded by Novartis Pharmaceuticals Corporation, East Hanover, NJ. Support for third-party writing assistance for this abstract, furnished by Kheng Bekdache, PhD, of Health Interactions, Inc, was provided by Novartis Pharmaceuticals Corporation, East Hanover, NJ.

Disclosure of Interests:

Howard Busch Speakers bureau: AbbVie, Amgen, Crescendo, Exagen, Genentech, Mallinckrodt, Novartis, Primus, Sanofi/Regeneron, and UCB, Jeffrey Curtis Grant/research support from: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, UCB, Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, UCB, Peter Hur Employee of: Novartis Pharmaceuticals Corporation, Esther Yi Employee of: Novartis Pharmaceuticals Corporation

AB0710 PATIENT PERCEPTIONS OF FIBROMYALGIA SYMPTOMS AND THE OVERLAP WITH AXIAL SPONDYLOARTHRITIS

Background:

In clinical practice, it is often challenging to distinguish fibromyalgia syndrome (FMS) from axial spondyloarthritis (axSpA), which includes ankylosing spondylitis and non-radiographic axSpA.1,2Early stages of axSpA may present with an onset similar to FMS,3and likewise patients with FMS may have symptoms that are similar to axSpA. Differentiating between axSpA and FMS can also be challenging for patients and cause confusion about their diagnosis.

Objectives:

To examine the prevalence of axSpA symptoms among patients with FMS and differences in the pathway to diagnosis among patients with and without concomitant axSpA.

Methods:

Adult US patients with FMS without concomitant rheumatoid arthritis or psoriatic arthritis in the ArthritisPower registry received email invitations to participate. Participants (pts) were asked whether they had a diagnosis of axSpA or ankylosing spondylitis and completed patient-reported outcome measures including Patient Reported Outcomes Measurement Information System (PROMIS) measures for Pain Interference, Sleep Disturbance and Fatigue, and the Bath Ankylosing Spondylitis Disease Activity Index (BASDAI). Pts then responded to a 57-item customized survey developed by the researchers in collaboration with patient partners. Results are descriptively reported.

Results:

As of January 2020, 231 pts completed the survey; 97% female, 89% White, mean (SD) age of 52 (11). Mean (SD) Pain Interference score was 68 (5); Sleep Disturbance 63 (8); Fatigue 68 (7); and BASDAI 46 (9). Of the pts, 40 (17%) reported concomitant axSpA, 64% osteoarthritis, 6% gout, 5% Crohn’s or ulcerative colitis, and 4% lupus. Half of all pts perceived their FMS to be ‘rarely’ or ‘never’ well managed and 80% felt that they have had an undiagnosed condition in addition to their FMS and their other current diagnoses. Three-fourths (75%) of pts reported being able to tell the difference between their FMS pain and pain they experience as a part of the concomitant disorder. Back pain lasting >3 months was reported by 95% of axSpA pts and 94% of non-axSpA pts and 12% reported all of the symptoms consistent with patient reported versions of the Assessment of SpondyloArthritis International Society (ASAS) criteria (back/buttock pain >3 months; age of symptom onset <45; sacroiliitis diagnosis; at least on spondyloarthritis feature) (Figure 1), and of these, 39% reported an axSpA diagnosis. More pts with axSpA received their FMS diagnosis by a rheumatologist (45%) than without (41%) (Figure 2), and of the pts without an axSpA diagnosis (n=191), only 6% had recalled their provider ever discussing with them the possibility of axSpA, including non-radiographic axSpA diagnosis. Half (53%) of pts with axSpA believe that their axSpA should have been diagnosed earlier, with 33% reporting that one reason for the delay was their doctors’ belief that FMS was the cause of any axSpA symptoms they experienced.

Conclusion:

Patients with FMS often experience symptoms of axSpA and the two conditions can occur concomitantly. Additional research is needed to improve the triage, diagnosis, and education of patients with FMS and symptoms of axSpA.

References:

[1]Roussou E, et al. Clin Ex Rheum Suppl. 2012;30(74):24-30.

[2]Kaskari D, et al. Mod Rheum. 2017;27(5):875-880.

[3]Hauser W, et al. Pain Rep. 2017;2(3):e598.

