Design and characteristics of the prophylactic intra-operative ventricular arrhythmia ablation in high-risk LVAD candidates (PIVATAL) trial.

BACKGROUND: The use of a Left Ventricular Assist Device (LVAD) in patients with advanced heart failure refractory to optimal medical management has progressed steadily over the past two decades. Data have demonstrated reduced LVAD efficacy, worse clinical outcome, and higher mortality for patients who experience significant ventricular tachyarrhythmia (VTA). We hypothesize that a novel prophylactic intra-operative VTA ablation protocol at the time of LVAD implantation may reduce the recurrent VTA and adverse events postimplant. METHODS: We designed a prospective, multicenter, open-label, randomized-controlled clinical trial enrolling 100 patients who are LVAD candidates with a history of VTA in the previous 5 years. Enrolled patients will be randomized in a 1:1 fashion to intra-operative VTA ablation (n = 50) versus conventional medical management (n = 50) with LVAD implant. Arrhythmia outcomes data will be captured by an implantable cardioverter defibrillator (ICD) to monitor VTA events, with a uniform ICD programming protocol. Patients will be followed prospectively over a mean of 18 months (with a minimum of 9 months) after LVAD implantation to evaluate recurrent VTA, adverse events, and procedural outcomes. Secondary endpoints include right heart function/hemodynamics, healthcare utilization, and quality of life. CONCLUSION: The primary aim of this first-ever randomized trial is to assess the efficacy of intra-operative ablation during LVAD surgery in reducing VTA recurrence and improving clinical outcomes for patients with a history of VTA.

Screening for chronic diseases: optimizing lead time through balancing prescribed frequency and individual adherence.

Screening for chronic diseases, such as cancer, is an important public health priority, but traditionally only the frequency or rate of screening has received attention. In this work, we study the importance of adhering to recommended screening policies and develop new methodology to better optimize screening policies when adherence is imperfect. We consider a progressive disease model with four states (healthy, undetectable preclinical, detectable preclinical, clinical), and overlay this with a stochastic screening-behavior model using the theory of renewal processes that allows us to capture imperfect adherence to screening programs in a transparent way. We show that decreased adherence leads to reduced efficacy of screening programs, quantified here using elements of the lead time distribution (i.e., the time between screening diagnosis and when diagnosis would have occurred clinically in the absence of screening). Under the assumption of an inverse relationship between prescribed screening frequency and individual adherence, we show that the optimal screening frequency generally decreases with increasing levels of non-adherence. We apply this model to an example in breast cancer screening, demonstrating how accounting for imperfect adherence affects the recommended screening frequency.

Traumatic Wounds of the Foot and Ankle: The Landstuhl Regional Medical Center Experience and Historical Perspectives.

As this is more of a reference article, I chose not to have an abstract similar to the paper I wrote in 2016 regarding flat feet in the military.

Criteria-Based Assessment of a Teleophthalmology Diabetic Retinopathy Evaluation Program in a Primary Care Setting.

Background:Studies have shown that teleophthalmology programs using a nonmydriatic camera in primary care settings can improve rates of diabetic retinopathy (DR) screening. However, such programs are not yet widespread due to common challenges in sustainability.Purpose:To comprehensively evaluate clinical and operational measures of an urban primary care clinic's 1-year pilot teleophthalmology DR evaluation program.Materials and Methods:This retrospective analysis used five metrics to evaluate the program: clinical diabetic retinal exam (DRE) rate, visual acuity and pathology, camera utilization, billing and insurance reimbursements, and outcomes of follow-up referrals.Results:Two hundred eleven patients were screened over 14 months. The DRE rate had more than doubled (34-75%). Of the patients, 55.9% had vision better than 20/50 in each eye and 21% with at least 1 eye worse than or equal to 20/70. DR was noted in 11% of patients. The program's first few months saw greatest camera use. Government and Medicare Advantage insurers were significantly (p < 0.001) less likely to reimburse than commercial insurers. Twenty-seven percent of patients screened had documented follow-up with an eye care provider within 16 months of their screening. Patients diagnosed with DR or recommended follow-up within 1 month were significantly (p < 0.001) more likely to schedule an appointment.Discussion:Challenges to program sustainability include efficient utilization, reimbursement from governmental insurers, and adherence to follow-up recommendations.Conclusions:Assessing teleophthalmology programs with the aforementioned five metrics allows for a comprehensive evaluation of impact and sustainability. This may be utilized to standardize the implementation and evaluation of such programs across diverse settings.

