Association study between drug prescriptions and Alzheimer's disease claims in a commercial insurance database.

In the ongoing effort to discover treatments for Alzheimer's disease (AD), there has been considerable focus on investigating the use of repurposed drug candidates. Mining of electronic health record data has the potential to identify novel correlated effects between commonly used drugs and AD. In this study, claims from members with commercial health insurance coverage were analyzed to determine the correlation between the use of various drugs on AD incidence and claim frequency. We found that, within the insured population, several medications for psychotic and mental illnesses were associated with higher disease incidence and frequency, while, to a lesser extent, antibiotics and anti-inflammatory drugs were associated with lower AD incidence rates. The observations thus provide a general overview of the prescription and claim relationships between various drug types and Alzheimer's disease, with insights into which drugs have possible implications on resulting AD diagnosis.

Meta-Prediction of Coronary Artery Disease Risk.

Coronary artery disease (CAD) remains the leading cause of mortality and morbidity worldwide. Recent advances in large-scale genome-wide association studies have highlighted the potential of genetic risk, captured as polygenic risk scores (PRS), in clinical prevention. However, the current clinical utility of PRS models is limited to identifying high-risk populations based on the top percentiles of genetic susceptibility. While some studies have attempted integrative prediction using genetic and non-genetic factors, many of these studies have been cross-sectional and focused solely on risk stratification. Our primary objective in this study was to integrate unmodifiable (age / genetics) and modifiable (clinical / biometric) risk factors into a prospective prediction framework which also produces actionable and personalized risk estimates for the purpose of CAD prevention in a heterogenous adult population. Thus, we present an integrative, omnigenic, meta-prediction framework that effectively captures CAD risk subgroups, primarily distinguished by degree and nature of genetic risk, with distinct risk reduction profiles predicted from standard clinical interventions. Initial model development considered ~ 2,000 predictive features, including demographic data, lifestyle factors, physical measurements, laboratory tests, medication usage, diagnoses, and genetics. To power our meta-prediction approach, we stratified the UK Biobank into two primary cohorts: 1) a prevalent CAD cohort used to train baseline and prospective predictive models for contributing risk factors and diagnoses, and 2) an incident CAD cohort used to train the final CAD incident risk prediction model. The resultant 10-year incident CAD risk model is composed of 35 derived meta-features from models trained on the prevalent risk cohort, most of which are predicted baseline diagnoses with multiple embedded PRSs. This model achieved an AUC of 0.81 and macro-averaged F1-score of 0.65, outperforming standard clinical scores and prior integrative models. We further demonstrate that individualized risk reduction profiles can be derived from this model, with genetic risk mediating the degree of risk reduction achieved by standard clinical interventions.

Genetically-informed prediction of short-term Parkinson's disease progression.

Parkinson's disease (PD) treatments modify disease symptoms but have not been shown to slow progression, characterized by gradual and varied motor and non-motor changes overtime. Variation in PD progression hampers clinical research, resulting in long and expensive clinical trials prone to failure. Development of models for short-term PD progression prediction could be useful for shortening the time required to detect disease-modifying drug effects in clinical studies. PD progressors were defined by an increase in MDS-UPDRS scores at 12-, 24-, and 36-months post-baseline. Using only baseline features, PD progression was separately predicted across all timepoints and MDS-UPDRS subparts in independent, optimized, XGBoost models. These predictions plus baseline features were combined into a meta-predictor for 12-month MDS UPDRS Total progression. Data from the Parkinson's Progression Markers Initiative (PPMI) were used for training with independent testing on the Parkinson's Disease Biomarkers Program (PDBP) cohort. 12-month PD total progression was predicted with an F-measure 0.77, ROC AUC of 0.77, and PR AUC of 0.76 when tested on a hold-out PPMI set. When tested on PDBP we achieve a F-measure 0.75, ROC AUC of 0.74, and PR AUC of 0.73. Exclusion of genetic predictors led to the greatest loss in predictive accuracy; ROC AUC of 0.66, PR AUC of 0.66-0.68 for both PPMI and PDBP testing. Short-term PD progression can be predicted with a combination of survey-based, neuroimaging, physician examination, and genetic predictors. Dissection of the interplay between genetic risk, motor symptoms, non-motor symptoms, and longer-term expected rates of progression enable generalizable predictions.

