A Bayesian hierarchical model for improving measurement of 5mC and 5hmC levels: Toward revealing associations between phenotypes and methylation states.

5-hydroxymethylcytosine (5hmC) is a methylation state linked with gene regulation, commonly found in cells of the central nervous system. 5hmC is associated with demethylation of cytosines from 5-methylcytosine (5mC) to the unmethylated state. The presence of 5hmC can be inferred by a paired experiment involving bisulfite and oxidation-bisulfite treatments on the same sample, followed by a methylation assay using a platform such as the Illumina Infinium MethylationEPIC BeadChip (EPIC). Existing methods for analysis of the resulting EPIC data are not ideal. Most approaches ignore the correlation between the two experiments and any imprecision associated with DNA damage from the additional treatment. Estimates of 5mC/5hmC levels free from these limitations are desirable to reveal associations between methylation states and phenotypes. We propose a hierarchical Bayesian method called Constrained HYdroxy Methylation Estimation (CHYME) to simultaneously estimate 5mC/5hmC signals as well as any associations between these signals and covariates or phenotypes, while accounting for the potential impact of DNA damage and dependencies induced by the experimental design. Simulations show that CHYME has valid type 1 error and better power than a range of alternative methods, including the popular OxyBS method and linear models on transformed proportions. Other methods we examined suffer from hugely inflated type 1 error for inference on 5hmC proportions. We use CHYME to explore genome-wide associations between 5mC/5hmC levels and cause of death in postmortem prefrontal cortex brain tissue samples. These analyses indicate that CHYME is a useful tool to reveal phenotypic associations with 5mC/5hmC levels.

Estimating the effects of copy-number variants on intelligence using hierarchical Bayesian models.

It is challenging to estimate the phenotypic impact of the structural genome changes known as copy-number variations (CNVs), since there are many unique CNVs which are nonrecurrent, and most are too rare to be studied individually. In recent work, we found that CNV-aggregated genomic annotations, that is, specifically the intolerance to mutation as measured by the pLI score (probability of being loss-of-function intolerant), can be strong predictors of intellectual quotient (IQ) loss. However, this aggregation method only estimates the individual CNV effects indirectly. Here, we propose the use of hierarchical Bayesian models to directly estimate individual effects of rare CNVs on measures of intelligence. Annotation information on the impact of major mutations in genomic regions is extracted from genomic databases and used to define prior information for the approach we call HBIQ. We applied HBIQ to the analysis of CNV deletions and duplications from three datasets and identified several genomic regions containing CNVs demonstrating significant deleterious effects on IQ, some of which validate previously known associations. We also show that several CNVs were identified as deleterious by HBIQ even if they have a zero pLI score, and the converse is also true. Furthermore, we show that our new model yields higher out-of-sample concordance (78%) for predicting the consequences of carrying known recurrent CNVs compared with our previous approach.

A novel statistical method for modeling covariate effects in bisulfite sequencing derived measures of DNA methylation.

Identifying disease-associated changes in DNA methylation can help us gain a better understanding of disease etiology. Bisulfite sequencing allows the generation of high-throughput methylation profiles at single-base resolution of DNA. However, optimally modeling and analyzing these sparse and discrete sequencing data is still very challenging due to variable read depth, missing data patterns, long-range correlations, data errors, and confounding from cell type mixtures. We propose a regression-based hierarchical model that allows covariate effects to vary smoothly along genomic positions and we have built a specialized EM algorithm, which explicitly allows for experimental errors and cell type mixtures, to make inference about smooth covariate effects in the model. Simulations show that the proposed method provides accurate estimates of covariate effects and captures the major underlying methylation patterns with excellent power. We also apply our method to analyze data from rheumatoid arthritis patients and controls. The method has been implemented in R package SOMNiBUS.

The PREvention Program for Alzheimer's RElated Delirium (PREPARED) cluster randomized trial: a study protocol.

