Faculty Bibliography

BACKGROUND/UNASSIGNED:The coronavirus disease 2019 (COVID-19) pandemic spurred a tremendous increase in the adoption and use of remote patient monitoring (RPM) for hypertension (HTN) management. However, limited evidence exists on the associations between frequency of utilization and uncontrolled blood pressure (BP). OBJECTIVES/UNASSIGNED:The present study comprehensively explores the associations between RPM use frequency and uncontrolled BP among a metropolitan-dwelling sample of hypertensive patients. METHODS/UNASSIGNED:Of 2,920 participants from a single urban health system, we employed a range of analytical perspectives to evaluate the RPM utilization-uncontrolled BP relationship across widely used engagement metrics: Frequency of BP transmission, digitally enabled clinician interactions, patient portal interactions, and a composite measure of utilization. Our dichotomized primary and secondary endpoints were BP >140/90 mm Hg and BP >130/80 mm Hg. RESULTS/UNASSIGNED:Fifty-nine percent of participants were females (59%), one-third (37%) were ≥65 years old, and Hispanic patients were most represented (39%). Our primary uncontrolled BP endpoint demonstrated strong adjusted associations with suboptimal RPM use across dichotomized measures: Low BP transmission (odds ratio [OR]: 2.02, 95% confidence interval [CI]: 1.41-2.96), low clinician interactions (OR: 1.83, 95% CI: 1.43-2.36), low patient portal interactions (OR: 1.83, 95% 1.46-2.30), and low overall engagement (OR: 3.50, 95% 2.77-4.46). Our causal evaluations mirrored these findings, showing moderate causal associations after comprehensive adjustment for confounding. Assessments using other data types, such as continuous and quartiles, showed significant associations and an apparent dose-response relationship, though not at a similar magnitude. CONCLUSION/UNASSIGNED:We observed strong associations between low RPM utilization and uncontrolled BP, with promising implications for patients with collectively high RPM use. These findings highlight the need to strengthen digital inclusion initiatives to improve RPM uptake and support existing efforts aimed at developing RPM clinical practice guidelines and expanding RPM reimbursement policies. Further research is warranted across diverse utilization components to better understand the linkages between engagement frequency and improved clinical outcomes.

An important goal of precision medicine is to personalize medical treatment by identifying individuals who are most likely to benefit from a specific treatment. The likely responder (LR) framework, which identifies a subpopulation where treatment response is expected to exceed a certain clinical threshold, plays a role in this effort. However, the LR framework, and more generally, data-driven subgroup analyses, often fail to account for uncertainty in the estimation of model-based data-driven subgrouping. We propose a simple two-stage approach that integrates subgroup identification with subsequent subgroup-specific inference on treatment effects. We incorporate model estimation uncertainty from the first stage into subgroup-specific treatment effect estimation in the second stage, by utilizing Bayesian posterior distributions from the first stage. We evaluate our method through simulations, demonstrating that the proposed Bayesian two-stage model produces better calibrated confidence intervals than naïve approaches. We apply our method to an international COVID-19 treatment trial, which shows substantial variation in treatment effects across data-driven subgroups.

Sensor-space EEG analyses typically rely on electrode layouts or data-driven components and rarely encode cortical geometry, making scalp patterns difficult to link to anatomy and to compare across participants. We introduce a sensor-space basis dictionary that explicitly integrates cortical geometry. Laplace-Beltrami (LB) eigenmodes are computed on a standard cortical template (fsaverage) and mapped by the lead-field matrix of a three-layer boundary-element (BEM) head model to yield cortex-anchored sensor-space harmonics. The leadfield-mapped LB dictionary spans scalp topographies, while preserving a meaningful spatial-frequency ordering inherited from the cortical manifold. We assess representational efficiency using ordinary least squares (OLS) projections of resting EEG (eyes-closed/open) across 59-, 32-, and 19-channel montages, and compare against spherical harmonics (SPH), principal components (PCA), and independent components (ICA). Efficiency is quantified by the variance explained of spatial configuration [Formula: see text] (by leading K modes) and the efficiency indices [Formula: see text] and [Formula: see text] (fewest modes reaching [Formula: see text] and 0.90) and between-condition consistency by ICC(3,1) of eyes-open/closed coefficients. The cortex-anchored basis shows higher early-K [Formula: see text] than SPH and PCA (e.g., 59-channel eyes-closed at [Formula: see text]: LB [Formula: see text] [95% CI: 0.54, 0.59] vs. SPH [Formula: see text] [0.42, 0.46], PCA [Formula: see text] [0.07, 0.09]) and reaches 70% and 90% variance with fewer modes (LB [Formula: see text]; SPH [Formula: see text]; PCA [Formula: see text]; ICA [Formula: see text]; LB [Formula: see text]; SPH [Formula: see text]; PCA [Formula: see text]; ICA [Formula: see text]). Mode-wise coefficient consistency (eyes-open vs. eyes-closed) is comparable between LB and SPH. By combining cortical eigenmodes with a forward head model, this approach yields a geometry-aligned, interpretable representation of sensor-space EEG that offers superior fidelity-complexity trade-offs at small K and a principled scaffold for low-dimensional EEG sensor space analysis.

