Expansion of biological pathways based on evolutionary inference.

The availability of diverse genomes makes it possible to predict gene function based on shared evolutionary history. This approach can be challenging, however, for pathways whose components do not exhibit a shared history but rather consist of distinct "evolutionary modules." We introduce a computational algorithm, clustering by inferred models of evolution (CLIME), which inputs a eukaryotic species tree, homology matrix, and pathway (gene set) of interest. CLIME partitions the gene set into disjoint evolutionary modules, simultaneously learning the number of modules and a tree-based evolutionary history that defines each module. CLIME then expands each module by scanning the genome for new components that likely arose under the inferred evolutionary model. Application of CLIME to ∼1,000 annotated human pathways and to the proteomes of yeast, red algae, and malaria reveals unanticipated evolutionary modularity and coevolving components. CLIME is freely available and should become increasingly powerful with the growing wealth of eukaryotic genomes.

Reformulating provider profiling by grouping providers treating similar patients prior to evaluating performance.

Standard approaches to comparing health providers' performance rely on hierarchical logistic regression models that adjust for patient characteristics at admission. Estimates from these models may be misleading when providers treat different patient populations and the models are misspecified. To address this limitation, we propose a novel profiling approach that identifies groups of providers treating similar populations of patients and then evaluates providers' performance within each group. The groups of providers are identified using a Bayesian multilevel finite mixture of general location models. To compare the performance of our proposed profiling approach to standard methods, we use patient-level data from 119 skilled nursing facilities in Massachusetts. We use simulated and observed outcome data to explore the performance of these profiling methods in different settings. In simulations, our proposed method classifies providers to groups with similar patients' admission characteristics. In addition, in the presence of limited overlap in patient characteristics across providers and misspecifications of the outcome model, the provider-level estimates obtained using our approach identified providers that under- and overperformed compared to the standard regression-based approaches more accurately.

A Bayesian MultiLayer Record Linkage Procedure to Analyze Post-Acute Care Recovery of Patients with Traumatic Brain Injury.

Understanding associations between injury severity and postacute care recovery for patients with traumatic brain injury (TBI) is crucial to improving care. Estimating these associations requires information on patients' injury, demographics, and healthcare utilization, which are dispersed across multiple data sets. Because of privacy regulations, unique identifiers are not available to link records across these data sets. Record linkage methods identify records that represent the same patient across data sets in the absence of unique identifiers. With a large number of records, these methods may result in many false links. Health providers are a natural grouping scheme for patients, because only records that receive care from the same provider can represent the same patient. In some cases, providers are defined within each data set, but they are not uniquely identified across data sets. We propose a Bayesian record linkage procedure that simultaneously links providers and patients. The procedure improves the accuracy of the estimated links compared to current methods. We use this procedure to merge a trauma registry with Medicare claims to estimate the association between TBI patients' injury severity and postacute care recovery.

Using Structured Observations to Evaluate the Effects of a Personalized Music Intervention on Agitated Behaviors and Mood in Nursing Home Residents With Dementia: Results From an Embedded, Pragmatic Randomized Controlled Trial.

OBJECTIVE: The objective of this research was to determine if a personalized music intervention reduced the frequency of agitated behaviors as measured by structured observations of nursing home (NH) residents with dementia. DESIGN: The design was a parallel, cluster-randomized, controlled trial. SETTING: The setting was 54 NH (27 intervention, 27 control) from four geographically-diverse, multifacility NH corporations. PARTICIPANTS: The participants were 976 NH residents (483 intervention, 493 control) with Alzheimer's disease or related dementias (66% with moderate to severe symptoms); average age 80.3 years (SD: 12.3) and 25.1% were Black. INTERVENTION: The intervention was individuals' preferred music delivered via a personalized music device. MEASUREMENT: The measurement tool was the Agitated Behavior Mapping Instrument, which captures the frequency of 13 agitated behaviors and five mood states during 3-minute observations. RESULTS: The results show that no verbally agitated behaviors were reported in a higher proportion of observations among residents in NHs randomized to receive the intervention compared to similar residents in NHs randomized to usual care (marginal interaction effect (MIE): 0.061, 95% CI: 0.028-0.061). Residents in NHs randomized to receive the intervention were also more likely to be observed experiencing pleasure compared to residents in usual care NHs (MIE: 0.038; 95% CI: 0.008-0.073)). There was no significant effect of the intervention on physically agitated behaviors, anger, fear, alertness, or sadness. CONCLUSIONS: The conclusions are that personalized music may be effective at reducing verbally-agitated behaviors. Using structured observations to measure behaviors may avoid biases of staff-reported measures.

