Seattle Institute for Biomedical and Clinical Research

Children with medical complexity (CMC) have chronic conditions often involving technology assistance, care from family caregivers and clinicians, and substantial health care needs. Previous studies have identified communication gaps between family caregivers of CMC and their child's clinicians, as well as a need for clinicians to better understand their lived experience. Approaches using photos may be a way for CMC and their family caregivers to share their experience with clinicians. To conduct a scoping review of the literature and describe findings from studies using photo-based approaches with CMC, their family caregivers, and clinicians. PubMed, PsycInfo, and Web of Science were searched following Joanna Briggs Institute scoping review guidelines. Abstract screening and full-text reviews were performed using Covidence software. Studies were eligible if they: (1) included CMC up to age 25, and/or their family caregivers, and/or clinicians; (2) used a photo-based approach (photo-elicitation, photovoice, photo-narrative); (3) were published in the last 20 years; and (4) were written in English. One hundred twenty-three studies were identified, including 83 identified through an initial search with a research center librarian, with additional studies identified through hand-searching. A final of 25 studies were included. Participants found photo-based approaches to sharing their experience to be both useful and enjoyable. A synthesis of the data presented in these studies yielded nine themes/dimensions describing the multidimensional experience of CMC, their family caregivers, and clinicians. These dimensions were organized into three areas: relational (independence vs. dependency, deepening relationships vs. straining relationships, and inclusion vs. stigmatization), navigational (feeling typical vs. feeling different, advocacy vs. subjugation, and participation vs. barriers), and emotional (predictability vs. uncertainty, well-being vs. exhaustion, and growth vs. loss). This scoping review describes how photo-based approaches may be used to explore the multidimensional experience of CMC, their family caregivers, and clinicians.

BackgroundAlzheimer's Disease and related dementias (ADRD) are under diagnosed and that under diagnosis if detrimental to patients, their families and the VA Health Care System. This crisis of under diagnosis exacerbates existing disparities in healthcare, disproportionately affect Black Americans (BAs) compared to White Americans (WAs). Using a machine learning (ML) model to assign ADRD‐risk scores may help to identify Veterans with undiagnosed ADRD and their risk of developing dementia in the near future.MethodWe previously developed a ML model based on 850+ variables extracted from structured and unstructured data from the VHA's vast electronic health records, using a training sample of 20,000 BA and 20,000 WA Veterans. Kaplan‐Meier curves were calculated separately for race and ML score quintile group. A Cox‐proportions hazards model was used to obtain an estimate of the hazard ratio (HR) for the risk of developing ADRD.ResultThe HR comparing the 75th to the 25th percentile of scores is estimated to be 2.20 (95% CI, 1.93‐2.51, p < 0.01). The Kaplan‐Meier plot showed differences in risk for converting to ADRD between races; for BAs, the curves of the highest two quintiles have visibly steeper slopes than those for other quintiles; for WAs, only the highest quintile have visibly steeper slopes than the other quintiles.ConclusionHigher ML scores were associated with higher risk of developing ADRD in the years following the index date. BAs in our sample were at a higher risk for developing ADRD compared to their WA counterparts with the same score.

Glucagon-like peptide-1 receptor agonists (GLP-1 RA) have emerged as a leading pharmacologic for managing weight gain across most populations, including peri and postmenopausal women who frequently suffer from weight gain. There is a paucity of data about this specific population and how they respond to these medications. This review aims to discuss the data available about the use and effects of GLP-1 RAs in the peri and postmenopausal populations. GLP-1 RAs are consistently the most effective pharmacologic for weight loss and can be a valuable tool for use in peri and postmenopausal women. Additional research is needed to determine the risks and benefits and ideal use of GLP-1 RAs in peri and postmenopausal women.