Disclosure of Interests:

Kelly Gavigan: None declared, W. Benjamin Nowell: None declared, Regan Reynolds: None declared, Laura Stradford: None declared, Jeffrey Curtis Grant/research support from: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, UCB, Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, UCB, Alexis Ogdie Grant/research support from: Pfizer, Novartis, Consultant of: Abbvie, Amgen, BMS, Celgene, Corrona, Janssen, Lilly, Pfizer, Novartis

FRI0057 A MODEL FOR QUANTIFYING THE EFFECT OF INFLAMMATION ON CARDIOVASCULAR DISEASE RISK PREDICTION IN RA PATIENTS

Background:

Patients with rheumatoid arthritis (RA) are at increased risk for cardiovascular disease (CVD)[1]. Quantifying the effect of inflammation on CVD risk is important because rheumatologists can reduce inflammation with effective RA medications. A new score has been developed for predicting the risk for a CVD event (MI, stroke or CV death) in RA patients. It combines serological measures of inflammation (the multi-biomarker disease activity [MBDA] score, a measure of RA disease activity; and three individual biomarkers [TNF-RI, MMP-3 and leptin]), with age and four conventional CVD risk factors (smoking, hypertension, diabetes and history of a high- risk CVD condition)[2]. To gain insight into the potential effect that treating inflammation may have on the CVD risk score, it would be useful to know how the score is affected by the level of inflammation.

Objectives:

Explore the quantitative contribution of inflammation to CVD risk score in individual RA patients.

Methods:

To quantify the effect of inflammation on the CVD risk score across a range of MBDA scores, a commercial dataset of 177,486 RA patients with ≥2 MBDA tests between October 2010 and June 2019 was split 2:1 into training and validation datasets. Curves showing variation in the CVD risk score across the spectrum of all possible MBDA scores (1-100) were generated for canonical patient types differing in the number of conventional risk factors (0 to 4) and age (45, 55, 65, 75, 85 years). To generate these curves, the contributions of TNF-RI, MMP-3 and leptin to the CVD risk score were treated in aggregate (denoted the molecular score) and estimated using a linear regression model of the difference in molecular scores vs. the difference in MBDA scores. This model for the molecular score was fit in the training dataset, then in the full dataset, with dataset (training or validation) and the interaction between dataset and change in MBDA score included as additional predictor variables. The method was considered validated if the F-test for the interaction variable was not significant at the 0.05 level.

Results:

The model for estimating the molecular score from the MBDA scores was validated and shown to fit the data well (Figure 1). The estimated molecular score was applied to the CVD risk score algorithm to generate curves that show how CVD risk score varies with MBDA score for several distinct patient types. These curves demonstrate that the predicted 3-year CVD risk increases continuously and markedly with increasing level of inflammation, as represented by the MBDA score (Figure 2). Age and the number of conventional risk factors also affected the predicted CVD risk, with older patients (Figure 2a) and those with more conventional risk factors (Figure 2b) being at higher risk for a CVD event.

Conclusion:

The level of CVD risk predicted by a new prognostic test for RA patients depends not only on conventional risk factors, which are relatively time invariant, but also varies greatly due to inflammation, which can potentially be reduced with RA treatment.

References:

[1]Agca et al (2017).Ann Rheum Dis.76(1):17-28. doi: 10.1136/annrheumdis-2016-209775.

[2]Curtis JR, Xie F, Crowson CS et al. (2019) ACR meeting abstract #446

Disclosure of Interests:

Rotem Ben-Shachar Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Darl Flake Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Richard Bamford Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Brent Mabey Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Eric Sasso Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Jeffrey Curtis Grant/research support from: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, UCB, Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, UCB

AB0750 CLINICAL CHARACTERISTICS AND TREATMENT PATTERNS OF PATIENTS WITH PSORIATIC ARTHRITIS WHO WERE PRESCRIBED BIOLOGICS: DATA FROM THE COLUMBUS REPOSITORY

Background:

Real-world data from electronic health records (EHR) allow examination of treatment patterns and clinical practice behaviors for psoriatic arthritis (PsA).

Objectives:

To describe physician and patient characteristics, and treatment patterns of patients with PsA who initiated secukinumab and other biologics using data from the Columbus Repository.

