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Background Wearables are increasingly used by patients with atrial fibrillation (AF) for symptom monitoring and health management, but their impact on patient health care use and psychological well‐being is not well understood. Methods and Results In this retrospective, propensity‐matched study of patients with AF, survey and electronic health record data were merged to compare AF‐specific health care use (outpatient/inpatient visits, rhythm‐related testing, and procedures) and informal health care use (telephone calls and patient portal messages) over a 9‐month period between wearable users and nonusers. We also examined the effects of wearable cardiac monitoring features (eg, heart rate alerts, irregular rhythm notification, and ECG) on patient behavior and well‐being. Of 172 patients with AF in this analysis (age, 72.6±9.0 years; 42% women), 83 used a wearable. Compared with nonusers, wearable users reported higher rates of symptom monitoring and preoccupation (P=0.03) and more AF treatment concerns (P=0.02). Moreover, 20% of wearable users experienced anxiety and always contacted their doctors in response to irregular rhythm notifications. After matching, AF‐specific health care use was significantly greater among wearable users compared with nonusers (P=0.04), including significantly higher rates of ECGs, echocardiograms/transesophageal echocardiogram, and ablation. Wearable users were also significantly more likely to use informal health care resources compared with nonusers (P=0.05). Conclusions Wearables were associated with higher rates of symptom monitoring and preoccupation, AF treatment concerns, AF‐specific health care use, and use of informal health care resources. Prospective, randomized studies are needed to understand the net effects of wearables and their alerts on patients, providers, and the health care system.

Background: The 2018 United Network for Organ Sharing organ allocation change aimed to distribute donor hearts to the sickest patients on the waitlist. Whether this change differentially affected outcomes in heart-only vs heart-kidney transplant recipients is unknown. Methods: This study used the Scientific Registry of Transplant Recipients to compare outcomes, including survival, of heart-only and heart-kidney transplant recipients from 2015 to 2021, from the old vs new allocation system, including use of mechanical circulatory support (MCS), prior to transplant. Results: During the study period, 16,696 patients underwent heart transplant alone (9,320 in the old and 7,376 in the new system) and 1,156 patients underwent heart-kidney transplant (529 in the old and 627 in the new system). For both heart and heart-kidney transplant populations, there was a 3- to 5-fold increase in the use of temporary MCS. Heart-only recipients had worse survival when temporary MCS was used in the old allocation system. Heart-only recipients with durable MCS had worse survival both in the old and the new allocation system. There was no difference in survival in heart-kidney recipients in the old vs new allocation system, regardless of MCS use. Conclusions: The new heart allocation system was associated with increased use of temporary MCS in both heart and heart-kidney recipients. However, this change only differentially affected survival in heart-only recipients with improved survival if on temporary MCS, but worse survival if on durable MCS. Unlike prior studies, heart-kidney recipients did not have different outcomes after the heart allocation change, which may reflect outcomes in more current times.

Understanding the heterogeneity of growth, response to therapy and progression dynamics in metastatic colorectal cancer (mCRC) remains critical. Here, the authors analyse lesion-specific response heterogeneity in 4,308 mCRC patients and find that organ-level progression sequence is associated with long-term survival.

Abstract Background Hereditary Hemorrhagic Telangiectasia (HHT) is a rare inherited disorder characterized by development of mucocutaneous telangiectases and visceral organ arteriovenous malformations, which can lead to recurrent, spontaneous bleeding and development of iron deficiency anemia. The primary objective of this study was to ascertain the relationship between epistaxis severity scores (ESS), laboratory values, genotype, and phenotype in HHT. Our secondary objective was to assess efficacy of systemic antifibrinolytic therapy in reducing ESS in HHT. Methodology We conducted a retrospective review of patients seen at the UNC HHT Center from January 1, 2009 to February 28, 2015. ESS, demographics, and results of genetic testing were abstracted from the medical record. Response to antifibrinolytic therapy was evaluated by comparing pre-post ESS. Results One hundred and forty nine patients were eligible with 116 having genetic testing and 33 without. Age, hemoglobin and ferritin levels were predictive of ESS. Of the 116 patients that underwent genetic testing: 63 had an ACVRL1 mutation, 40 had an ENG mutation, 2 had a SMAD4 mutation, and 11 patients had no pathologic HHT genetic variation detected. Compared to patients without a detectable HHT-associated genetic variation, patients with a HHT-associated genetic variation had higher ESS scores (p 4 were started on antifibrinolytic therapy (tranexamic acid or aminocaproic acid) and had a post-treatment ESS recorded. All patients had a decrease in ESS of > 0.71 (minimal meaningful difference), but patients taking antifibrinolytics displayed larger decreases. No patients on antifibrinolytics experienced a VTE with median follow up of 13 months. Conclusions We demonstrate that the ESS correlates with age, hemoglobin and ferritin. Additionally, we demonstrate that HHT patients with genetic mutations have higher ESS scores. Our data demonstrate that antifibrinolytics are effective in decreasing epistaxis severity and safe with long-term use in HHT patients.