Abstract Disclosure: B.M. Biller: Consulting Fee; Self; HRA Pharmaceuticals, Novartis Pharmaceuticals, Sparrow, Strongbridge Biopharma. Grant Recipient; Self; Strongbridge Biopharma. A. Gilis-Januszewska: Speaker; Self; Ipsen, Novartis Pharmaceuticals, Novo Nordisk, Recordati, Teva Pharmaceutical Industries Ltd.. M. Doknic: None. A.M. Pico: None. M. Fleseriu: Consulting Fee; Self; Ascendis, GlaxoSmithKline, Johnson &amp;Johnson, Karyopharm, Novo Nordisk, Pfizer, Inc., Pharmacosmos. Research Investigator; Self; Ascendis. G. Raverot: None. A.M. Isidori: None. Y. Takahashi: None. J.M. Garcia: None. J.M. Silverstein: None. I. Bancos: None. E. Huang: Employee; Self; Ascendis Pharma, Inc. J. Kang: Employee; Self; Ascendis Pharma, Inc. A.S. Komirenko: Employee; Self; Ascendis Pharma, Inc. L. Domrzalski: Employee; Self; Ascendis Pharma, Inc. A. Shu: Employee; Self; Ascendis Pharma, Inc. K. Yuen: Advisory Board Member; Self; Amryt, Ascendis, Corcept, Crinetics, Novo Nordisk, Recordati, Strongbridge, Xeris. Grant Recipient; Self; Amryt, Ascendis, Corcept, Crinetics. Speaker; Self; Corcept, Novo Nordisk, Recordati. Background: Adult GHD (aGHD) is characterized by metabolic abnormalities due to insufficient growth hormone (GH) production. Lonapegsomatropin was designed to provide sustained release of unmodified somatropin, with the identical amino acid sequence as endogenous GH, and once-weekly injection reduces the burden of daily GH replacement therapy. MethodsForesiGHt was a randomized, parallel-3-arm, placebo(PL)-controlled (double-blind) and active-controlled (open-label) trial designed to establish the efficacy and safety of lonapegsomatropin in aGHD. It evaluated 259 adults with GHD across 21 countries who were GH treatment-naïve or not treated with GH in the prior year, randomized 1:1:1 to receive lonapegsomatropin, once-weekly PL, or daily somatropin (dGH). Fixed dosing was based on age and oral estrogen intake, and designed to be comparable across lonapegsomatropin and dGH arms. Following a 12-week (w) titration period to target maintenance dose, fixed doses were administered for 26w. ResultsBaseline (BL) characteristics were similar between arms. Lonapegsomatropin demonstrated superiority on the primary efficacy endpoint of change from BL in trunk percent fat at Week 38 vs PL (lonapegsomatropin -1.7% vs PL 0.4%, LS mean difference -2.0%, p &amp;lt; 0.0001) and on the key secondary efficacy endpoints of change from BL in total body lean mass (lonapegsomatropin 1.6 kg vs PL -0.1 kg, LS mean difference 1.7 kg, p &amp;lt; 0.0001) and change from BL in trunk fat mass (lonapegsomatropin -0.5 kg vs PL 0.2 kg, LS mean difference -0.7 kg, p = 0.0053). Mean total exposure and maintenance doses were similar for lonapegsomatropin and dGH arms, with larger changes from BL in average IGF-I SDS mean at Week 38 in the lonapegsomatropin arm (1.4) vs dGH arm (0.5). To assess outcomes at comparable weekly IGF-I exposure, a post-hoc analysis was performed in a subset of participants with average IGF-I SDS ≤1.75 SDS at Week 38. Mean IGF-I SDS for these subsets were similar (lonapegsomatropin -0.1, dGH -0.5), and comparable effects in fat and lean tissue compartments were demonstrated (change from BL in trunk percent fat mean -2.4% and -2.6% respectively; change from BL in total body lean mass mean 1.7 kg and 1.4 kg respectively). In the safety population, the incidence of severe AEs was low (lonapegsomatropin, 3.4%; placebo, 1.2%; dGH, 2.3%) and the incidence of treatment-related AEs was similar (lonapegsomatropin 24.7%, dGH 22.1%). Injection site reaction incidence was low and similar for lonapegsomatropin (4.5%), dGH (5.8%) and placebo (4.8%), and A1c levels remained stable in all treatment arms. ConclusionsThe results of the foresiGHt trial indicate that lonapegsomatropin is a safe, efficacious, and tolerable replacement for endogenous GH. Once-weekly dosing may be impactful for adults with GHD who typically manage multiple other medical therapies. Presentation: 6/3/2024