Financial Performance of Accountable Care Organizations: A 5-Year National Empirical Analysis.

GOALS: Of 513 accountable care organizations (ACOs) participating in the Medicare Shared Savings Program (MSSP) in 2020, 67% generated a positive shared savings of approximately $2.3 billion. This research aimed to examine their financial performance trends and drivers over time. METHODS: The unit of analysis was the ACO in each year of the study period from 2016 to 2020. The dependent variable was the ACOs' total shared savings earned annually per beneficiary. The independent variables included ACO age, risk model, clinician staffing type, and provider type (hybrid, hospital-led, or physician-led). Covariates were the average risk score among beneficiaries, payer type, and calendar year. The Centers for Medicare & Medicaid Services (CMS) public use files (PUFs) and a commercial healthcare data aggregator were the data sources. RESULTS: ACOs' earned shared savings grew annually by 35%, while the proportions of ACOs with positive shared savings grew by 21%. For 1-year increase in ACO age, an additional $0.57 of shared savings per beneficiary was observed. ACOs with two-sided risk contracting were associated with an average marginal increase of $109 in shared savings per beneficiary compared to ACOs with one-sided risk contracting. Primary care physicians were associated with the greatest increase in earned shared savings per beneficiary. In contrast, nurse practitioners/physician assistants/clinical nurse specialists were associated with a reduction in earned shared savings. Under a one-sided risk model, hospital-led ACOs were associated with $18 higher average shared savings earning per beneficiary compared to hybrid ACOs, while physician-led ACOs were associated with lower average saved shared earnings per beneficiary at -$2 compared to hybrid ACOs. Provider-type results were not statistically significant at the 5% nominal level. No statistically significant differences were observed between provider types under a two-sided risk model. PRACTICAL APPLICATIONS: For all ACO provider types, building broader primary care provider networks was correlated with positive financial results. Future research should examine whether ACOs are conducting specific preventive screenings for cancer or monitoring conditions such as diabetes, hypertension, heart disease, obesity, mental disorders, and joint disorders. Such studies may answer health policy and strategy questions about the effects of incentives for improved ACO performance in serving a healthier population.

Bayesian record linkage with variables in one file.

In many healthcare and social science applications, information about units is dispersed across multiple data files. Linking records across files is necessary to estimate the associations of interest. Common record linkage algorithms only rely on similarities between linking variables that appear in all the files. Moreover, analysis of linked files often ignores errors that may arise from incorrect or missed links. Bayesian record linking methods allow for natural propagation of linkage error, by jointly sampling the linkage structure and the model parameters. We extend an existing Bayesian record linkage method to integrate associations between variables exclusive to each file being linked. We show analytically, and using simulations, that the proposed method can improve the linking process, and can result in accurate inferences. We apply the method to link Meals on Wheels recipients to Medicare enrollment records.

The potential impact of proxy reports for symptom experience and care quality and experience in advanced cancer.

OBJECTIVES: As the US tests models of care for the seriously ill, patient perceptions of the quality of care are important. Proxies are often needed for this group. We sought to understand the potential impact of proxy reports for the assessment of care quality and experience in cancer. METHODS: Secondary data analysis of a deidentified prospective study that included surveys of perceived care quality, including symptom management, from patients with advanced cancer receiving chemotherapy and their caregivers. Surveys were administered at diagnosis (time 1) and treatment (time 2), with top-box scoring used for analysis. Overall concordance was assessed using metrics including Gwet's AC1. The proportion of the highest scores by respondent type within 2 subgroups were examined: (1) symptom burden and (2) practice setting. RESULTS: Data from 83 dyads were analyzed. Proxies and patients frequently reported the highest scores for quality (time 1: proxies: 77% and patients: 80%). At time 1, 14% of proxies and 10% of patients reported an unmet need for symptom palliation. Most patients reporting an unmet need gave the top score for quality (75%), but fewer proxies did so (45%). Proxy and patient reports were similar within practice settings. Concordance was at least moderate (nearly all outcomes >0.5 and some >0.8) by Gwet's AC1. SIGNIFICANCE OF RESULTS: Findings of at least moderate concordance and similar experience outcomes within subgroups suggest the use of proxies may not change estimates substantially. However, consideration should be taken when evaluating symptom management, particularly if such evaluations inform assessment of provider performance.

