Personalized prediction of antidepressant v. placebo response: evidence from the EMBARC study

Stratified Care in Cognitive Behavioural Therapy: A Comparative Evaluation of Predictive Modelling Approaches for Individualized Treatment: La stratification des soins pour l'orientation vers une thérapie cognitivo-comportementale: une évaluation comparative des approches de modélisation prédictive pour un traitement individualisé

OBJECTIVE

In response to high demand and prolonged wait times for cognitive behavioural therapy (CBT) in Ontario, Canada, we developed predictive models to stratify patients into high- or low-intensity treatment, aiming to optimize limited healthcare resources.

METHOD

Using client records (n = 953) from Ontario Shores Centre for Mental Health Sciences (January 2017-2021), we estimated four binary outcome models to assign patients into complex and standard cases based on the probability of reliable improvement in Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder-7 (GAD-7) scores. We evaluated two choices of cut-offs for patient complexity assignment: models at an ROC (receiver operating characteristic)-derived cut-off and a 0.5 probability cut-off. Final model effectiveness was assessed by assigning treatment intensity (high-intensity or low-intensity CBT) based on the combined performance of both GAD-7 and PHQ-9 models. We compared the treatment assignment recommendations provided by the models to those assigned by clinical assessors.

RESULTS

The predictive models demonstrated higher accuracy in selecting treatment modality compared to provider-assigned treatment selection. The ROC cut-off achieved the highest prediction accuracy of case complexity (0.749). The predictive models exhibited large sensitivity and specificity trade-offs (which influence the rates of patient assignment to high-intensity CBT) despite having similar accuracy statistics (ROC cut-off = 0.749, 0.5 cut-off = 0.690), emphasizing the impact of cut-off choices when implementing predictive models.

CONCLUSIONS

Overall, our findings suggest that the predictive model has the potential to improve the allocation of CBT services by shifting incoming clients with milder symptoms of depression to low-intensity CBT, with those at highest risk of not improving beginning in high-intensity CBT. We have demonstrated that models can have significant sensitivity and specificity trade-offs depending on the chosen acceptable threshold for the model to make positive predictions of case complexity. Further research could assess the use of the predictive model in real-world clinical settings.

Clinical and sociodemographic features of the Texas resilience against depression (T-RAD) study: Findings from the initial cohort

OBJECTIVE

The burden of major depressive disorder is compounded by a limited understanding of its risk factors, the limited efficacy of treatments, and the lack of precision approaches to guide treatment selection. The Texas Resilience Against Depression (T-RAD) study was designed to explore the etiology of depression by collecting comprehensive socio-demographic, clinical, behavioral, neurophysiological/neuroimaging, and biological data from depressed individuals (D2K) and youth at risk for depression (RAD).

METHODS

This report details the baseline sociodemographic, clinical, and functional features from the initial cohort (D2K N = 1040, RAD N = 365).

RESULTS

Of the total T-RAD sample, n = 1078 (76.73 %) attended ≥2 in-person visits, and n = 845 (60.14 %) attended ≥4 in-person visits. Most D2K (84.82 %) had a primary diagnosis of any depressive disorder, with a bipolar disorder diagnosis being prevalent (13.49 %). RAD participants (75.89 %) did not have a psychiatric diagnosis, but other non-depressive diagnoses were present. D2K participants had 9-item Patient Health Questionnaire scores at or near the moderate range (10.58 ± 6.42 > 24 yrs.; 9.73 ± 6.12 10-24 yrs). RAD participants were in the non-depressed range (2.19 ± 2.65). While the age ranges in D2K and RAD differ, the potential to conduct analyses that compare at-risk and depressed youth is a strength of the study. The opportunity to examine the trajectory of depressive symptoms in the D2K cohort over the lifespan is unique.

LIMITATIONS

As a longitudinal study, missing data were common.

CONCLUSION

T-RAD will allow data to be collected from multiple modalities on a clinically well-characterized sample. These data will drive important discoveries on diagnosis, treatment, and prevention of depression.

Initial evaluation of a personalized advantage index to determine which individuals may benefit from mindfulness-based cognitive therapy for suicide prevention

OBJECTIVE

Develop and evaluate a treatment matching algorithm to predict differential treatment response to Mindfulness-Based Cognitive Therapy for suicide prevention (MBCT-S) versus enhanced treatment-as-usual (eTAU).

METHODS

Analyses used data from Veterans at high-risk for suicide assigned to either MBCT-S (n = 71) or eTAU (n = 69) in a randomized clinical trial. Potential predictors (n = 55) included available demographic, clinical, and neurocognitive variables. Random forest models were used to predict risk of suicidal event (suicidal behaviors, or ideation resulting in hospitalization or emergency department visit) within 12 months following randomization, characterize the prediction, and develop a Personalized Advantage Index (PAI).

RESULTS

A slightly better prediction model emerged for MBCT-S (AUC = 0.70) than eTAU (AUC = 0.63). Important outcome predictors for participants in the MBCT-S arm included PTSD diagnosis, decisional efficiency on a neurocognitive task (Go/No-Go), prior-year mental health residential treatment, and non-suicidal self-injury. Significant predictors for participants in the eTAU arm included past-year acute psychiatric hospitalizations, past-year outpatient psychotherapy visits, past-year suicidal ideation severity, and attentional control (indexed by Stroop task). A moderation analysis showed that fewer suicidal events occurred among those randomized to their PAI-indicated optimal treatment.

CONCLUSIONS

PAI-guided treatment assignment may enhance suicide prevention outcomes. However, prior to real-world application, additional research is required to improve model accuracy and evaluate model generalization.

A Tutorial Introduction to Heterogeneous Treatment Effect Estimation with Meta-learners

Psychotherapy has been proven to be effective on average, though patients respond very differently to treatment. Understanding which characteristics are associated with treatment effect heterogeneity can help to customize therapy to the individual patient. In this tutorial, we describe different meta-learners, which are flexible algorithms that can be used to estimate personalized treatment effects. More specifically, meta-learners decompose treatment effect estimation into multiple prediction tasks, each of which can be solved by any machine learning model. We begin by reviewing necessary assumptions for interpreting the estimated treatment effects as causal, and then give an overview over key concepts of machine learning. Throughout the article, we use an illustrative data example to show how the different meta-learners can be implemented in R. We also point out how current popular practices in psychotherapy research fit into the meta-learning framework. Finally, we show how heterogeneous treatment effects can be analyzed, and point out some challenges in the implementation of meta-learners.

Can the potential benefit of individualizing treatment be assessed using trial summary statistics alone?

Individualizing treatment assignment can improve outcomes for diseases with patient-to-patient variability in comparative treatment effects. When a clinical trial demonstrates that some patients improve on treatment while others do not, it is tempting to assume that treatment effect heterogeneity exists. However, if outcome variability is mainly driven by factors other than variability in the treatment effect, investigating the extent to which covariate data can predict differential treatment response is a potential waste of resources. Motivated by recent meta-analyses assessing the potential of individualizing treatment for major depressive disorder using only summary statistics, we provide a method that uses summary statistics widely available in published clinical trial results to bound the benefit of optimally assigning treatment to each patient. We also offer alternate bounds for settings in which trial results are stratified by another covariate. Our upper bounds can be especially informative when they are small, as there is then little benefit to collecting additional covariate data. We demonstrate our approach using summary statistics from a depression treatment trial. Our methods are implemented in the rct2otrbounds R package.

Predictive modeling of antidepressant efficacy based on cognitive neuropsychological theory

BACKGROUND

We aimed to develop a clinical predictive model based on the cognitive neuropsychological (CNP) theory and machine-learning to examine SSRI efficacy in the treatment of MDD.

METHODS

Baseline assessments including clinical symptoms (HAMD, HAMA, BDI, and TEPS scores), negative biases (NEO-PI-R-N and NCPBQ scores), sociodemographic characteristics (social support and SES), and a 5-min eye-opening resting-state EEG were completed by 69 participants with first-episode major depressive disorder (MDD) and 36 healthy controls. The clinical symptoms and negative bias were again assessed after an 8-week treatment of depression with selective serotonin reuptake inhibitors (SSRIs). A multi-modality machine-learning model was developed to predict the effectiveness of SSRI antidepressants.

RESULTS

At baseline, we observed significant differences between MDD patients and healthy controls in terms of social support, clinical symptoms, and negative bias characteristics (p < 0.001). A negative association was found (p < 0.05) between neuroticism and alpha asymmetry in both the central and central-parietal areas, as well as between negative cognitive processing bias and alpha asymmetry in the parietal region. Compared to responders, non-responders exhibited less negative cognitive processing bias and greater alpha asymmetry in both central and central-parietal regions. Importantly, we developed a multi-modality machine-learning model with 83 % specificity using the above salient features.