Differential dysregulation of granule subsets in WASH-deficient neutrophil leukocytes resulting in inflammation.

Dysregulated secretion in neutrophil leukocytes associates with human inflammatory disease. The exocytosis response to triggering stimuli is sequential; gelatinase granules modulate the initiation of the innate immune response, followed by the release of pro-inflammatory azurophilic granules, requiring stronger stimulation. Exocytosis requires actin depolymerization which is actively counteracted under non-stimulatory conditions. Here we show that the actin nucleator, WASH, is necessary to maintain azurophilic granules in their refractory state by granule actin entrapment and interference with the Rab27a-JFC1 exocytic machinery. On the contrary, gelatinase granules of WASH-deficient neutrophil leukocytes are characterized by decreased Rac1, shortened granule-associated actin comets and impaired exocytosis. Rac1 activation restores exocytosis of these granules. In vivo, WASH deficiency induces exacerbated azurophilic granule exocytosis, inflammation, and decreased survival. WASH deficiency thus differentially impacts neutrophil granule subtypes, impairing exocytosis of granules that mediate the initiation of the neutrophil innate response while exacerbating pro-inflammatory granule secretion.

Impact of polygenic risk communication: an observational mobile application-based coronary artery disease study.

We developed a smartphone application, MyGeneRank, to conduct a prospective observational cohort study (NCT03277365) involving the automated generation, communication, and electronic capture of response to a polygenic risk score (PRS) for coronary artery disease (CAD). Adults with a smartphone and an existing 23andMe genetic profiling self-referred to the study. We evaluated self-reported actions taken in response to personal CAD PRS information, with special interest in the initiation of lipid-lowering therapy. 19% (721/3,800) of participants provided complete responses for baseline and follow-up use of lipid-lowering therapy. 20% (n = 19/95) of high CAD PRS vs 7.9% (n = 8/101) of low CAD PRS participants initiated lipid-lowering therapy at follow-up (p-value = 0.002). Both the initiation of statin and non-statin lipid-lowering therapy was associated with degree of CAD PRS: 15.2% (n = 14/92) vs 6.0% (n = 6/100) for statins (p-value = 0.018) and 6.8% (n = 8/118) vs 1.6% (n = 2/123) for non-statins (p-value = 0.022) in high vs low CAD PRS, respectively. High CAD PRS was also associated with earlier initiation of lipid lowering therapy (average age of 52 vs 65 years in high vs low CAD PRS respectively, p-value = 0.007). Overall, degree of CAD PRS was associated with use of any lipid-lowering therapy at follow-up: 42.4% (n = 56/132) vs 28.5% (n = 37/130) (p-value = 0.009). We find that digital communication of personal CAD PRS information is associated with increased and earlier lipid-lowering initiation in individuals of high CAD PRS. Loss to follow-up is the primary limitation of this study. Alternative communication routes, and long-term studies with EHR-based outcomes are needed to understand the generalizability and durability of this finding.

The freshman sleep and health (FRoSH) study: Examining sleep and weight gain in incoming college freshmen.

OBJECTIVE: Examine how changes in sleep duration, objectively measured by activity trackers, impact weight gain in incoming college freshman. Participants: Incoming college freshmen, age ≥ 18. Methods: We measured weight and daily sleep duration before college entry and through the 1st college quarter. Additionally, we examined changes in sleep variability, activity levels and smartphone screen time use as possible predictors of weight gain. Results: 75 participants completed the study. Total sleep duration decreased from 437.9 ± SD 57.3 minutes at baseline to 416.5 ± SD 68.6 minutes by the end of the first quarter (p = 6.6 × 10-3). (BMI) did not change significantly in this cohort. Higher sleep variability at baseline and an increase in sleep variability were associated with increases in BMI. Smartphone screen use was note to be high (235.2 ± SD 110.3 minutes/day) at the end of the first quarter. Conclusions: College weight gain may be affected by factors other than sleep duration, including sleep variability.Supplemental data for this article can be accessed online at https://doi.org/10.1080/07448481.2022.2032720.

AudioChip: A Deep Phenotyping Approach for Deconstructing and Quantifying Audiological Phenotypes of Self-Reported Speech Perception Difficulties.