Risk Factors of Skilled Nursing Facility Admissions and the Interrelation With Hospitalization and Amount of Informal Caregiving Received.

BACKGROUND: The correlations between skilled nursing facility (SNF) admissions, number of hospitalizations, and informal caregiving hours received after adjusting for physical and cognitive function and sociodemographic covariates are not well understood. OBJECTIVE: The objective of this study was to better understand risk factors for SNF admissions and the interrelation with hospitalizations and amount of informal caregiving received, this study applied a novel joint modeling analysis to simultaneously explore the correlation and shared information between the 3 outcomes. RESEARCH DESIGN: This was an observational follow-up study. SUBJECTS: Data from 4836 older Americans included in the 2011-2015 rounds of the National Health and Aging Trends Study were linked with Centers for Medicare & Medicaid Services. MEASURES: We jointly modeled SNF admission, hospital admissions, and informal caregiving hours received while accounting for possible risk factors. We addressed missing values by multiple imputation with chained equations. RESULTS: SNF admission evidenced a strong positive correlation with hospital admission, and SNF admission evidenced a weak positive correlation with the informal caregiving hours received after adjustment for important risk factors. Non-Hispanic White race/ethnicity, living alone, not being Medicaid eligible, Alzheimer disease and related dementias diagnosis, activities of daily living disabilities, and frailty were associated with increased risk of SNF admissions and any/number of hospital admission. Lower educational level was also associated with the latter. Medicaid eligibility was the only factor not associated with any nor numbers of informal caregiving hours received. CONCLUSIONS: Sociodemographic and health factors were important for predicting SNF admissions. After adjustment for important risk factors, SNF evidenced a strong positive correlation with the number of hospitalizations and a weak positive correlation with the hours of informal caregiving received.

Effects of dipeptidyl peptidase-4 inhibitors and sulphonylureas on cognitive and physical function in nursing home residents.

AIMS: Dipeptidyl peptidase-4 inhibitors (DPP4Is) may mitigate hypoglycaemia-mediated declines in cognitive and physical functioning compared with sulphonylureas (SUs), yet comparative studies are unavailable among older adults, particularly nursing home (NH) residents. We evaluated the effects of DPP4Is versus SUs on cognitive and physical functioning among NH residents. MATERIALS AND METHODS: This new-user cohort study included long-stay NH residents aged ≥65 years from the 2007-2010 national US Minimum Data Set (MDS) clinical assessments and linked Medicare claims. We measured cognitive decline from the validated 6-point MDS Cognitive Performance Scale, functional decline from the validated 28-point MDS Activities of Daily Living scale, and hospitalizations or emergency department visits for altered mental status from Medicare claims. We compared 180-day outcomes in residents who initiated a DPP4I versus SU after 1:1 propensity score matching using Cox regression models. RESULTS: The matched cohort (N = 1784) had a mean ± SD age of 80 ± 8 years and 73% were women. Approximately 46% had no or mild cognitive impairment and 35% had no or mild functional impairment before treatment initiation. Compared with SU users, DPP4I users had lower 180-day rates of cognitive decline [hazard ratio (HR) = 0.61, 95% confidence interval (CI) 0.31-1.19], altered mental status events (HR = 0.71, 95% CI 0.39-1.27), and functional decline (HR = 0.89, 95% CI 0.51-1.56), but estimates were imprecise. CONCLUSIONS: Rates of cognitive and functional decline may be reduced among older NH residents using DPP4Is compared with SUs, but larger studies with greater statistical power should resolve the remaining uncertainty by providing more precise effect estimates.

Multiple imputation procedures for estimating causal effects with multiple treatments with application to the comparison of healthcare providers.

