Faculty Bibliography

BACKGROUND:Emerging evidence suggests the potential mediating role of microbiome in health disparities. However, no analytic framework can be directly used to analyze microbiome as a mediator between health disparity and clinical outcome, due to the non-manipulable nature of the exposure and the unique structure of microbiome data, including high dimensionality, sparsity, and compositionality. METHODS:Considering the modifiable and quantitative features of the microbiome, we propose a microbial causal mediation model framework, SparseMCMM_HD, to uncover the mediating role of microbiome in health disparities, by depicting a plausible path from a non-manipulable exposure (e.g., ethnicity or region) to the outcome through the microbiome. The proposed SparseMCMM_HD rigorously defines and quantifies the manipulable disparity measure that would be eliminated by equalizing microbiome profiles between comparison and reference groups and innovatively and successfully extends the existing microbial mediation methods, which are originally proposed under potential outcome or counterfactual outcome study design, to address health disparities. RESULTS:Through three body mass index (BMI) studies selected from the curatedMetagenomicData 3.4.2 package and the American gut project: China vs. USA, China vs. UK, and Asian or Pacific Islander (API) vs. Caucasian, we exhibit the utility of the proposed SparseMCMM_HD framework for investigating the microbiome's contributions in health disparities. Specifically, BMI exhibits disparities and microbial community diversities are significantly distinctive between reference and comparison groups in all three applications. By employing SparseMCMM_HD, we illustrate that microbiome plays a crucial role in explaining the disparities in BMI between ethnicities or regions. 20.63%, 33.09%, and 25.71% of the overall disparity in BMI in China-USA, China-UK, and API-Caucasian comparisons, respectively, would be eliminated if the between-group microbiome profiles were equalized; and 15, 18, and 16 species are identified to play the mediating role respectively. CONCLUSIONS:The proposed SparseMCMM_HD is an effective and validated tool to elucidate the mediating role of microbiome in health disparity. Three BMI applications shed light on the utility of microbiome in reducing BMI disparity by manipulating microbial profiles. Video Abstract.

INTRODUCTION/BACKGROUND:Cognitive activity and awareness during cardiac arrest (CA) are reported but ill understood. This first of a kind study examined consciousness and its underlying electrocortical biomarkers during cardiopulmonary resuscitation (CPR). METHODS:) monitoring into CPR during in-hospital CA (IHCA). Survivors underwent interviews to examine for recall of awareness and cognitive experiences. A complementary cross-sectional community CA study provided added insights regarding survivors' experiences. RESULTS:=43%) normal EEG activity (delta, theta and alpha) consistent with consciousness emerged as long as 35-60 minutes into CPR. CONCLUSIONS:Consciousness. awareness and cognitive processes may occur during CA. The emergence of normal EEG may reflect a resumption of a network-level of cognitive activity, and a biomarker of consciousness, lucidity and RED (authentic "near-death" experiences).

OBJECTIVE:Chronic mental and physical fatigue and post-exertional malaise are the more debilitating symptoms of long COVID-19. The study objective was to explore factors contributing to exercise intolerance in long COVID-19 to guide development of new therapies. Exercise capacity data of patients referred for a cardiopulmonary exercise test (CPET) and included in a COVID-19 Survivorship Registry at one urban health center were retrospectively analyzed. RESULTS:pulse peak % predicted (of 79 ± 12.9) was reduced, supporting impaired energy metabolism as a mechanism of exercise intolerance in long COVID, n = 59. We further identified blunted rise in heart rate peak during maximal CPET. Our preliminary analyses support therapies that optimize bioenergetics and improve oxygen utilization for treating long COVID-19.

A novel functional additive model is proposed, which is uniquely modified and constrained to model nonlinear interactions between a treatment indicator and a potentially large number of functional and/or scalar pretreatment covariates. The primary motivation for this approach is to optimize individualized treatment rules based on data from a randomized clinical trial. We generalize functional additive regression models by incorporating treatment-specific components into additive effect components. A structural constraint is imposed on the treatment-specific components in order to provide a class of additive models with main effects and interaction effects that are orthogonal to each other. If primary interest is in the interaction between treatment and the covariates, as is generally the case when optimizing individualized treatment rules, we can thereby circumvent the need to estimate the main effects of the covariates, obviating the need to specify their form and thus avoiding the issue of model misspecification. The methods are illustrated with data from a depression clinical trial with electroencephalogram functional data as patients' pretreatment covariates.