BACKGROUND:Anxiety disorders may partly stem from altered neurodevelopment of attention-related networks. Neonatal alterations in resting-state functional connectivity (rsFC) among the dorsal attention (DAN); frontal parietal (FPN); salience (SN); and default mode networks (DMN)) relate to fearful temperament, a risk marker for anxiety. Nevertheless, little research examines development of these networks beyond the first months of life, particularly in fearful infants. This study examines how changes in these networks in the first two years of life relate to fearful temperament. METHODS:Using data from the Baby Connectome Project (from 180 infants across 396 sessions), we conducted independent components analysis to extract rsFC among the DMN, SN, DAN, and FPN. Longitudinal modeling characterized 1) age-related changes (slope) in rsFC through age two; 2) relations between rsFC change (slope) and fearfulness at age 2; 3) relations between rsFC and fearfulness trajectories (slope and intercept) over the first two years of life. RESULTS:Age-related decreases occurred in rsFC in DAN - FPN and DMN - SN. Smaller decreases in DAN - FPN rsFC over time related to greater fear at age 2, and to increases in fearfulness over time. High initial DAN-FPN rsFC and low initial DAN - SN rsFC also related to increasing fearfulness over time. CONCLUSION/CONCLUSIONS:This study provides the first evidence that changes in attention-related brain networks are related to early-life fearfulness, a robust early-life risk marker of anxiety.

BACKGROUND:Hypertension (HTN) is a critical public health concern that disproportionately impacts racial/ethnic minorities. The recent COVID-19 pandemic spurred rapid adoption of virtual HTN treatment programs such as remote patient monitoring programs (RPM), including among minority populations. However, it is unclear how utilization patterns differ across racial/ethnic groups and what the implications are for HTN outcomes. OBJECTIVE:The present study examines whether the association between RPM utilization and uncontrolled BP differs by race/ethnicity among hypertensive patients enrolled in an RPM program. METHODS:This study includes an urban sample of HTN patients who were 18 ≥ years old who have been in their RPM programs for three consecutive months or longer. Our primary exposure measures are three widely used dichotomized RPM engagement metrics and uncontrolled BP outcomes were dichotomized as BP ≥ 140/90 and ≥ 130/80. We tested for effect modification by race/ethnicity across RPM utilization variables using multivariable logistic regression models. RESULTS:Of 2920 participants, 59% were females, 37% were ≥ 65 years old, and Hispanic patients were the most represented race/ethnicity group (39%). Percentage-uncontrolled was 25% non-Hispanic Black, 21% Hispanic, and 20% among non-Hispanic White patients. Compared to non-Hispanic White patients with high RPM utilization, patients with no BP transmission had higher odds of uncontrolled BP: White (OR=1.72; 95% CI: 1.07-2.75), Black (OR=2.11; 95% CI: 1.32-3.39), and Other race (OR=2.36; 95% CI: 1.41-3.96). Similar patterns were observed for low clinician interactions and low portal use. CONCLUSION/CONCLUSIONS:Disparities in RPM utilization and BP outcomes in our study parallel reported inequities in digital technology utilization and uncontrolled BP in the U.S. Future studies should aim to understand how utilization trends among various vulnerable populations influence HTN outcomes. Such findings may help inform efforts aimed at streamlining access and utilization of RPM to reduce utilization disparities and promote better BP control.

Widespread hyperalgesia, characterized by pain sensitivity beyond the primary pain site, is a common yet under-characterized feature across chronic pain conditions, including chronic pancreatitis (CP). In this exploratory study, we identified a candidate neural biosignature of widespread hyperalgesia using high-density electroencephalography (EEG) in patients with chronic low back pain (cLBP). Specifically, stimulus-evoked delta, theta, and alpha oscillatory activity in the bilateral medial orbitofrontal cortex (mOFC) differentiated cLBP patients with widespread hyperalgesia from healthy controls. To examine cross-condition generalizability and advance predictive biomarker development for CP, we applied this mOFC-derived EEG biosignature to an independent cohort of patients with CP. The biosignature distinguished CP patients with widespread hyperalgesia and predicted individual treatment responses to peripherally targeted endoscopic therapy. These preliminary findings provide early support for a shared cortical signature of central sensitization across pain conditions and offer translational potential for developing EEG-based predictive tools for treatment response in CP.