Choosing between multiple healthcare providers requires us to simultaneously compare the expected outcomes under each provider. This comparison is complex because the composition of patients treated by each provider may differ. Similar issues arise when simultaneously comparing the adverse effects of interventions using non-randomized data. To simultaneously estimate the effects of multiple providers/interventions we propose procedures that explicitly impute the set of potential outcomes for each subject. The procedures are based on different specifications of the generalized additive models (GAM) and the Bayesian additive regression trees (BART). We compare the performance of the proposed procedures to previously proposed matching and weighting procedures using an extensive simulation study for continuous outcomes. Our simulations show that when the distributions of the covariates across treatment groups have adequate overlap, the multiple imputation procedures based on separate BART or GAM models in each treatment group are generally superior to weighting based methods and have similar and sometimes better performance than matching on the logit of the generalized propensity score. Another advantage of these multiple imputation procedures is the ability to provide point and interval estimates to a wide range of causal effect estimands. We apply the proposed procedures to comparing multiple nursing homes in Massachusetts for readmission outcomes. The proposed approach can be applied to other causal effects applications with multiple treatments.

Review and evaluation of imputation methods for multivariate longitudinal data with mixed-type incomplete variables.

Estimating relationships between multiple incomplete patient measurements requires methods to cope with missing values. Multiple imputation is one approach to address missing data by filling in plausible values for those that are missing. Multiple imputation procedures can be classified into two broad types: joint modeling (JM) and fully conditional specification (FCS). JM fits a multivariate distribution for the entire set of variables, but it may be complex to define and implement. FCS imputes missing data variable-by-variable from a set of conditional distributions. In many studies, FCS is easier to define and implement than JM, but it may be based on incompatible conditional models. Imputation methods based on multilevel modeling show improved operating characteristics when imputing longitudinal data, but they can be computationally intensive, especially when imputing multiple variables simultaneously. We review current MI methods for incomplete longitudinal data and their implementation on widely accessible software. Using simulated data from the National Health and Aging Trends Study, we compare their performance for monotone and intermittent missing data patterns. Our simulations demonstrate that in a longitudinal study with a limited number of repeated observations and time-varying variables, FCS-Standard is a computationally efficient imputation method that is accurate and precise for univariate single-level and multilevel regression models. When the analyses comprise multivariate multilevel models, FCS-LMM-latent is a statistically valid procedure with overall more accurate estimates, but it requires more intensive computations. Imputation methods based on generalized linear multilevel models can lead to biased subject-level variance estimates when the statistical analyses involve hierarchical models.

Effect of advance care planning video on do-not-hospitalize orders for nursing home residents with advanced illness.

BACKGROUND: The purpose of the study is to evaluate the effect of an Advance Care Planning (ACP) Video Program on documented Do-Not-Hospitalize (DNH) orders among nursing home (NH) residents with advanced illness. METHODS: Secondary analysis on a subset of NHs enrolled in a cluster-randomized controlled trial (41 NHs in treatment arm implemented the ACP Video Program: 69 NHs in control arm employed usual ACP practices). Participants included long (> 100 days) and short (≤ 100 days) stay residents with advanced illness (advanced dementia or cardiopulmonary disease (chronic obstructive pulmonary disease or congestive heart failure)) in NHs from March 1, 2016 to May 31, 2018 without a documented Do-Not-Hospitalize (DNH) order at baseline. Logistic regression with covariate adjustments was used to estimate the impact of the resident being in a treatment versus control NH on: the proportion of residents with new DNH orders during follow-up; and the proportion of residents with any hospitalization during follow-up. Clustering at the facility-level was addressed using hierarchical models. RESULTS: The cohort included 6,117 residents with advanced illness (mean age (SD) = 82.8 (8.4) years, 65% female). Among long-stay residents (n = 3,902), 9.3% (SE, 2.2; 95% CI 5.0-13.6) and 4.2% (SE, 1.1; 95% CI 2.1-6.3) acquired a new DNH order in the treatment and control arms, respectively (average marginal effect, (AME) 5.0; SE, 2.4; 95% CI, 0.3-9.8). Among short-stay residents with advanced illness (n = 2,215), 8.0% (SE, 1.6; 95% CI 4.6-11.3) and 3.5% (SE 1.0; 95% CI 1.5-5.5) acquired a new DNH order in the treatment and control arms, respectively (AME 4.4; SE, 2.0; 95% CI, 0.5-8.3). Proportion of residents with any hospitalizations did not differ between arms in either cohort. CONCLUSIONS: Compared to usual care, an ACP Video Program intervention increased documented DNH orders among NH residents with advanced disease but did not significantly reduce hospitalizations. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02612688 .

Predicting Progression from Gestational Diabetes to Impaired Glucose Tolerance Using Peridelivery Data: An Observational Study.

