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Development and Validation of a Treatment Benefit Index to Identify Hospitalized Patients With COVID-19 Who May Benefit From Convalescent Plasma

Park, Hyung; Tarpey, Thaddeus; Liu, Mengling; Goldfeld, Keith; Wu, Yinxiang; Wu, Danni; Li, Yi; Zhang, Jinchun; Ganguly, Dipyaman; Ray, Yogiraj; Paul, Shekhar Ranjan; Bhattacharya, Prasun; Belov, Artur; Huang, Yin; Villa, Carlos; Forshee, Richard; Verdun, Nicole C; Yoon, Hyun Ah; Agarwal, Anup; Simonovich, Ventura Alejandro; Scibona, Paula; Burgos Pratx, Leandro; Belloso, Waldo; Avendaño-Solá, Cristina; Bar, Katharine J; Duarte, Rafael F; Hsue, Priscilla Y; Luetkemeyer, Anne F; Meyfroidt, Geert; Nicola, André M; Mukherjee, Aparna; Ortigoza, Mila B; Pirofski, Liise-Anne; Rijnders, Bart J A; Troxel, Andrea; Antman, Elliott M; Petkova, Eva
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.
PMCID:8790670
PMID: 35076698
ISSN: 2574-3805
CID: 5153212

Association of Convalescent Plasma Treatment With Clinical Status in Patients Hospitalized With COVID-19: A Meta-analysis

Troxel, Andrea B; Petkova, Eva; Goldfeld, Keith; Liu, Mengling; Tarpey, Thaddeus; Wu, Yinxiang; Wu, Danni; Agarwal, Anup; Avendaño-Solá, Cristina; Bainbridge, Emma; Bar, Katherine J; Devos, Timothy; Duarte, Rafael F; Gharbharan, Arvind; Hsue, Priscilla Y; Kumar, Gunjan; Luetkemeyer, Annie F; Meyfroidt, Geert; Nicola, André M; Mukherjee, Aparna; Ortigoza, Mila B; Pirofski, Liise-Anne; Rijnders, Bart J A; Rokx, Casper; Sancho-Lopez, Arantxa; Shaw, Pamela; Tebas, Pablo; Yoon, Hyun-Ah; Grudzen, Corita; Hochman, Judith; Antman, Elliott M
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.
PMCID:8790669
PMID: 35076699
ISSN: 2574-3805
CID: 5153222

Elucidating age and sex-dependent association between frontal EEG asymmetry and depression: An application of multiple imputation in functional regression

Ciarleglio, Adam; Petkova, Eva; Harel, Ofer
Frontal power asymmetry (FA), a measure of brain function derived from electroencephalography, is a potential biomarker for major depressive disorder (MDD). Though FA is functional in nature, it is typically reduced to a scalar value prior to analysis, possibly obscuring its relationship with MDD and leading to a number of studies that have provided contradictory results. To overcome this issue, we sought to fit a functional regression model to characterize the association between FA and MDD status, adjusting for age, sex, cognitive ability, and handedness using data from a large clinical study that included both MDD and healthy control (HC) subjects. Since nearly 40% of the observations are missing data on either FA or cognitive ability, we propose an extension of multiple imputation (MI) by chained equations that allows for the imputation of both scalar and functional data. We also propose an extension of Rubin's Rules for conducting valid inference in this setting. The proposed methods are evaluated in a simulation and applied to our FA data. For our FA data, a pooled analysis from the imputed data sets yielded similar results to those of the complete case analysis. We found that, among young females, HCs tended to have higher FA over the θ, α, and β frequency bands, but that the difference between HC and MDD subjects diminishes and ultimately reverses with age. For males, HCs tended to have higher FA in the β frequency band, regardless of age. Young male HCs had higher FA in the θ and α bands, but this difference diminishes with increasing age in the α band and ultimately reverses with increasing age in the θ band.
PMCID:8959477
PMID: 35350190
ISSN: 0162-1459
CID: 5191132

A single-index model with a surface-link for optimizing individualized dose rules

Park, Hyung; Petkova, Eva; Tarpey, Thaddeus; Ogden, R Todd
This paper focuses on the problem of modeling and estimating interaction effects between covariates and a continuous treatment variable on an outcome, using a single-index regression. The primary motivation is to estimate an optimal individualized dose rule and individualized treatment effects. To model possibly nonlinear interaction effects between patients' covariates and a continuous treatment variable, we employ a two-dimensional penalized spline regression on an index-treatment domain, where the index is defined as a linear projection of the covariates. The method is illustrated using two applications as well as simulation experiments. A unique contribution of this work is in the parsimonious (single-index) parametrization specifically defined for the interaction effect term.
PMCID:9306450
PMID: 35873662
ISSN: 1061-8600
CID: 5387832

Robust index of confidence weighted learning for optimal individualized treatment rule estimation

