Rationale and design of a single-center randomized trial to compare the graft patency between the radial artery and the no-touch saphenous vein in coronary artery bypass grafting surgery (GRAFT-CAB Study).

BACKGROUND: Previous studies suggested only the radial artery and the No-touch (NT) technique were effective in reducing graft occlusion after coronary artery bypass grafting (CABG) surgery. However, there is no randomized trial comparing these 2 graft conduits. The optimum second conduit for CABG remains undetermined. MATERIALS AND METHODS: This study is a prospective, single-center randomized clinical trial, aiming to compare the graft patency between the radial artery and the NT vein graft. All patients undergoing isolated CABG with left internal mammary artery (LIMA) plus at least 2 additional grafts will be considered eligible. About 774 cases (516 in the radial artery group and 258 in the NT vein group) will be enrolled in over 1 to 2 years. Participants will be randomized and allocated to two bypass strategies: the LIMA plus 1 radial artery and 1 conventional vein graft, or the LIMA plus 2 NT vein grafts. The primary outcome is graft occlusion at 1 year after CABG evaluated by CT angiography. The secondary outcomes include graft occlusion at 3 and 5 years and major adverse cardiac or cerebrovascular events at 1, 3, and 5 years follow-ups. DISCUSSION: This study will define whether or not the NT vein has a lower graft occlusion rate than the radial artery in short and mid-term follow-ups, and provide new evidence for the second conduit choice in CABG surgery. TRIAL REGISTRATION: ClinicalTrials.gov NCT06014047. Registered on October 15th, 2023.

STING agonist, SMA-2, inhibits clear cell renal cell carcinoma through improving tumor microenvironment.

Clear cell renal cell carcinoma (ccRCC) is the most prevalent and lethal subtype of kidney cancer, patients with ccRCC usually have very poor prognosis and short survival. Therefore, it is urgent to develop more effective therapeutics or medications to suppress ccRCC progression. Here, we demonstrated that STING agonist, MSA-2 significantly inhibits tumor progress and prolongs the survival of ccRCC mice by promoting cytokines secretion. Moreover, MSA-2 triggered the trafficking and infiltration of CD8+ T cells, supported by the generation of a chemokine milieu that promoted recruitment and modulation of the immunosuppressive TME in ccRCC. These findings suggest that MSA-2 potentially serves an effective and preferable adjuvant immunotherapy of ccRCC.

Music Therapy for Pain Management for People With Advanced Cancer: A Randomized Controlled Trial.

OBJECTIVE: To improve mechanistic understanding, this randomized controlled trial examined anxiety, mood, emotional support, and pain-related self-efficacy as mediators of music therapy for pain management in people with advanced cancer. METHODS: People with advanced cancer who had chronic pain were randomized (1:1) to 6 weekly individual music therapy or social attention control sessions. We measured mediators and pain outcomes (pain interference and pain intensity) using self-report measures at baseline, session 4, and post-intervention. We included outcome expectancy/treatment credibility, music reward, adult playfulness, and baseline pain interference and pain intensity as moderators. RESULTS: Participants (n = 92) had a mean age of 56 years. Most were female (71.7%), white (47.8%) or Black (39.1%), and had stage IV cancer (75%). Self-efficacy was found to be a significant mediator of music therapy for pain intensity (indirect effect ab = 0.79, 95% CI 0.01-1.82) and pain interference (indirect effect ab = 1.16, 95% CI 0.02-2.51), while anxiety, mood, and emotional support were not. The mediating effect of pain-related self-efficacy was significantly moderated by baseline pain interference but not by the other moderators. CONCLUSIONS: The findings suggest that the impact of music therapy on chronic pain is mediated by self-efficacy. This knowledge can help optimize music therapy interventions for chronic pain management for people with advanced cancer by capitalizing on teaching music-based self-management strategies. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT03432247.

Preventing prefrontal dysfunction by tDCS modulates stress-induced creativity impairment in women: an fNIRS study.