CONCLUSIONS

Research results support the CNP theory of depression treatment. To some extent, the multimodal clinical model constructed based on the CNP theory effectively predicted the efficacy of this treatment in this population.

LIMITATIONS

Small sample and only focus on the mechanisms of delayed-onset SSRI treatment.

Individualized Functional Brain System Topologies and Major Depression: Relationships Among Patch Sizes and Clinical Profiles and Behavior

BACKGROUND

Neuroimaging studies of major depression have typically been conducted using group-level approaches. However, given interindividual differences in brain systems, there is a need for individualized approaches to brain systems mapping and putative links toward diagnosis, symptoms, and behavior.

METHODS

We used an iterative parcellation approach to map individualized brain systems in 328 participants from a multisite, placebo-controlled clinical trial. We hypothesized that participants with depression would show abnormalities in salience, control, default, and affective systems, which would be associated with higher levels of self-reported anhedonia, anxious arousal, and worse cognitive performance. Within hypothesized brain systems, we compared patch sizes (number of vertices) between depressed and healthy control groups. Within depressed groups, abnormal patches were correlated with hypothesized clinical and behavioral measures.

RESULTS

Significant group differences emerged in hypothesized patches of 1) the lateral salience system (parietal operculum; t326 = -3.11, p = .002) and 2) the control system (left medial posterior prefrontal cortex region; z = -3.63, p < .001), with significantly smaller patches in these regions in participants with depression than in healthy control participants. Results suggest that participants with depression with significantly smaller patch sizes in the lateral salience system and control system regions experience greater anxious arousal and cognitive deficits.

CONCLUSIONS

The findings imply that neural features mapped at the individual level may relate meaningfully to diagnosis, symptoms, and behavior. There is strong clinical relevance in taking an individualized brain systems approach to mapping neural functional connectivity because these associated region patch sizes may help advance our understanding of neural features linked to psychopathology and foster future patient-specific clinical decision making.

Understanding symptom profiles of depression with the PHQ-9 in a community sample using network analysis

BACKGROUND

Depression is one of the most prevalent mental health conditions in the world. However, the heterogeneity of depression has presented obstacles for research concerning disease mechanisms, treatment indication, and personalization. The current study used network analysis to analyze and compare profiles of depressive symptoms present in community samples, considering the relationship between symptoms.

METHODS

Cross-sectional measures of depression using the Patient Health Questionnaire - 9 items (PHQ-9) were collected from community samples using data from participants scoring above a clinical threshold of ≥10 points (N = 2,023; 73.9% female; mean age 49.87, SD = 17.40). Data analysis followed three steps. First, a profiling algorithm was implemented to identify all possible symptom profiles by dichotomizing each PHQ-9 item. Second, the most prevalent symptom profiles were identified in the sample. Third, network analysis for the most prevalent symptom profiles was carried out to identify the centrality and covariance of symptoms.

RESULTS

Of 382 theoretically possible depression profiles, only 167 were present in the sample. Furthermore, 55.6% of the symptom profiles present in the sample were represented by only eight profiles. Network analysis showed that the network and symptoms' relationship varied across the profiles.

CONCLUSIONS

Findings indicate that the vast number of theoretical possible ways to meet the criteria for major depressive disorder (MDD) is significantly reduced in empirical samples and that the most common profiles of symptoms have different networks and connectivity patterns. Scientific and clinical consequences of these findings are discussed in the context of the limitations of this study.

What predicts response to sertraline for people with depression in primary care? a secondary data analysis of moderators in the PANDA trial

PURPOSE

Antidepressants are a first-line treatment for depression, yet many patients do not respond. There is a need to understand which patients have greater treatment response but there is little research on patient characteristics that moderate the effectiveness of antidepressants. This study examined potential moderators of response to antidepressant treatment.

METHODS

The PANDA trial investigated the clinical effectiveness of sertraline (n = 326) compared with placebo (n = 329) in primary care patients with depressive symptoms. We investigated 11 potential moderators of treatment effect (age, employment, suicidal ideation, marital status, financial difficulty, education, social support, family history of depression, life events, health and past antidepressant use). Using multiple linear regression, we investigated the appropriate interaction term for each of these potential moderators with treatment as allocated.

RESULTS

Family history of depression was the only variable with weak evidence of effect modification (p-value for interaction = 0.048), such that those with no family history of depression may have greater benefit from antidepressant treatment. We found no evidence of effect modification (p-value for interactions≥0.29) by any of the other ten variables.

CONCLUSION

Evidence for treatment moderators was extremely limited, supporting an approach of continuing discuss antidepressant treatment with all patients presenting with moderate to severe depressive symptoms.

Features of immunometabolic depression as predictors of antidepressant treatment outcomes: pooled analysis of four clinical trials

BACKGROUND

Profiling patients on a proposed 'immunometabolic depression' (IMD) dimension, described as a cluster of atypical depressive symptoms related to energy regulation and immunometabolic dysregulations, may optimise personalised treatment.

AIMS

To test the hypothesis that baseline IMD features predict poorer treatment outcomes with antidepressants.

METHOD

Data on 2551 individuals with depression across the iSPOT-D (n = 967), CO-MED (n = 665), GENDEP (n = 773) and EMBARC (n = 146) clinical trials were used. Predictors included baseline severity of atypical energy-related symptoms (AES), body mass index (BMI) and C-reactive protein levels (CRP, three trials only) separately and aggregated into an IMD index. Mixed models on the primary outcome (change in depressive symptom severity) and logistic regressions on secondary outcomes (response and remission) were conducted for the individual trial data-sets and pooled using random-effects meta-analyses.

RESULTS

Although AES severity and BMI did not predict changes in depressive symptom severity, higher baseline CRP predicted smaller reductions in depressive symptoms (n = 376, βpooled = 0.06, P = 0.049, 95% CI 0.0001-0.12, I2 = 3.61%); this was also found for an IMD index combining these features (n = 372, βpooled = 0.12, s.e. = 0.12, P = 0.031, 95% CI 0.01-0.22, I2= 23.91%), with a higher - but still small - effect size compared with CRP. Confining analyses to selective serotonin reuptake inhibitor users indicated larger effects of CRP (βpooled = 0.16) and the IMD index (βpooled = 0.20). Baseline IMD features, both separately and combined, did not predict response or remission.

CONCLUSIONS

Depressive symptoms of people with more IMD features improved less when treated with antidepressants. However, clinical relevance is limited owing to small effect sizes in inconsistent associations. Whether these patients would benefit more from treatments targeting immunometabolic pathways remains to be investigated.

Adolescent, parent, and provider attitudes toward a machine learning based clinical decision support system for selecting treatment for youth depression

BACKGROUND

Machine learning based clinical decision support systems (CDSSs) have been proposed as a means of advancing personalized treatment planning for disorders, such as depression, that have a multifaceted etiology, course, and symptom profile. However, machine learning based models for treatment selection are rare in the field of psychiatry. They have also not yet been translated for use in clinical practice. Understanding key stakeholder attitudes toward machine learning based CDSSs is critical for developing plans for their implementation that promote uptake by both providers and families.

METHODS

In Study 1, a prototype machine learning based Clinical Decision Support System for Youth Depression (CDSS-YD) was demonstrated to focus groups of adolescents with a diagnosis of depression (n = 9), parents (n = 11), and behavioral health providers (n = 8). Qualitative analysis was used to assess their attitudes towards the CDSS-YD. In Study 2, behavioral health providers were trained in the use of the CDSS-YD and they utilized the CDSS-YD in a clinical encounter with 6 adolescents and their parents as part of their treatment planning discussion. Following the appointment, providers, parents, and adolescents completed a survey about their attitudes regarding the use of the CDSS-YD.

RESULTS

All stakeholder groups viewed the CDSS-YD as an easy to understand and useful tool for making personalized treatment decisions, and families and providers were able to successfully use the CDSS-YD in clinical encounters. Parents and adolescents viewed their providers as having a critical role in the use the CDSS-YD, and this had implications for the perceived trustworthiness of the CDSS-YD. Providers reported that clinic productivity metrics would be the primary barrier to CDSS-YD implementation, with the creation of protected time for training, preparation, and use as a key facilitator.

CONCLUSIONS

Machine learning based CDSSs, if proven effective, have the potential to be widely accepted tools for personalized treatment planning. Successful implementation will require addressing the system-level barrier of having sufficient time and energy to integrate it into practice.

Revealing subgroup-specific mechanisms of change via moderated mediation: A meditation intervention example

OBJECTIVE

Effective psychosocial interventions exist for numerous mental health conditions. However, despite decades of research, limited progress has been made in clarifying the mechanisms that account for their beneficial effects. We know that many treatments work, but we know relatively little about why they work. Mechanisms of change may be obscured due to prior research collapsing across heterogeneous subgroups of patients with differing underlying mechanisms of response. Studies identifying baseline individual characteristics that predict differential response (i.e., moderation) may inform research on why (i.e., mediation) a particular subgroup has better outcomes to an intervention via tests of moderated mediation.