OBJECTIVES: About 15% of U.S. adults report speech perception difficulties despite showing normal audiograms. Recent research suggests that genetic factors might influence the phenotypic spectrum of speech perception difficulties. The primary objective of the present study was to describe a conceptual framework of a deep phenotyping method, referred to as AudioChipping, for deconstructing and quantifying complex audiometric phenotypes. DESIGN: In a sample of 70 females 18 to 35 years of age with normal audiograms (from 250 to 8000 Hz), the study measured behavioral hearing thresholds (250 to 16,000 Hz), distortion product otoacoustic emissions (1000 to 16,000 Hz), click-evoked auditory brainstem responses (ABR), complex ABR (cABR), QuickSIN, dichotic digit test score, loudness discomfort level, and noise exposure background. The speech perception difficulties were evaluated using the Speech, Spatial, and Quality of Hearing Scale-12-item version (SSQ). A multiple linear regression model was used to determine the relationship between SSQ scores and audiometric measures. Participants were categorized into three groups (i.e., high, mid, and low) using the SSQ scores before performing the clustering analysis. Audiometric measures were normalized and standardized before performing unsupervised k-means clustering to generate AudioChip. RESULTS: The results showed that SSQ and noise exposure background exhibited a significant negative correlation. ABR wave I amplitude, cABR offset latency, cABR response morphology, and loudness discomfort level were significant predictors for SSQ scores. These predictors explained about 18% of the variance in the SSQ score. The k-means clustering was used to split the participants into three major groups; one of these clusters revealed 53% of participants with low SSQ. CONCLUSIONS: Our study highlighted the relationship between SSQ and auditory coding precision in the auditory brainstem in normal-hearing young females. AudioChip was useful in delineating and quantifying internal homogeneity and heterogeneity in audiometric measures among individuals with a range of SSQ scores. AudioChip could help identify the genotype-phenotype relationship, document longitudinal changes in auditory phenotypes, and pair individuals in case-control groups for the genetic association analysis.

Mobile Health-Collected Biophysical Markers in Children with Serious Illness-Related Pain.

Context: There is an ongoing established need to develop engaging pain assessment strategies to provide more effective individualized care to pediatric patients with serious illnesses. This study explores the acceptability of wireless devices as one option. Objective: To evaluate the ability of wrist-wearable technology to collect physiological data from children with serious illnesses. Methods: Single-site prospective observational study conducted between September 2017 and September 2018 at Rady Children's Hospital, San Diego, California, inpatient wards. Pediatric patients with diagnoses of cancer and sickle cell disease admitted to the hospital for acute-on-chronic pain and taking opioid pain medications were asked to complete two 24-hour continuous monitoring periods with the Empatica E4 wristband. Results: Data collected from the device correlated with manually obtained vital signs. Children responded favorably to wearing the device. Participants with reported subjective pain versus no pain had average heart rate increased by 16.4 bpm, skin temperature decreased by 3.5°C, and electrodermal activity decreased by 0.27. Conclusions: This study shows the possibility of collecting continuous biophysical data in a nonobtrusive manner in seriously ill children experiencing acute-on-chronic pain using wearable devices. It provides the framework for larger studies to explore the utility of such data in relation to metrics of pain and suffering in this patient population.

Objectively measured sleep fragmentation is associated with incident delirium in older hospitalized patients: Analysis of data collected from an randomized controlled trial.

Delirium may lead to poor outcomes in hospitalized older adults, and sleep deprivation may contribute to its pathogenesis. Thus, we sought to measure sleep duration and fragmentation using wrist-worn actigraphy in older, hospitalized patients with and without delirium, and to determine if actigraphy-based parameters could be used to predict delirium prior to clinical recognition. We conducted a secondary analysis of data from a recent, randomized clinical trial aimed at preventing inpatient delirium. Participants (n = 70) were aged ≥ 65 years admitted to an internal medicine service. Delirium was defined by the Confusion Assessment Method, or altered mental status identified by a clinician. Sleep measurements were actigraphy-based, and included total sleep time, median sleep bout duration and other measures of sleep fragmentation. We found that total sleep duration was similar between patients with (n = 17) and without (n = 53) delirium (mean 384.9 ± SD 162.7 versus mean 456.6 ± SD 135.8 min; p = .081). Mean sleep bout times were shorter in delirious versus never-delirious patients (median 6.1 [interquartile range 4.3-8.9] versus 7.9 [interquartile range 5.7-11.3] min, p = .048). Patients with delirium had more short sleep bouts (< 10 min) and fewer longer sleep bouts (> 30 min) compared with those without delirium. Increased sleep fragmentation was present prior to the clinical recognition of delirium. Overall, delirium was associated with increased sleep fragmentation detected by actigraphy, and sleep fragmentation might be useful as a biomarker for delirium prediction in the future.

