Abstract
Background and Objectives
Sustained implementation of new programs in complex care systems like nursing homes is challenging. This prospective qualitative evaluation examined factors ...affecting the sustainability of the Staff Training in Assisted Living Residences in Veterans Health Administration (STAR-VA) program in Veterans Health Administration (VA) Community Living Centers (CLC, i.e., nursing homes). STAR-VA is an evidence-based interdisciplinary, resident-centered, behavioral approach for managing distress behaviors in dementia.
Evaluation Design and Methods
In 2019, we conducted 39 semistructured phone interviews with STAR-VA key informants across 20 CLCs. We identified a priori themes based on the Organizational Memory Framework, which includes 7 Knowledge Reservoirs (KRs): people, routines, artifacts, relationships, organizational information space, culture, and structure. We conducted content-directed analysis of transcripts to identify factors to program sustainment.
Results
We identified 9 sustainment facilitators across KRs: engaged site leaders and champions, regular meetings and trainings, written documentation and resources, regular and open communication, available educational tools (e.g., handouts and posters), adequate spaces, leadership support on many levels, staff buy-in across disciplines, and staff competencies and recognition. Ten barriers across KRs included: staffing concerns, inconsistent/inefficient routines, inconsistent documentation, lack of written policies, communication gaps, nonstandardized use of tools, constraints with meeting spaces and regulations on posting information, limited leadership support, division among staff, and missing performance expectations.
Discussion and Implications
Findings inform tailored strategies for optimizing STAR-VA program sustainment in CLCs, including the development of a sustained implementation guide, implementation resources, regional communities of practice, and STAR-VA integration into national CLC quality improvement routines for team communication and problem-solving.
Evidence-based practices to manage distress behaviors in dementia (DBD) are not consistently implemented despite demonstrated effectiveness. The Veterans Health Administration (VA) trained teams to ...implement Staff Training in Assisted Living Residences (STAR)-VA, an intervention to manage DBD in VA nursing home settings, or Community Living Centers (CLCs). This paper summarizes multiyear formative evaluation results including challenges, adaptations, and lessons learned to support sustained integration into usual care across CLCs nationwide.
STAR was selected as an evidence-based practice for DBD, adapted for and piloted in VA (STAR-VA), and implemented through a train-the-trainer program from 2013 to 2018. Training and consultation were provided to 92 CLC teams. Evaluation before and after training and consultation included descriptive statistics of measures of clinical impact and survey feedback from site teams regarding self-confidence, engagement, resource quality, and content analysis of implementation facilitators and challenges.
STAR-VA training and consultation increased staff confidence and resulted in significant decreases in DBD, depression, anxiety, and agitation for Veterans engaged in the intervention. Implementation outcomes demonstrated feasibility and identified facilitators and barriers. Key findings were interpreted using implementation frameworks and informed subsequent modifications to sustain implementation.
STAR-VA successfully prepared teams to manage DBD and resulted in improved outcomes. Lessons learned include importance of behavioral health-nursing partnerships, continuous engagement, iterative feedback and adaptations, and sustainment planning. Evaluation of sustainment factors has informed selection of implementation strategies to address sustainment barriers. Lessons learned have implications for integrating team-based practices into system-level practice.
We identify factors associated with sustainment of an intervention (STAR-VA) to address distress behaviors in dementia (DBD), guided by the Organizational Memory Knowledge Reservoir (KR) framework, ...compared across 2 types of outcomes: (1) site performance improvement on a clinical outcome, the magnitude of change in levels of DBD, and (2) self-rated adherence to STAR-VA core components, a process outcome.
We used a cross-sectional sequential explanatory mixed methods design guided by the Organizational Memory Framework.
We selected 20 of 79 sites that completed STAR-VA training and consultation based on rankings on 2 outcomes-change in an indicator of DBD and reported adherence to STAR-VA core components. We recruited key informants most knowledgeable about STAR-VA resulting in a sample of 43% behavioral coordinators, 36% nurse champions, and 21% nurse leaders.