Abstract Disclosure: L. Caeiro: None. L.J. Anderson: None. H. Liu: None. J.M. Garcia: None. Androgen deprivation therapy (ADT) is the primary treatment for advanced and metastatic prostate cancer. However, it induces adverse effects like reduced muscle mass and function, increased adiposity, and insulin resistance (IR). While muscle wasting is a known complication of ADT-induced hypogonadism, the extent to which other factors determine the variability in muscle loss remains unknown. In other diseases, IR is associated with muscle wasting, but its role in this setting of ADT has yet to be addressed. We hypothesized that patients with greater IR pre-ADT would display larger decreases of muscle mass and function after six months of ADT. Insulin and glucose (to calculate HOMA-IR), body composition, and physical function were assessed in patients with prostate cancer prior to and after three and six months of ADT. Body composition was assessed by dual x-ray absorptiometry and physical function by VO2 peak and 6-minute walk test (6MWT). Fasting blood samples were obtained to measure insulin and glucose in plasma. Pre-ADT, HOMA-IR was directly correlated with body mass index (BMI) (r=0.65, p&amp;lt;0.001, n=49), fat mass (r=0.55, p&amp;lt;0.001, n=49), and appendicular skeletal muscle index (ASMI) (r=0.33, p=0.021, n=49) and was inversely correlated with VO2 peak (r= -0.39, p=0.007, n=46). After three months, fat mass (p=0.015) and IR (p=0.0014) increased and VO2 peak decreased (p=0.0007), which persisted at the six-month visit. Appendicular lean mass and ASMI decreased after six months (p&amp;lt;0.0001). Pre HOMA-IR was negatively associated with delta BMI (r=-0.49, p&amp;lt;0.001, n=46), delta lean mass (r=-0.58, p&amp;lt;0.001, n=46), delta ASMI (r=-0.52, p&amp;lt;0.001, n=46), and delta 6MWT (r=-0.37, p=0.011, n=45) and positively correlated with delta VO2 peak (r=0.44, p=0.007, n=36). No associations were found with Pre-ADT HOMA-IR and changes in fat mass. In conclusion, those with greater Pre-ADT HOMA-IR lost more muscle mass, body weight, and 6MWT while unexpectedly showing less decrease in VO2 peak. This data suggests that HOMA-IR could potentially serve as a predictor of muscle mass loss in patients undergoing ADT. We propose that the significant decrease in muscle mass seen with ADT can be exacerbated by pre-existing IR. Presentation: 6/3/2024

Large (De Serres stage [IV-V]) head and neck lymphatic malformations (HNLMs) often have multiple, high-risk, invasive treatments (ITs) to address functional compromise. Logically reducing HNLM ITs should reduce treatment risk. We tested whether delaying HNLM ITs reduces total IT number. Consecutive HNLM patients (n= 199) between 2010 and 2017, aged 0-18 years. ITs (surgery or sclerotherapy) were offered for persistent or dysfunction causing HNLMs. Treatment effectiveness categorized by IT number: optimal (0-1), acceptable (2-5), or suboptimal (>5). Clinical data were summarized, and outcome associations tested (χ2 ). Relative risk (RR) with a Poisson working model tested whether HNLM observation or IT delay (>6 months post-diagnosis) predicts treatment success (i.e., ≤1 IT). Median age at HNLM diagnosis was 1.3 months (interquartile range [IQR] 0-45 m) with 107/199(54%) male. HNLM were stage I-III (174 [88%]), IV-V (25 [13%]). Initial treatment was observation (70 [35%]), invasive (129 [65%]). Treatment outcomes were optimal (137 [69%]), acceptable (36 [18%]), and suboptimal (26 [13%]). Suboptimal outcome associations: EXIT procedure, stage IV-V, oral location, and tracheotomy (p< 0.001). Stage I-III HNLMs were initially observed compared with stage I-III having ITs within 6 months of HNLM diagnosis, had a 82% lower relative treatment failure risk ([i.e., >1 IT], RR=0.09, 95% CI 0.02-0.36, p< 0.001). Stage I-III HNLMs with non-delayed ITs had reduced treatment failure risk compared with IV-V (RR=0.47, 95% CI 0.33-0.66, p< 0.001). Observation and delayed IT in stage I-III HNLM ("Grade 1") is safe and reduces IT (i.e., ≤1 IT). Stage IV-V HNLMs ("Grade 2") with early IT have a greater risk of multiple ITs. 4 Laryngoscope, 133:956-962, 2023.