Harnessing wearable device data to improve state-level real-time surveillance of influenza-like illness in the USA: a population-based study.

BACKGROUND: Acute infections can cause an individual to have an elevated resting heart rate (RHR) and change their routine daily activities due to the physiological response to the inflammatory insult. Consequently, we aimed to evaluate if population trends of seasonal respiratory infections, such as influenza, could be identified through wearable sensors that collect RHR and sleep data. METHODS: We obtained de-identified sensor data from 200 000 individuals who used a Fitbit wearable device from March 1, 2016, to March 1, 2018, in the USA. We included users who wore a Fitbit for at least 60 days and used the same wearable throughout the entire period, and focused on the top five states with the most Fitbit users in the dataset: California, Texas, New York, Illinois, and Pennsylvania. Inclusion criteria included having a self-reported birth year between 1930 and 2004, height greater than 1 m, and weight greater than 20 kg. We excluded daily measurements with missing RHR, missing wear time, and wear time less than 1000 min per day. We compared sensor data with weekly estimates of influenza-like illness (ILI) rates at the state level, as reported by the US Centers for Disease Control and Prevention (CDC), by identifying weeks in which Fitbit users displayed elevated RHRs and increased sleep levels. For each state, we modelled ILI case counts with a negative binomial model that included 3-week lagged CDC ILI rate data (null model) and the proportion of weekly Fitbit users with elevated RHR and increased sleep duration above a specified threshold (full model). We also evaluated weekly change in ILI rate by linear regression using change in proportion of elevated Fitbit data. Pearson correlation was used to compare predicted versus CDC reported ILI rates. FINDINGS: We identified 47 249 users in the top five states who wore a Fitbit consistently during the study period, including more than 13·3 million total RHR and sleep measures. We found the Fitbit data significantly improved ILI predictions in all five states, with an average increase in Pearson correlation of 0·12 (SD 0·07) over baseline models, corresponding to an improvement of 6·3-32·9%. Correlations of the final models with the CDC ILI rates ranged from 0·84 to 0·97. Week-to-week changes in the proportion of Fitbit users with abnormal data were associated with week-to-week changes in ILI rates in most cases. INTERPRETATION: Activity and physiological trackers are increasingly used in the USA and globally to monitor individual health. By accessing these data, it could be possible to improve real-time and geographically refined influenza surveillance. This information could be vital to enact timely outbreak response measures to prevent further transmission of influenza cases during outbreaks. FUNDING: Partly supported by the US National Institutes of Health National Center for Advancing Translational Sciences.

Genotype imputation and variability in polygenic risk score estimation.

BACKGROUND: Polygenic risk scores (PRSs) are a summarization of an individual's genetic risk for a disease or trait. These scores are being generated in research and commercial settings to study how they may be used to guide healthcare decisions. PRSs should be updated as genetic knowledgebases improve; however, no guidelines exist for their generation or updating. METHODS: Here, we characterize the variability introduced in PRS calculation by a common computational process used in their generation-genotype imputation. We evaluated PRS variability when performing genotype imputation using 3 different pre-phasing tools (Beagle, Eagle, SHAPEIT) and 2 different imputation tools (Beagle, Minimac4), relative to a WGS-based gold standard. Fourteen different PRSs spanning different disease architectures and PRS generation approaches were evaluated. RESULTS: We find that genotype imputation can introduce variability in calculated PRSs at the individual level without any change to the underlying genetic model. The degree of variability introduced by genotype imputation differs across algorithms, where pre-phasing algorithms with stochastic elements introduce the greatest degree of score variability. In most cases, PRS variability due to imputation is minor (< 5 percentile rank change) and does not influence the interpretation of the score. PRS percentile fluctuations are also reduced in the more informative tails of the PRS distribution. However, in rare instances, PRS instability at the individual level can result in singular PRS calculations that differ substantially from a whole genome sequence-based gold standard score. CONCLUSIONS: Our study highlights some challenges in applying population genetics tools to individual-level genetic analysis including return of results. Rare individual-level variability events are masked by a high degree of overall score reproducibility at the population level. In order to avoid PRS result fluctuations during updates, we suggest that deterministic imputation processes or the average of multiple iterations of stochastic imputation processes be used to generate and deliver PRS results.