OBJECTIVE: This article aimed to develop a predictive model to identify persons with recent gestational diabetes mellitus (GDM) most likely to progress to impaired glucose tolerance postpartum. STUDY DESIGN: We conducted an observational study among persons with GDM in their most recent pregnancy, defined by Carpenter-Coustan criteria. Participants were followed up from delivery through 1-year postpartum. We used lasso regression with k-fold cross validation to develop a multivariable model to predict progression to impaired glucose tolerance, defined as HbA1c≥5.7%, at 1-year postpartum. Predictive ability was assessed by the area under the curve (AUC), sensitivity, specificity, and positive and negative predictive values (PPV and NPV). RESULTS: Of 203 participants, 71 (35%) had impaired glucose tolerance at 1-year postpartum. The final model had an AUC of 0.79 (95% confidence interval [CI]: 0.72, 0.85) and included eight indicators of weight, body mass index, family history of type 2 diabetes, GDM in a prior pregnancy, GDM diagnosis<24 weeks' gestation, and fasting and 2-hour plasma glucose at 2 days postpartum. A cutoff point of ≥ 0.25 predicted probability had sensitivity of 80% (95% CI: 69, 89), specificity of 58% (95% CI: 49, 67), PPV of 51% (95% CI: 41, 61), and NPV of 85% (95% CI: 76, 91) to identify women with impaired glucose tolerance at 1-year postpartum. CONCLUSION: Our predictive model had reasonable ability to predict impaired glucose tolerance around delivery for persons with recent GDM. KEY POINTS: · We developed a predictive model to identify persons with GDM most likely to develop IGT postpartum.. · The final model had an AUC of 0.79 (95% CI: 0.72, 0.85) and included eight clinical indicators.. · If validated, our model could help prioritize diabetes prevention efforts among persons with GDM..

Assessment of patient perception of treatment assignment and patient-reported outcomes in a cannabis use disorder trial.

Background: Blinding is a cornerstone of trial methodology. Prior work indicates participant-perceived assignment may be associated with trial outcomes. Less is known about how perception changes over time and if this is associated with outcomes.Objectives: To evaluate if participants change their perception of assignment over time in a blinded trial, and if perception is associated with different types of patient-reported outcomes (PROs).Methods: This was a secondary analysis of data from the Achieving Cannabis Cessation-Evaluating N-Acetylcysteine Treatment (ACCENT) trial, which evaluated the efficacy of N-acetylcysteine (NAC) relative to placebo for treating cannabis use disorder. Participants (N = 234; 164 men, 70 women) were asked at weeks 5 and 9 what treatment (placebo or NAC) they believed they were receiving. We included PROs proximal (cannabis-associated problems, craving) and distal (anxiety) to the intervention. Analysis was by multiple linear regression and mixed models.Results: Approximately 20% of participants in both arms changed their perception over time. Relative to participants who consistently perceived assignment to placebo, participants who consistently perceived assignment to NAC did not always have comparatively better average scores (coefficient -3.3 [95% CI: -7.0, 0.5]). In some analyses, participants who switched to guessing NAC from placebo had comparatively better average scores (coefficient -3.0 [95% CI: -9.3, 3.4]), but this was inconsistent across outcomes or strata defined by actual assignment or guess accuracy.Conclusion: The study suggests that the proportion of individuals who switch their perception over time is modest. However, this group may influence the estimates of intervention effects on some PROs.

Mindfulness-Based College: A Stage 1 Randomized Controlled Trial for University Student Well-Being.

OBJECTIVE: To evaluate effects of a mindfulness-based program, adapted to the young adult life course stage (age, 18-29 years), named Mindfulness-Based College (MB-College). The primary outcome was a young adult health summary score, composed of key health risk factors: body mass index, physical activity, fruit and vegetable intake, alcohol consumption, stress, loneliness, and sleep duration. Secondary outcomes were hypothesized self-regulation mechanisms, including attention control, interoceptive awareness, and emotion regulation. METHODS: This was a stage 1 randomized controlled trial of the 9-week MB-College program (n = 47) versus enhanced usual care control (n = 49) including students from three universities. Assessments were at baseline, during the beginning of the college term when stress is typically lower, and at MB-College completion (3-month follow-up), when term-related stress is typically higher. Intention-to-treat, linear regression analyses estimated the marginal effects of MB-College versus control on the outcomes. RESULTS: MB-College participants (mean age = 20 years, 68% female, 37% racial minorities) demonstrated improved health summary scores at follow-up compared with control participants whose health summary scores worsened (marginal effect for MB-College versus control = 0.23; p = .004). Effects on loneliness were pronounced (marginal effect = -3.11 for the Revised University of Los Angeles Loneliness Scale score; p = .03). Secondary analyses showed significant impacts of MB-College on hypothesized self-regulation mechanisms (e.g., Sustained Attention to Response Task correct no-go percent, p = .0008; Multidimensional Assessment of Interoceptive Awareness, p < .0001; Center for Epidemiologic Studies-Depression scale, p = .03). CONCLUSIONS: Findings of this early stage clinical trial suggest that MB-College may foster well-being in young adults.Trial Registration: NCT03124446.