Protocol paper: Stepped wedge cluster randomized trial translating the ABCS into optimizing cardiovascular care for people living with HIV.

People living with HIV (PWH) are at higher risk for cardiovascular disease (CVD) and stroke in comparison to their non-infected counterparts. The ABCS (aspirin-blood pressure control-cholesterol control-smoking cessation) reduce atherosclerotic (ASCVD) risk in the general population, but little is known regarding strategies for promoting the ABCS among PWH. Guided by the Consolidated Framework for Implementation Research (CFIR), we designed multilevel implementation strategies that target PWH and their clinicians to promote appropriate use of the ABCS based on a 10-year estimated ASCVD risk. Implementation strategies include patient coaching, automated texting, peer phone support, academic detailing and audit and feedback for the patient's clinician. We are evaluating implementation through a stepped wedge cluster randomized trial based on the Reach-Effectiveness-Adoption-Maintenance/Qualitative-Evaluation-for-Systematic-Translation (RE-AIM/QuEST) mixed methods framework that integrates quantitative and qualitative assessments. The primary outcome is change in ASCVD risk. Findings will have important implications regarding strategies for reducing ASCVD risk among PWH.

Modeling the rate of HIV testing from repeated binary data amidst potential never-testers.

Many longitudinal studies with a binary outcome measure involve a fraction of subjects with a homogeneous response profile. In our motivating data set, a study on the rate of human immunodeficiency virus (HIV) self-testing in a population of men who have sex with men (MSM), a substantial proportion of the subjects did not self-test during the follow-up study. The observed data in this context consist of a binary sequence for each subject indicating whether or not that subject experienced any events between consecutive observation time points, so subjects who never self-tested were observed to have a response vector consisting entirely of zeros. Conventional longitudinal analysis is not equipped to handle questions regarding the rate of events (as opposed to the odds, as in the classical logistic regression model). With the exception of discrete mixture models, such methods are also not equipped to handle settings in which there may exist a group of subjects for whom no events will ever occur, i.e. a so-called "never-responder" group. In this article, we model the observed data assuming that events occur according to some unobserved continuous-time stochastic process. In particular, we consider the underlying subject-specific processes to be Poisson conditional on some unobserved frailty, leading to a natural focus on modeling event rates. Specifically, we propose to use the power variance function (PVF) family of frailty distributions, which contains both the gamma and inverse Gaussian distributions as special cases and allows for the existence of a class of subjects having zero frailty. We generalize a computational algorithm developed for a log-gamma random intercept model (Conaway, 1990. A random effects model for binary data. Biometrics46, 317-328) to compute the exact marginal likelihood, which is then maximized to obtain estimates of model parameters. We conduct simulation studies, exploring the performance of the proposed method in comparison with competitors. Applying the PVF as well as a Gaussian random intercept model and a corresponding discrete mixture model to our motivating data set, we conclude that the group assigned to receive follow-up messages via SMS was self-testing at a significantly lower rate than the control group, but that there is no evidence to support the existence of a group of never-testers.

Reactions to Testing HIV Negative: Measurement and Associations with Sexual Risk Behaviour Among Young MSM Who Recently Tested HIV Negative.

Receiving an HIV-positive test result is associated with reduced condomless anal sex (CAS), but little is known about negative test results. The recent development of the Inventory of Reactions to Testing HIV Negative confirmed that there are diverse reactions to receiving a negative test result, which have implications for risk behaviour. The goals of the current study were to validate the measure in a sample of young men who have sex with men who recently tested HIV-negative (N = 1113) and to examine its associations with CAS. Factor analysis identified four factors, three of which were the same as the original factors (Reinforced Safety, Luck, and Invulnerability) and one that was novel (Reinforced Risk). Construct validity was demonstrated with associations between subscales and constructs from the IMB model of HIV prevention. Lower Reinforced Safety and higher Luck and Reinforced Risk were associated with more CAS. Associations between Reinforced Safety and Luck with CAS were stronger for those who reported more lifetime HIV tests. Findings highlight the importance of reactions to testing HIV-negative and suggest that they become more important with repeated testing.