Cyclin E Overexpression in Human Mammary Epithelial Cells Promotes Epithelial Cancer-Specific Copy Number Alterations.

Cyclin E, a key cell cycle regulatory protein, has been linked to oncogenesis when dysregulated. We have previously shown that overexpression of cyclin E causes replication stress, leading to failure to complete replication at specific chromosomal loci during S phase of the cell cycle. This in turn promotes chromosomal damage during anaphase. Here we show that non-transformed human mammary epithelial cell clones that survive such aberrant mitoses have a specific and reproducible pattern of chromosomal Copy Number Alterations (CNAs) that we have characterized and termed the cyclin E CNA signature. Using a number of computational approaches, we show that this signature resembles one specific CNA pattern enriched in differentiated epithelial-like tumors of the breast and ovary. Analysis of the CNA profile of these clones provides a potential mechanism for cyclin E-mediated oncogenesis.

Trends in Prices of Popular Brand-Name Prescription Drugs in the United States.

Importance: High and continually increasing pharmaceutical drug spending is a major health and health policy concern in the United States. Objective: To demonstrate trends in prices among popular brand-name prescription drugs. Design, Setting, and Participants: This economic evaluation of drug prices focuses on 49 top-selling brand-name medications in the United States. Pharmacy claims data from January 1, 2012, through December 31, 2017, were obtained from Blue Cross Blue Shield Axis, a database that includes data from more than 35 million individuals with private pharmaceutical insurance. Drugs that exceeded $500 million in US sales or $1 billion in worldwide sales were examined. Main Outcomes and Measures: The median sum of out-of-pocket and insurance costs paid by patients or insurers for common prescriptions, presented annually and monthly, was the primary outcome. Results: In total, 132 brand-name prescription drugs were identified in 2017 that met the inclusion criteria. Of this total, the study focused on 49 top-selling drugs that exceeded 100 000 pharmacy claims. Substantial cost increases among these drugs was near universal, with a 76% median cost increase from January 2012 through December 2017, and almost all drugs (48 [98%]) displaying regular annual or biannual price increases. Of the 36 drugs that have been available since 2012, 28 (78%) have seen an increase in insurer and out-of-pocket costs by more than 50%, and 16 (44%) have more than doubled in price. Insulins (ie, Novolog, Humalog, and Lantus) and tumor necrosis factor inhibitors (ie, Humira and Enbrel) demonstrated highly correlated price increases, coinciding with some of the largest growth in drug costs. Relative price changes did not differ between drugs that entered the market in the past 3 to 6 years and those that have been on the market longer (number of drugs, 13 vs 36; median, 29% increase from January 2015 through December 2017; P = .81) nor between drugs with or without a Food and Drug Administration-approved therapeutic equivalent (number of drugs, 17 vs 32; median, 79% vs 73%; P = .21). Changes in prices paid were highly correlated with third-party estimates of changes in drug net prices (ρ = 0.55; P = 3.8 × 10-5), suggesting that the current rebate system, which incentivizes high list prices and greater reliance on rebates, increases overall costs. Conclusions and Relevance: The growth of drug spending in the United States associated with government-protected market exclusivity is likely to continue; greater price transparency is warranted.

Identification of paroxysmal atrial fibrillation subtypes in over 13,000 individuals.