We collected data with key informants at each Community Living Center (CLC) from December 2018 to June 2019. We analyzed data using within-case and cross-case matrixes created from the coded transcripts for each a priori KR domain. We then assessed if there were any similarities or differences for CLCs in comparable DBD performance and STAR-VA adherence categories.
We found 4 KRs that differentiated sustainment factors based on CLC implementation process and clinical outcomes-3 KRs related to DBD performance (people, relationships, and routines) and 2 related to STAR-VA adherence (relationships and culture).
This evaluation found several knowledge retention mechanisms that differ in high and low performance/adherence sites. Our findings highlight knowledge retention/sustainment strategies based on site functioning to support sustainment strategies in the CLC. Understanding sustainment factors as they relate to clinical and process outcomes is innovative and can be used to support CLCs struggling with sustainment. More research is needed to inform tailored sustainment efforts based on site functioning in the nursing home setting.
Operational Risk is one of the most difficult risks to model. It is a large and diverse category covering anything from cyber losses to mis-selling fines; and from processing errors to HR issues. ...Data is usually lacking, particularly for low frequency, high impact losses, and consequently there can be a heavy reliance on expert judgement. This paper seeks to help actuaries and other risk professionals tasked with the challenge of validating models of operational risks. It covers the loss distribution and scenario-based approaches most commonly used to model operational risks, as well as Bayesian Networks. It aims to give a comprehensive yet practical guide to how one may validate each of these and provide assurance that the model is appropriate for a firm’s operational risk profile.
The objective of this study was to test and revise a staff assessment of person-centered care (PCC) within the Veterans Health Administration (VA) Community Living Center (CLC) setting.
Starting with ...measures of PCC initially developed through the Better Jobs Better Care (BJBC) study, we conducted cognitive interviews with CLC staff to assess applicability to the VA setting. We then (a) modified the questionnaire based on respondent feedback, (b) administered the revised survey via Internet to 265 staff at 8 VA CLCs, and (c) examined the psychometric properties of the revised 50-item BJBC PCC instrument using multitrait analysis.
Scale reliabilities met the criterion for group comparisons (alpha levels ranged from 0.84 to 0.91). The pattern of item correlations and intra- and interscale correlations indicating convergent and discriminant validity, respectively, were both 100%.
Our results support the broader use of the BJBC survey within VA. In addition, given the high levels of internal consistency reliability of the current scales, it is likely that a psychometrically sound short form of the instrument could be created. Further research on construct and convergent validity are warranted to support the broader application of the instrument.
Introduction: In myeloid neoplasms, spliceosome mutations are found in approximately 50% of patients and are often associated with a poor prognosis. The mutations occur in recurrent hotspot regions, ...although whether different point mutations affecting the same gene have the same impact remains uncertain. Furthermore, the mechanism through which spliceosome mutations exert a clonal advantage over wild-type cells in the hematopoietic stem/progenitor cell (HSPC) compartment remains poorly understood. Single-cell multiomic techniques combining mutation analysis with RNA-sequencing are a powerful approach to study the effect of specific mutations through an intra-patient comparison of mutant and wild-type HSPCs. Up to now, this approach has not been used to study the effects of specific spliceosome mutations. To this end, we developed experimental and computational pipelines to enable joint genotyping and alternative splicing analysis from the same single cell. We further demonstrated the utility of our approach to identify specific altered splicing events in HSPCs derived from myeloproliferative neoplasm (MPN) patients in association with specific U2AF1 hotspot mutations (S34 and Q157). Methods: >20,000 lineage-CD34+ HSPCs were derived from healthy controls and MPN patients. Among those included for analysis, six patients had JAK2V617F (n=5) or CALR mutations (n=1), of which, two patients had additional U2AF1S34 mutations and three of them, U2AF1Q157 mutations. Parallel single-cell genotyping and short-read RNA-sequencing were performed using TARGET-seq (Rodriguez-Meira et al., 2019). Subsequent alternative splicing analysis was performed using the MARVEL software (https://github.com/wenweixiong/MARVEL). On average, 10,600 splice junctions were detected per cell. Confirmation of candidate alternative splicing events was performed using Nanopore and PacBio long-read sequencing. Results: Single-cell genotyping revealed U2AF1S34 and U2AF1Q157 cells were highly clonal and constituted 85.4% (on average) of the entire HSPC compartment. In contrast, single JAK2V617F mutant patients showed a much more variable clonal burden in the HSPC compartment. We performed differential splicing analysis to identify aberrant splicing events in U2AF1S34 and U2AF1Q157 cells. Differential splicing analysis identified 423 of 2,979 (14.2%) and 1,041 of 6,857 (15.2%) splicing events that were differentially spliced between U2AF1S34 and U2AF1Q157 vs. U2AF1WT cells, respectively (Fig. A). Notably, using this single-cell approach, we successfully recapitulated previously identified U2AF1S34-mediated spliced genes including GNAS and H2AFY, as well as a number of novel aberrantly spliced genes in HSPCs. The repertoire of differentially spliced events was distinct for U2AF1S34 vs. U2AF1Q157 cells. Specifically, only 14 overlapping differentially spliced events were associated with both U2AF1S34 and U2AF1Q157 genotypes. In addition, GSEA analysis identified dysregulation of ribonucleoprotein processes exclusively in U2AF1S34 cells and dysregulation of cell cycle exclusively in U2AF1Q157 cells. This suggests that U2AF1S34 and U2AF1Q157 mutations regulate different molecular pathways in HSPCs and might give rise to clonal expansions through different mechanisms. We next performed long-read sequencing with Nanopore and PacBio to confirm the full isoform sequence of candidate splicing events (Fig. B). Long-read sequencing revealed GNAS to be differentially spliced in U2AF1 S34cells due to a combination of skipped-exon and variable 5‘ and 3‘ untranslated regions. The inclusion of the skipped-exon has been experimentally shown to hyperactivate Gαs protein and downstream ERK/MAPK signalling pathway (Wheeler et al., 2021). Each cell on average expressed two GNAS isoforms (range: 0 to 5). Notably, short-read RNA-sequencing would have limited capacity to detect these isoforms, thus highlighting the advantage of single-cell long-read RNA-sequencing in deconvoluting complex splicing events in the same cell. Conclusion: In this study we demonstrate the proof-of-principle of joint genotyping and alternative splicing analysis at single-cell resolution to identify disease-relevant splicing alterations. We anticipate our approach will enable and accelerate novel biomarker discovery with therapeutic implications in myeloid neoplasms and beyond.
SF3B1 is the most prevalent splicing factor mutated in myeloid neoplasms, detected in 20-30% of patients with myelodysplasia (MDS) and associated with a milder disease phenotype. SF3B1 mutations are ...typically single nucleotide variations occurring in hotspots in the HEAT domain. Disease-specific SF3B1 hotspot predilection is observed; for example, K700E is most common in MDS and R625 in uveal melanoma. The K666 hotspot has been associated with increased risk of MDS disease progression to acute myeloid leukemia (AML). However, the prognostic impact across all myeloid neoplasms inclusive of myeloproliferative neoplasms (MPN) and MDS/MPN overlap syndromes has not been established. Mutation information was collated from available datasets inclusive of unpublished clinical trial cohorts across patients with myeloid neoplasms (MDS, MPN, MDS/MPN, AML) and patients with solid malignancies and clonal hematopoiesis (CH). SF3B1 hotspot mutations were correlated with additional somatic mutations, disease type and other clinical parameters. Overall, 11,744 patients with myeloid