Promoting Resilience in Stress Management (PRISM), a psychosocial intervention for adolescents and young adults (AYAs) with serious illness, enhances resilience resources via four skills-based training sessions. A recent randomized controlled trial showed PRISM improved health-related quality of life (HRQOL) compared to usual care (UC). This post hoc exploratory analysis aimed to better understand the effect of PRISM on HRQOL by describing changes in HRQOL subdomain scores. English-speaking AYAs (12–25 years) with cancer were randomized to PRISM or UC. At enrollment and six months later, HRQOL was assessed using the Pediatric Quality of Life Inventory (PedsQL) Generic Short Form (SF-15) and Cancer Module. Scores at each time point were summarized descriptively and individual HRQOL trajectories were categorized (<70 vs. ≥70). “Positive” trajectories indicate participants maintained scores ≥70 or improved from <70 to ≥70 during the study period. Baseline assessments were completed by 92 participants (48 PRISM, 44 UC); six-month assessments were completed by 74 participants (36 PRISM, 38 UC). For the SF-15, positive trajectories in psychosocial domains were more common with PRISM; trajectories in the physical subdomain were similar across groups. For the Cancer Module, positive trajectories were more common with PRISM in the following subdomains: nausea, treatment anxiety, worry, cognitive, physical appearance, and communication. From this, we conclude PRISM may improve HRQOL, especially in psychosocial domains of wellbeing.

Abstract High-dimensional single-cell cytometry is routinely used to characterize patient responses to cancer immunotherapy and other treatments. This has produced a wealth of datasets ripe for exploration but whose biological and technical heterogeneity make them difficult to analyze with current tools. We introduce a new interpretable machine learning method for single-cell mass and flow cytometry studies, FAUST, that robustly performs unbiased cell population discovery and annotation. FAUST processes data on a per-sample basis and returns biologically interpretable cell phenotypes that can be compared across studies, making it well-suited for the analysis and integration of complex datasets. We demonstrate how FAUST can be used for candidate biomarker discovery and validation by applying it to a flow cytometry dataset from a Merkel cell carcinoma anti-PD-1 trial and discover new CD4+ and CD8+ effector-memory T cell correlates of outcome co-expressing PD-1, HLA-DR, and CD28. We then use FAUST to validate these correlates in an independent CyTOF dataset from a published metastatic melanoma trial. Importantly, existing state-of-the-art computational discovery approaches as well as prior manual analysis did not detect these or any other statistically significant T cell sub-populations associated with anti-PD-1 treatment in either data set. We further validate our methodology by using FAUST to replicate the discovery of a previously reported myeloid correlate in a different published melanoma trial, and validate the correlate by identifying itde novoin two additional independent trials. FAUST’s phenotypic annotations can be used to perform cross-study data integration in the presence of heterogeneous data and diverse immunophenotyping staining panels, enabling hypothesis-driven inference about cell sub-population abundance through a multivariate modeling framework we callPhenotypic andFunctionalDifferentialAbundance (PFDA). We demonstrate this approach on data from myeloid and T cell panels across multiple trials. Together, these results establish FAUST as a powerful and versatile new approach for unbiased discovery in single-cell cytometry.