Functional status decline as a measure of adverse events in home health care: an observational study

BACKGROUND: Research that examines the quality of home health care is complex because no gold standard exists for measuring adverse outcomes, and because the patient and clinician populations are highly heterogeneous. The objectives in this study are to develop models to predict functional decline for three indices of functional status as measures of adverse events in home health care and determine which index is most appropriate for risk-adjusting for future quality research. METHODS: Data come from the Outcomes and Assessment Information Set (OASIS) from a large urban home health care agency and other agency data. Prognostic data yields 49,437 episodes, while follow-up data yields 47,684 episodes. We tested three indices defined as substantial decline in three or more (gt3_ADLs), two or more (gt2_ADLs), and one or more (gt1_ADLs) ADLs. Multivariate logistic regression determines the performance of the models for each index as measured by the c-statistic and Hosmer-Lemeshow chi square (χ(2)). RESULTS: Frequencies for gt3_ADLs, gt2_ADLs, and gt1_ADLs are 212 (0.43%), 783 (1.58%), and 4,271 (8.64%) respectively. Follow-up results are comparable with frequencies of 218 (0.46%), 763 (1.60%), and 3,949 (8.28%) for each index. Gt3_ADLs does not produce valid models. The model for gt2_ADLs consistently yields a higher c-statistic compared to gt1_ADLs (0.754 vs. 0.679, respectively). Both indices' models yield non-significant Hosmer-Lemeshow chi square indicating reasonable model fit. Findings for gt2_ADLs and gt1_ADLs are consistent over time as indicated by follow-up data results. CONCLUSION: Gt2_ADLs yields the best models as indicated by a high c-statistic and a non-significant Hosmer-Lemeshow χ(2), both of which exhibit exceptional consistency. We conclude that gt2_ADLs may be preferable in defining ADL adverse events in the context of home health care

Feasibility of deploying peer coaches to mentor frontline home health aides and promote mobility among individuals recovering from a stroke: pilot test of a randomized controlled trial

Background Each year, approximately 100,000 individuals receive home health services after a stroke. Evidence has shown the benefits of home-based stroke rehabilitation, but little is known about resource-efficient ways to enhance its effectiveness, nor has anyone explored the value of leveraging low-cost home health aides (HHAs) to reinforce repetitive task training, a key component of home-based rehabilitation. We developed and piloted a Stroke Homehealth Aide Recovery Program (SHARP) that deployed specially trained HHAs as “peer coaches” to mentor frontline aides and help individuals recovering from stroke increase their mobility through greater adherence to repetitive exercise regimens. We assessed the feasibility of SHARP and its readiness for a full-scale randomized controlled trial (RCT). Specifically, we examined (1) the practicability of recruitment and randomization procedures, (2) program acceptability, (3) intervention fidelity, and (4) the performance of outcome measures. Methods This was a feasibility study including a pilot RCT. Target enrollment was 60 individuals receiving post-stroke home health services, who were randomized to SHARP + usual home care or usual care only. The protocol specified a 30-day intervention with four planned in-home coach visits, including one joint coach/physical therapist visit. The primary participant outcome was 60-day change in mobility, using the performance-based Timed Up and Go and 4-Meter Walk Gait Speed tests. Interviews with participants, coaches, physical therapists, and frontline aides provided acceptability data. Enrollment figures, visit tracking reports, and audio recordings provided intervention fidelity data. Mixed methods included thematic analysis of qualitative data and quantitative analysis of structured data to examine the intervention feasibility and performance of outcome measures. Results Achieving the 60-participant enrollment target required modifying participant eligibility criteria to accommodate a decline in the receipt of HHA services among individuals receiving home care after a stroke. This modification entailed intervention redesign. Acceptability was high among coaches and participants but lower among therapists and frontline aides. Intervention fidelity was mixed: 87% of intervention participants received all four planned coach visits; however, no joint coach/therapist visits occurred. Sixty-day follow-up retention was 78%. However, baseline and follow-up performance-based primary outcome mobility assessments could be completed for only 55% of participants. Conclusions The trial was not feasible in its current form. Before progressing to a definitive trial, significant program redesign would be required to address issues affecting enrollment, coach/HHA/therapist coordination, and implementation of performance-based outcome measures. Trial registration ClinicalTrials.gov, NCT04840407 . Retrospectively registered on 9 April 202

Electron excitation and energy transfer rates for H2O in the upper atmosphere

Recent measurements of the cross sections for electronic state excitations in H2O have made it possible to calculate rates applicable to these excitation processes. We thus present here calculations of electron energy transfer rates for electronic and vibrational state excitations in H2O, as well as rates for excitation of some of these states by atmospheric thermal and auroral secondary electrons. The calculation of these latter rates is an important first step towards our aim of including water into a statistical equilibrium model of the atmosphere under auroral conditions.Comment: 15 pages, 8 figure

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

Model averaging, optimal inference, and habit formation

Postulating that the brain performs approximate Bayesian inference generates principled and empirically testable models of neuronal function-the subject of much current interest in neuroscience and related disciplines. Current formulations address inference and learning under some assumed and particular model. In reality, organisms are often faced with an additional challenge-that of determining which model or models of their environment are the best for guiding behavior. Bayesian model averaging-which says that an agent should weight the predictions of different models according to their evidence-provides a principled way to solve this problem. Importantly, because model evidence is determined by both the accuracy and complexity of the model, optimal inference requires that these be traded off against one another. This means an agent's behavior should show an equivalent balance. We hypothesize that Bayesian model averaging plays an important role in cognition, given that it is both optimal and realizable within a plausible neuronal architecture. We outline model averaging and how it might be implemented, and then explore a number of implications for brain and behavior. In particular, we propose that model averaging can explain a number of apparently suboptimal phenomena within the framework of approximate (bounded) Bayesian inference, focusing particularly upon the relationship between goal-directed and habitual behavior

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

An increase in dietary n-3 fatty acids decreases a marker of bone resorption in humans

Human, animal, and in vitro research indicates a beneficial effect of appropriate amounts of omega-3 (n-3) polyunsaturated fatty acids (PUFA) on bone health. This is the first controlled feeding study in humans to evaluate the effect of dietary plant-derived n-3 PUFA on bone turnover, assessed by serum concentrations of N-telopeptides (NTx) and bone-specific alkaline phosphatase (BSAP). Subjects (n = 23) consumed each diet for 6 weeks in a randomized, 3-period crossover design: 1) Average American Diet (AAD; [34% total fat, 13% saturated fatty acids (SFA), 13% monounsaturated fatty acids (MUFA), 9% PUFA (7.7% LA, 0.8% ALA)]), 2) Linoleic Acid Diet (LA; [37% total fat, 9% SFA, 12% MUFA, 16% PUFA (12.6% LA, 3.6% ALA)]), and 3) α-Linolenic Acid Diet (ALA; [38% total fat, 8% SFA, 12% MUFA, 17% PUFA (10.5% LA, 6.5% ALA)]). Walnuts and flaxseed oil were the predominant sources of ALA. NTx levels were significantly lower following the ALA diet (13.20 ± 1.21 nM BCE), relative to the AAD (15.59 ± 1.21 nM BCE) (p < 0.05). Mean NTx level following the LA diet was 13.80 ± 1.21 nM BCE. There was no change in levels of BSAP across the three diets. Concentrations of NTx were positively correlated with the pro-inflammatory cytokine TNFα for all three diets. The results indicate that plant sources of dietary n-3 PUFA may have a protective effect on bone metabolism via a decrease in bone resorption in the presence of consistent levels of bone formation