neoplasms; MDS (n=4275), AML (n=3526), MDS/MPN (n=192), MPN (n=3751) and 24,146 patients with solid malignancies and CH were included in this analysis. SF3B1 mutations were present in 11.1% of patients; 21.3% MDS, 4.1% AML, 51% MDS/MPN and 3.7% MPN. In MDS, SF3B1 mutations were predominantly present in low risk MDS (23.8%) as compared with 4.1% in intermediate to high risk MDS. This contrasts with MPN where SF3B1 mutations were present at a higher frequency in more advanced MPN (5.5% in treatment-resistant ET, 8.8% in all MF cases and further augmented at 14% in JAK inhibitor resistant high-risk thrombocytopenic MF) as compared with earlier phase MPN (2.1% and 1.3% in ET and PV respectively). In MPN SF3B1-mutant cases, JAK-STAT signaling driver mutation frequency and distribution were in line with published data; 65.6% JAK2V617F-mutated, 18.8% CALR-mutated, 8.5% MPL-mutated and 7% triple negative. As previously described, SF3B1 K700E hotspot mutations were most prevalent (frequency 49.1%), followed by K666, H622 and R625 hotspot mutations at 20.6%, 7.6% and 6.1% respectively. K700E was enriched as expected in MDS cohorts and K666 hotspot mutations were enriched in AML (42%). Although less common in MDS (11.6 - 17.8%), the frequency of K666 mutation correlated with higher IPSS-M risk category in MDS (p=0.001; chi-squared test). In contrast to MDS, SF3B1 K666 was the dominant hotspot in MPN ranging 46-70% in frequency across subtypes from earlier to more advanced MPN such as MF (Fig 1A). SF3B1 K700E mutations were more often co-mutated with epigenetic mutations such as in DNMT3A, TET2, EZH2 and KMT2C genes (Fig 1B) whereas SF3B1 K666 mutations were significantly co-mutated with NPM1 and FLT3 in AML cases (odds ratio, OR, 25.6 and 4.2 respectively, p-value <0.0001) and JAK2V617F in MPN cases (OR 3.2, p<0.0001). SF3B1 K666 mutations also showed a trend towards significant co-occurrence with adverse prognostic mutations in AML; PHF6, PTPN11 and RUNX1. No differences were observed for SF3B1 hotspots for CALR or MPL-mutated MPN but numbers were too few to draw conclusions. In patients with SF3B1 mutations, 58% were male and K666 hotspot was more frequent in males (21% as compared with 11% in females, p<0.001, chi-squared test). There were no hotspot differences observed in age, ethnicity, bloods counts or karyotype where data was available for this analysis. In CH, SF3B1 mutations were detected in 0.5% with K700E (34.2%) and K666 (26.7%) frequencies equivalent (p=0.24). In summary, this large multi-cohort myeloid neoplasm analysis reveals myeloid disease-specific SF3B1 hotspot propensity. The K666 hotspot was frequent in MPN, particularly in persons with advanced disease where SF3B1 mutations are more prevalent than earlier phase MPN. We conclude that different SF3B1 hotspots are associated with distinct clinical phenotypes and in chronic myeloid malignancies; K666 SF3B1 mutation is associated with adverse clinical course. Additional studies to elucidate the distinct biological effects of different SF3B1 mutations are warranted.
Efforts to identify the genetic underpinnings of rare undiagnosed diseases increasingly involve the use of next-generation sequencing and comparative genomic hybridization methods. These efforts are ...limited by a lack of knowledge regarding gene function, and an inability to predict the impact of genetic variation on the encoded protein function. Diagnostic challenges posed by undiagnosed diseases have solutions in model organism research, which provides a wealth of detailed biological information. Model organism geneticists are by necessity experts in particular genes, gene families, specific organs, and biological functions. Here, we review the current state of research into undiagnosed diseases, highlighting large efforts in North America and internationally, including the Undiagnosed Diseases Network (UDN) (Supplemental Material, File S1) and UDN International (UDNI), the Centers for Mendelian Genomics (CMG), and the Canadian Rare Diseases Models and Mechanisms Network (RDMM). We discuss how merging human genetics with model organism research guides experimental studies to solve these medical mysteries, gain new insights into disease pathogenesis, and uncover new therapeutic strategies.
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