Methods:

EHR data from adult patients with PsA who were prescribed a new biologic therapy between January 2018 and March 2019 (index date) were included from the Columbus Repository, which collects clinical records from a network of US rheumatology providers. Demographics, disease characteristics, and treatment patterns, as well as physicians’ characteristics, were reported for patients who were prescribed secukinumab vs other biologics (abatacept, adalimumab, etanercept, certolizumab pegol, golimumab, infliximab, infliximab-dyyb, infliximab-abda, ustekinumab, and ixekizumab). Treatment groups were mutually exclusive and only the most recently prescribed biologic was represented. Categorical variables were summarized using frequency counts and percentages and continuous variables were presented using means and standard deviations.

Results:

As of March 2019, 234 patients initiated secukinumab and 806 initiated other biologics for PsA treatment; 62 physicians prescribed biologics for PsA. Overall, 73% of physicians’ offices had a single provider contributing patients to the analysis, and 76% of physicians were located in the South US region. Secukinumab initiators were younger (55.2 vs 57.3 years), more likely to be male (44% vs 31%), and had higher BMI (34.0 vs 31.9 kg/m2) vs other biologic initiators. Almost all disease activity measures evaluated had a large proportion (> 80%) of missing data; among those with nonmissing data, secukinumab initiators had numerically higher mean (SD) RAPID3 score vs other biologic initiators (12.6 [6.5] vs 11.6 [7.1]). Overall, 70% of secukinumab initiators and 48% of other biologic initiators were biologic experienced (Figure 1). Comorbidities were similar between groups (Figure 2). The most common reasons for discontinuation of prior biologic were the biologic was no longer required and lack of efficacy (Table 1).

Table 1.

Treatment Patterns Among Patients With PsA at the Index Date

Secukinumab(N = 234)Other Biologic(N = 806)SMD*PValueReason for discontinuing prior biologic treatment, n (%)N = 164N = 3850.200.67No longer required64 (39)136 (35)Lack of efficacy28 (17)75 (19)Cost or administrative5 (3)10 (3)Side effects5 (3)9 (2)Lack of tolerability03 (1)Patient fear of side effects1 (1)0Other25 (15)63 (16)Missing36 (22)89 (23)Prior medication use, n (%)NSAIDs109 (47)365 (45)0.030.78Opioids89 (38)252 (31)0.140.06Steroids68 (29)265 (33)0.080.31DMARDsMethotrexate83 (35)340 (42)0.140.08Sulfasalazine25 (11)93 (12)0.030.81Apremilast53 (23)104 (13)0.26< 0.01Tofacitinib10 (4)36 (4)0.011.00No. of prior biologics, mean (SD)0.95 (0.82)0.62 (0.77)0.41< 0.01

SMD, standardized mean difference.

* Comparisons with SMD > 0.1 were suggestive of clinically relevant differences.

Conclusion:

Secukinumab initiators with PsA were more likely to be male and biologic experienced, have a higher BMI and higher RAPID3 scores indicative of more active disease vs those initiating other biologics. Additional structured and unstructured elements may need to be captured on EHR platforms to gain clarity on disease activity and treatment decisions.

Acknowledgments:

This study was funded by Novartis Pharmaceuticals Corporation, East Hanover, NJ. Support for third-party writing assistance for this abstract, furnished by Kheng Bekdache, PhD, of Health Interactions, Inc, was provided by Novartis Pharmaceuticals Corporation, East Hanover, NJ.

Disclosure of Interests:

Howard Busch Speakers bureau: AbbVie, Amgen, Crescendo, Exagen, Genentech, Mallinckrodt, Novartis, Primus, Sanofi/Regeneron, and UCB, Jeffrey Curtis Grant/research support from: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, UCB, Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, UCB, Peter Hur Employee of: Novartis Pharmaceuticals Corporation

SAT0370 TUMOUR NECROSIS FACTOR INHIBITOR THERAPY DOES NOT REDUCE THE INCIDENCE OF COMORBIDITIES AND EXTRA-ARTICULAR MANIFESTATIONS IN ANKYLOSING SPONDYLITIS: AN ANALYSIS OF THREE US CLAIMS DATABASES

Background:

Comorbidities and extra-articular manifestations (EAMs) substantially increase disease burden and mortality risk in patients (pts) with ankylosing spondylitis (AS).1,2Tumour necrosis factor inhibitors (TNFi) are highly efficacious and effective in AS treatment (tx), and are used after inadequate response to non-steroidal anti-inflammatory drugs.3,4However, the impact of TNFi on the incidence of comorbidities and EAMs in pts with AS is unknown.5

Objectives:

To determine the incidence of comorbidities and EAMs in TNFi vs non-TNFi treated pts with AS in the US.