Background Anger and extreme stress can trigger potentially fatal cardiovascular events in susceptible people. Political elections, such as the 2016 US presidential election, are significant stressors. Whether they can trigger cardiac arrhythmias is unknown. Methods and Results In this retrospective case‐crossover study, we linked cardiac device data, electronic health records, and historic voter registration records from 2436 patients with implanted cardiac devices. The incidence of arrhythmias during the election was compared with a control period with Poisson regression. We also tested for effect modification by demographics, comorbidities, political affiliation, and whether an individual's political affiliation was concordant with county‐level election results. Overall, 2592 arrhythmic events occurred in 655 patients during the hazard period compared with 1533 events in 472 patients during the control period. There was a significant increase in the incidence of composite outcomes for any arrhythmia (incidence rate ratio [IRR], 1.77 [95% CI, 1.42–2.21]), supraventricular arrhythmia (IRR, 1.82 [95% CI, 1.36–2.43]), and ventricular arrhythmia (IRR, 1.60 [95% CI, 1.22–2.10]) during the election relative to the control period. There was also an increase in specific types of arrhythmia, including atrial fibrillation (IRR, 1.50 [95% CI, 1.06–2.11]), supraventricular tachycardia (IRR, 3.7 [95% CI, 2.2–6.2]), nonsustained ventricular tachycardia (IRR, 1.7 [95% CI, 1.3–2.2]), and daily atrial fibrillation burden (P

Construction of regulatory networks using cross-sectional expression profiling of genes is desired, but challenging. The Directed Acyclic Graph (DAG) provides a general framework to infer causal effects from observational data. However, most existing DAG methods assume that all nodes follow the same type of distribution, which prohibit a joint modeling of continuous gene expression and categorical variables. We present a new mixed DAG (mDAG) algorithm to infer the regulatory pathway from mixed observational data containing both continuous variables (e.g. expression of genes) and categorical variables (e.g. categorical phenotypes or single nucleotide polymorphisms). Our method can identify upstream causal factors and downstream effectors closely linked to a variable and generate hypotheses for causal direction of regulatory pathways. We propose a new permutation method to test the conditional independence of variables of mixed types, which is the key for mDAG. We also utilize an L1 regularization in mDAG to ensure it can recover a large sparse DAG with limited sample size. We demonstrate through extensive simulations that mDAG outperforms two well-known methods in recovering the true underlying DAG. We apply mDAG to a cross-sectional immunological study of Chlamydia trachomatis infection and successfully infer the regularity network of cytokines. We also apply mDAG to a large cohort study, generating sensible mechanistic hypotheses underlying plasma adiponectin level. The R package mDAG is publicly available from CRAN at https://CRAN.R-project.org/package=mDAG.

Retirement from employment involves disruption in daily routines and has been associated with positive and negative changes in physical activity. Walking is the most common physical activity among older Americans. The factors that influence changes in walking after retirement are unknown. The study objective was to identify correlates of within-person change in recreational walking (for leisure) and transport walking (to get places) during the retirement transition among a multi-ethnic cohort of adults (N = 928) from six US communities. Correlates were measured at the individual (e.g., gender), interpersonal (e.g., social support), and community (e.g., density of walking destinations) levels at study exams between 2000 and 2012. Comparing pre- and post-retirement measures (average 4.5 years apart), 50% of participants increased recreational walking by 60 min or more per week, 31% decreased by 60 min or more per week, and 19% maintained their recreational walking. Forty-one percent of participants increased transport walking by 60 min or more per week, 40% decreased by 60 min or more per week, and 19% maintained their transport walking after retirement. Correlates differed for recreational and transport walking and for increases compared to decreases in walking. Self-rated health, chronic conditions, and perceptions of the neighborhood walking environment were associated with changes in both types of walking after retirement. Further, some correlates differed by gender and retirement age. Findings can inform the targeting of interventions to promote walking during the retirement transition. Keywords: Walking, Retirement, Built environment, Transportation, Leisure activities, Cohort study