Stress is a major external factor threatening creative activity. The study explored whether left-lateralized activation in the dorsolateral prefrontal cortex manipulated through transcranial direct current stimulation could alleviate stress-induced impairment in creativity. Functional near-infrared spectroscopy was used to explore the underlying neural mechanisms. Ninety female participants were randomly assigned to three groups that received stress induction with sham stimulation, stress induction with true stimulation (anode over the left and cathode over the right dorsolateral prefrontal cortex), and control manipulation with sham stimulation, respectively. Participants underwent the stress or control task after the transcranial direct current stimulation manipulation, and then completed the Alternative Uses Task to measure creativity. Behavioral results showed that transcranial direct current stimulation reduced stress responses in heart rate and anxiety. The functional near-infrared spectroscopy results revealed that transcranial direct current stimulation alleviated dysfunction of the prefrontal cortex under stress, as evidenced by higher activation of the dorsolateral prefrontal cortex and frontopolar cortex, as well as stronger inter-hemispheric and intra-hemispheric functional connectivity within the prefrontal cortex. Further analysis demonstrated that the cortical regulatory effect prevented creativity impairment induced by stress. The findings validated the hemispheric asymmetry hypothesis regarding stress and highlighted the potential for brain stimulation to alleviate stress-related mental disorders and enhance creativity.

Using Continuous Glucose Monitoring to Passively Classify Naturalistic Binge Eating and Vomiting Among Adults With Binge-Spectrum Eating Disorders: A Preliminary Investigation.

OBJECTIVE: Binge eating and self-induced vomiting are common, transdiagnostic eating disorder (ED) symptoms. Efforts to understand these behaviors in research and clinical settings have historically relied on self-report measures, which may be biased and have limited ecological validity. It may be possible to passively detect binge eating and vomiting using data collected by continuous glucose monitors (CGMs; minimally invasive sensors that measure blood glucose levels), as these behaviors yield characteristic glucose responses. METHOD: This study developed machine learning classification algorithms to classify binge eating and vomiting among 22 adults with binge-spectrum EDs using CGM data. Participants wore Dexcom G6 CGMs and reported eating episodes and disordered eating symptoms using ecological momentary assessment for 2 weeks. Group-level random forest models were generated to distinguish binge eating from typical eating episodes and to classify instances of vomiting. RESULTS: The binge eating model had accuracy of 0.88 (95% CI: 0.83, 0.92), sensitivity of 0.56, and specificity of 0.90. The vomiting model demonstrated accuracy of 0.79 (95% CI: 0.62, 0.91), sensitivity of 0.88, and specificity of 0.71. DISCUSSION: Results suggest that CGM may be a promising avenue for passively classifying binge eating and vomiting, with implications for innovative research and clinical applications.

Continuous glucose monitoring as an objective measure of meal consumption in individuals with binge-spectrum eating disorders: A proof-of-concept study.

OBJECTIVE: Going extended periods of time without eating increases risk for binge eating and is a primary target of leading interventions for binge-spectrum eating disorders (B-EDs). However, existing treatments for B-EDs yield insufficient improvements in regular eating and subsequently, binge eating. These unsatisfactory clinical outcomes may result from limitations in assessment and promotion of regular eating in therapy. Detecting the absence of eating using passive sensing may improve clinical outcomes by facilitating more accurate monitoring of eating behaviours and powering just-in-time adaptive interventions. We developed an algorithm for detecting meal consumption (and extended periods without eating) using continuous glucose monitor (CGM) data and machine learning. METHOD: Adults with B-EDs (N = 22) wore CGMs and reported eating episodes on self-monitoring surveys for 2 weeks. Random forest models were run on CGM data to distinguish between eating and non-eating episodes. RESULTS: The optimal model distinguished eating and non-eating episodes with high accuracy (0.82), sensitivity (0.71), and specificity (0.94). CONCLUSIONS: These findings suggest that meal consumption and extended periods without eating can be detected from CGM data with high accuracy among individuals with B-EDs, which may improve clinical efforts to target dietary restriction and improve the field's understanding of its antecedents and consequences.

Sample size optimization for clinical trials using graphical approaches for multiplicity adjustment.