Racial/Ethnic Differences in 30-Day Mortality for Heart Failure and Pneumonia in the Veterans Health Administration Using Claims-based, Clinical, and Social Risk-adjustment Variables.

BACKGROUND: Prior studies have identified lower mortality in Black Veterans compared with White Veterans after hospitalization for common medical conditions, but these studies adjusted for comorbid conditions identified in administrative claims. OBJECTIVES: The objectives of this study were to compare mortality for non-Hispanic White (hereafter, "White"), non-Hispanic Black (hereafter, "Black"), and Hispanic Veterans hospitalized for heart failure (HF) and pneumonia and determine whether observed mortality differences varied according to whether claims-based comorbid conditions and/or clinical variables were included in risk-adjustment models. RESEARCH DESIGN: This was an observational study. SUBJECTS: The study cohort included 143,520 admissions for HF and 127,782 admissions for pneumonia for Veterans hospitalized in 132 Veterans Health Administration (VA) Medical Centers between January 2009 and September 2015. MEASURES: The primary independent variable was racial/ethnic group (ie, Black, Hispanic, and non-Hispanic White), and the outcome was all-cause mortality 30 days following admission. To compare mortality by race/ethnicity, we used logistic regression models that included different combinations of claims-based, clinical, and sociodemographic variables. For each model, we estimated the average marginal effect (AME) for Black and Hispanic Veterans relative to White Veterans. RESULTS: Among the 143,520 (127,782) hospitalizations for HF (pneumonia), the average patient age was 71.6 (70.9) years and 98.4% (97.1%) were male. The unadjusted 30-day mortality rates for HF (pneumonia) were 7.2% (11.0%) for White, 4.1% (10.4%) for Black and 8.4% (16.9%) for Hispanic Veterans. Relative to White Veterans, when only claims-based variables were used for risk adjustment, the AME (95% confidence interval) for the HF [pneumonia] cohort was -2.17 (-2.45, -1.89) [0.08 (-0.41, 0.58)] for Black Veterans and 1.32 (0.49, 2.15) [4.51 (3.65, 5.38)] for Hispanic Veterans. When clinical variables were incorporated in addition to claims-based ones, the AME, relative to White Veterans, for the HF [pneumonia] cohort was -1.57 (-1.88, -1.27) [-0.83 (-1.31, -0.36)] for Black Veterans and 1.50 (0.71, 2.30) [3.30 (2.49, 4.11)] for Hispanic Veterans. CONCLUSIONS: Compared with White Veterans, Black Veterans had lower mortality, and Hispanic Veterans had higher mortality for HF and pneumonia. The inclusion of clinical variables into risk-adjustment models impacted the magnitude of racial/ethnic differences in mortality following hospitalization. Future studies examining racial/ethnic disparities should consider including clinical variables for risk adjustment.

Does Knowledge of Treatment Assignment Affect Patient Report of Symptoms, Function, and Health Status? An Evaluation Using Multiple Myeloma Trials.

OBJECTIVES: Unblinded trials are common in oncology, but patient knowledge of treatment assignment may bias response to questionnaires. We sought to ascertain the extent of possible bias arising from patient knowledge of treatment assignment. METHODS: This is a retrospective analysis of data from 2 randomized trials in multiple myeloma, 1 double-blind and 1 open label. We compared changes in patient reports of symptoms, function, and health status from prerandomization (screening) to baseline (pretreatment but postrandomization) across control and investigational arms in the 2 trials. Changes from prerandomization scores at ~2 and 6 months on treatment were evaluated only across control arms to avoid comparisons between 2 different experimental drugs. All scores were on 0- to 100-point scales. Inverse probability weighting, entropy balancing, and multiple imputation using propensity score splines were used to compare score changes across similar groups of patients. RESULTS: Minimal changes from screening were seen at baseline in all arms. In the control arm, mean changes of <7 points were seen for all domains at 2 and 6 months. The effect of unblinding at 6 months in social function was a decline of less than 6 points (weighting: -3.09; 95% confidence interval -8.41 to 2.23; balancing: -4.55; 95% confidence interval -9.86 to 0.76; imputation: -5.34; 95% confidence interval -10.64 to -0.04). CONCLUSION: In this analysis, we did not find evidence to suggest that there was a meaningful differential effect on how patients reported their symptoms, function or health status after knowing their treatment assignment.