METHOD

In a recent randomized controlled trial comparing a 4-week meditation app with a control condition in school system employees (N = 662), we previously developed a "Personalized Advantage Index" (PAI) using baseline characteristics, which identified a subgroup of individuals who derived relatively greater benefit from meditation training. Here, we tested whether the effect of mindfulness acquisition in mediating group differences in outcome was moderated by PAI scores.

RESULTS

A significant index of moderated mediation (IMM = 1.22, 95% CI [0.30, 2.33]) revealed that the effect of mindfulness acquisition in mediating group differences in outcome was only significant among those individuals with PAI scores predicting relatively greater benefit from the meditation app.

CONCLUSIONS

Subgroups of individuals may differ meaningfully in the mechanisms that mediate their response to an intervention. Considering subgroup-specific mediators may accelerate progress on clarifying mechanisms of change underlying psychosocial interventions and may help inform which specific interventions are most beneficial for whom. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A common symptom geometry of mood improvement under sertraline and placebo associated with distinct neural patterns

Importance

Understanding the mechanisms of major depressive disorder (MDD) improvement is a key challenge to determine effective personalized treatments.

Objective

To perform a secondary analysis quantifying neural-to-symptom relationships in MDD as a function of antidepressant treatment.

Design

Double blind randomized controlled trial.

Setting

Multicenter.

Participants

Patients with early onset recurrent depression from the public Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study.

Interventions

Either sertraline or placebo during 8 weeks (stage 1), and according to response a second line of treatment for 8 additional weeks (stage 2).

Main Outcomes and Measures

To identify a data-driven pattern of symptom variations during these two stages, we performed a Principal Component Analysis (PCA) on the variations of individual items of four clinical scales measuring depression, anxiety, suicidal ideas and manic-like symptoms, resulting in a univariate measure of clinical improvement. We then investigated how initial clinical and neural factors predicted this measure during stage 1. To do so, we extracted resting-state global brain connectivity (GBC) at baseline at the individual level using a whole-brain functional network parcellation. In turn, we computed a linear model for each brain parcel with individual data-driven clinical improvement scores during stage 1 for each group.

Results

192 patients (127 women), age 37.7 years old (standard deviation: 13.5), were included. The first PC (PC1) capturing 20% of clinical variation was similar across treatment groups at stage 1 and stage 2, suggesting a reproducible pattern of symptom improvement. PC1 patients' scores significantly differed according to treatment during stage 1, whereas no difference of response was evidenced between groups with the Clinical Global Impressions (CGI). Baseline GBC correlated to stage 1 PC1 scores in the sertraline, but not in the placebo group.

Conclusions and Relevance

Using data-driven reduction of symptoms scales, we identified a common profile of symptom improvement across placebo and sertraline. However, the neural patterns of baseline that mapped onto symptom improvement distinguished between treatment and placebo. Our results underscore that mapping from data-driven symptom improvement onto neural circuits is vital to detect treatment-responsive neural profiles that may aid in optimal patient selection for future trials.

Using Wearable Devices and Speech Data for Personalized Machine Learning in Early Detection of Mental Disorders: Protocol for a Participatory Research Study

BACKGROUND

Early identification of mental disorder symptoms is crucial for timely treatment and reduction of recurring symptoms and disabilities. A tool to help individuals recognize warning signs is important. We posit that such a tool would have to rely on longitudinal analysis of patterns and trends in the individual's daily activities and mood, which can now be captured through data from wearable activity trackers, speech recordings from mobile devices, and the individual's own description of their mental state. In this paper, we describe such a tool developed by our team to detect early signs of depression, anxiety, and stress.

OBJECTIVE

This study aims to examine three questions about the effectiveness of machine learning models constructed based on multimodal data from wearables, speech, and self-reports: (1) How does speech about issues of personal context differ from speech while reading a neutral text, what type of speech data are more helpful in detecting mental health indicators, and how is the quality of the machine learning models influenced by multilanguage data? (2) Does accuracy improve with longitudinal data collection and how, and what are the most important features? and (3) How do personalized machine learning models compare against population-level models?

METHODS

We collect longitudinal data to aid machine learning in accurately identifying patterns of mental disorder symptoms. We developed an app that collects voice, physiological, and activity data. Physiological and activity data are provided by a variety of off-the-shelf fitness trackers, that record steps, active minutes, duration of sleeping stages (rapid eye movement, deep, and light sleep), calories consumed, distance walked, heart rate, and speed. We also collect voice recordings of users reading specific texts and answering open-ended questions chosen randomly from a set of questions without repetition. Finally, the app collects users' answers to the Depression, Anxiety, and Stress Scale. The collected data from wearable devices and voice recordings will be used to train machine learning models to predict the levels of anxiety, stress, and depression in participants.

RESULTS

The study is ongoing, and data collection will be completed by November 2023. We expect to recruit at least 50 participants attending 2 major universities (in Canada and Mexico) fluent in English or Spanish. The study will include participants aged between 18 and 35 years, with no communication disorders, acute neurological diseases, or history of brain damage. Data collection complied with ethical and privacy requirements.

CONCLUSIONS

The study aims to advance personalized machine learning for mental health; generate a data set to predict Depression, Anxiety, and Stress Scale results; and deploy a framework for early detection of depression, anxiety, and stress. Our long-term goal is to develop a noninvasive and objective method for collecting mental health data and promptly detecting mental disorder symptoms.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/48210.

Adolescent, parent, and provider attitudes toward a machine-learning based clinical decision support system for selecting treatment for youth depression

Background

Machine-learning based clinical decision support systems (CDSSs) have been proposed as a means of advancing personalized treatment planning for disorders, such as depression, that have a multifaceted etiology, course, and symptom profile. However, machine-learning based models for treatment selection are rare in the field of psychiatry. They have also not yet been translated for use in clinical practice. Understanding key stakeholder attitudes toward machine learning-based CDSSs is critical for developing plans for their implementation that promote uptake by both providers and families.

Methods

In Study 1, a machine-learning based Clinical Decision Support System for Youth Depression (CDSS-YD) was demonstrated to focus groups of adolescents with a diagnosis of depression (n = 9), parents (n = 11), and behavioral health providers (n = 8). Qualitative analysis was used to assess their attitudes towards the CDSS-YD. In Study 2, behavioral health providers were trained in the use of the CDSS-YD and they utilized the CDSS-YD in a clinical encounter with 6 adolescents and their parents as part of their treatment planning discussion. Following the appointment, providers, parents, and adolescents completed a survey about their attitudes regarding the use of the CDSS-YD.

Results

All stakeholder groups viewed the CDSS-YD as an easy to understand and useful tool for making personalized treatment decisions, and families and providers were able to successfully use the CDSS-YD in clinical encounters. Parents and adolescents viewed their providers as having a critical role in the use the CDSS-YD, and this had implications for the perceived trustworthiness of the CDSS-YD. Providers reported that clinic productivity metrics would be the primary barrier to CDSS-YD implementation, with the creation of protected time for training, preparation, and use as a key facilitator.

Conclusions

The CDSS-YD has the potential to be a widely accepted and useful tool for personalized treatment planning. Successful implementation will require addressing the system-level barrier of having sufficient time and energy to integrate it into practice.

Exploring alternative approaches to precision medicine through genomics and artificial intelligence - a systematic review

The core idea behind precision medicine is to pinpoint the subpopulations that differ from one another in terms of disease risk, drug responsiveness, and treatment outcomes due to differences in biology and other traits. Biomarkers are found through genomic sequencing. Multi-dimensional clinical and biological data are created using these biomarkers. Better analytic methods are needed for these multidimensional data, which can be accomplished by using artificial intelligence (AI). An updated review of 80 latest original publications is presented on four main fronts-preventive medicine, medication development, treatment outcomes, and diagnostic medicine-All these studies effectively illustrated the significance of AI in precision medicine. Artificial intelligence (AI) has revolutionized precision medicine by swiftly analyzing vast amounts of data to provide tailored treatments and predictive diagnostics. Through machine learning algorithms and high-resolution imaging, AI assists in precise diagnoses and early disease detection. AI's ability to decode complex biological factors aids in identifying novel therapeutic targets, allowing personalized interventions and optimizing treatment outcomes. Furthermore, AI accelerates drug discovery by navigating chemical structures and predicting drug-target interactions, expediting the development of life-saving medications. With its unrivaled capacity to comprehend and interpret data, AI stands as an invaluable tool in the pursuit of enhanced patient care and improved health outcomes. It's evident that AI can open a new horizon for precision medicine by translating complex data into actionable information. To get better results in this regard and to fully exploit the great potential of AI, further research is required on this pressing subject.

