Faculty Bibliography

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.

The purpose of this study was to test the feasibility and safety of High-Level Mobility (HLM) training on adults with Acquired Brain Injury (ABI). Our hypotheses were that HLM training would be feasible and safe. This study was a pilot randomized control trial with a Simple Skill Group (SSG) and a Complex Skill Group (CSG). Both groups received 12 sessions over 8 weeks and completed 4 testing sessions over 16 weeks. The SSG focused on locomotion, while CSG focused on the acquisition of running. Feasibility was assessed in terms of process, resources, management, and scientific metrics, including safety. Among the 41 participants meeting inclusion criteria, 28 consented (CSG, n = 13, SSG, n = 15), 20 completed the assigned protocol and 8 withdrew (CSG n = 4, SSG n = 4). Adherence rate to assigned protocol was 100%. There were two Adverse Events (AEs), 1 over 142 SSG sessions and 1 over 120 CSG sessions. The AE Odd Ratio (OR) (CSG:SSG) was 1.18 (95% CI: 0.07, 19.15). The data support our hypotheses that HLM training is feasible and safe on ambulatory adults with ABI.

Importance:Identifying which patients with COVID-19 are likely to benefit from COVID-19 convalescent plasma (CCP) treatment may have a large public health impact. Objective:To develop an index for predicting the expected relative treatment benefit from CCP compared with treatment without CCP for patients hospitalized for COVID-19 using patients' baseline characteristics. Design, Setting, and Participants:This prognostic study used data from the COMPILE study, ie, a meta-analysis of pooled individual patient data from 8 randomized clinical trials (RCTs) evaluating CCP vs control in adults hospitalized for COVID-19 who were not receiving mechanical ventilation at randomization. A combination of baseline characteristics, termed the treatment benefit index (TBI), was developed based on 2287 patients in COMPILE using a proportional odds model, with baseline characteristics selected via cross-validation. The TBI was externally validated on 4 external data sets: the Expanded Access Program (1896 participants), a study conducted under Emergency Use Authorization (210 participants), and 2 RCTs (with 80 and 309 participants). Exposure:Receipt of CCP. Main Outcomes and Measures:World Health Organization (WHO) 11-point ordinal COVID-19 clinical status scale and 2 derivatives of it (ie, WHO score of 7-10, indicating mechanical ventilation to death, and WHO score of 10, indicating death) at day 14 and day 28 after randomization. Day 14 WHO 11-point ordinal scale was used as the primary outcome to develop the TBI. Results:A total of 2287 patients were included in the derivation cohort, with a mean (SD) age of 60.3 (15.2) years and 815 (35.6%) women. The TBI provided a continuous gradation of benefit, and, for clinical utility, it was operationalized into groups of expected large clinical benefit (B1; 629 participants in the derivation cohort [27.5%]), moderate benefit (B2; 953 [41.7%]), and potential harm or no benefit (B3; 705 [30.8%]). Patients with preexisting conditions (diabetes, cardiovascular and pulmonary diseases), with blood type A or AB, and at an early COVID-19 stage (low baseline WHO scores) were expected to benefit most, while those without preexisting conditions and at more advanced stages of COVID-19 could potentially be harmed. In the derivation cohort, odds ratios for worse outcome, where smaller odds ratios indicate larger benefit from CCP, were 0.69 (95% credible interval [CrI], 0.48-1.06) for B1, 0.82 (95% CrI, 0.61-1.11) for B2, and 1.58 (95% CrI, 1.14-2.17) for B3. Testing on 4 external datasets supported the validation of the derived TBIs. Conclusions and Relevance:The findings of this study suggest that the CCP TBI is a simple tool that can quantify the relative benefit from CCP treatment for an individual patient hospitalized with COVID-19 that can be used to guide treatment recommendations. The TBI precision medicine approach could be especially helpful in a pandemic.

Importance:COVID-19 convalescent plasma (CCP) is a potentially beneficial treatment for COVID-19 that requires rigorous testing. Objective:To compile individual patient data from randomized clinical trials of CCP and to monitor the data until completion or until accumulated evidence enables reliable conclusions regarding the clinical outcomes associated with CCP. Data Sources:From May to August 2020, a systematic search was performed for trials of CCP in the literature, clinical trial registry sites, and medRxiv. Domain experts at local, national, and international organizations were consulted regularly. Study Selection:Eligible trials enrolled hospitalized patients with confirmed COVID-19, not receiving mechanical ventilation, and randomized them to CCP or control. The administered CCP was required to have measurable antibodies assessed locally. Data Extraction and Synthesis:A minimal data set was submitted regularly via a secure portal, analyzed using a prespecified bayesian statistical plan, and reviewed frequently by a collective data and safety monitoring board. Main Outcomes and Measures:Prespecified coprimary end points-the World Health Organization (WHO) 11-point ordinal scale analyzed using a proportional odds model and a binary indicator of WHO score of 7 or higher capturing the most severe outcomes including mechanical ventilation through death and analyzed using a logistic model-were assessed clinically at 14 days after randomization. Results:Eight international trials collectively enrolled 2369 participants (1138 randomized to control and 1231 randomized to CCP). A total of 2341 participants (median [IQR] age, 60 [50-72] years; 845 women [35.7%]) had primary outcome data as of April 2021. The median (IQR) of the ordinal WHO scale was 3 (3-6); the cumulative OR was 0.94 (95% credible interval [CrI], 0.74-1.19; posterior probability of OR <1 of 71%). A total of 352 patients (15%) had WHO score greater than or equal to 7; the OR was 0.94 (95% CrI, 0.69-1.30; posterior probability of OR <1 of 65%). Adjusted for baseline covariates, the ORs for mortality were 0.88 at day 14 (95% CrI, 0.61-1.26; posterior probability of OR <1 of 77%) and 0.85 at day 28 (95% CrI, 0.62-1.18; posterior probability of OR <1 of 84%). Heterogeneity of treatment effect sizes was observed across an array of baseline characteristics. Conclusions and Relevance:This meta-analysis found no association of CCP with better clinical outcomes for the typical patient. These findings suggest that real-time individual patient data pooling and meta-analysis during a pandemic are feasible, offering a model for future research and providing a rich data resource.