Graphical approach provides a useful framework for multiplicity adjustment in clinical trials with multiple endpoints. When designing a graphical approach, initial weight and transition probability for the endpoints are often assigned based on clinical importance. For example, practitioners may prefer putting more weights on some primary endpoints. The clinical preference can be formulated as a constrain in the sample size optimization problem. However, there has been a lack of theoretical guidance on how to specify initial weight and transition probability in a graphical approach to meet the clinical preference but at the same time to minimize the sample size needed for a power requirement. To fill this gap, we propose statistical methods to optimize sample size over initial weight and transition probability in a graphical approach under a common setting, which is to use marginal power for each endpoint in a trial design. Importantly, we prove that some of the commonly used graphical approaches such as putting all initial weights on one endpoint are suboptimal. Our methods are flexible, which can be used for both single-arm trials and randomized controlled trials with either continuous or binary or mixed types of endpoints. Additionally, we prove the existence of optimal solution where all marginal powers are placed exactly at the prespecified values, assuming continuity. Two hypothetical clinical trial designs are presented to illustrate the application of our methods under different scenarios. Results are first presented for a design with two endpoints and are further generalized to three or more endpoints. Our findings are helpful to guide the design of a graphical approach and the sample size calculation in clinical trials.

The Role of Physical Activity in Long-term Weight Loss: 36-month Results From a Randomized Controlled Trial.

BACKGROUND: Physical activity (PA) may promote long-term weight loss, but facilitating high levels of PA in behavioral weight loss programs is challenging. PURPOSE: This study reports the 36-month follow-up of a behavioral weight loss trial that tested the efficacy of increasing the emphasis on PA during treatment and using traditional or acceptance-based therapy (ABT) for this purpose. We also examined the extent to which long-term weight loss differed by PA pattern and tested if individual differences in eating behavior moderated this relationship. METHODS: Participants (N = 320) were randomized to (1) standard behavioral weight loss treatment (BT), (2) BT with a focus on PA, or (3) ABT with a focus on PA. Weight loss and PA were measured at 24- and 36-month follow-up. RESULTS: There were no differences between conditions in weight loss or PA at 24 or 36 months. Participants consistently engaging in high PA experienced the greatest weight losses. The positive impact of PA on weight loss was more pronounced among those with low emotional eating and those who believed that exercise did not affect their appetite. CONCLUSIONS: Findings emphasize the difficulty of improving long-term PA among adults with overweight/obesity beyond what standard behavioral weight loss treatment achieves. This study highlights the need to develop new PA treatment strategies, and suggests that ABT for weight loss may be more effective when applied to eating behavior versus PA. Results also demonstrate the importance of addressing problematic eating behavior and cognitions to fully realize the benefits of PA for weight loss. CLINICAL TRIAL INFORMATION: ClinicalTrials.gov identifier: NCT02363010.

Multidimensional brain-age prediction reveals altered brain developmental trajectory in psychiatric disorders.

Brain-age prediction has emerged as a novel approach for studying brain development. However, brain regions change in different ways and at different rates. Unitary brain-age indices represent developmental status averaged across the whole brain and therefore do not capture the divergent developmental trajectories of various brain structures. This staggered developmental unfolding, determined by genetics and postnatal experience, is implicated in the progression of psychiatric and neurological disorders. We propose a multidimensional brain-age index (MBAI) that provides regional age predictions. Using a database of 556 individuals, we identified clusters of imaging features with distinct developmental trajectories and built machine learning models to obtain brain-age predictions from each of the clusters. Our results show that the MBAI provides a flexible analysis of region-specific brain-age changes that are invisible to unidimensional brain-age. Importantly, brain-ages computed from region-specific feature clusters contain complementary information and demonstrate differential ability to distinguish disorder groups (e.g., depression and oppositional defiant disorder) from healthy controls. In summary, we show that MBAI is sensitive to alterations in brain structures and captures distinct regional change patterns that may serve as biomarkers that contribute to our understanding of healthy and pathological brain development and the characterization and diagnosis of psychiatric disorders.

Optimizing digital health technologies to improve therapeutic skill use and acquisition alongside enhanced cognitive-behavior therapy for binge-spectrum eating disorders: Protocol for a randomized controlled trial.

