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The effectiveness of PTSD treatment for adolescents in the juvenile justice system: A systematic review

Baetz, Carly Lyn; Branson, Christopher Edward; Weinberger, Emily; Rose, Raquel E; Petkova, Eva; Horwitz, Sarah McCue; Hoagwood, Kimberly Eaton
OBJECTIVE:The objective of this study was to systematically review existing empirical evidence on the effectiveness of trauma-specific treatment for justice-involved adolescents and evaluate the impact of the interventions on the reduction of posttraumatic stress disorder (PTSD) symptoms, co-occurring mental health symptoms, and juvenile justice-related outcomes. METHOD/METHODS:A systematic literature search was conducted using a four-step process. Studies were included if they used a manualized, trauma-specific treatment with at least one control or comparison group and a sample comprised exclusively of justice-involved adolescents. RESULTS:In total, 1,699 unique records were identified, and 56 full-text articles were reviewed, of which 7 met the criteria for inclusion. Trauma-specific interventions led to a decrease in PTSD symptoms compared with a control group in four of seven studies, and two studies also demonstrated a reduction in trauma-related depressive symptoms. Finally, juvenile justice-related outcomes were measured in only four studies, with one study finding moderately reduced rates of delinquent behavior and recidivism following trauma-specific treatment. CONCLUSIONS:The results from this systematic review suggest that trauma-specific treatment interventions have promising effects for justice-involved adolescents. However, the results reveal a dearth of quality intervention research for treating youths with histories of trauma in the justice system. Significant gaps in the literature are highlighted, and suggestions for future directions are discussed. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
PMID: 34410809
ISSN: 1942-969x
CID: 5069842

A sparse additive model for treatment effect-modifier selection

Park, Hyung; Petkova, Eva; Tarpey, Thaddeus; Ogden, R Todd
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.
PMID: 32808656
ISSN: 1468-4357
CID: 4566752

Efficacy and Safety of COVID-19 Convalescent Plasma in Hospitalized Patients: A Randomized Clinical Trial