Rapid fluid removal (ultrafiltration, UF) is associated with higher cardiovascular morbidity and mortality among individuals receiving maintenance hemodialysis (HD). Fluid removal rates that exceed vascular refill rates can result in hemodynamic instability, end-organ damage to the heart, kidneys, gut and brain, among other organs, and patient symptoms. There are no known evidence-based HD treatment strategies to reduce harm from higher UF rates. Ultrafiltration profiling, the practice of varying UF rates to maximize fluid removal during periods of greatest hydration and plasma oncotic pressure, has been proposed as an HD treatment intervention that may reduce UF rate-related complications. This study is a randomized 4-phase cross-over trial in which participants are successively alternated between study arms with intervening washout periods, and treatment order is randomized. After 4-week screening and 6-week baseline periods, participants are randomized to HD with conventional UF or HD with UF profiling for a period of 3 weeks followed by a 1-week washout period before crossing over. Participants cross into conventional UF and UF profiling phases twice (2 phases per arm). The primary outcomes of interest are intradialytic hypotension (nadir intradialytic systolic blood pressure

Modern biomedical datasets are increasingly high-dimensional and exhibit complex correlation structures. Generalized linear mixed models (GLMMs) have long been employed to accountforsuch dependencies.However, properspecification ofthe fixed and randomeffectsin GLMMs is increasingly difficult in high dimensions, and computational complexity grows with increasing dimension of the random effects. We present a novel reformulation of the GLMM using a factor model decomposition of the random effects, enabling scalable computation of GLMMsin high dimensions by reducing the latent space from a large number of random effects to a smaller set of latent factors. We also extend our prior work to estimate model parameters using a modified Monte Carlo Expectation Conditional Minimization algorithm, allowing us to perform variable selection on both the fixed and random effects simultaneously. We show through simulation that through this factor model decomposition, our method can fit high-dimensional penalized GLMMs faster than comparable methods and more easily scale to larger dimensions not previously seen in existing approaches. [ABSTRACT FROM AUTHOR]

Generalized linear mixed models (GLMMs) are widely used in research for their ability to model correlated outcomes with non-Gaussian conditional distributions. The proper selection of fixed and random effects is a critical part of the modeling process since model misspecification may lead to significant bias. However, the joint selection of fixed and random effects has historically been limited to lower-dimensional GLMMs, largely due to the use of criterion-based model selection strategies. Here we present the R package glmmPen, one of the first to select fixed and random effects in higher dimension using a penalized GLMM modeling framework. Model parameters are estimated using a Monte Carlo Expectation Conditional Minimization (MCECM) algorithm, which leverages Stan and RcppArmadillo for increased computational efficiency. Our package supports the Binomial, Gaussian, and Poisson families and multiple penalty functions. In this manuscript we discuss the modeling procedure, estimation scheme, and software implementation through application to a pancreatic cancer subtyping study. Simulation results show our method has good performance in selecting both the fixed and random effects in high dimensional GLMMs. [ABSTRACT FROM AUTHOR]

The article focuses on providing proofs and supporting lemmas for "An Efficient Greedy Search Algorithm for High-dimensional Linear Discriminant Analysis." Topics include concentration inequalities for differences in covariance matrices and discriminant vectors; conditions ensuring the convergence of covariance matrix estimates; and results are crucial for validating the efficiency and accuracy of the proposed algorithm in high-dimensional linear discriminant analysis.

In modern scientific research, data heterogeneity is commonly observed due to the abundance of complex data. We propose a factor regression model for data with heterogeneous subpopulations. The proposed model can be represented as a decomposition of heterogeneous and homogeneous terms. The heterogeneous term is driven by latent factors in different subpopulations. The homogeneous term captures common variation in the covariates and shares common regression coefficients across subpopulations. Our proposed model attains a good balance between a global model and a group-specific model. The global model ignores data heterogeneity, while the group-specific model fits each subgroup separately. We prove the estimation and prediction consistency for our proposed estimators, and show that it has better convergence rates than the group-specific and the global models. We show that the extra cost of estimating latent factors is asymptotically negligible and the minimax rate is still attainable. We further demonstrate the robustness of our proposed method by studying its prediction error under a mis-specified group-specific model. Finally, we conduct simulation studies and analyze a dataset from Alzheimer's Disease Neuroimaging Initiative and an aggregated microarray dataset to further demonstrate the competitiveness and interpretability of our proposed factor regression model. [ABSTRACT FROM AUTHOR]