Methods:

This was a retrospective, observational cohort study using data from 3 healthcare insurance claims databases: Multi-Payer Claims Database (MPCD Optum Insight; 2007–2010), Truven MarketScan®(2010–2014) and US Medicare Fee-for-Service Claims database (2006–2014). Eligible pts: ≥20 years (yrs) for MarketScan/MPCD or ≥65 yrs for Medicare, had an AS diagnosis (≥2 International Classification of Disease, 9thversion [ICD-9] diagnosis codes of 720.0 from a rheumatologist) and ≥12 months’ continuous medical and pharmacy enrolment prior to AS diagnosis (AS index date). Pts with AS not receiving tx were excluded. Tx exposure was reported from the first date of a new prescription/administration of an AS tx (no prior exposure) after the AS index date. Crude incidence rates (IR; shown as cases/100 pt-yrs) were calculated for EAMs (uveitis, psoriasis [PSO], psoriatic arthritis [PsA], inflammatory bowel disease [IBD]), with follow-up until the earliest of: death, lost medical/pharmacy coverage, study period end, first outcome occurrence, tx switch/discontinuation. Hazard ratios (HRs) of comorbidities (hospitalised infection, solid cancers) and EAMs for propensity score (PS)-matched pt groups were calculated using Cox proportional hazard regression models. Pts with the specific comorbidity/EAM of interest prior to AS index date were excluded. PS analyses assessed probability of TNFi initiation vs non-TNFi tx and adjusted for factors including comorbidities and demographics. HRs with confidence intervals crossing 1 are not reported.

Results:

20,460 pts with AS were eligible (MPCD: 2,384; MarketScan: 9,032; Medicare: 9,044). In all databases, crude IR of EAMs were higher for TNFi vs non-TNFi treated pts (Figure 1). In the PS-matched cohort, incidences of hospitalised infections were lower in TNFi vs non-TNFi treated pts from the MarketScan and Medicare databases (Figure 2). Higher incidences of solid cancers and EAMs were observed in TNFi vs non-TNFi treated pts; Medicare data (Figure 2). A higher risk of PsA and PSO was seen in TNFi vs non-TNFi treated pts; MarketScan data (Figure 2). PS-matched cohort data from the MPCD database were non-significant.

Conclusion:

Despite strong efficacy in treating AS-related signs and symptoms, similar incidence of comorbidities and increased incidence of some EAMs (IBD, PSO/PsA, uveitis) was seen in TNFi vs non-TNFi treated pts in the PS-matched analyses. This may be due to channelling of pts with more severe AS to receive TNFi, despite the PS-matched analysis aiming to overcome this. Moreover, prior medical history of Medicare pts may not be captured in the database, as pts are typically older with longer disease durations. While these results confirm previous findings,6a prospective observational study is required to generalise to pts outside the US.

References:

[1]Stolwijk C. Ann Rheum Dis 2015;74:1373–8;

[2]Bremander A. Arthritis Care Res 2011;63:550–6;

[3]Braun J. Scand J Rheumatol 2005;34:178–90;

[4]Ji X. Front Pharmacol 2019;10:1476;

[5]Maxwell LJ. Cochrane Database Syst Rev 2015:CD005468;

[6]Walsh J. J Pharm Health Serv Res 2018;9:115–21.

Acknowledgments:

This study was funded by UCB Pharma. Editorial services were provided by Costello Medical.