Zhang, Jinchun; Troxel, Andrea B.; Petkova, Eva
Determination of optimal individual treatment rules (ITR) is a rapidly growing area in precision medicine; various parametric and non-parametric methods have been proposed. Existing methods, however, focus on the mean outcome and thus are sensitive to outliers, skewed and heavy-tailed outcome distributions. In this paper, we propose an optimal ITR estimation framework using a weighted classifier with robust weights based on measures of similarity. Compared to previous methods in the literature, this two-stage nonparametric model is novel and enjoys several advantages. First, due to its non-parametric nature, it is more flexible than regression-based parametric and semi-parametric models. Second, the similarity-based confidence index is essentially a weighted sum of indicator functions depending on the sign of pairwise outcome differences; therefore, it is robust to outliers, skewed and heavy-tailed outcome distributions. The performance of the proposed approach is demonstrated via simulation studies and an analysis of data from a randomized clinical trial for depression.
SCOPUS:85121322571
ISSN: 2049-1573
CID: 5115202

Prospective individual patient data meta-analysis: Evaluating convalescent plasma for COVID-19

Goldfeld, Keith S; Wu, Danni; Tarpey, Thaddeus; Liu, Mengling; Wu, Yinxiang; Troxel, Andrea B; Petkova, Eva
As the world faced the devastation of the COVID-19 pandemic in late 2019 and early 2020, numerous clinical trials were initiated in many locations in an effort to establish the efficacy (or lack thereof) of potential treatments. As the pandemic has been shifting locations rapidly, individual studies have been at risk of failing to meet recruitment targets because of declining numbers of eligible patients with COVID-19 encountered at participating sites. It has become clear that it might take several more COVID-19 surges at the same location to achieve full enrollment and to find answers about what treatments are effective for this disease. This paper proposes an innovative approach for pooling patient-level data from multiple ongoing randomized clinical trials (RCTs) that have not been configured as a network of sites. We present the statistical analysis plan of a prospective individual patient data (IPD) meta-analysis (MA) from ongoing RCTs of convalescent plasma (CP). We employ an adaptive Bayesian approach for continuously monitoring the accumulating pooled data via posterior probabilities for safety, efficacy, and harm. Although we focus on RCTs for CP and address specific challenges related to CP treatment for COVID-19, the proposed framework is generally applicable to pooling data from RCTs for other therapies and disease settings in order to find answers in weeks or months, rather than years.
PMID: 34164838
ISSN: 1097-0258
CID: 4918612

Predicting multiscan MRI outcomes in children with neurodevelopmental conditions following MRI simulator training

Simhal, Anish K; Filho, José O A; Segura, Patricia; Cloud, Jessica; Petkova, Eva; Gallagher, Richard; Castellanos, F Xavier; Colcombe, Stan; Milham, Michael P; Di Martino, Adriana
Pediatric brain imaging holds significant promise for understanding neurodevelopment. However, the requirement to remain still inside a noisy, enclosed scanner remains a challenge. Verbal or visual descriptions of the process, and/or practice in MRI simulators are the norm in preparing children. Yet, the factors predictive of successfully obtaining neuroimaging data remain unclear. We examined data from 250 children (6-12 years, 197 males) with autism and/or attention-deficit/hyperactivity disorder. Children completed systematic MRI simulator training aimed to habituate to the scanner environment and minimize head motion. An MRI session comprised multiple structural, resting-state, task and diffusion scans. Of the 201 children passing simulator training and attempting scanning, nearly all (94%) successfully completed the first structural scan in the sequence, and 88% also completed the following functional scan. The number of successful scans decreased as the sequence progressed. Multivariate analyses revealed that age was the strongest predictor of successful scans in the session, with younger children having lower success rates. After age, sensorimotor atypicalities contributed most to prediction. Results provide insights on factors to consider in designing pediatric brain imaging protocols.
PMCID:8517836
PMID: 34649041
ISSN: 1878-9307
CID: 5068032

A constrained single-index regression for estimating interactions between a treatment and covariates

Park, Hyung; Petkova, Eva; Tarpey, Thaddeus; Ogden, R Todd
We consider a single-index regression model, uniquely constrained to estimate interactions between a set of pretreatment covariates and a treatment variable on their effects on a response variable, in the context of analyzing data from randomized clinical trials. We represent interaction effect terms of the model through a set of treatment-specific flexible link functions on a linear combination of the covariates (a single index), subject to the constraint that the expected value given the covariates equals zero, while leaving the main effects of the covariates unspecified. We show that the proposed semiparametric estimator is consistent for the interaction term of the model, and that the efficiency of the estimator can be improved with an augmentation procedure. The proposed single-index regression provides a flexible and interpretable modeling approach to optimizing individualized treatment rules based on patients' data measured at baseline, as illustrated by simulation examples and an application to data from a depression clinical trial. This article is protected by copyright. All rights reserved.
PMID: 32573759
ISSN: 1541-0420
CID: 4493012