Successful Community Discharge Among Older Adults With Traumatic Brain Injury in Skilled Nursing Facilities.

OBJECTIVE: To identify patient, injury, and functional status characteristics associated with successful discharge to the community following a skilled nursing facility (SNF) stay among older adults hospitalized following traumatic brain injury (TBI). SETTING: Skilled nursing facilities. PARTICIPANTS: Medicare fee-for-service beneficiaries admitted to an SNF after hospitalization for TBI. DESIGN: Retrospective cohort study using Medicare administrative data merged with the National Trauma Data Bank using a multilayered Bayesian record linkage approach. MAIN OUTCOME MEASURE: Successful community discharge: discharged alive within 100 days of SNF admission and remaining in the community for 30 days or more without dying or admission to a healthcare facility. RESULTS: Medicaid enrollment, incontinence, decreased independence with activities of daily living, and cognitive impairment were associated with lower odds of successful discharge, whereas race "other" was associated with higher odds of successful discharge. Injury factors including worse injury severity (Glasgow Coma Scale and Abbreviated Injury Scale scores) and fall-related injury mechanism were not associated with successful discharge. CONCLUSION: Among older adults with TBI who discharge to an SNF, sociodemographic and functional status characteristics are associated with successful discharge and may be useful to clinicians for discharge planning. Acute injury severity indices may have limited utility in predicting discharge disposition once a patient is admitted to an SNF for post-acute care.

Does Consumption of Baker's Yeast (Saccharomyces cerevisiae) by Black Soldier Fly (Diptera: Stratiomyidae) Larvae Affect Their Fatty Acid Composition?

Fatty acids are important compounds for insects, but the requirements for essential fatty acids may differ between insect species. Most of the fatty acids are acquired through the insect's diet; therefore, supplementing the diet with baker's yeast (Saccharomyces cerevisiae Meyen ex E.C. Hansen), which produces unsaturated fatty acids, was predicted to affect the fatty acid composition of the insect. The tested insect was the black soldier fly (BSF) (Hermetia illucens L.), that is used as a source of protein and fat in feed. Therefore, there is importance for BSF larvae (BSFL) nutritional composition, especially the unsaturated fatty acids content, which is one of the nutritional limitations for mammalian diets. The dominant fatty acids of the tested BSFL were the saturated fatty acids: lauric, myristic, and palmitic acids, as found in other BSF studies. Oleic acid (c18:1) and linoleic acid (C18:2) were the abundant unsaturated fatty acids in the BSFL. The proportion of linoleic acid was higher in the substrate with the supplemental yeast; however, this did not affect its proportion in the larvae. The higher proportion of linoleic acid may have been exploited as a source for production of saturated lauric acid. Therefore, providing unsaturated fatty acids to the substrate through supplemental baker's yeast is not the most efficient way to increase the proportion of unsaturated fatty acids in the larvae.

Using the potential outcome framework to estimate optimal sample size for cluster randomized trials: a simulation-based algorithm.

In cluster randomized trials (CRTs) groups rather than individuals are randomized to different interventions. Individuals' responses within clusters are commonly more similar than those across clusters. This dependency introduces complexity when calculating the number of clusters required to reach a specified statistical power for nominal significance levels and effect sizes. Current CRTs' sample size estimation approaches rely on asymptotic-based formulae or Monte Carlo methods. We propose a new Monte Carlo procedure which is based on the potential outcomes framework. By explicitly defining the causal estimand, the data generating, the sampling, and the treatment assignment mechanisms, this procedure allows for sample size calculations in a broad range of study designs including sample size calculations in finite and infinite populations. It can also address financial and administrative considerations by allowing for unequal allocation of clusters. The R package CRTsampleSearch implements the method and we provide examples for using this package.