Dimensions of temperament and character as predictors of antidepressant discontinuation, response and adverse reactions during treatment with nortriptyline and escitalopram

BACKGROUND

Personality traits may predict antidepressant discontinuation and response. However, previous studies were rather small, only explored a few personality traits and did not include adverse drug effects nor the interdependency between antidepressant discontinuation patterns and response.

METHODS

GENDEP included 589 patients with unipolar moderate-severe depression treated with escitalopram or nortriptyline for 12 weeks. Seven personality dimensions were measured using the self-reported 240-item Temperament and Character Inventory-Revised (TCI-R). We applied Cox proportional models to study discontinuation patterns, logistic and linear regression to investigate response and remission after 8 and 12 weeks, and mixed-effects linear models regarding time-varying treatment response and adverse drug reactions.

RESULTS

Low harm avoidance, low cooperativeness, high self-transcendence and high novelty seeking were associated with higher risks for antidepressant discontinuation, independent of depressed mood, adverse drug reactions, drug, sex and age. Regression analyses showed that higher novelty seeking and cooperativeness scores were associated with a greater likelihood of response and remission after 8 and 12 weeks, respectively, but we found no correlations with response in the mixed-effects models. Only high harm avoidance was associated with more self-reported adverse effects.

CONCLUSIONS

This study, representing the largest investigation between several personality traits and response to two different antidepressants, suggests that correlations between personality traits and antidepressant treatment response may be confounded by differential rates of discontinuation. Future trials on personality in the treatment of depression need to consider this interdependency and study whether interventions aiming at improving compliance for some personality types may improve response to antidepressants.

Brain Ventricle and Choroid Plexus Morphology as Predictor of Treatment Response: Findings from the EMBARC Study

Recent observations suggest a role of the choroid plexus (CP) and cerebral ventricle volume (CV), to identify treatment resistance of major depressive disorder (MDD). We tested the hypothesis that these markers are associated with clinical improvement in subjects from the EMBARC study, as implied by a recent pilot study. The EMBARC study characterized biological markers in a randomized placebo-controlled trial of sertraline vs. placebo in patients with MDD. Association of baseline volumes of CV, CP and of the corpus callosum (CC) with treatment response after 4 weeks treatment were evaluated. 171 subjects (61 male, 110 female) completed the 4 week assessments; gender, site and age were taken into account for this analyses. As previously reported, no treatment effect of sertraline was observed, but prognostic markers for clinical improvement were identified. Responders (n = 54) had significantly smaller volumes of the CP and lateral ventricles, whereas the volume of mid-anterior and mid-posterior CC was significantly larger compared to non-responders (n = 117). A positive correlation between CV volume and CP volume was observed, whereas a negative correlation between CV volume and both central-anterior and central-posterior parts of the CC emerged. In an exploratory way correlations between enlarged VV and CP volume on the one hand and signs of metabolic syndrome, in particular triglyceride plasma concentrations, were observed. A primary abnormality of CP function in MDD may be associated with increased ventricles, compression of white matter volume, which may affect treatment response speed or outcome. Metabolic markers may mediate this relationship.

Methodological and Quality Flaws in the Use of Artificial Intelligence in Mental Health Research: Systematic Review

BACKGROUND

Artificial intelligence (AI) is giving rise to a revolution in medicine and health care. Mental health conditions are highly prevalent in many countries, and the COVID-19 pandemic has increased the risk of further erosion of the mental well-being in the population. Therefore, it is relevant to assess the current status of the application of AI toward mental health research to inform about trends, gaps, opportunities, and challenges.

OBJECTIVE

This study aims to perform a systematic overview of AI applications in mental health in terms of methodologies, data, outcomes, performance, and quality.

METHODS

A systematic search in PubMed, Scopus, IEEE Xplore, and Cochrane databases was conducted to collect records of use cases of AI for mental health disorder studies from January 2016 to November 2021. Records were screened for eligibility if they were a practical implementation of AI in clinical trials involving mental health conditions. Records of AI study cases were evaluated and categorized by the International Classification of Diseases 11th Revision (ICD-11). Data related to trial settings, collection methodology, features, outcomes, and model development and evaluation were extracted following the CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies) guideline. Further, evaluation of risk of bias is provided.

RESULTS

A total of 429 nonduplicated records were retrieved from the databases and 129 were included for a full assessment-18 of which were manually added. The distribution of AI applications in mental health was found unbalanced between ICD-11 mental health categories. Predominant categories were Depressive disorders (n=70) and Schizophrenia or other primary psychotic disorders (n=26). Most interventions were based on randomized controlled trials (n=62), followed by prospective cohorts (n=24) among observational studies. AI was typically applied to evaluate quality of treatments (n=44) or stratify patients into subgroups and clusters (n=31). Models usually applied a combination of questionnaires and scales to assess symptom severity using electronic health records (n=49) as well as medical images (n=33). Quality assessment revealed important flaws in the process of AI application and data preprocessing pipelines. One-third of the studies (n=56) did not report any preprocessing or data preparation. One-fifth of the models were developed by comparing several methods (n=35) without assessing their suitability in advance and a small proportion reported external validation (n=21). Only 1 paper reported a second assessment of a previous AI model. Risk of bias and transparent reporting yielded low scores due to a poor reporting of the strategy for adjusting hyperparameters, coefficients, and the explainability of the models. International collaboration was anecdotal (n=17) and data and developed models mostly remained private (n=126).

CONCLUSIONS

These significant shortcomings, alongside the lack of information to ensure reproducibility and transparency, are indicative of the challenges that AI in mental health needs to face before contributing to a solid base for knowledge generation and for being a support tool in mental health management.

New and emerging approaches to treat psychiatric disorders

Psychiatric disorders are highly prevalent, often devastating diseases that negatively impact the lives of millions of people worldwide. Although their etiological and diagnostic heterogeneity has long challenged drug discovery, an emerging circuit-based understanding of psychiatric illness is offering an important alternative to the current reliance on trial and error, both in the development and in the clinical application of treatments. Here we review new and emerging treatment approaches, with a particular emphasis on the revolutionary potential of brain-circuit-based interventions for precision psychiatry. Limitations of circuit models, challenges of bringing precision therapeutics to market and the crucial advances needed to overcome these obstacles are presented.

Optimizing and Accelerating the Development of Precision Pain Treatments for Chronic Pain: IMMPACT Review and Recommendations

Large variability in the individual response to even the most-efficacious pain treatments is observed clinically, which has led to calls for a more personalized, tailored approach to treating patients with pain (ie, "precision pain medicine"). Precision pain medicine, currently an aspirational goal, would consist of empirically based algorithms that determine the optimal treatments, or treatment combinations, for specific patients (ie, targeting the right treatment, in the right dose, to the right patient, at the right time). Answering this question of "what works for whom" will certainly improve the clinical care of patients with pain. It may also support the success of novel drug development in pain, making it easier to identify novel treatments that work for certain patients and more accurately identify the magnitude of the treatment effect for those subgroups. Significant preliminary work has been done in this area, and analgesic trials are beginning to utilize precision pain medicine approaches such as stratified allocation on the basis of prespecified patient phenotypes using assessment methodologies such as quantitative sensory testing. Current major challenges within the field include: 1) identifying optimal measurement approaches to assessing patient characteristics that are most robustly and consistently predictive of inter-patient variation in specific analgesic treatment outcomes, 2) designing clinical trials that can identify treatment-by-phenotype interactions, and 3) selecting the most promising therapeutics to be tested in this way. This review surveys the current state of precision pain medicine, with a focus on drug treatments (which have been most-studied in a precision pain medicine context). It further presents a set of evidence-based recommendations for accelerating the application of precision pain methods in chronic pain research. PERSPECTIVE: Given the considerable variability in treatment outcomes for chronic pain, progress in precision pain treatment is critical for the field. An array of phenotypes and mechanisms contribute to chronic pain; this review summarizes current knowledge regarding which treatments are most effective for patients with specific biopsychosocial characteristics.

Multi-omics data integration methods and their applications in psychiatric disorders

To study mental illness and health, in the past researchers have often broken down their complexity into individual subsystems (e.g., genomics, transcriptomics, proteomics, clinical data) and explored the components independently. Technological advancements and decreasing costs of high throughput sequencing has led to an unprecedented increase in data generation. Furthermore, over the years it has become increasingly clear that these subsystems do not act in isolation but instead interact with each other to drive mental illness and health. Consequently, individual subsystems are now analysed jointly to promote a holistic understanding of the underlying biological complexity of health and disease. Complementing the increasing data availability, current research is geared towards developing novel methods that can efficiently combine the information rich multi-omics data to discover biologically meaningful biomarkers for diagnosis, treatment, and prognosis. However, clinical translation of the research is still challenging. In this review, we summarise conventional and state-of-the-art statistical and machine learning approaches for discovery of biomarker, diagnosis, as well as outcome and treatment response prediction through integrating multi-omics and clinical data. In addition, we describe the role of biological model systems and in silico multi-omics model designs in clinical translation of psychiatric research from bench to bedside. Finally, we discuss the current challenges and explore the application of multi-omics integration in future psychiatric research. The review provides a structured overview and latest updates in the field of multi-omics in psychiatry.