Risk Factors Associated With Repeated HIV Testing Among Internet-Using Men Who Have Sex With Men.

Men who have sex with men (MSM) represent a disproportionately impacted risk group for HIV incidence among at-risk U.S. POPULATIONS: Few studies have identified risk factors associated with HIV testing frequency both within and outside of traditional health care settings. MSM enrolled in a prospective cohort were mailed at-home specimen collection kits and followed for a year. Incidence density rate ratios (IDRR) of testing were calculated, and generalized estimating equations were used to analyze the association between HIV testing and behavioral factors. The incidence rate of testing was higher among Black MSM than White MSM (IDRR: 1.3, 95% confidence interval CI [1.1, 1.5]) and higher among MSM who reported 3+ condomless anal intercourse partners (CAI) than MSM who reported no CAI (IDRR: 1.6, 95% CI [1.3, 2.0]). Increasing availability of HIV testing outside traditional health care settings, including at-home testing kits, in conjunction with targeted behavioral interventions and biomedical treatment preventions is needed.

Estimating the palliative effect of percutaneous endoscopic gastrostomy in an observational registry using principal stratification and generalized propensity scores.

Clinical disease registries offer a rich collection of valuable patient information but also pose challenges that require special care and attention in statistical analyses. The goal of this paper is to propose a statistical framework that allows for estimating the effect of surgical insertion of a percutaneous endogastrostomy (PEG) tube for patients living with amyotrophic lateral sclerosis (ALS) using data from a clinical registry. Although all ALS patients are informed about PEG, only some patients agree to the procedure which, leads to the potential for selection bias. Assessing the effect of PEG is further complicated by the aggressively fatal disease, such that time to death competes directly with both the opportunity to receive PEG and clinical outcome measurements. Our proposed methodology handles the "censoring by death" phenomenon through principal stratification and selection bias for PEG treatment through generalized propensity scores. We develop a fully Bayesian modeling approach to estimate the survivor average causal effect (SACE) of PEG on BMI, a surrogate outcome measure of nutrition and quality of life. The use of propensity score methods within the principal stratification framework demonstrates a significant and positive effect of PEG treatment, particularly when time of treatment is included in the treatment definition.

Model selection and inference for censored lifetime medical expenditures.

Identifying factors associated with increased medical cost is important for many micro- and macro-institutions, including the national economy and public health, insurers and the insured. However, assembling comprehensive national databases that include both the cost and individual-level predictors can prove challenging. Alternatively, one can use data from smaller studies with the understanding that conclusions drawn from such analyses may be limited to the participant population. At the same time, smaller clinical studies have limited follow-up and lifetime medical cost may not be fully observed for all study participants. In this context, we develop new model selection methods and inference procedures for secondary analyses of clinical trial data when lifetime medical cost is subject to induced censoring. Our model selection methods extend a theory of penalized estimating function to a calibration regression estimator tailored for this data type. Next, we develop a novel inference procedure for the unpenalized regression estimator using perturbation and resampling theory. Then, we extend this resampling plan to accommodate regularized coefficient estimation of censored lifetime medical cost and develop postselection inference procedures for the final model. Our methods are motivated by data from Southwest Oncology Group Protocol 9509, a clinical trial of patients with advanced nonsmall cell lung cancer, and our models of lifetime medical cost are specific to this population. But the methods presented in this article are built on rather general techniques and could be applied to larger databases as those data become available.

Modeling clinical outcome using multiple correlated functional biomarkers: A Bayesian approach.