OBJECTIVE: Adjunctive mobile health (mHealth) technologies offer promise for improving treatment response to enhanced cognitive-behavior therapy (CBT-E) among individuals with binge-spectrum eating disorders, but research on the key "active" components of these technologies has been very limited. The present study will use a full factorial design to (1) evaluate the optimal combination of complexity of two commonly used mHealth components (i.e., self-monitoring and microinterventions) alongside CBT-E and (2) test whether the optimal complexity level of these interventions is moderated by baseline self-regulation. Secondary aims of the present study include evaluating target engagement associated with each level of these intervention components and quantifying the component interaction effects (i.e., partially additive, fully additive, or synergistic effects). METHOD: Two hundred and sixty-four participants with binge-spectrum eating disorders will be randomized to six treatment conditions determined by the combination of self-monitoring condition (i.e., standard self-monitoring or skills monitoring) and microinterventions condition (i.e., no microinterventions, automated microinterventions, or just-in-time adaptive interventions) as an augmentation to 16 sessions of CBT-E. Treatment outcomes will be measured using the Eating Disorder Examination and compared by treatment condition using multilevel models. RESULTS: Results will clarify the "active" components in mHealth interventions for binge eating. DISCUSSION: The present study will provide critical insight into the efficacy of commonly used digital intervention components (i.e., skills monitoring and microinterventions) alongside CBT-E. Furthermore, results of this study may inform personalization of digital intervention intensity based on patient profiles of self-regulation. PUBLIC SIGNIFICANCE: This study will examine the relative effectiveness of commonly used components of application-based interventions as an augmentation to cognitive-behavioral therapy for binge eating. Findings from this study will inform the development of an optimized digital intervention for individuals with binge eating.

Cost-effective Bi2WO6 for efficient degradation of rhodamine B and tetracycline.

The morphology-controlled synthesis of nanostructured photocatalysts by an environmentally friendly and low-cost method provides a feasible way to realize practical applications of photocatalysts. Herein, Bi2WO6 (BWO) nanophotocatalysts with mulberry shape, sheet-like, and round-cake morphologies have been successfully synthesized through a highly facile solvothermal process by simply adjusting the solvothermal temperature or utilizing selective addition of ethylene glycol as an orientation agent without using strong acids and bases and/or hazardous chemicals. The ratio of ethylene glycol and glacial acetic acid can affect the morphology and oxygen vacancy content of BWO, thereby influencing the photocatalytic performance. The photocatalytic activity of the as-prepared samples was evaluated by degradation of rhodamine B (RhB) and tetracycline under visible-light irradiation. The results indicated that all the BWO samples exhibited morphology-associated photocatalytic activity, and the sheet-like structure of BWO obtained via solvothermal treatment at 120 °C with ethylene glycol and glacial acetic acid ratio of 1:3 achieved the maximum specific surface area and possessed abundant oxygen vacancies, exhibiting outstanding photocatalytic activity for degradation of RhB and tetracycline. The degradation rate of RhB reached 100% within 20 min. To the best of our knowledge, this value is one of the most remarkable values for pristine BWO photocatalysts. Radical capture experiments demonstrated that hydroxyl radicals (·OH) play major roles compared with electrons (e-) and holes (h+) in the photocatalytic degradation process. A possible mechanism for the photocatalytic degradation of pollutants was proposed to better understand the reaction process. We believe that the more economical, efficient and greener methodology can provide guidance to develop highly efficient photocatalysts with favourable morphology and structure. Supplementary Information: The online version contains supplementary material available at 10.1007/s10854-022-09654-z.

Investigating the temporal pattern of neuroimaging-based brain age estimation as a biomarker for Alzheimer's Disease related neurodegeneration.