A prospective study of long-term outcomes among hospitalized COVID-19 patients with and without neurological complications

Frontera, Jennifer A; Yang, Dixon; Lewis, Ariane; Patel, Palak; Medicherla, Chaitanya; Arena, Vito; Fang, Taolin; Andino, Andres; Snyder, Thomas; Madhavan, Maya; Gratch, Daniel; Fuchs, Benjamin; Dessy, Alexa; Canizares, Melanie; Jauregui, Ruben; Thomas, Betsy; Bauman, Kristie; Olivera, Anlys; Bhagat, Dhristie; Sonson, Michael; Park, George; Stainman, Rebecca; Sunwoo, Brian; Talmasov, Daniel; Tamimi, Michael; Zhu, Yingrong; Rosenthal, Jonathan; Dygert, Levi; Ristic, Milan; Ishii, Haruki; Valdes, Eduard; Omari, Mirza; Gurin, Lindsey; Huang, Joshua; Czeisler, Barry M; Kahn, D Ethan; Zhou, Ting; Lin, Jessica; Lord, Aaron S; Melmed, Kara; Meropol, Sharon; Troxel, Andrea B; Petkova, Eva; Wisniewski, Thomas; Balcer, Laura; Morrison, Chris; Yaghi, Shadi; Galetta, Steven
BACKGROUND:Little is known regarding long-term outcomes of patients hospitalized with COVID-19. METHODS:We conducted a prospective study of 6-month outcomes of hospitalized COVID-19 patients. Patients with new neurological complications during hospitalization who survived were propensity score-matched to COVID-19 survivors without neurological complications hospitalized during the same period. The primary 6-month outcome was multivariable ordinal analysis of the modified Rankin Scale(mRS) comparing patients with or without neurological complications. Secondary outcomes included: activities of daily living (ADLs;Barthel Index), telephone Montreal Cognitive Assessment and Neuro-QoL batteries for anxiety, depression, fatigue and sleep. RESULTS:Of 606 COVID-19 patients with neurological complications, 395 survived hospitalization and were matched to 395 controls; N = 196 neurological patients and N = 186 controls completed follow-up. Overall, 346/382 (91%) patients had at least one abnormal outcome: 56% had limited ADLs, 50% impaired cognition, 47% could not return to work and 62% scored worse than average on ≥1 Neuro-QoL scale (worse anxiety 46%, sleep 38%, fatigue 36%, and depression 25%). In multivariable analysis, patients with neurological complications had worse 6-month mRS (median 4 vs. 3 among controls, adjusted OR 1.98, 95%CI 1.23-3.48, P = 0.02), worse ADLs (aOR 0.38, 95%CI 0.29-0.74, P = 0.01) and were less likely to return to work than controls (41% versus 64%, P = 0.04). Cognitive and Neuro-QOL metrics were similar between groups. CONCLUSIONS:Abnormalities in functional outcomes, ADLs, anxiety, depression and sleep occurred in over 90% of patients 6-months after hospitalization for COVID-19. In multivariable analysis, patients with neurological complications during index hospitalization had significantly worse 6-month functional outcomes than those without.
PMCID:8113108
PMID: 34000678
ISSN: 1878-5883
CID: 4876752

The Early Screening for Autism and Communication Disorders: Field-testing an autism-specific screening tool for children 12 to 36 months of age

Wetherby, Amy M; Guthrie, Whitney; Hooker, Jessica L; Delehanty, Abigail; Day, Taylor N; Woods, Juliann; Pierce, Karen; Manwaring, Stacy S; Thurm, Audrey; Ozonoff, Sally; Petkova, Eva; Lord, Catherine
LAY ABSTRACT/UNASSIGNED:There is a critical need for accurate screening tools for autism spectrum disorder in very young children so families can access tailored intervention services as early as possible. However, there are few screeners designed for children 18-24 months. Developing screeners that pick up on the signs of autism spectrum disorder in very young children has proved even more challenging. In this study, we examined a new autism-specific parent-report screening tool, the Early Screening for Autism and Communication Disorders for children between 12 and 36 months of age. Field-testing was done in five sites with 471 children screened for communication delays in primary care or referred for familial risk or concern for autism spectrum disorder. The Early Screening for Autism and Communication Disorders was tested in three age groups: 12-17, 18-23, and 24-36 months. A best-estimate diagnosis of autism spectrum disorder, developmental delay, or typical development was made. Analyses examined all 46 items and identified 30 items that best discriminated autism spectrum disorder from the non-spectrum groups. Cutoffs were established for each age group with good sensitivity and specificity. Results provide preliminary support for the accuracy of the Early Screening for Autism and Communication Disorders as an autism-specific screener in children 12-36 months with elevated risk of communication delay or autism spectrum disorder.
PMID: 33962531
ISSN: 1461-7005
CID: 4866892