In some biomedical studies, biomarkers are measured repeatedly along some spatial structure or over time and are subject to measurement error. In these studies, it is often of interest to evaluate associations between a clinical endpoint and these biomarkers (also known as functional biomarkers). There are potentially two levels of correlation in such data, namely, between repeated measurements of a biomarker from the same subject and between multiple biomarkers from the same subject; none of the existing methods accounts for correlation between multiple functional biomarkers. We propose a Bayesian approach to model a clinical outcome of interest (e.g. risk for colorectal cancer) in the presence of multiple functional biomarkers while accounting for potential correlation. Our simulations show that the proposed approach achieves good performance in finite samples under various settings. In the presence of substantial or moderate correlation, the proposed approach outperforms an existing approach that does not account for correlation. The proposed approach is applied to a study of biomarkers of risk for colorectal neoplasms and our results show that the risk for colorectal cancer is associated with two functional biomarkers, APC and TGF-α, in particular, with their values in the region between the proliferating and differentiating zones of colorectal crypts.

Hierarchical Feature Selection Incorporating Known and Novel Biological Information: Identifying Genomic Features Related to Prostate Cancer Recurrence.

Our work is motivated by a prostate cancer study aimed at identifying mRNA and miRNA biomarkers that are predictive of cancer recurrence after prostatectomy. It has been shown in the literature that incorporating known biological information on pathway memberships and interactions among biomarkers improves feature selection of high-dimensional biomarkers in relation to disease risk. Biological information is often represented by graphs or networks, in which biomarkers are represented by nodes and interactions among them are represented by edges; however, biological information is often not fully known. For example, the role of microRNAs (miRNAs) in regulating gene expression is not fully understood and the miRNA regulatory network is not fully established, in which case new strategies are needed for feature selection. To this end, we treat unknown biological information as missing data (i.e., missing edges in graphs), different from commonly encountered missing data problems where variable values are missing. We propose a new concept of imputing unknown biological information based on observed data and define the imputed information as the novel biological information. In addition, we propose a hierarchical group penalty to encourage sparsity and feature selection at both the pathway level and the within-pathway level, which, combined with the imputation step, allows for incorporation of known and novel biological information. While it is applicable to general regression settings, we develop and investigate the proposed approach in the context of semiparametric accelerated failure time models motivated by our data example. Data application and simulation studies show that incorporation of novel biological information improves performance in risk prediction and feature selection and the proposed penalty outperforms the extensions of several existing penalties.

On Huberized calibration regression for censored medical cost data.

Authors have observed that the distribution of medical expenditures has features that do not lend it to parametric modeling and can present significant challenges for least-squares-type estimators, even on a logarithmic scale. In this note, we discuss caveats and extensions of coefficient estimation in the bivariate accelerated lifetime model of medical cost and survival time on covariates. We consider the setting where medical cost is observed only when the event occurs and potential right-censoring of the event time induces a dependent censoring mechanism on cost. We adopt Huang's (2002) estimation framework using the weighted log-rank estimating equations and investigate his proposal for robust mark-scale coefficient estimation. Due to modeling restrictions on the joint distribution of survival time and cost, we conclude that his robust mark-scale coefficient estimator would benefit from a time-scale adjustment. We use basic principles from robust estimation to define a new weighted marked process that subsequently leads to a new time-corrected robust regression calibration estimator. Our simulation studies illustrate how the proposed estimator has desirable operating characteristics, including reduced sensitivity to extreme values in the cost distribution, smaller finite sample bias and variance than earlier proposals. We illustrate the method in an analysis of lifetime medical cost data from a lung cancer study conducted by the Southwest Oncology Group.

Evaluating interventions to promote routine preventive screenings: a comparison of analytical outcomes.