BACKGROUND: Paroxysmal atrial fibrillation (PAF) is broadly defined despite high variability in the occurrence and duration of PAF episodes. OBJECTIVE: The purpose of this study was to identify rhythm patterns in a large cohort of individuals with PAF who wore an ambulatory single-lead electrocardiogram (ECG) patch sensor as part of standard clinical care. METHODS: We performed a retrospective analysis of longitudinal rhythm data obtained from 13,293 individuals with PAF. RESULTS: In this study, 7934 men and 5359 women with PAF wore an ambulatory single-lead ECG patch sensor for 11.4 days on average, experiencing 1,041,504 PAF episodes. The median daily rate of PAF was 1.21 episodes per day (interquartile range [IQR] 0.31-4.99), and the median maximum duration per individual was 7.5 hours (IQR 2.4-18.6 hours). There was an inverse relationship between the duration of PAF episodes and the frequency in which they occurred, which became pronounced at moderate and high overall burdens of AF. This produced a spectrum of PAF flanked by 2 distinct subtypes of the disease: the staccato subtype, characterized by many, short AF episodes; and the legato subtype, characterized by fewer, longer episodes. Longer but less frequent episodes became more common with increasing age. Only 49.4% of individuals experienced an episode in the first 24 hours of monitoring, increasing to 89.7% after 1 week of monitoring. CONCLUSION: We identified subtypes of the disease that we labeled staccato and legato. Although further study is required, these subtypes may result from differing elements of pathophysiology and disease progression, and may confer differing stroke risks.

Genome-wide association study of habitual physical activity in over 377,000 UK Biobank participants identifies multiple variants including CADM2 and APOE.

BACKGROUND/OBJECTIVES: Physical activity (PA) protects against a wide range of diseases. Habitual PA appears to be heritable, motivating the search for specific genetic variants that may inform efforts to promote PA and target the best type of PA for each individual. SUBJECTS/METHODS: We used data from the UK Biobank to perform the largest genome-wide association study of PA to date, using three measures based on self-report (nmax = 377,234) and two measures based on wrist-worn accelerometry data (nmax = 91,084). We examined genetic correlations of PA with other traits and diseases, as well as tissue-specific gene expression patterns. With data from the Atherosclerosis Risk in Communities (ARIC; n = 8,556) study, we performed a meta-analysis of our top hits for moderate-to-vigorous PA (MVPA). RESULTS: We identified ten loci across all PA measures that were significant in both a basic and a fully adjusted model (p < 5 × 10-9). Upon meta-analysis of the nine top hits for MVPA with results from ARIC, eight were genome-wide significant. Interestingly, among these, the rs429358 variant in the APOE gene was the most strongly associated with MVPA, whereby the allele associated with higher Alzheimer's risk was associated with greater MVPA. However, we were not able to rule out possible selection bias underlying this result. Variants in CADM2, a gene previously implicated in obesity, risk-taking behavior and other traits, were found to be associated with habitual PA. We also identified three loci consistently associated (p < 5 × 10-5) with PA across both self-report and accelerometry, including CADM2. We found genetic correlations of PA with educational attainment, chronotype, psychiatric traits, and obesity-related traits. Tissue enrichment analyses implicate the brain and pituitary gland as locations where PA-associated loci may exert their actions. CONCLUSIONS: These results provide new insight into the genetic basis of habitual PA, and the genetic links connecting PA with other traits and diseases.

The personal and clinical utility of polygenic risk scores.

Initial expectations for genome-wide association studies were high, as such studies promised to rapidly transform personalized medicine with individualized disease risk predictions, prevention strategies and treatments. Early findings, however, revealed a more complex genetic architecture than was anticipated for most common diseases - complexity that seemed to limit the immediate utility of these findings. As a result, the practice of utilizing the DNA of an individual to predict disease has been judged to provide little to no useful information. Nevertheless, recent efforts have begun to demonstrate the utility of polygenic risk profiling to identify groups of individuals who could benefit from the knowledge of their probabilistic susceptibility to disease. In this context, we review the evidence supporting the personal and clinical utility of polygenic risk profiling.

Melatonin and Sleep in Preventing Hospitalized Delirium: A Randomized Clinical Trial.