An exploratory machine learning approach to identify placebo responders in pharmacological binge eating disorder trials

Randomized, placebo-controlled trials for binge eating disorder (BED) have revealed highly variable, and often marked, rates of short-term placebo response. Several quantitative based analyses in patients with BED have inconsistently demonstrated which patient factors attribute to an increase in placebo response. The objective of this study is to utilize machine learning (ML) algorithms to identify moderators of placebo response in patients with BED. Data were pooled from 12 randomized placebo-controlled trials evaluating different treatment options for BED. The final dataset consisted of 189 adults receiving placebo with complete information of baseline variables. Placebo responders were defined as patients experiencing ≥75% reduction in binge eating frequency (BEF) at study end point. Nine patient prerandomization variables were included as predictors. Patients were divided into training and testing subsets according to an 75%:25% distribution while preserving the proportion of placebo responders. All analysis was performed in the software Pumas 2.0. Gaussian Naïve Bayes algorithm showed the best cross-validation accuracy (~64%) and was chosen as the final algorithm. Shapley analysis suggested that patients with low baseline BEF and anxiety status were strong moderators of placebo response. Upon applying the final algorithm on the test dataset, the resulting sensitivity was 88% and prediction accuracy was 72%. This is the first application of ML to identify moderators of placebo response in BED. The results of this analysis confirm previous findings of lesser baseline disease severity and adds that patients with no anxiety are more susceptible to placebo response.

Integrated Module of Multidimensional Omics for Peripheral Biomarkers (iMORE) in patients with major depressive disorder: rationale and design of a prospective multicentre cohort study

INTRODUCTION

Major depressive disorder (MDD) represents a worldwide burden on healthcare and the response to antidepressants remains limited. Systems biology approaches have been used to explore the precision therapy. However, no reliable biomarker clinically exists for prognostic prediction at present. The objectives of the Integrated Module of Multidimensional Omics for Peripheral Biomarkers (iMORE) study are to predict the efficacy of antidepressants by integrating multidimensional omics and performing validation in a real-world setting. As secondary aims, a series of potential biomarkers are explored for biological subtypes.

METHODS AND ANALYSIS

iMore is an observational cohort study in patients with MDD with a multistage design in China. The study is performed by three mental health centres comprising an observation phase and a validation phase. A total of 200 patients with MDD and 100 healthy controls were enrolled. The protocol-specified antidepressants are selective serotonin reuptake inhibitors and serotonin-norepinephrine reuptake inhibitors. Clinical visits (baseline, 4 and 8 weeks) include psychiatric rating scales for symptom assessment and biospecimen collection for multiomics analysis. Participants are divided into responders and non-responders based on treatment response (>50% reduction in Montgomery-Asberg Depression Rating Scale). Antidepressants' responses are predicted and biomarkers are explored using supervised learning approach by integration of metabolites, cytokines, gut microbiomes and immunophenotypic cells. The accuracy of the prediction models constructed is verified in an independent validation phase.

ETHICS AND DISSEMINATION

The study was approved by the ethics committee of Shanghai Mental Health Center (approval number 2020-87). All participants need to sign a written consent for the study entry. Study findings will be published in peer-reviewed journals.

TRIAL REGISTRATION NUMBER

NCT04518592.

A systematic meta-review of patient-level predictors of psychological therapy outcome in major depressive disorder

BACKGROUND

Psychological therapies are effective for treating major depressive disorder, but current clinical guidelines do not provide guidance on the personalization of treatment choice. Established predictors of psychotherapy treatment response could help inform machine learning models aimed at predicting individual patient responses to different therapy options. Here we sought to comprehensively identify known predictors.

METHODS

EMBASE, Medline, PubMed, PsycINFO were searched for systematic reviews with or without meta-analysis published until June 2020 to identify individual patient-level predictors of response to psychological treatments. 3113 abstracts were identified and 300 articles assessed. We qualitatively synthesized our findings by predictor category (sociodemographic; symptom profile; social support; personality features; affective, cognitive, and behavioural; comorbidities; neuroimaging; genetics) and treatment type. We used the AMSTAR 2 to evaluate the quality of included reviews.

RESULTS

Following screening and full-text assessment, 27 systematic reviews including 12 meta-analyses were eligible for inclusion. 74 predictors emerged for various psychological treatments, primarily cognitive behavioural therapy, interpersonal therapy, and mindfulness-based cognitive therapy.

LIMITATIONS

A paucity of studies examining predictors of psychological treatment outcome, as well as methodological heterogeneities and publication biases limit the strength of the identified predictors.

CONCLUSIONS

The synthesized predictors could be used to supplement clinical decision-making in selecting psychological therapies based on individual patient characteristics. These predictors could also be used as a priori input features for machine learning models aimed at predicting a given patient's likelihood of response to different treatment options for depression, and may contribute toward the development of patient-specific treatment recommendations in clinical guidelines.

Personalized Prediction of Response to Smartphone-Delivered Meditation Training: Randomized Controlled Trial

BACKGROUND

Meditation apps have surged in popularity in recent years, with an increasing number of individuals turning to these apps to cope with stress, including during the COVID-19 pandemic. Meditation apps are the most commonly used mental health apps for depression and anxiety. However, little is known about who is well suited to these apps.

OBJECTIVE

This study aimed to develop and test a data-driven algorithm to predict which individuals are most likely to benefit from app-based meditation training.

METHODS

Using randomized controlled trial data comparing a 4-week meditation app (Healthy Minds Program [HMP]) with an assessment-only control condition in school system employees (n=662), we developed an algorithm to predict who is most likely to benefit from HMP. Baseline clinical and demographic characteristics were submitted to a machine learning model to develop a "Personalized Advantage Index" (PAI) reflecting an individual's expected reduction in distress (primary outcome) from HMP versus control.

RESULTS

A significant group × PAI interaction emerged (t658=3.30; P=.001), indicating that PAI scores moderated group differences in outcomes. A regression model that included repetitive negative thinking as the sole baseline predictor performed comparably well. Finally, we demonstrate the translation of a predictive model into personalized recommendations of expected benefit.

CONCLUSIONS

Overall, the results revealed the potential of a data-driven algorithm to inform which individuals are most likely to benefit from a meditation app. Such an algorithm could be used to objectively communicate expected benefits to individuals, allowing them to make more informed decisions about whether a meditation app is appropriate for them.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04426318; https://clinicaltrials.gov/ct2/show/NCT04426318.

Components of Behavioral Activation Therapy for Depression Engage Specific Reinforcement Learning Mechanisms in a Pilot Study

Background

Behavioral activation is an evidence-based treatment for depression. Theoretical considerations suggest that treatment response depends on reinforcement learning mechanisms. However, which reinforcement learning mechanisms are engaged by and mediate the therapeutic effect of behavioral activation remains only partially understood, and there are no procedures to measure such mechanisms.

Objective

To perform a pilot study to examine whether reinforcement learning processes measured through tasks or self-report are related to treatment response to behavioral activation.

Method

The pilot study enrolled 13 outpatients (12 completers) with major depressive disorder, from July of 2018 through February of 2019, into a nine-week trial with BA. Psychiatric evaluations, decision-making tests and self-reported reward experience and anticipations were acquired before, during and after the treatment. Task and self-report data were analysed by using reinforcement-learning models. Inferred parameters were related to measures of depression severity through linear mixed effects models.

Results

Treatment effects during different phases of the therapy were captured by specific decision-making processes in the task. During the weeks focusing on the active pursuit of reward, treatment effects were more pronounced amongst those individuals who showed an increase in Pavlovian appetitive influence. During the weeks focusing on the avoidance of punishments, treatment responses were more pronounced in those individuals who showed an increase in Pavlovian avoidance. Self-reported anticipation of reinforcement changed according to formal RL rules. Individual differences in the extent to which learning followed RL rules related to changes in anhedonia.

Conclusions

In this pilot study both task- and self-report-derived measures of reinforcement learning captured individual differences in treatment response to behavioral activation. Appetitive and aversive Pavlovian reflexive processes appeared to be modulated by separate psychotherapeutic interventions, and the modulation strength covaried with response to specific interventions. Self-reported changes in reinforcement expectations are also related to treatment response.

Trial Registry Name

Set Your Goal: Engaging in GO/No-Go Active Learning, #NCT03538535, http://www.clinicaltrials.gov.