BACKGROUND: Often in public health, we are interested in promoting routine preventive screenings (e.g., blood glucose monitoring, hypertension screening, or mammography). Evaluating novel interventions to encourage frequent screenings using randomized controlled trials can help inform evidence-based health promotion programs. When the desired behavior change is a recurrent event, specifying the most meaningful study outcomes may prove challenging. METHODS: To understand the efficiency of multiple approaches for evaluating an intervention seeking to increase regular health screenings we (a) simulated several replications of a trial with a positive intervention effect under various censoring scenarios, (b) formulated three different analytical outcome definitions (screening a certain number of times during the entire study period versus not, screening at least once within a clinically meaningful time period versus not, "hazard" or instantaneous rate of screening), and (c) compared them with regard to interpreting results and estimating power at different sample sizes. RESULTS: Approaches which better utilize detailed prospective data, while also accounting for within-participant correlations, are less likely to miss the actual underlying benefits conferred by a new prevention strategy compared to relying on a dichotomous measure derived from aggregating events over the study duration. Such approaches are also more powerful in realistic scenarios wherein some participants are lost to follow-up over time. CONCLUSIONS: Researchers should carefully consider the choice of analytical outcomes and strive to employ more efficient approaches that model comprehensive event-specific information, rather than summarizing repeated measures into less-informative dichotomous responses, while designing and conducting trials with recurrent preventive screenings.

Variable selection in the presence of missing data: resampling and imputation.

In the presence of missing data, variable selection methods need to be tailored to missing data mechanisms and statistical approaches used for handling missing data. We focus on the mechanism of missing at random and variable selection methods that can be combined with imputation. We investigate a general resampling approach (BI-SS) that combines bootstrap imputation and stability selection, the latter of which was developed for fully observed data. The proposed approach is general and can be applied to a wide range of settings. Our extensive simulation studies demonstrate that the performance of BI-SS is the best or close to the best and is relatively insensitive to tuning parameter values in terms of variable selection, compared with several existing methods for both low-dimensional and high-dimensional problems. The proposed approach is further illustrated using two applications, one for a low-dimensional problem and the other for a high-dimensional problem.

Activated CD4+CCR5+ T cells in the rectum predict increased SIV acquisition in SIVGag/Tat-vaccinated rhesus macaques.

An effective T-cell-based AIDS vaccine should induce strong HIV-specific CD8(+) T cells in mucosal tissues without increasing the availability of target cells for the virus. Here, we evaluated five immunization strategies that include Human adenovirus-5 (AdHu5), Chimpanzee adenovirus-6 (AdC6) or -7 (AdC7), Vaccinia virus (VV), and DNA given by electroporation (DNA/EP), all expressing Simian immunodeficiency virus group specific antigen/transactivator of transcription (SIV(mac239Gag/Tat)). Five groups of six rhesus macaques (RMs) each were vaccinated with DNA/EP-AdC6-AdC7, VV-AdC6-AdC7, DNA/-EP-VV-AdC6, DNA/EP-VV-AdC7, or AdHu5-AdHu5-AdHu5 and were challenged repeatedly with low-dose intrarectal SIVmac239. Upon challenge, there were no significant differences among study groups in terms of virus acquisition or viral load after infection. When taken together, the immunization regimens did not protect against SIV acquisition compared with controls but did result in an ∼ 1.6-log decline in set-point viremia. Although all immunized RMs had detectable SIV-specific CD8(+) T cells in blood and rectal mucosa, we found no correlation between the number or function of these SIV-specific CD8(+) T cells and protection against SIV acquisition. Interestingly, RMs experiencing breakthrough infection showed significantly higher prechallenge levels of CD4(+)C-C chemokine receptor type 5 (CCR5)(+)HLA-DR(+) T cells in the rectal biopsies (RB) than animals that remained uninfected. In addition, among the infected RMs, the percentage of CD4(+)CCR5(+)Ki-67(+) T cells in RBs prechallenge correlated with higher early viremia. Overall, these data suggest that the levels of activated CD4(+)CCR5(+) target T cells in the rectal mucosa may predict the risk of SIV acquisition in RMs vaccinated with vectors that express SIVGag/Tat.

Global transcriptome analysis of formalin-fixed prostate cancer specimens identifies biomarkers of disease recurrence.