Neuroimaging-based brain-age estimation via machine learning has emerged as an important new approach for studying brain aging. The difference between one's estimated brain age and chronological age, the brain age gap (BAG), has been proposed as an Alzheimer's Disease (AD) biomarker. However, most past studies on the BAG have been cross-sectional. Quantifying longitudinal changes in an individual's BAG temporal pattern would likely improve prediction of AD progression and clinical outcome based on neurophysiological changes. To fill this gap, our study conducted predictive modeling using a large neuroimaging dataset with up to 8 years of follow-up to examine the temporal patterns of the BAG's trajectory and how it varies by subject-level characteristics (sex, APOEɛ4 carriership) and disease status. Specifically, we explored the pattern and rate of change in BAG over time in individuals who remain stable with normal cognition or mild cognitive impairment (MCI), as well as individuals who progress to clinical AD. Combining multimodal imaging data in a support vector regression model to estimate brain age yielded improved performance over single modality. Multilevel modeling results showed the BAG followed a linear increasing trajectory with a significantly faster rate in individuals with MCI who progressed to AD compared to cognitively normal or MCI individuals who did not progress. The dynamic changes in the BAG during AD progression were further moderated by sex and APOEɛ4 carriership. Our findings demonstrate the BAG as a potential biomarker for understanding individual specific temporal patterns related to AD progression.

Classification model with weighted regularization to improve the reproducibility of neuroimaging signature selection.

Machine learning (ML) has been extensively applied in brain imaging studies to aid the diagnosis of psychiatric disorders and the selection of potential biomarkers. Due to the high dimensionality of imaging data and heterogeneous subtypes of psychiatric disorders, the reproducibility of ML results in brain imaging studies has drawn increasing attention. The reproducibility in brain imaging has been primarily examined in terms of prediction accuracy. However, achieving high prediction accuracy and discovering relevant features are two separate but related goals. An important yet under-investigated problem is the reproducibility of feature selection in brain imaging studies. We propose a new metric to quantify the reproducibility of neuroimaging feature selection via bootstrapping. We estimate the reproducibility index (R-index) for each feature as the reciprocal coefficient of variation of absolute mean difference across a larger number of bootstrap samples. We then integrate the R-index in regularized classification models as penalty weight. Reproducible features with a larger R-index are assigned smaller penalty weights and thus are more likely to be selected by our proposed models. Both simulated and multimodal neuroimaging data are used to examine the performance of our proposed models. Results show that our proposed R-index models are effective in separating informative features from noise features. Additionally, the proposed models yield similar or higher prediction accuracy than the standard regularized classification models while further reducing coefficient estimation error. Improvements achieved by the proposed models are essential to advance our understanding of the selected brain imaging features as well as their associations with psychiatric disorders.

A data-driven approach to post-stroke aphasia classification and lesion-based prediction.

Aphasia is an acquired impairment in the production or comprehension of language, typically caused by left hemisphere stroke. The subtyping framework used in clinical aphasiology today is based on the Wernicke-Lichtheim model of aphasia formulated in the late 19th century, which emphasizes the distinction between language production and comprehension. The current study used a data-driven approach that combined modern statistical, machine learning, and neuroimaging tools to examine behavioural deficit profiles and their lesion correlates and predictors in a large cohort of individuals with post-stroke aphasia. First, individuals with aphasia were clustered based on their behavioural deficit profiles using community detection analysis (CDA) and these clusters were compared with the traditional aphasia subtypes. Random forest classifiers were built to evaluate how well individual lesion profiles predict cluster membership. The results of the CDA analyses did not align with the traditional model of aphasia in either behavioural or neuroanatomical patterns. Instead, the results suggested that the primary distinction in aphasia (after severity) is between phonological and semantic processing rather than between production and comprehension. Further, lesion-based classification reached 75% accuracy for the CDA-based categories and only 60% for categories based on the traditional fluent/non-fluent aphasia distinction. The results of this study provide a data-driven basis for a new approach to classification of post-stroke aphasia subtypes in both research and clinical settings.

Glucose variability: A physiological correlate of eating disorder behaviors among individuals with binge-spectrum eating disorders.