A standard competing risks set-up requires both time to event and cause of failure to be fully observable for all subjects. However, in application, the cause of failure may not always be observable, thus impeding the risk assessment. In some extreme cases, none of the causes of failure is observable. In the case of a recurrent episode of Plasmodium vivax malaria following treatment, the patient may have suffered a relapse from a previous infection or acquired a new infection from a mosquito bite. In this case, the time to relapse cannot be modeled when a competing risk, a new infection, is present. The efficacy of a treatment for preventing relapse from a previous infection may be underestimated when the true cause of infection cannot be classified. In this paper, we developed a novel method for classifying the latent cause of failure under a competing risks set-up, which uses not only time to event information but also transition likelihoods between covariates at the baseline and at the time of event occurrence. Our classifier shows superior performance under various scenarios in simulation experiments. The method was applied to Plasmodium vivax infection data to classify recurrent infections of malaria. [ABSTRACT FROM AUTHOR]

We prospectively evaluated adolescent organized physical activity (PA) as a factor in adult female bone traits. Annual DXA scans accompanied semiannual records of anthropometry, maturity, and PA for 42 participants in this preliminary analysis (criteria: appropriately timed DXA scans at ~1 year premenarche [predictor] and ~5 years postmenarche [dependent variable]). Regression analysis evaluated total adolescent interscan PA and PA over 3 maturity subphases as predictors of young adult bone outcomes: 1) bone mineral content (BMC), geometry, and strength indices at nondominant distal radius and femoral neck; 2) subhead BMC; 3) lumbar spine BMC. Analyses accounted for baseline gynecological age (years pre- or postmenarche), baseline bone status, adult body size and interscan body size change. Gymnastics training was evaluated as a potentially independent predictor, but did not improve models for any outcomes (p > .07). Premenarcheal bone traits were strong predictors of most adult outcomes (semipartial r2 = .21-0.59, p ≤ .001). Adult 1/3 radius and subhead BMC were predicted by both total PA and PA 1-3 years postmenarche (p < .03). PA 3-5 years postmenarche predicted femoral narrow neck width, endosteal diameter, and buckling ratio (p < .05). Thus, participation in organized physical activity programs throughout middle and high school may reduce lifetime fracture risk in females. [ABSTRACT FROM AUTHOR]

Clinical studies with time-to-event outcomes often collect measurements of a large number of time-varying covariates over time (e.g., clinical assessments or neuroimaging biomarkers) in order to build a time-sensitive prognostic model. However, resource-intensive or invasive (e.g., lumbar puncture) data-collection processes mean that biomarkers may be measured infrequently and, thus, not be available at every observed event time point. Therefore, leveraging all available time-varying biomarkers is important to improving our models event occurrence. We propose a kernel smoothing-based approach that borrows information across subjects to remedy the problem of infrequent and unbalanced biomarker measurements under a time-varying hazards model. A penalized pseudo-likelihood function is proposed for estimation, and an efficient augmented penalization minimization algorithm related to the alternating direction method of multipliers is adopted for computation. Given several regularity conditions, used to control the approximation bias and stochastic variability, we show that even in the presence of ultrahigh dimensionality, the proposed method selects important biomarkers with high probability. We use simulation studies to show that our method outperforms existing methods in terms of estimation and selection performance. Finally, we apply the proposed method to real data to model time-to-disease conversion using longitudinal, whole-brain structural magnetic resonance imaging biomarkers. The results show substantial improvement in performance over that of current standards, including using baseline measures only. [ABSTRACT FROM AUTHOR]

Data from a large number of covariates with known population totals are frequently observed in survey studies. These auxiliary variables contain valuable information that can be incorporated into an estimation of the population total of a survey variable in order to improve the estimation precision. We consider a generalized regression estimator formulated under a model-assisted framework, in which a regression model is used for the available covariates, and the estimator retains the basic design-based properties. The generalized regression estimator is shown to improve the efficiency of the design-based Horvitz-Thompson estimator when the number of covariates is fixed. We investigate the performance of the generalized regression estimator when the number of covariates p is allowed to diverge as the sample size n increases. We examine two approaches. First, the model parameter is estimated using the weighted least squares method when p < n. Second, the Lasso method is employed when the model parameter is sparse. We show that under an assisted model and certain conditions on the joint distribution of the covariates, as well as the divergence rates of n and p, the generalized regression estimator is asymptotically more efficient than the Horvitz-Thompson estimator, and is robust against a model misspecification. We also study the consistency of the variance estimation for the generalized regression estimator. Our theoretical results are corroborated by simulation studies and an example. [ABSTRACT FROM AUTHOR]