Prostate cancer remains the second leading cause of cancer death in American men and there is an unmet need for biomarkers to identify patients with aggressive disease. In an effort to identify biomarkers of recurrence, we performed global RNA sequencing on 106 formalin-fixed, paraffin-embedded prostatectomy samples from 100 patients at three independent sites, defining a 24-gene signature panel. The 24 genes in this panel function in cell-cycle progression, angiogenesis, hypoxia, apoptosis, PI3K signaling, steroid metabolism, translation, chromatin modification, and transcription. Sixteen genes have been associated with cancer, with five specifically associated with prostate cancer (BTG2, IGFBP3, SIRT1, MXI1, and FDPS). Validation was performed on an independent publicly available dataset of 140 patients, where the new signature panel outperformed markers published previously in terms of predicting biochemical recurrence. Our work also identified differences in gene expression between Gleason pattern 4 + 3 and 3 + 4 tumors, including several genes involved in the epithelial-to-mesenchymal transition and developmental pathways. Overall, this study defines a novel biomarker panel that has the potential to improve the clinical management of prostate cancer.

Healthcare provider intervention on smoking and quit attempts among HIV-positive versus HIV-negative MSM smokers in Chengdu, China.

Given the implications for smoking among HIV-positive individuals and high smoking and HIV rates among men who have sex with men (MSM) in China, we examined sociodemographic, smoking-related, psychosocial, and substance use factors in relation to HIV status; receiving some sort of healthcare provider intervention regarding smoking; and having made a quit attempt in the past year in a sample of MSM smokers in Chengdu. We conducted a cross-sectional survey of 381 MSM smokers recruited by a nongovernmental organization in Chengdu in 2012-2013. Of these, 350 disclosed their HIV status and 344 (188 HIV-positive and 156 HIV-negative) provided completed data. Half (50.0%) reported at least one quit attempt in their lifetime; 30.5% reported a quit attempt in the past year. The majority (59.4%) reported that a healthcare provider had intervened in some way (assessed smoking, advised quitting, provided assistance), most commonly by assessing smoking status (50.0%). HIV-positive individuals were more likely to report a healthcare provider intervening on their smoking (p < .001). Those who received provider intervention were more likely to have attempted to quit ever (p = .009) and in the past year (p < .001). Those HIV-positive were more likely to have attempted to quit since diagnosis if a provider had intervened (p = .001). Multivariate regression documented that being HIV-positive (p < .001), greater cigarette consumption (p = .02), less frequent drinking (p = .03), and greater depressive symptoms (p = .003) were significant correlates of healthcare provider intervention. Multivariate regression also found that healthcare provider intervention (p = .003), older age (p = .01), and higher autonomous motivation (p = .007) were significant correlates of attempting to quit in the past year. Given the impact of healthcare provider intervention regarding smoking on quit attempts among MSM, greater training and support is needed to promote consistent intervention on smoking in the clinical setting among HIV-positive and HIV-negative MSM smokers.

Low-dose metabolism of benzene in humans: science and obfuscation.

Benzene is a ubiquitous air pollutant that causes human leukemia and hematotoxic effects. Although the mechanism by which benzene causes toxicity is unclear, metabolism is required. A series of articles by Kim et al. used air and biomonitoring data from workers in Tianjin, China, to investigate the dose-specific metabolism (DSM) of benzene over a wide range of air concentrations (0.03-88.9 p.p.m.). Kim et al. concluded that DSM of benzene is greatest at air concentrations <1 p.p.m. This provocative finding motivated the American Petroleum Institute to fund a study by Price et al. to reanalyze the original data. Although their formal 'reanalysis' reproduced Kim's finding of enhanced DSM at sub-p.p.m. benzene concentrations, Price et al. argued that Kim's methods were inappropriate for assigning benzene exposures to low exposed subjects (based on measurements of urinary benzene) and for adjusting background levels of metabolites (based on median values from the 60 lowest exposed subjects). Price et al. then performed uncertainty analyses under alternative approaches, which led them to conclude that '… the Tianjin data appear to be too uncertain to support any conclusions …' regarding the DSM of benzene. They also argued that the apparent low-dose metabolism of benzene could be explained by 'lung clearance.' In addressing these criticisms, we show that the methods and arguments presented by Price et al. are scientifically unsound and that their results are unreliable.