BACKGROUND: Delirium is a significant cause of morbidity and mortality among older people admitted to both acute and long-term care facilities (LTCFs). Multicomponent interventions have been shown to reduce delirium incidence in the acute care setting (30-73%) by acting on modifiable risk factors. Little work, however, has focused on using this approach to reduce delirium incidence in LTCFs. METHODS: The objective is to assess the effectiveness of the multicomponent PREPARED Trial intervention in reducing the following primary outcomes: incidence, severity, duration, and frequency of delirium episodes in cognitively impaired residents. This 4-year, parallel-design, cluster randomized study will involve nursing staff and residents in 45-50 LTCFs in Montreal, Canada. Participating public and private LTCFs (clusters) that provide 24-h nursing care will be assigned to either the PREPARED Trial intervention or the control (usual care) arm of the study using a covariate constrained randomization procedure. Approximately 400-600 LTC residents aged 65 and older with dementia and/or cognitive impairment will be enrolled in the study and followed for 18 weeks. Residents must be at risk of delirium, delirium-free at baseline and have resided at the facility for at least 2 weeks. Residents who are unable to communicate verbally, have a history of specific psychiatric conditions, or are receiving end-of-life care will be excluded. The PREPARED Trial intervention consists of four main components: a decision tree, an instruction manual, a training package, and a toolkit. Primary study outcomes will be assessed weekly. Functional autonomy and cognitive levels will be assessed at the beginning and end of follow-up, while information pertaining to modifiable delirium risk factors, medical consultations, and facility transfers will be collected retrospectively for the duration of the follow-up period. Primary outcomes will be reported at the level of intervention assignment. All researchers analyzing the data will be blinded to group allocation. DISCUSSION: This large-scale intervention study will contribute significantly to the development of evidence-based clinical guidelines for delirium prevention in this frail elderly population, as it will be the first to evaluate the efficacy of a multicomponent delirium prevention program translated into LTC clinical practice on a large scale. TRIAL REGISTRATION: NCT03718156 , ClinicalTrials.gov .

Constrained instruments and their application to Mendelian randomization with pleiotropy.

In Mendelian randomization (MR), inference about causal relationship between a phenotype of interest and a response or disease outcome can be obtained by constructing instrumental variables from genetic variants. However, MR inference requires three assumptions, one of which is that the genetic variants only influence the outcome through phenotype of interest. Pleiotropy, that is, the situation in which some genetic variants affect more than one phenotype, can invalidate these genetic variants for use as instrumental variables; thus a naive analysis will give biased estimates of the causal relation. Here, we present new methods (constrained instrumental variable [CIV] methods) to construct valid instrumental variables and perform adjusted causal effect estimation when pleiotropy exists and when the pleiotropic phenotypes are available. We demonstrate that a smoothed version of CIV performs approximate selection of genetic variants that are valid instruments, and provides unbiased estimates of the causal effects. We provide details on a number of existing methods, together with a comparison of their performance in a large series of simulations. CIV performs robustly across different pleiotropic violations of the MR assumptions. We also analyzed the data from the Alzheimer's disease (AD) neuroimaging initiative (ADNI; Mueller et al., 2005. Alzheimer's Dementia, 11(1), 55-66) to disentangle causal relationships of several biomarkers with AD progression.

Latent class analysis of the multivariate Delirium Index in long-term care settings.

ABSTRACTBackground:A few studies examine the time evolution of delirium in long-term care (LTC) settings. In this work, we analyze the multivariate Delirium Index (DI) time evolution in LTC settings. METHODS: The multivariate DI was measured weekly for six months in seven LTC facilities, located in Montreal and Quebec City. Data were analyzed using a hidden Markov chain/latent class model (HMC/LC). RESULTS: The analysis sample included 276 LTC residents. Four ordered latent classes were identified: fairly healthy (low "disorientation" and "memory impairment," negligible other DI symptoms), moderately ill (low "inattention" and "disorientation," medium "memory impairment"), clearly sick (low "disorganized thinking" and "altered level of consciousness," medium "inattention," "disorientation," "memory impairment" and "hypoactivity"), and very sick (low "hypoactivity," medium "altered level of consciousness," high "inattention," "disorganized thinking," "disorientation" and "memory impairment"). Four course types were also identified: stable, improvement, worsening, and non-monotone. Class order was associated with increasing cognitive impairment, frequency of both prevalent/incident delirium and dementia, mortality rate, and decreasing performance in ADL. CONCLUSION: Four ordered latent classes and four course types were found in LTC residents. These results are similar to those reported previously in acute care (AC); however, the proportion of very sick residents at enrolment was larger in LTC residents than in AC patients. In clinical settings, these findings could help identify participants with a chronic clinical disorder. Our HMC/LC approach may help understand coexistent disorders, e.g. delirium and dementia.