Stepped wedge cluster randomized trials (SWCRTs) often face challenges related to potential confounding by time. Traditional frequentist methods may not provide adequate coverage of an intervention's true effect using confidence intervals, whereas Bayesian approaches show potential for better coverage of intervention effects. However, Bayesian methods remain underexplored in the context of SWCRTs. To bridge this gap, we propose two innovative Bayesian hierarchical penalized spline models. Our first model accommodates large numbers of clusters and time periods, focusing on immediate intervention effects. To evaluate this approach, we compared this model to traditional frequentist methods. We then extend our approach to account for time-varying intervention effects, conducting a comprehensive comparison with an existing Bayesian monotone effect curve model and alternative frequentist methods. The proposed models were applied in the Primary Palliative Care for Emergency Medicine stepped wedge trial to evaluate the effectiveness of the intervention. Through extensive simulations and real-world application, we demonstrate the robustness of our proposed Bayesian models. Notably, the Bayesian immediate effect model consistently achieves the nominal coverage probability, providing more reliable interval estimations while maintaining high estimation accuracy. Furthermore, our proposed Bayesian time-varying effect model represents a significant advancement over the existing Bayesian monotone effect curve model, offering improved accuracy and reliability in estimation while also achieving higher coverage probability than alternative frequentist methods. To the best of our knowledge, this marks the first development of Bayesian hierarchical spline modeling for SWCRTs. Our proposed models offer promising tools for researchers and practitioners, enabling more precise evaluation of intervention impacts.

This paper presents a Bayesian regression model relating scalar outcomes to brain functional connectivity represented as symmetric positive definite (SPD) matrices. Unlike many proposals that simply vectorize the matrix-valued connectivity predictors, thereby ignoring their geometric structure, the method presented here respects the Riemannian geometry of SPD matrices by using a tangent space modeling. Dimension reduction is performed in the tangent space, relating the resulting low-dimensional representations to the responses. The dimension reduction matrix is learned in a supervised manner with a sparsity-inducing prior imposed on a Stiefel manifold to prevent overfitting. Our method yields a parsimonious regression model that allows uncertainty quantification of all model parameters and identification of key brain regions that predict the outcomes. We demonstrate the performance of our approach in simulation settings and through a case study to predict Picture Vocabulary scores using data from the Human Connectome Project.

This paper presents a Bayesian reformulation of covariate-assisted principal regression for covariance matrix outcomes to identify low-dimensional components in the covariance associated with covariates. By introducing a geometric approach to the covariance matrices and leveraging Euclidean geometry, we estimate dimension reduction parameters and model covariance heterogeneity based on covariates. This method enables joint estimation and uncertainty quantification of relevant model parameters associated with heteroscedasticity. We demonstrate our approach through simulation studies and apply it to analyze associations between covariates and brain functional connectivity using data from the Human Connectome Project.

BACKGROUND/UNASSIGNED:Precision medicine has led to the development of targeted treatment strategies tailored to individual patients based on their characteristics and disease manifestations. Although precision medicine often focuses on a single health outcome for individualized treatment decision rules (ITRs), relying only on a single outcome rather than all available outcomes information leads to suboptimal data usage when developing optimal ITRs. METHODS/UNASSIGNED:To address this limitation, we propose a Bayesian multivariate hierarchical model that leverages the wealth of correlated health outcomes collected in clinical trials. The approach jointly models mixed types of correlated outcomes, facilitating the "borrowing of information" across the multivariate outcomes, and results in a more accurate estimation of heterogeneous treatment effects compared to using single regression models for each outcome. We develop a treatment benefit index, which quantifies the relative treatment benefit of the experimental treatment over the control treatment, based on the proposed multivariate outcome model. RESULTS/UNASSIGNED:We demonstrate the strengths of the proposed approach through extensive simulations and an application to an international Coronavirus Disease 2019 (COVID-19) treatment trial. Simulation results indicate that the proposed method reduces the occurrence of erroneous treatment decisions compared to a single regression model for a single health outcome. Additionally, the sensitivity analysis demonstrates the robustness of the model across various study scenarios. Application of the method to the COVID-19 trial exhibits improvements in estimating the individual-level treatment efficacy (indicated by narrower credible intervals for odds ratios) and optimal ITRs. CONCLUSION/UNASSIGNED:The study jointly models mixed types of outcomes in the context of developing ITRs. By considering multiple health outcomes, the proposed approach can advance the development of more effective and reliable personalized treatment.