Disclosure of Interests:

Atul Deodhar Grant/research support from: AbbVie, Eli Lilly, GSK, Novartis, Pfizer, UCB, Consultant of: AbbVie, Amgen, Boehringer Ingelheim, Bristol Myer Squibb (BMS), Eli Lilly, GSK, Janssen, Novartis, Pfizer, UCB, Speakers bureau: AbbVie, Amgen, Boehringer Ingelheim, Bristol Myer Squibb (BMS), Eli Lilly, GSK, Janssen, Novartis, Pfizer, UCB, Kevin Winthrop Grant/research support from: Bristol-Myers Squibb, Consultant of: AbbVie, Bristol-Myers Squibb, Eli Lilly, Galapagos, Gilead, GSK, Pfizer Inc, Roche, UCB, Rhonda Bohn Consultant of: UCB Pharma, Benjamin Chan: None declared, Robert Suruki Employee of: UCB Pharma, Jeffrey Stark Employee of: UCB Pharma, Huifeng Yun Grant/research support from: Bristol-Myers Squibb and Pfizer, Sarah Siegel: None declared, Lang Chen: None declared, Jeffrey Curtis Grant/research support from: AbbVie, Amgen, Bristol-Myers Squibb, Corona, Crescendo, Genentech, Janssen, Pfizer, Roche and UCB Pharma, Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, Corona, Crescendo, Genentech, Janssen, Pfizer, Roche and UCB Pharma

Psychiatry heal thyself: a lifestyle intervention targeting mental health staff to enhance uptake of lifestyle interventions for people prescribed antipsychotic medication

Introduction

People experiencing severe mental illness (SMI) face a shortened life expectancy of up to 20 years, primarily due to preventable cardiovascular (CV) diseases. Lifestyle interventions are effective in reducing CV risk, yet examples of service-wide interventions are lacking. Staff culture remains a barrier to the successful implementation of lifestyle interventions. The Keeping the Body in Mind (KBIM) program, established by SESLHD (Australia), aims to close the gap in life expectancy through multidisciplinary teams, including clinical nurse consultants, dieticians, exercise physiologists, and peer support workers. Prior to the KBIM rollout, an individualized lifestyle intervention called Keeping Our Staff In Mind (KoSiM) was offered to all district mental health staff.

Objective

KoSiM examined the effectiveness of a staff intervention to improve physical health, confidence, knowledge and attitudes of mental health staff.

Methods

Mental health staffs were invited to participate in an online survey and a 4-week individualized intervention including personalised health screening and lifestyle advice, with a 16-week follow-up. Outcomes assessed included: attitudes, confidence and knowledge regarding metabolic health, weight, waist circumference (WC), blood pressure, sleep, diet, physical activity and exercise capacity.

Results

Of a total of 702 staff, 204 completed the survey (29%). Among those completing the survey, 154 staff (75%) participated in the intervention. A mean decrease in waist circumference of 2 ± 2.7 cm, (P < 0.001) was achieved. Among staffs that were overweight or obese at baseline, 75% achieved a decrease in WC.

Conclusion

Improving staff culture regarding physical health interventions is an important step in integrating lifestyle interventions into routine care.

Disclosure of interest

The authors have not supplied their declaration of competing interest.

Meta-analysis of natural, unnatural and cause-specific mortality rates following discharge from in-patient psychiatric facilities

BACKGROUND

People discharged from in-patient psychiatric facilities have highly elevated rates of suicide, and there is increasing concern about natural mortality among the seriously mentally ill.

METHOD

A meta-analysis of English-language, peer-reviewed longitudinal studies of mortality among patients discharged from in-patient psychiatric facilities was conducted using papers published in MEDLINE, PsycINFO or EMBASE (from 1 January 1960 to 1 April 2018) located using the terms ((suicid*).ti AND (hospital OR discharg* OR inpatient OR in-patient OR admit*)).ab and ((mortality OR outcome* OR death*) AND (psych* OR mental*)).ti AND (admit* OR admis* OR hospital* OR inpatient* OR in-patient* OR discharg*).ab. Pooled mortality rates for aggregated natural and unnatural causes, and the specific causes of suicide, accident, homicide, vascular, neoplastic, respiratory, gastrointestinal, infectious and metabolic death were calculated using a random-effects meta-analytic model. Between-study heterogeneity was investigated using subgroup analysis and metaregression.

RESULTS

The pooled natural death rate of 1128 per 100 000 person-years exceeded the pooled unnatural deaths of 479 per 100 000 person-year among studies with varying periods of follow-up. Natural deaths significantly exceeded unnatural deaths among studies with a mean follow-up of longer than 2 years, and vascular deaths exceeded suicide deaths among studies with mean period of follow-up of 5 years or longer.

CONCLUSION

Suicide may be the largest single cause of death in the short term after discharge from in-patient psychiatric facilities but vascular disease is the major cause of mortality in the medium- and long-term.