Beyond using composite measures to analyze the effect of unmet supportive care needs on caregivers' anxiety and depression.

OBJECTIVE: Caregiver research has relied on composite measures (eg, count) of unmet supportive care needs to determine relationships with anxiety and depression. Such composite measures assume that all unmet needs have a similar impact on outcomes. The purpose of this study is to identify individual unmet needs most associated with caregivers' anxiety and depression. METHODS: Two hundred nineteen caregivers completed the 44-item Supportive Care Needs Survey and the Hospital Anxiety and Depression Scale (minimal clinically important difference = 1.5) at 6 to 8 months and 1, 2, 3.5, and 5 years following the patients' cancer diagnosis. The list of needs was reduced using partial least square regression, and those with a variance importance in projection >1 were analyzed using Bayesian model averaging. RESULTS: Across time, 8 items remained in the top 10 based on prevalence and were labelled "core." Three additional ones were labelled "frequent," as they remained in the top 10 from 1 year onwards. Bayesian model averaging identified a maximum of 3 significant unmet needs per time point-all leading to a difference greater than the minimal clinically important difference. For depression, none of the core unmet needs were significant, rather significance was noted for frequent needs and needs that were not prevalent. For anxiety, 3/8 core and 3/3 frequent unmet needs were significant. CONCLUSIONS: Those unmet needs that are most prevalent are not necessarily the most significant ones, and findings provide an evidence-based framework to guide the development of caregiver interventions. A broader contribution is proposing a different approach to identify significant unmet needs.

How Older Adults Experience an Emergency Department Visit: Development and Validation of Measures.

STUDY OBJECTIVE: This study aims to develop and validate measures of experiences of an emergency department (ED) visit suitable for use by older adults or their family members. METHODS: A cohort of patients aged 75 years and older who were discharged home was recruited at 4 EDs. At 1 week after the visit, patients or family members were interviewed by telephone to assess problems experienced at the visit. Twenty-six questions based on 6 domains of care found in the literature were developed: 16 questions were administered to all patients; 10 questions were administered to bed patients only. Scales were developed with multiple correspondence analysis. Regression analyses were used to validate the scales, using 2 validation criteria: perceived overall quality of care and willingness to return to the same ED. RESULTS: Four hundred twelve patients completed the 1-week interview, 197 ambulatory and 215 bed patients; family members responded for 75 patients. Two scales were developed, assessing personal care and communication (8 questions; α=.63) and waiting times (2 questions; α=.79). Both scales were significantly independently associated with perceived overall quality of care and willingness to return to the same ED. CONCLUSION: Two scales assessing important aspects of ED care experienced by older adults are ready for further evaluation in other settings.

Gene networks show associations with seed region connectivity.

Primary patterns in adult brain connectivity are established during development by coordinated networks of transiently expressed genes; however, neural networks remain malleable throughout life. The present study hypothesizes that structural connectivity from key seed regions may induce effects on their connected targets, which are reflected in gene expression at those targeted regions. To test this hypothesis, analyses were performed on data from two brains from the Allen Human Brain Atlas, for which both gene expression and DW-MRI were available. Structural connectivity was estimated from the DW-MRI data and an approach motivated by network topology, that is, weighted gene coexpression network analysis (WGCNA), was used to cluster genes with similar patterns of expression across the brain. Group exponential lasso models were then used to predict gene cluster expression summaries as a function of seed region structural connectivity patterns. In several gene clusters, brain regions located in the brain stem, diencephalon, and hippocampal formation were identified that have significant predictive power for these expression summaries. These connectivity-associated clusters are enriched in genes associated with synaptic signaling and brain plasticity. Furthermore, using seed region based connectivity provides a novel perspective in understanding relationships between gene expression and connectivity. Hum Brain Mapp 38:3126-3140, 2017. © 2017 Wiley Periodicals, Inc.

funtooNorm: an R package for normalization of DNA methylation data when there are multiple cell or tissue types.