Is it worth it to personalize the treatment of PTSD? - A variance-ratio meta-analysis and estimation of treatment effect heterogeneity in RCTs of PTSD

Several evidence-based treatments for posttraumatic stress disorder (PTSD) are recommended by international guidelines (e.g., APA, NICE). While their average effects are in general high, non-response rates indicate differential treatment effects. Here, we used a large database of RCTs on psychotherapy for PTSD to determine a reliable estimate of this heterogeneity in treatment effects (HTE) by applying Bayesian variance ratio meta-analysis. In total, 66 studies with a total of 8803 patients were included in our study. HTE was found for all psychological treatments, with varying degrees of certainty, only slight differences between psychological treatments, and active control groups yielding a smaller variance ratio compared to waiting list control groups. Across all psychological treatment and control group types, the estimate for the intercept was 0.12, indicating a 12% higher variance of posttreatment values in the intervention groups after controlling for differences in treatment outcomes. This study is the first to determine the maximum increase in treatment effects of psychological treatments for PTSD by personalization. The results indicate that there is comparatively high heterogeneity in treatment effects across all psychological treatment and control groups, which in turn allow personalizing psychological treatments by using treatment selection approaches.

Which adolescents are well-suited to app-based mindfulness training? A randomized clinical trial and data-driven approach for personalized recommendations

OBJECTIVE

Rumination heightens risk for depression and anxiety, which increase substantially during adolescence. Smartphone apps offer a convenient and cost-effective means for adolescents to access mindfulness training, which may reduce rumination. Despite their increasing popularity, it is unclear which adolescents benefit from mindfulness apps.

METHOD

Adolescents (n = 152) with elevated trait rumination were randomly assigned to 3 weeks of app-based mindfulness training or a mood-monitoring control. Multilevel models tested group differences in state rumination change, assessed via ecological momentary assessment. Baseline adolescent characteristics were submitted to elastic net regularization models to develop a "Personalized Advantage Index" indicating an individual's expected outcome from the mindfulness app relative to the mood-monitoring control. Finally, we translated a predictive model (developed in an external sample) for personalized recommendations of expected benefit from the mindfulness app.

RESULTS

Adolescents in the mindfulness app condition reported significantly greater reductions in rumination than adolescents in the control condition. Individuals predicted to have better outcomes from the mindfulness app relative to mood monitoring had significantly greater reductions in rumination if randomly assigned to the mindfulness condition. In contrast, between-condition differences in outcome were not significant for adolescents predicted to have better outcomes in the mood-monitoring condition.

CONCLUSIONS

Findings support the efficacy of a mindfulness app to reduce state rumination in adolescents, particularly among adolescents high in trait rumination. A predictive model is put forth, which could be used to objectively communicate expected mindfulness app outcomes to adolescents prior to engagement in app-based mindfulness training. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Praxisorientierte Forschung in der Psychotherapie

Zusammenfassung. In den letzten Jahrzehnten hat sich durch randomisiert-kontrollierte Studien (RCTs) eine breite Evidenzbasis von Psychotherapie mit mittleren bis großen Effekten für verschiedene psychische Störungen gebildet. Neben der Bestimmung dieser Wirksamkeit („Efficacy“) ebneten Studien zur Wirksamkeit unter alltäglichen Routinebedingungen („Effectiveness“) historisch den Weg zur Entwicklung eines praxisorientierten Forschungsparadigmas. Im Beitrag wird argumentiert, dass im Rahmen dieses Paradigmas praxisbasierte Studien eine wertvolle Ergänzung zu RCTs darstellen, da sie existierende Probleme in der Psychotherapieforschung adressieren können. In der gegenwärtigen praxisorientierten Forschung liefern dabei neue Ansätze aus der personalisierten Medizin und Methoden aus der ‚Computational Psychiatry‘ wichtige Anhaltspunkte zur Optimierung von Effekten in der Psychotherapie. Im Kontext der Personalisierung werden bspw. klinische multivariable Prädiktionsmodelle entwickelt, welche durch Rückmeldeschleifen an Praktiker_innen kurzfristig ein evidenzbasiertes Outcome-Monitoring ermöglicht und langfristig das Praxis-Forschungsnetzwerk in Deutschland stärkt. Am Ende des Beitrags werden zukünftige Richtungen für die praxisorientierte Forschung im Sinne des ‘Precision Mental Health Care’ -Paradigmas abgeleitet und diskutiert.

Folk Classification and Factor Rotations: Whales, Sharks, and the Problems With the Hierarchical Taxonomy of Psychopathology (HiTOP)

The Hierarchical Taxonomy of Psychopathology (HiTOP) uses factor analysis to group people with similar self-reported symptoms (i.e., like-goes-with-like). It is hailed as a significant improvement over other diagnostic taxonomies. However, the purported advantages and fundamental assumptions of HiTOP have received little, if any scientific scrutiny. We critically evaluated five fundamental claims about HiTOP. We conclude that HiTOP does not demonstrate a high degree of verisimilitude and has the potential to hinder progress on understanding the etiology of psychopathology. It does not lend itself to theory-building or taxonomic evolution, and it cannot account for multifinality, equifinality, or developmental and etiological processes. In its current form, HiTOP is not ready to use in clinical settings and may result in algorithmic bias against underrepresented groups. We recommend a bifurcation strategy moving forward in which the DSM is used in clinical settings while researchers focus on developing a falsifiable theory-based classification system.

Personalized prescriptions of therapeutic skills from patient characteristics: An ecological momentary assessment approach

OBJECTIVE

Rather than relying on a single psychotherapeutic orientation, most clinicians draw from a range of therapeutic approaches to treat their clients. To date, no data-driven approach exists for personalized predictions of which skill domain would be most therapeutically beneficial for a given patient. The present study combined ecological momentary assessment (EMA) and machine learning to test a data-driven approach for predicting patient-specific skill-outcome associations.

METHOD

Fifty (M_age = 37 years old, 54% female, 84% White) adults received training in behavioral therapy (BT) and dialectical behavior therapy (DBT) skills within a behavioral health partial hospital program (PHP). Following discharge, patients received four EMA surveys per day for 2 weeks (total observations = 2,036) assessing the use of therapeutic skills and positive/negative affect (PA/NA). Clinical and demographic characteristics were submitted to elastic net regularization to predict, via cross-validation, patient-specific associations between the use of BT versus DBT skills and level of PA/NA.

RESULTS

Cross-validated accuracy was 81% (sensitivity = 93% and specificity = 63%) in predicting whether a patient would exhibit a stronger association between the use of BT versus DBT skills and PA level. Predictors of positive DBT skills-PA associations included higher levels of nonsuicidal self-injury (NSSI) and sleep disturbance, whereas predictors of positive BT skills-PA relations included higher emotional lability and anxiety disorder comorbidity, and lower psychomotor retardation/agitation and worthlessness/guilt. Corresponding models with NA yielded no predictors.

CONCLUSIONS

Findings from this initial proof-of-concept study highlight the potential of data-driven approaches to inform personalized prescriptions of which skill domains may be most therapeutically beneficial for a given patient. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Measurement-Based and Data-Informed Psychological Therapy

Outcome measurement in the field of psychotherapy has developed considerably in the last decade. This review discusses key issues related to outcome measurement, modeling, and implementation of data-informed and measurement-based psychological therapy. First, an overview is provided, covering the rationale of outcome measurement by acknowledging some of the limitations of clinical judgment. Second, different models of outcome measurement are discussed, including pre-post, session-by-session, and higher-resolution intensive outcome assessments. Third, important concepts related to modeling patterns of change are addressed, including early response, dose-response, and nonlinear change. Furthermore, rational and empirical decision tools are discussed as the foundation for measurement-based therapy. Fourth, examples of clinical applications are presented, which show great promise to support the personalization of therapy and to prevent treatment failure. Finally, we build on continuous outcome measurement as the basis for a broader understanding of clinical concepts and data-driven clinical practice in the future. Expected final online publication date for the Annual Review of Clinical Psychology, Volume 18 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Validation of Machine Learning-Based Individualized Treatment for Depressive Disorder Using Target Trial Emulation

This study aims to develop and validate the use of machine learning-based prediction models to select individualized pharmacological treatment for patients with depressive disorder. This study used data from Taiwan’s National Health Insurance Research Database. Patients with incident depressive disorders were included in this study. The study outcome was treatment failure, which was defined as psychiatric hospitalization, self-harm hospitalization, emergency visits, or treatment change. Prediction models based on the Super Learner ensemble were trained separately for the initial and the next-step treatments if the previous treatments failed. An individualized treatment strategy was developed for selecting the drug with the lowest probability of treatment failure for each patient as the model-selected regimen. We emulated clinical trials to estimate the effectiveness of individualized treatments. The area under the curve of the prediction model using Super Learner was 0.627 and 0.751 for the initial treatment and the next-step treatment, respectively. Model-selected regimens were associated with reduced treatment failure rates, with a 0.84-fold (95% confidence interval (CI) 0.82–0.86) decrease for the initial treatment and a 0.82-fold (95% CI 0.80–0.83) decrease for the next-step. In emulation of clinical trials, the model-selected regimen was associated with a reduced treatment failure rate.