OBJECTIVES: Elevated glucose variability may be one mechanism that increases risk for significant psychological and physiological health conditions among individuals with binge-spectrum eating disorders (B-EDs), given the impact of eating disorder (ED) behaviors on blood glucose levels. This study aimed to characterize glucose variability among individuals with B-EDs compared with age-matched, sex-matched, and body mass index-matched controls, and investigate the association between frequency of ED behaviors and glucose variability. METHODS: Participants were 52 individuals with B-EDs and 22 controls who wore continuous glucose monitors to measure blood glucose levels and completed ecological momentary assessment surveys to measure ED behaviors for 1 week. Independent samples t-tests compared individuals with B-EDs and controls and multiple linear regression models examined the association between ED behaviors and glucose variability. RESULTS: Individuals with B-EDs demonstrated numerically higher glucose variability than controls (t = 1.42, p = .08, d = 0.43), although this difference was not statistically significant. When controlling for covariates, frequency of ED behaviors was significantly, positively associated with glucose variability (t = 3.17, p = .003) with medium effect size (f2  = 0.25). Post hoc analyses indicated that binge eating frequency was significantly associated with glucose variability, while episodes of 5+ hours without eating were not. DISCUSSION: Glucose variability among individuals with B-EDs appears to be positively associated with engagement in ED behaviors, particularly binge eating. Glucose variability may be an important mechanism by which adverse health outcomes occur at elevated rates in B-EDs and warrants future study. PUBLIC SIGNIFICANCE: This study suggests that some individuals with binge ED and bulimia nervosa may experience elevated glucose variability, a physiological symptom that is linked to a number of adverse health consequences. The degree of elevation in glucose variability is positive associated with frequency of eating disorder behaviors, especially binge eating.

Validation of the food craving Acceptance and action questionnaire (FAAQ) in a weight loss-seeking sample.

OBJECTIVE: The Food Craving Acceptance and Action Questionnaire (FAAQ) was developed to measure psychological flexibility around food-related internal experiences (e.g., thoughts, feelings, urges) and has two subscales, acceptance and willingness. However, the FAAQ factor structure has not yet been systematically validated with a clinically relevant sample. METHODS: Two weight-loss treatment seeking samples (total N = 462; 80.4% female) ages 18 to 70 (M = 52.6, SD = 9.8) completed the FAAQ before and after group-based treatment of overweight or obesity. RESULTS: Confirmatory factor analysis on the FAAQ's previously observed two-factor model produced poor model fit. An alternative 7-item model removing specific items that contributed to poor fit and were conceptually relevant to remove provided good model fit. The resulting revised 7-item FAAQ (items 1,3,6 removed) had adequate internal consistency and significant predictive validity for the Total score and subscales, and showed initial construct validity for the Total score. CONCLUSIONS: Results from this study suggest researchers and clinicians should now use the 7-item FAAQ-II, which retains the Willingness and Acceptance subscales. Future research is needed with other relevant samples to confirm the FAAQ-II's factor structure and psychometric properties.

Traditional versus developmental measures of weight suppression: Exploring their relationships with bulimic psychopathology.

OBJECTIVE: Weight suppression (WS) is related to a wide variety of eating disorder characteristics. However, individuals with eating disorders usually reach their highest premorbid weight while still developing physically. Therefore, a more sensitive index of individual differences in highest premorbid weight may be one that compares highest premorbid z-BMI to current z-BMI (called developmental weight suppression [DWS] here). METHOD: In this exploratory study, we compared the relationships between traditional weight suppression (TWS) and DWS and a variety of measures related to bulimic psychopathology in 91 females (M age, 25.2; 60.5% White), with clinical or sub-clinical bulimia nervosa. RESULTS: TWS and DWS were correlated (r = 0.40, p < 0.001). TWS was only significantly related to a measure of physical activity whereas DWS was related to 14 outcomes. DWS showed consistent positive relations with behavioural outcomes (e.g., binge eating) but consistent negative relations with cognitive/affective outcomes (e.g., weight concerns). CONCLUSIONS: Findings indicated much more consistent relationships between the novel DWS measure and bulimic characteristics than with the TWS measure. DWS showed both positive and negative relations with bulimic symptoms, though these findings require replication to confirm their validity. Consistent evidence indicated that the two WS measures served as mutual suppressor variables.

Pitfalls in brain age analyses.