MOTIVATION: DNA methylation patterns are well known to vary substantially across cell types or tissues. Hence, existing normalization methods may not be optimal if they do not take this into account. We therefore present a new R package for normalization of data from the Illumina Infinium Human Methylation450 BeadChip (Illumina 450 K) built on the concepts in the recently published funNorm method, and introducing cell-type or tissue-type flexibility. RESULTS: funtooNorm is relevant for data sets containing samples from two or more cell or tissue types. A visual display of cross-validated errors informs the choice of the optimal number of components in the normalization. Benefits of cell (tissue)-specific normalization are demonstrated in three data sets. Improvement can be substantial; it is strikingly better on chromosome X, where methylation patterns have unique inter-tissue variability. AVAILABILITY AND IMPLEMENTATION: An R package is available at https://github.com/GreenwoodLab/funtooNorm, and has been submitted to Bioconductor at http://bioconductor.org.

Recruitment challenges in clinical research: Survey of potential participants in a diagnostic study of ovarian cancer.

OBJECTIVE: Recruiting participants in clinical research is challenging. Certain groups, such as older adults, rural residents, and individuals with lower socio-economic status, are typically underrepresented. Here, we explore perceived motivators and barriers among potential participants in a diagnostic study of ovarian cancer. METHODS: Women aged 50 and older who answered a mail survey in Montreal, Canada, were asked to assess their eligibility to participate in the ongoing Diagnosing Ovarian cancer Early (DOvE) Study. If 'eligible', they were asked whether they planned to participate in DOvE. Using modified Poisson regression, we examined responders' self-assessment of eligibility, intention to participate, and reasons for why or why not, as a function of socio-demographic and health indicators. RESULTS: Of 826 responders, 33.1% misclassified themselves with respect to eligibility. Among 532 self-assessed eligible women, 56.4% planned to participate in the study. The majority of women not planning to participate preferred to be assessed by their physicians (a reason more commonly reported by those with lower education or income) or believed they were not at risk of ovarian cancer (despite having no fewer risk factors). "Inconvenience" was also a commonly reported reason, especially among rural residents. Women who planned to participate often perceived a benefit (e.g. to rule out ovarian cancer, or to receive a quick check-up). CONCLUSIONS: Recruitment, particularly of underrepresented groups, in clinical studies may be enhanced by involving primary care providers, facilitating access to study sites, and providing clear information about the disease under study (including risk factors) and eligibility criteria.

Use of medications that antagonize mediators of inflammatory responses may reduce the risk of delirium in older adults: a nested case-control study.

OBJECTIVE: The objective of this study is to explore whether the use of medications that antagonize mediators of inflammatory responses reduces the risk of delirium in older adults. METHODS: A nested case-control study was conducted using data from a prospective study of delirium in older long-term care residents from 7 long-term care facilities in Montreal and Quebec City, Canada. The Confusion Assessment Method was used to diagnose incident delirium. The use of medications that antagonize mediators of inflammatory responses was determined by examining facility pharmacy databases and coding medications received daily by each resident. Risk sets were built using incidence density sampling: each risk set consisted of a case with incident delirium and all controls without incident delirium at the same date and facility. Conditional logistic regression was used to assess the association of exposure to inflammation antagonist medications with the incidence of delirium. RESULTS: Of 254 residents, 95 developed incident delirium during 24 weeks (cases); each case was matched with up to 35 controls. Unadjusted and adjusted odds ratios (95% CI) of delirium for residents exposed to at least one inflammation antagonist medication were 0.53 (0.34, 0.81) and 0.60 (0.38, 0.92), respectively. Estimates of the risk of incident delirium associated with specific medications and medication classes were mostly protective but not statistically significant. CONCLUSION: The use of medications that antagonize mediators of inflammatory responses may reduce the risk of delirium in older adults. Despite study limitations, the findings merit further investigation using larger patient samples, more precise measures of exposure and better control of potential confounding variables. Copyright © 2016 John Wiley & Sons, Ltd.

