A large veteran's hospital participated in a year-long collaborative project across 9 hospitals to reduce serious injury from falls in acute care, targeting medical-surgical units. The primary objective of this project was to develop and test a set of interventions (bundles) to prevent serious physical injury (fractures and hemorrhagic bleeds) from patient falls. The interventions were implemented using tests of change on 2 medical-surgical units focused on engaging unit-based staff and combining innovations for vulnerable populations at greatest risk for injury if they fall.

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Background:Apremilast (APR), an oral phosphodiesterase 4 (PDE4) inhibitor, modulates inflammatory mediators1and has demonstrated efficacy in treating oral ulcers in a phase III Behçet’s syndrome study (BCT-002 [RELIEF]).2Objectives:To conduct an exploratory analysis of genetic polymorphisms, plasma biomarkers, and blood leukocytes with clinical response in RELIEF.Methods:Subjects with active Behçet’s disease (BD) were randomized (1:1) to APR 30 mg twice daily or placebo (PBO). The primary clinical efficacy endpoint was the area under the curve for the number of oral ulcers through Week 12 (AUCWk0-12). Among the 207 subjects enrolled, 140 provided consent for DNA genotyping, 116 for plasma biomarker testing, and 96 for leukocyte subset testing. Genotyping was performed on the Illumina Omni2.5 BeadChip (Covance Genomics Laboratory). TNF-α, IL-6, interferon-γ, and IL-17A levels were measured using Simoa Single Molecule Array; IL-8 and IL-23 were measured using the Human DiscoveryMAP multiplex panel (Myriad RBM). Th17, Treg, and CD3 T cells were counted using bisulfite-specific RT-PCR (Epiontis Gmbh). A rank ANCOVA model was used to estimate between-treatment differences (APR vs. PBO) in percent change from baseline for each biomarker/leukocyte subtype over the 12 weeks of treatment. Within each treatment group, the correlation of percent change from baseline at Week 12 in biomarker/leukocyte subtype with the primary efficacy endpoint AUCWk0-12was examined using a univariate regression model. A separate regression model was used to assess the interaction between treatment and the biomarker/leukocyte subtype clinical response.Results:Pharmacogenetic analysis of BD risk variants in HLA-B, IL-10, TLR2, ACE, TNF, GIMAP, PDGFRL, and UBAC2 + 55 genes associated with PDE4 biology yielded no candidate variants that were significantly associated with response to APR or PBO at a Bonferroni-correctedPvalue of 2 x 10−6. Clinical response to APR with respect to HLA-B51 yielded an odds ratio (OR) of 1.21 (95% CI, 0.53-2.75), indicating no significant relationship (Figure 1). Pharmacodynamic changes for IL-6, IL-3, IL-17A, IL-23, and TNF-α were not statistically significant. APR treatment was associated with a significant change in interferon-γ (mean: +107.4%; median: −19.2%) vs. PBO (mean: +78.8%; median: +7.9%) (P=0.0077). Using a univariate regression model, TNF-α showed strong positive correlation with AUCWk0-12in the APR group (r=0.90;P=0.0140); IL-8 had weak positive correlation with AUCWk0-12in the APR group (r=0.04;P=0.0333). A significant negative correlation was observed between the percent change from baseline in the number of Th17 cells and AUCWk0-12in the APR group (r=−0.79;P=0.0392) and a significant positive correlation was observed with the percent change from baseline in the number of Treg cells and AUCWk0-12in the PBO group (r=0.94;P=0.0182). Of all the biomarkers and leukocyte subtypes examined in a regression model using treatment as a factor, only Treg had a statistically significant treatment interaction (P=0.0069).Conclusion:Although there were no genetic predictors of clinical response to APR treatment, strong correlation was observed between the percent change from baseline in plasma TNF-α with AUCWk0-12in the APR group. A negative correlation was observed between percent change from baseline in Th17 cells and AUCWk0-12in the APR group and a positive association was observed between Treg cells and AUCW0-12in the PBO group.References:[1]Schafer P.Biochem Pharmacol. 2012; 83:1583-1590. 2. Hatemi G, et al. Presented at: ACR/ARHP Annual Meeting; November 8–13, 2019; Atlanta, GA. Presentation 0946.Disclosure of Interests: :Joseph Maranville Employee of: Celgene Corporation – employment at the time of study conduct, Irina Medvedeva Employee of: Celgene Corporation – employment at the time of study conduct, Robert Yang Employee of: Celgene Corporation – employment at the time of study conduct, Mindy Chen Employee of: Amgen Inc. – employment; Celgene Corporation – employment at the time of the conduct, Lorraine (Ruoying) Fang Employee of: Celgene Corporation – employment at the time of study conduct, Sandra Collazo Employee of: Amgen Inc. – employment; Celgene Corporation – employment at the time of the conduct, Shannon McCue Employee of: Amgen Inc. – employment; Celgene Corporation – employment at the time of the conduct, Peter Schafer Employee of: Bristol-Myers Squibb – employment; Celgene Corporation – employment at the time of study conduct