Cognitive Control Deficits in Depression: A Novel Target to Improve Suboptimal Outcomes in Childhood

Cognitive control deficits are one of three primary endophenotypes in depression, and the enhanced targeting of these deficits in clinical and research work is expected to lead to improved depression outcomes. Cognitive control is a set of self-regulatory processes responsible for goal-oriented behavior that predicts clinical/functional outcomes across the spectrum of brain-based disorders. In depression, cognitive control deficits emerge by the first depressive episode, persist during symptom remission, and worsen over the course of depression. In addition, the presence of these deficits predicts a poor response to evidence-based depression treatments, including psychotherapy and antidepressant medication. This is particularly relevant to childhood depression, as 1%-2% of children are diagnosed with depression, yet there are very limited evidence-based treatment options. Cognitive control deficits may be a previously underaddressed factor contributing to poor outcomes, although there remains a dearth of research examining the topic. The investigators describe the prior literature on cognitive control in depression to argue for the need for increased focus on this endophenotype. They then describe cognitive control-focused clinical and research avenues that would likely lead to improved treatments and outcomes for this historically undertreated aspect of childhood depression.

Treatment selection using prototyping in latent-space with application to depression treatment

Machine-assisted treatment selection commonly follows one of two paradigms: a fully personalized paradigm which ignores any possible clustering of patients; or a sub-grouping paradigm which ignores personal differences within the identified groups. While both paradigms have shown promising results, each of them suffers from important limitations. In this article, we propose a novel deep learning-based treatment selection approach that is shown to strike a balance between the two paradigms using latent-space prototyping. Our approach is specifically tailored for domains in which effective prototypes and sub-groups of patients are assumed to exist, but groupings relevant to the training objective are not observable in the non-latent space. In an extensive evaluation, using both synthetic and Major Depressive Disorder (MDD) real-world clinical data describing 4754 MDD patients from clinical trials for depression treatment, we show that our approach favorably compares with state-of-the-art approaches. Specifically, the model produced an 8% absolute and 23% relative improvement over random treatment allocation. This is potentially clinically significant, given the large number of patients with MDD. Therefore, the model can bring about a much desired leap forward in the way depression is treated today.

Computing schizophrenia: ethical challenges for machine learning in psychiatry

Recent advances in machine learning (ML) promise far-reaching improvements across medical care, not least within psychiatry. While to date no psychiatric application of ML constitutes standard clinical practice, it seems crucial to get ahead of these developments and address their ethical challenges early on. Following a short general introduction concerning ML in psychiatry, we do so by focusing on schizophrenia as a paradigmatic case. Based on recent research employing ML to further the diagnosis, treatment, and prediction of schizophrenia, we discuss three hypothetical case studies of ML applications with view to their ethical dimensions. Throughout this discussion, we follow the principlist framework by Tom Beauchamp and James Childress to analyse potential problems in detail. In particular, we structure our analysis around their principles of beneficence, non-maleficence, respect for autonomy, and justice. We conclude with a call for cautious optimism concerning the implementation of ML in psychiatry if close attention is paid to the particular intricacies of psychiatric disorders and its success evaluated based on tangible clinical benefit for patients.

App-based Mindfulness Training for Adolescent Rumination: Predictors of Immediate and Cumulative Benefit

Objectives

Rumination is a transdiagnostic risk factor for depression and anxiety, which surge during the adolescent years. Mindfulness training - with its emphasis on metacognitive awareness and present-moment attention - may be effective at reducing rumination. Mindfulness apps offer a convenient, engaging, and cost-effective means for accessing mindfulness training for teens. Despite their increasing popularity among adolescents, no study to date has investigated which teens are well-suited to app-based mindfulness training.

Methods

Eighty adolescents (M age = 14.01 years, 45% girls) with elevated rumination were enrolled in a 3-week trial of app-based mindfulness training. Repeated daily ecological momentary assessment (EMA) surveys assessed problem-focused and emotion-focused rumination immediately prior to and following each mindfulness exercise. Elastic net regularization (ENR) models tested baseline predictors of "immediate" (post-mindfulness exercise) and "cumulative" (post-3-week intervention) benefit from app-based mindfulness training.

Results

Ninety percent (72/80) of adolescents completed the 3-week trial, and the mean number of mindfulness exercises completed was 28.7. Baseline adolescent characteristics accounted for 14%-25% of the variance in outcomes (i.e., reduction in problem-focused or emotion-focused rumination). Higher baseline rumination, and lower emotional suppression, predicted better immediate and cumulative outcomes. In contrast, female gender and older age predicted better immediate, but not cumulative, outcomes. Differences in results across outcome timeframes (immediate vs. cumulative) are discussed.

Conclusions

Findings from this study highlight the potential of data-driven approaches to inform which adolescent characteristics may predict benefit from engaging with an app-based mindfulness training program. Additional research is needed to test these predictive models against a comparison (non-mindfulness) condition.

Predicting outcomes of psychotherapy for depression with electronic health record data

Predictive analytics with electronic health record (EHR) data holds promise for improving outcomes of psychiatric care. This study evaluated models for predicting outcomes of psychotherapy for depression in a clinical practice setting. EHR data from two large integrated health systems (Kaiser Permanente Colorado and Washington) included 5,554 new psychotherapy episodes with a baseline Patient Health Questionnaire (PHQ-9) score ≥ 10 and a follow-up PHQ-9 14–180 days after treatment initiation. Baseline predictors included demographics and diagnostic, medication, and encounter history. Prediction models for two outcomes—follow-up PHQ-9 score and treatment response (≥ 50% PHQ-9 reduction)—were trained in a random sample of 70% of episodes and validated in the remaining 30%. Two methods were used for modeling: generalized linear regression models with variable selection and random forests. Sensitivity analyses considered alternate predictor, outcome, and model specifications. Predictions of follow-up PHQ-9 scores poorly estimated observed outcomes (mean squared error = 31 for linear regression, 40 for random forest). Predictions of treatment response had low discrimination (AUC = 0.57 for logistic regression, 0.61 for random forest), low classification accuracy, and poor calibration. Sensitivity analyses showed similar results. We note that prediction model performance may vary for settings with different care or EHR documentation practices. In conclusion, prediction models did not accurately predict depression treatment outcomes despite using rich EHR data and advanced analytic techniques. Health systems should proceed cautiously when considering prediction models for psychiatric outcomes using baseline intake information. Transparent research should be conducted to evaluate performance of any model intended for clinical use.

Recent trends in the management of depression in persons with cancer

PURPOSE OF REVIEW

Depression is a prevalent comorbidity in cancer that significantly increases the risk for numerous negative health outcomes. This review updates the current evidence base for management of depression in cancer, highlighting new research directions based on the inflammatory hypothesis of depression.

RECENT FINDINGS

Research on pharmacotherapy and psychotherapy for depression in cancer has shown mixed efficacy partly because of methodological issues arising from the phenomenology of depression in cancer. After decades of stagnancy, more recent high-quality clinical trials are beginning to provide an evidence base to guide treatment. Inflammatory cytokine-associated depression is a subtype of depression that may have particular relevance in cancer, opening new avenues to explore therapeutic targets and biobehavioral impacts of interventions, which may improve cancer outcomes.

SUMMARY

The continuum of severity in cancer-related depression is important to consider in management approaches. Choice of treatment should be personalized to the patient and their symptom profile as there is currently insufficient evidence to recommend any particular medication or psychotherapy over another. Psychological interventions should be considered first line for mild-to-moderate depression, and pharmacological treatment added for more severe depression, which can be optimally delivered within a collaborative care model.

VIDEO ABSTRACT

http://links.lww.com/YCO/A62.