Partial and no recovery from delirium after hospital discharge predict increased adverse events.

Background: The implications of partial and no recovery from delirium after hospital discharge are not clear. We sought to explore whether partial and no recovery from delirium among recently discharged patients predicted increased adverse events (emergency room visits, hospitalisations, death) during the subsequent 3 months. Method: Prospective study of recovery from delirium in older hospital inpatients. The Confusion Assessment Method was used to diagnose delirium in hospital and determine recovery status after discharge (T0). Adverse events were determined during the 3 months T0. Survival analysis to the first adverse event and counting process modelling for one or more adverse events were used to examine associations between recovery status (ordinal variable, 0, 1 or 2 for full, partial or no recovery, respectively) and adverse events. Results: Of 278 hospital inpatients with delirium, 172 were discharged before the assessment of recovery status (T0). Delirium recovery status at T0 was determined for 152: 25 had full recovery, 32 had partial recovery and 95 had no recovery. Forty-four patients had at least one adverse event during the subsequent 3 months. In multivariable analysis of one or more adverse events, poorer recovery status predicted increased adverse events; the hazard ratio (HR) (95% confidence interval, CI) was 1.72 (1.09, 2.71). The association of recovery status with adverse events was stronger among patients without dementia. Conclusion: Partial and no recovery from delirium after hospital discharge appear to predict increased adverse events during the subsequent 3 months These findings have potentially important implications for in-hospital and post-discharge management and policy.

Baseline Psychological Treatment Reduces the Effect of Coaching in a Randomised Trial of a Depression Self-Care Intervention.

OBJECTIVE: To explore the effects of baseline psychological and antidepressant medication treatment in a trial of lay telephone coaching in a low-intensity, supported depression self-care intervention. METHOD: A single blind, individually randomised, pragmatic trial was conducted among primary care adults with chronic physical conditions and comorbid depressive symptoms. Eligible subjects were randomised to receive a depression self-care toolkit with (intervention group) or without (control group) telephone coaching provided by trained lay coaches. For this brief communication, a secondary analysis of the trial data focused on the effects of baseline psychological and antidepressant treatments on mental health outcomes (Patient Health Questionnaire 9 [PHQ-9], SF-12 Mental Component Summary [MCS], Generalized Anxiety Disorder 7 [GAD-7]) and satisfaction with the intervention. RESULTS: In total, 223 patients were randomised, and 165 (74.0%) completed both 3- and 6-month follow-ups. There were 2 significant interactions of baseline treatment and study group for 6-month mental health outcomes. A significant benefit of coaching on 6-month PHQ-9 was seen only among participants who were not receiving baseline psychological treatment. A smaller interaction was found for baseline antidepressant medications and 6-month mental health. There was a significant main effect for baseline psychological treatment and lower 6-month satisfaction. CONCLUSIONS: Depressed patients receiving baseline psychological treatment may not benefit from lay coaching offered as part of a low-intensity depression self-care intervention.

Longitudinal patterns of delirium severity scores in long-term care settings.

BACKGROUND: The delirium index (DI) is a valid measure of delirium severity. We proposed to describe longitudinal patterns of severity scores in older long-term care (LTC) residents. METHODS: A prospective cohort study of 280 residents in seven LTC facilities in Montreal and Quebec City, Canada, was conducted. DI, Barthel Index, Mini-Mental State Examination, Charlson Comorbidity Index, Cornell Scale for Depression in Dementia, dementia assessment by an MD, and prevalent or incident probable delirium defined according to the Confusion Assessment Method were completed at baseline. The DI was also assessed weekly for 6 months. Demographic characteristics were abstracted from resident charts. Cluster analysis for longitudinal data was used to describe longitudinal patterns of DI scores. RESULTS: During the 24 weeks following enrolment, 28 (10.0%) of 280 residents who had prevalent delirium and 76 (27.1%) who had incident delirium were included in our analysis. Average observation period was 18.3 weeks. Four basic types of time evolution patterns were discovered: Improvement, Worsening, Fluctuating, and Steady, including 22%, 18%, 25%, and 35%, of the residents, respectively. With the exception of the Worsening pattern, the average trajectory was stabilized at the 4th week or earlier. Poor baseline cognitive and physical function and greater severity of delirium predicted worse trajectories over 24 weeks. CONCLUSIONS: The longitudinal patterns of DI scores found in LTC residents resemble those found in an earlier study of delirium in acute care (AC) settings. However, compared to AC patients, LTC residents have a smaller DI variability over time, a less frequent Improvement pattern, and more frequent Worsening and Fluctuating patterns.