Over the past decade, there has been an abundance of research on the difference between age and age predicted using brain features, which is commonly referred to as the "brain age gap." Researchers have identified that the brain age gap, as a linear transformation of an out-of-sample residual, is dependent on age. As such, any group differences on the brain age gap could simply be due to group differences on age. To mitigate the brain age gap's dependence on age, it has been proposed that age be regressed out of the brain age gap. If this modified brain age gap is treated as a corrected deviation from age, model accuracy statistics such as R2 will be artificially inflated to the extent that it is highly improbable that an R2 value below .85 will be obtained no matter the true model accuracy. Given the limitations of proposed brain age analyses, further theoretical work is warranted to determine the best way to quantify deviation from normality.

Prediction of eating disorder treatment response trajectories via machine learning does not improve performance versus a simpler regression approach.

OBJECTIVE: Patterns of response to eating disorder (ED) treatment are heterogeneous. Advance knowledge of a patient's expected course may inform precision medicine for ED treatment. This study explored the feasibility of applying machine learning to generate personalized predictions of symptom trajectories among patients receiving treatment for EDs, and compared model performance to a simpler logistic regression prediction model. METHOD: Participants were adolescent girls and adult women (N = 333) presenting for residential ED treatment. Self-report progress assessments were completed at admission, discharge, and weekly throughout treatment. Latent growth mixture modeling previously identified three latent treatment response trajectories (Rapid Response, Gradual Response, and Low-Symptom Static Response) and assigned a trajectory type to each patient. Machine learning models (support vector, k-nearest neighbors) and logistic regression were applied to these data to predict a patient's response trajectory using data from the first 2 weeks of treatment. RESULTS: The best-performing machine learning model (evaluated via area under the receiver operating characteristics curve [AUC]) was the radial-kernel support vector machine (AUCRADIAL = 0.94). However, the more computationally-intensive machine learning models did not improve predictive power beyond that achieved by logistic regression (AUCLOGIT = 0.93). Logistic regression significantly improved upon chance prediction (MAUC[NULL] = 0.50, SD = .01; p <.001). DISCUSSION: Prediction of ED treatment response trajectories is feasible and achieves excellent performance, however, machine learning added little benefit. We discuss the need to explore how advance knowledge of expected trajectories may be used to plan treatment and deliver individualized interventions to maximize treatment effects.

Comprehensive Cognitive-Behavioral Interventions Augment Diaphragmatic Breathing for Rumination Syndrome: A Proof-of-Concept Trial.

BACKGROUND: Rumination syndrome (RS) is often treated in medical settings with 1-2 sessions of diaphragmatic breathing to target reflexive abdominal wall contraction in response to conditioned cues (e.g., food). However, many patients remain symptomatic and require additional behavioral interventions. AIMS: In an attempt to augment diaphragmatic breathing with additional interventions, we tested the proof-of-concept of a comprehensive Cognitive-Behavioral Therapy (CBT) for RS. METHODS: In an uncontrolled trial, adults with RS completed a 5-8 session CBT protocol, delivered by one of two psychology behavioral health providers. CBT included two main phases: awareness training and diaphragmatic breathing (Phase 1) and modularized interventions chosen by the therapist and patient to target secondary maintenance mechanisms (Phase 2). At pre-treatment, post-treatment, and 3-month follow-up, participants completed a semi-structured interview on RS symptoms with an independent evaluator. RESULTS: Of 10 eligible individuals (ages 20-67 years, 50% female) offered treatment, all 10 initiated treatment and eight completed it. All participants endorsed high treatment credibility at Session 1. Permutation-based repeated measures ANOVA showed participants achieved large reductions in regurgitations across treatment [F(1,7) = 17.7, p = .007, η p2 = .69]. Although participants reduced regurgitations with diaphragmatic breathing during Phase 1, addition of other CBT strategies in Phase 2 produced further large reductions [F(1,7) = 6.3, p = .04, η p2 = .47]. Of eight treatment completers, treatment gains were maintained at 3-month follow-up for n = 6. CONCLUSIONS: Findings provide evidence of feasibility, acceptability, and proof-of-concept for a comprehensive CBT for RS that includes interventions in addition to diaphragmatic breathing to target secondary maintenance mechanisms. Randomized controlled trials are needed.