Ortigoza, Mila B; Yoon, Hyunah; Goldfeld, Keith S; Troxel, Andrea B; Daily, Johanna P; Wu, Yinxiang; Li, Yi; Wu, Danni; Cobb, Gia F; Baptiste, Gillian; O'Keeffe, Mary; Corpuz, Marilou O; Ostrosky-Zeichner, Luis; Amin, Amee; Zacharioudakis, Ioannis M; Jayaweera, Dushyantha T; Wu, Yanyun; Philley, Julie V; Devine, Megan S; Desruisseaux, Mahalia S; Santin, Alessandro D; Anjan, Shweta; Mathew, Reeba; Patel, Bela; Nigo, Masayuki; Upadhyay, Rabi; Kupferman, Tania; Dentino, Andrew N; Nanchal, Rahul; Merlo, Christian A; Hager, David N; Chandran, Kartik; Lai, Jonathan R; Rivera, Johanna; Bikash, Chowdhury R; Lasso, Gorka; Hilbert, Timothy P; Paroder, Monika; Asencio, Andrea A; Liu, Mengling; Petkova, Eva; Bragat, Alexander; Shaker, Reza; McPherson, David D; Sacco, Ralph L; Keller, Marla J; Grudzen, Corita R; Hochman, Judith S; Pirofski, Liise-Anne; Parameswaran, Lalitha; Corcoran, Anthony T; Rohatgi, Abhinav; Wronska, Marta W; Wu, Xinyuan; Srinivasan, Ranjini; Deng, Fang-Ming; Filardo, Thomas D; Pendse, Jay; Blaser, Simone B; Whyte, Olga; Gallagher, Jacqueline M; Thomas, Ololade E; Ramos, Danibel; Sturm-Reganato, Caroline L; Fong, Charlotte C; Daus, Ivy M; Payoen, Arianne Gisselle; Chiofolo, Joseph T; Friedman, Mark T; Wu, Ding Wen; Jacobson, Jessica L; Schneider, Jeffrey G; Sarwar, Uzma N; Wang, Henry E; Huebinger, Ryan M; Dronavalli, Goutham; Bai, Yu; Grimes, Carolyn Z; Eldin, Karen W; Umana, Virginia E; Martin, Jessica G; Heath, Timothy R; Bello, Fatimah O; Ransford, Daru Lane; Laurent-Rolle, Maudry; Shenoi, Sheela V; Akide-Ndunge, Oscar Bate; Thapa, Bipin; Peterson, Jennifer L; Knauf, Kelly; Patel, Shivani U; Cheney, Laura L; Tormey, Christopher A; Hendrickson, Jeanne E
Importance/UNASSIGNED:There is clinical equipoise for COVID-19 convalescent plasma (CCP) use in patients hospitalized with COVID-19. Objective/UNASSIGNED:To determine the safety and efficacy of CCP compared with placebo in hospitalized patients with COVID-19 receiving noninvasive supplemental oxygen. Design, Setting, and Participants/UNASSIGNED:CONTAIN COVID-19, a randomized, double-blind, placebo-controlled trial of CCP in hospitalized adults with COVID-19, was conducted at 21 US hospitals from April 17, 2020, to March 15, 2021. The trial enrolled 941 participants who were hospitalized for 3 or less days or presented 7 or less days after symptom onset and required noninvasive oxygen supplementation. Interventions/UNASSIGNED:A unit of approximately 250 mL of CCP or equivalent volume of placebo (normal saline). Main Outcomes and Measures/UNASSIGNED:The primary outcome was participant scores on the 11-point World Health Organization (WHO) Ordinal Scale for Clinical Improvement on day 14 after randomization; the secondary outcome was WHO scores determined on day 28. Subgroups were analyzed with respect to age, baseline WHO score, concomitant medications, symptom duration, CCP SARS-CoV-2 titer, baseline SARS-CoV-2 serostatus, and enrollment quarter. Outcomes were analyzed using a bayesian proportional cumulative odds model. Efficacy of CCP was defined as a cumulative adjusted odds ratio (cOR) less than 1 and a clinically meaningful effect as cOR less than 0.8. Results/UNASSIGNED:Of 941 participants randomized (473 to placebo and 468 to CCP), 556 were men (59.1%); median age was 63 years (IQR, 52-73); 373 (39.6%) were Hispanic and 132 (14.0%) were non-Hispanic Black. The cOR for the primary outcome adjusted for site, baseline risk, WHO score, age, sex, and symptom duration was 0.94 (95% credible interval [CrI], 0.75-1.18) with posterior probability (P[cOR<1] = 72%); the cOR for the secondary adjusted outcome was 0.92 (95% CrI, 0.74-1.16; P[cOR<1] = 76%). Exploratory subgroup analyses suggested heterogeneity of treatment effect: at day 28, cORs were 0.72 (95% CrI, 0.46-1.13; P[cOR<1] = 93%) for participants enrolled in April-June 2020 and 0.65 (95% CrI, 0.41 to 1.02; P[cOR<1] = 97%) for those not receiving remdesivir and not receiving corticosteroids at randomization. Median CCP SARS-CoV-2 neutralizing titer used in April to June 2020 was 1:175 (IQR, 76-379). Any adverse events (excluding transfusion reactions) were reported for 39 (8.2%) placebo recipients and 44 (9.4%) CCP recipients (P = .57). Transfusion reactions occurred in 2 (0.4) placebo recipients and 8 (1.7) CCP recipients (P = .06). Conclusions and Relevance/UNASSIGNED:In this trial, CCP did not meet the prespecified primary and secondary outcomes for CCP efficacy. However, high-titer CCP may have benefited participants early in the pandemic when remdesivir and corticosteroids were not in use. Trial Registration/UNASSIGNED:ClinicalTrials.gov Identifier: NCT04364737.
PMID: 34901997
ISSN: 2168-6114
CID: 5084962

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

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