Estimating the Prevalence of Ovarian Cancer Symptoms in Women Aged 50 Years or Older: Problems and Possibilities.

Diagnostic testing is recommended in women with "ovarian cancer symptoms." However, these symptoms are nonspecific. The ongoing Diagnosing Ovarian Cancer Early (DOVE) Study in Montreal, Quebec, Canada, provides diagnostic testing to women aged 50 years or older with symptoms lasting for more than 2 weeks and less than 1 year. The prevalence of ovarian cancer in DOVE is 10 times that of large screening trials, prompting us to estimate the prevalence of these symptoms in this population. We sent a questionnaire to 3,000 randomly sampled women in 2014-2015. Overall, 833 women responded; 81.5% reported at least 1 symptom, and 59.7% reported at least 1 symptom within the duration window specified in DOVE. We explored whether such high prevalence resulted from low survey response by applying inverse probability weighting to correct the estimates. Older women and those from deprived areas were less likely to respond, but only age was associated with symptom reporting. Prevalence was similar in early and late responders. Inverse probability weighting had a minimal impact on estimates, suggesting little evidence of nonresponse bias. This is the first study investigating symptoms that have proven to identify a subset of women with a high prevalence of ovarian cancer. However, the high frequency of symptoms warrants further refinements before symptom-triggered diagnostic testing can be implemented.

Descent of the fetal head (station) during the first stage of labor.

BACKGROUND: High station at specific points in the first stage of labor, such as a floating head on admission, or at 4-cm dilation or when arrest of dilation occurs, is associated with higher rates of failure to deliver vaginally. Therefore it could be useful to know if station is within an expected range at a given dilation during first stage. Arrest of descent disorders have been defined thus far on criteria applicable in the second stage. Statistical modeling is an attractive methodology to characterize the relationship between station and dilation because the resulting mathematical expressions could be used as a reference for comparison in the future. In addition, they can be used to produce a finely graded assessment of descent using numerical terms such as percentile rankings. A 2-step approach to potentially improving the assessment of station could be to develop a statistical model that describes the general relationship between station and dilation in the first stage of uncomplicated births and then determine if such a model would have identified births with complications related to poor labor progress. Given the complex nature of labor data, especially the imprecision of dilation and station measurement, it is not immediately evident that such a model is identifiable or what its precision would be. OBJECTIVE: We sought to characterize in mathematical terms the relationship of station to dilation during the first stage of labor for nulliparous and multiparous women with spontaneous vaginal births. STUDY DESIGN: This retrospective cohort study included 28,121 exams from 5555 women with singleton cephalic presentations at ≥37 weeks' gestation with electronic fetal monitoring tracings, who delivered vaginally without instrumentation and had 5-minute Apgar scores >6 at 2 academic community referral hospitals in 2012 through 2013. Women with a previous cesarean birth were excluded. We used longitudinal statistical techniques suitable to biological data that were irregularly sampled with repeated measures over time. RESULTS: A linear relationship was observed between station and dilation. For both nulliparous and multiparous women the final model was a linear regression with random effects for intercept and slope and a first-order autoregressive correlation structure. The 5th-95th range of station at any given dilation spanned about 3-4 cm. CONCLUSION: Our results demonstrate a general trend of increasing descent of the presenting part as dilation advances during the first stage of labor in women who delivered vaginally without instrumentation. We propose that the mathematical expressions describing this relationship may be valuable in the assessment of first-stage labor progression.

Frequency of full, partial and no recovery from subsyndromal deliriumin older hospital inpatients.