PURPOSE: Studies suggest that melatonin may prevent delirium, a condition of acute brain dysfunction occurring in 20%-30% of hospitalized older adults that is associated with increased morbidity and mortality. We examined the effect of melatonin on delirium prevention in hospitalized older adults while measuring sleep parameters as a possible underlying mechanism. METHODS: This was a randomized clinical trial measuring the impact of 3 mg of melatonin nightly on incident delirium and both objective and subjective sleep in inpatients age ≥65 years, admitted to internal medicine wards (non-intensive care units). Delirium incidence was measured by bedside nurses using the confusion assessment method. Objective sleep measurements (nighttime sleep duration, total sleep time per 24 hours, and sleep fragmentation as determined by average sleep bout length) were obtained via actigraphy. Subjective sleep quality was measured using the Richards Campbell Sleep Questionnaire. RESULTS: Delirium occurred in 22.2% (8/36) of subjects who received melatonin vs in 9.1% (3/33) who received placebo (P = .19). Melatonin did not significantly change objective or subjective sleep measurements. Nighttime sleep duration and total sleep time did not differ between subjects who became delirious vs those who did not, but delirious subjects had more sleep fragmentation (sleep bout length 7.0 ± 3.0 vs 9.5 ± 5.3 min; P = .03). CONCLUSIONS: Melatonin given as a nightly dose of 3 mg did not prevent delirium in non-intensive care unit hospitalized patients or improve subjective or objective sleep.

Validation of a genetic risk score for atrial fibrillation: A prospective multicenter cohort study.

BACKGROUND: Atrial fibrillation (AF) is the most commonly encountered arrhythmia and is associated with an elevated risk of stroke. Improving the identification of patients with the highest risk for AF to enable appropriate surveillance and treatment, if necessary, is critical to reducing AF-associated morbidity and mortality. Multiple common single nucleotide polymorphisms (SNPs) are unequivocally associated with the lifetime risk of AF. In the current study we aimed to prospectively validate an AF genetic risk score (GRS) in previously undiagnosed patients at risk for AF. METHODS AND FINDINGS: Individuals 40 years of age or older with 1 clinical risk factor for AF, presenting with symptoms of AF, or with a first diagnosis of AF, were enrolled for genetic testing and ambulatory cardiac rhythm monitoring with an adhesive patch monitor or a long-term Holter monitor (mean wear time 10 days 21 hours and 13 days 18 hours, respectively). An AF event was the first diagnosis of AF by ECG, patch monitor, or long-term Holter monitor. The AF GRS was determined for each participant based on the weighted contribution of 12 genetic risk loci. Of 904 participants, 85 manifested AF. Their mean age was 66.2 (SD 11.8) years; 38% of participants were male. Participants in the highest quintile of AF GRS were more likely (odds ratio 3.11; 95% CI 1.27-7.58; p = 0.01) to have had an AF event than participants in the lowest quintile after adjusting for age, sex, smoking status, BMI, hypertension, diabetes mellitus, heart failure, and prior myocardial infarction. Study limitations included an ethnically homogenous population, a restricted rhythm monitoring period, and the evolving discovery of SNPs associated with AF. CONCLUSIONS: Prospective assessment of a GRS for AF identified participants with elevated risk of AF beyond established clinical criteria. Accordingly, a GRS for AF could be incorporated into overall risk assessment to better identify patients at the highest risk of developing AF, although further testing in larger populations is needed to confirm these findings. TRIAL REGISTRATION: ClinicalTrials.gov NCT01970969.

Effect of co-payment on behavioral response to consumer genomic testing.

Existing research in consumer behavior suggests that perceptions and usage of a product post-purchase depends, in part, on how the product was marketed, including price paid. In the current study, we examine the effect of providing an out-of-pocket co-payment for consumer genomic testing (CGT) on consumer post-purchase behavior using both correlational field evidence and a hypothetical online experiment. Participants were enrolled in a longitudinal cohort study of the impact of CGT and completed behavioral assessments before and after receipt of CGT results. Most participants provided a co-payment for the test (N = 1668), while others (N = 369) received fully subsidized testing. The two groups were compared regarding changes in health behaviors and post-test use of health care resources. Participants who paid were more likely to share results with their physician (p = .012) and obtain follow-up health screenings (p = .005) relative to those who received fully subsidized testing. A follow-up online experiment in which participants (N = 303) were randomized to a "fully-subsidized" versus "co-payment" condition found that simulating provision of a co-payment significantly increased intentions to seek follow-up screening tests (p = .050) and perceptions of the test results as more trustworthy (p = .02). Provision of an out-of-pocket co-payment for CGT may influence consumer's post-purchase behavior consistent with a price placebo effect. Cognitive dissonance or sunk cost may help explain the increase in screening propensity among paying consumers. Such individuals may obtain follow-up screenings to validate their initial decision to expend personal resources to obtain CGT.