BACKGROUND/UNASSIGNED:The optimal revascularization approach in patients with heart failure with reduced ejection fraction (HFrEF) and ischemic heart disease ("ischemic cardiomyopathy") is unknown. Physician preferences regarding clinical equipoise for mode of revascularization and their willingness to consider offering enrollment in a randomized trial to patients with ischemic cardiomyopathy have not been characterized. METHODS/UNASSIGNED:We conducted two anonymous online surveys: 1) a clinical case scenario-based survey to assess willingness to offer clinical trial enrollment for a patient with ischemic cardiomyopathy (overall response rate to email invitation 0.45 %), and 2) a Delphi consensus-building survey to identify specific areas of clinical equipoise (overall response rate to email invitation 37 %). RESULTS/UNASSIGNED:< 0.0001). In 17 scenarios (11.8 %), there was no difference in CABG or PCI appropriateness ratings, suggesting clinical equipoise in these settings. CONCLUSIONS/UNASSIGNED:Our findings demonstrate willingness to consider offering enrollment in a randomized clinical trial and areas of clinical equipoise, two factors that support the feasibility of a randomized trial to compare clinical outcomes after revascularization with CABG vs. PCI in selected patients with ischemic cardiomyopathy, suitable coronary anatomy and co-morbidity profile.

This paper develops a Bayesian model with a flexible link function connecting a binary treatment response to a linear combination of covariates and a treatment indicator and the interaction between the two. Generalized linear models allowing data-driven link functions are often called "single-index models" and are among popular semi-parametric modeling methods. In this paper, we focus on modeling heterogeneous treatment effects, with the goal of developing a treatment benefit index (TBI) incorporating prior information from historical data. The model makes inference on a composite moderator of treatment effects, summarizing the effect of the predictors within a single variable through a linear projection of the predictors. This treatment benefit index can be useful for stratifying patients according to their predicted treatment benefit levels and can be especially useful for precision health applications. The proposed method is applied to a COVID-19 treatment study.

Evolving medical technologies have motivated the development of treatment decision rules (TDRs) that incorporate complex, costly data (e.g., imaging). In clinical practice, we aim for TDRs to be valuable by reducing unnecessary testing while still identifying the best possible treatment for a patient. Regardless of how well any TDR performs in the target population, there is an associated degree of uncertainty about its optimality for a specific patient. In this paper, we aim to quantify, via a confidence measure, the uncertainty in a TDR as patient data from sequential procedures accumulate in real-time. We first propose estimating confidence using the distance of a patient's vector of covariates to a treatment decision boundary, with further distances corresponding to higher certainty. We further propose measuring confidence through the conditional probabilities of ultimately (with all possible information available) being assigned a particular treatment, given that the same treatment is assigned with the patient's currently available data or given the treatment recommendation made using only the currently available patient data. As patient data accumulate, the treatment decision is updated and confidence reassessed until a sufficiently high confidence level is achieved. We present results from simulation studies and illustrate the methods using a motivating example from a depression clinical trial. Recommendations for practical use of the measures are proposed.

A pressing challenge in medical research is to identify optimal treatments for individual patients. This is particularly challenging in mental health settings where mean responses are often similar across multiple treatments. For example, the mean longitudinal trajectory for patients treated with an active drug versus placebo may be similar trajectories, but different treatments may exhibit trajectory shapes that are distinct from trajectories in other treatment groups. Most precision medicine approaches using longitudinal data often ignore information from the longitudinal data structure. This paper investigates a powerful precision medicine approach by examining the impact of baseline covariates on longitudinal outcome trajectories to guide treatment decisions instead of traditional scalar outcome measures derived from longitudinal data, such as a change score. We introduce a method of estimating "biosignatures" defined as linear combinations of baseline characteristics (i.e., a single index) that optimally separate longitudinal trajectories among different treatment groups. The criterion used is to maximize the Kullback"“Leibler Divergences between different treatment outcome distributions. The approach is illustrated via simulation studies and a depression clinical trial. The approach is also contrasted with more traditional methods and compares performance in the presence of missing data.

Sparse additive modeling is a class of effective methods for performing high-dimensional nonparametric regression. This article develops a sparse additive model focused on estimation of treatment effect modification with simultaneous treatment effect-modifier selection. We propose a version of the sparse additive model uniquely constrained to estimate the interaction effects between treatment and pretreatment covariates, while leaving the main effects of the pretreatment covariates unspecified. The proposed regression model can effectively identify treatment effect-modifiers that exhibit possibly nonlinear interactions with the treatment variable that are relevant for making optimal treatment decisions. A set of simulation experiments and an application to a dataset from a randomized clinical trial are presented to demonstrate the method.