OBJECTIVES: To determine the frequencies of full, partial and no recovery from subsyndromal delirium (SSD) in older hospital inpatients. A secondary objective was to compare the recovery status of patients with SSD or delirium. METHODS: SSD was defined as acute onset of one or more Confusion Assessment Method core symptoms of delirium (fluctuation, inattention, disorganized thinking and altered level of consciousness) not meeting criteria for delirium and not progressing to delirium. The recovery status of medical or surgical inpatients aged 65 and older with SSD was assessed approximately 1 and 3 months after enrolment. Primary outcome categories were full recovery (no core symptoms of delirium), partial recovery (presence of one or more core symptoms but fewer symptoms than at enrolment), no recovery (same number of core symptoms as at enrolment) or death. Nominal logistic regression was used to compare the recovery status of patients with SSD or delirium. RESULTS: Twenty-eight patients with SSD were enrolled. At the first follow-up, the frequencies of full, partial and no recovery and death were 40%, 12%, 32% and 16%, respectively; at the second follow-up, the frequencies were 54%, 8%, 21% and 17%, respectively. The frequency of full recovery was much higher in patients with SSD than delirium. CONCLUSION: Small study sample size notwithstanding, the majority (54%) of patients with SSD recovered fully, but a substantial proportion (29%) had a protracted course. It may be important to monitor the longer-term course of SSD and develop strategies to ensure full recovery in all patients.

Six-month trajectories of self-reported depressive symptoms in long-term care.

BACKGROUND: Depression is a common problem in long-term care (LTC) settings. We sought to characterize depression symptom trajectories over six months among older residents, and to identify resident characteristics at baseline that predict symptom trajectory. METHODS: This study was a secondary analysis of data from a six-month prospective, observational, and multi-site study. Severity of depressive symptoms was assessed with the 15-item Geriatric Depression Scale (GDS) at baseline and with up to six monthly follow-up assessments. Participants were 130 residents with a Mini-Mental State Examination score of 15 or more at baseline and of at least two of the six monthly follow-up assessments. Individual resident GDS trajectories were grouped using hierarchical clustering. The baseline predictors of a more severe trajectory were identified using the Proportional Odds Model. RESULTS: Three clusters of depression symptom trajectory were found that described "lower," "intermediate," and "higher" levels of depressive symptoms over time (mean GDS scores for three clusters at baseline were 2.2, 4.9, and 9.0 respectively). The GDS scores in all groups were generally stable over time. Baseline predictors of a more severe trajectory were as follows: Initial GDS score of 7 or more, female sex, LTC residence for less than 12 months, and corrected visual impairment. CONCLUSIONS: The six-month course of depressive symptoms in LTC is generally stable. Most residents who experience a more severe symptom trajectory can be identified at baseline.

Estimating genome-wide significance for whole-genome sequencing studies.

Although a standard genome-wide significance level has been accepted for the testing of association between common genetic variants and disease, the era of whole-genome sequencing (WGS) requires a new threshold. The allele frequency spectrum of sequence-identified variants is very different from common variants, and the identified rare genetic variation is usually jointly analyzed in a series of genomic windows or regions. In nearby or overlapping windows, these test statistics will be correlated, and the degree of correlation is likely to depend on the choice of window size, overlap, and the test statistic. Furthermore, multiple analyses may be performed using different windows or test statistics. Here we propose an empirical approach for estimating genome-wide significance thresholds for data arising from WGS studies, and we demonstrate that the empirical threshold can be efficiently estimated by extrapolating from calculations performed on a small genomic region. Because analysis of WGS may need to be repeated with different choices of test statistics or windows, this prediction approach makes it computationally feasible to estimate genome-wide significance thresholds for different analysis choices. Based on UK10K whole-genome sequence data, we derive genome-wide significance thresholds ranging between 2.5 × 10(-8) and 8 × 10(-8) for our analytic choices in window-based testing, and thresholds of 0.6 × 10(-8) -1.5 × 10(-8) for a combined analytic strategy of testing common variants using single-SNP tests together with rare variants analyzed with our sliding-window test strategy.