Extraversion but not depression predicts reward sensitivity: Revisiting the measurement of anhedonic phenotypes

Recently, increasing efforts have been made to define and measure dimensional phenotypes associated with psychiatric disorders. One example is a probabilistic reward task developed by Pizzagalli, Jahn, and O'Shea (2005) to assess anhedonia, by measuring response to a differential reinforcement schedule. This task has been used in many studies, which have connected blunted reward response in the task to depressive symptoms, across clinical groups and in the general population. The current study attempted to replicate these findings in a large community sample and also investigated possible associations with Extraversion, a personality trait linked to reward sensitivity. Participants (N = 299) completed the probabilistic reward task, as well as the Beck Depression Inventory, Personality Inventory for the DSM-5, Big Five Inventory, and Big Five Aspect Scales. Our direct replication attempts used bivariate correlations and analysis of variance models. Follow-up and extension analyses used structural equation models to assess relations among reward sensitivity, depression, Extraversion, and Neuroticism. No significant associations were found between reward sensitivity and depression, thus failing to replicate previous findings. Reward sensitivity (both modeled as response bias aggregated across blocks and as response bias controlling for baseline) showed positive associations with Extraversion, but not Neuroticism. Findings suggest reward sensitivity as measured by this task may be related primarily to Extraversion and its pathological manifestations, rather than to depression per se, consistent with existing models that conceptualize depressive symptoms as combining features of Neuroticism and low Extraversion. Findings are discussed in broader contexts of dimensional psychopathology frameworks, replicable science, and behavioral task reliability. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Peripheral inflammatory biomarkers define biotypes of bipolar depression

We identified biologically relevant moderators of response to tumor necrosis factor (TNF)-α inhibitor, infliximab, among 60 individuals with bipolar depression. Data were derived from a 12-week, randomized, placebo-controlled clinical trial secondarily evaluating the efficacy of infliximab on a measure of anhedonia (i.e., Snaith-Hamilton Pleasure Scale). Three inflammatory biotypes were derived from peripheral cytokine measurements using an iterative, machine learning-based approach. Infliximab-randomized participants classified as biotype 3 exhibited lower baseline concentrations of pro- and anti-inflammatory cytokines and soluble TNF receptor-1 and reported greater pro-hedonic improvements, relative to those classified as biotype 1 or 2. Pretreatment biotypes also moderated changes in neuroinflammatory substrates relevant to infliximab's hypothesized mechanism of action. Neuronal origin-enriched extracellular vesicle (NEV) protein concentrations were reduced to two factors using principal axis factoring: phosphorylated nuclear factorκB (p-NFκB), Fas-associated death domain (p-FADD), and IκB kinase (p-IKKα/β) and TNF receptor-1 (TNFR1) comprised factor "NEV1," whereas phosphorylated insulin receptor substrate-1 (p-IRS1), p38 mitogen-activated protein kinase (p-p38), and c-Jun N-terminal kinase (p-JNK) constituted "NEV2". Among infliximab-randomized subjects classified as biotype 3, NEV1 scores were decreased at weeks 2 and 6 and increased at week 12, relative to baseline, and NEV2 scores increased over time. Decreases in NEV1 scores and increases in NEV2 scores were associated with greater reductions in anhedonic symptoms in our classification and regression tree model (r² = 0.22, RMSE = 0.08). Our findings provide preliminary evidence supporting the hypothesis that the pro-hedonic effects of infliximab require modulation of multiple TNF-α signaling pathways, including NF-κB, IRS1, and MAPK.

The promise of machine learning in predicting treatment outcomes in psychiatry

For many years, psychiatrists have tried to understand factors involved in response to medications or psychotherapies, in order to personalize their treatment choices. There is now a broad and growing interest in the idea that we can develop models to personalize treatment decisions using new statistical approaches from the field of machine learning and applying them to larger volumes of data. In this pursuit, there has been a paradigm shift away from experimental studies to confirm or refute specific hypotheses towards a focus on the overall explanatory power of a predictive model when tested on new, unseen datasets. In this paper, we review key studies using machine learning to predict treatment outcomes in psychiatry, ranging from medications and psychotherapies to digital interventions and neurobiological treatments. Next, we focus on some new sources of data that are being used for the development of predictive models based on machine learning, such as electronic health records, smartphone and social media data, and on the potential utility of data from genetics, electrophysiology, neuroimaging and cognitive testing. Finally, we discuss how far the field has come towards implementing prediction tools in real-world clinical practice. Relatively few retrospective studies to-date include appropriate external validation procedures, and there are even fewer prospective studies testing the clinical feasibility and effectiveness of predictive models. Applications of machine learning in psychiatry face some of the same ethical challenges posed by these techniques in other areas of medicine or computer science, which we discuss here. In short, machine learning is a nascent but important approach to improve the effectiveness of mental health care, and several prospective clinical studies suggest that it may be working already.

Explainable AI enables clinical trial patient selection to retrospectively improve treatment effects in schizophrenia

Background

Heterogeneity among patients’ responses to treatment is prevalent in psychiatric disorders. Personalized medicine approaches—which involve parsing patients into subgroups better indicated for a particular treatment—could therefore improve patient outcomes and serve as a powerful tool in patient selection within clinical trials. Machine learning approaches can identify patient subgroups but are often not “explainable” due to the use of complex algorithms that do not mirror clinicians’ natural decision-making processes.

Methods

Here we combine two analytical approaches—Personalized Advantage Index and Bayesian Rule Lists—to identify paliperidone-indicated schizophrenia patients in a way that emphasizes model explainability. We apply these approaches retrospectively to randomized, placebo-controlled clinical trial data to identify a paliperidone-indicated subgroup of schizophrenia patients who demonstrate a larger treatment effect (outcome on treatment superior than on placebo) than that of the full randomized sample as assessed with Cohen’s d. For this study, the outcome corresponded to a reduction in the Positive and Negative Syndrome Scale (PANSS) total score which measures positive (e.g., hallucinations, delusions), negative (e.g., blunted affect, emotional withdrawal), and general psychopathological (e.g., disturbance of volition, uncooperativeness) symptoms in schizophrenia.

Results

Using our combined explainable AI approach to identify a subgroup more responsive to paliperidone than placebo, the treatment effect increased significantly over that of the full sample (p < 0.0001 for a one-sample t-test comparing the full sample Cohen’s d = 0.82 and a generated distribution of subgroup Cohen’s d’s with mean d = 1.22, std d = 0.09). In addition, our modeling approach produces simple logical statements (if–then-else), termed a “rule list”, to ease interpretability for clinicians. A majority of the rule lists generated from cross-validation found two general psychopathology symptoms, disturbance of volition and uncooperativeness, to predict membership in the paliperidone-indicated subgroup.

Conclusions

These results help to technically validate our explainable AI approach to patient selection for a clinical trial by identifying a subgroup with an improved treatment effect. With these data, the explainable rule lists also suggest that paliperidone may provide an improved therapeutic benefit for the treatment of schizophrenia patients with either of the symptoms of high disturbance of volition or high uncooperativeness.

Trial Registration: clincialtrials.gov identifier: NCT 00,083,668; prospectively registered May 28, 2004

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-021-01510-0.

Morphometry of the Hippocampus Across the Adult Life-Span in Patients with Depressive Disorders: Association with Neuroticism

Neuroticism is one of the main endophenotypes of major depressive disorder (MDD) and is closely related to the negative effect systems of Research Domain Criteria (RDoC) domains. The relationship between neuroticism and aging is dynamic and complex. Moreover, reduced hippocampal volumes are probably the most frequently reported structural neuroimaging finding associated with MDD. However, it remains unclear to what extent hippocampal abnormalities are linked with age and neuroticism changes in people with depression through the adult life span. This study aimed to examine the interplay between aging and neuroticism on hippocampal morphometric across the adult life-span in a relative large sample of patients with depressive disorders (114 patients, 73 females, age range: 18-74 years) and healthy control (HC) subjects (112 healthy controls, 72 females, age range: 19-72 years). MDD patients showed reduced bilateral hippocampal volumes. The effect of aging on the left hippocampal showed linear and the right hippocampal volume non-linear trajectories throughout the adult life span in healthy groups and MDD groups respectively. The hippocampal atrophy was dynamically impacted by depression at the early stages of adult life. Furthermore, we observed that right hippocampal volume reduction was associated with higher neuroticism in depressive patients younger than 30.65 years old. Our results suggest that the age-related atrophy in the right hippocampal volume was more affected by individual differences in neuroticism among younger depressive patients. Hippocampal volume reduction as a vulnerability factor for early-onset and major geriatric depression may have a distinct endophenotype.

Major Depressive Disorder in Older Patients as an Inflammatory Disorder: Implications for the Pharmacological Management of Geriatric Depression

Depression is a common and highly disabling condition in older adults. It is a heterogenous disorder and there is emerging evidence of a link between inflammation and depression in older patients, with a possible inflammatory subtype of depression. Persistent low-level inflammation, from several sources including psychological distress and chronic disease, can disrupt monoaminergic and glutaminergic systems to create dysfunctional brain networks. Despite the evidence for the role of inflammation in depression, there is insufficient evidence to recommend use of any putative anti-inflammatory agent in the treatment of depression in older adults at this stage. Further characterisation of markers of inflammation and stratification of participants with elevated rates of inflammatory markers in treatment trials is needed.

Predicting Response to Brain Stimulation in Depression: a Roadmap for Biomarker Discovery

PURPOSE OF REVIEW

Clinical response to brain stimulation treatments for depression is highly variable. A major challenge for the field is predicting an individual patient's likelihood of response. This review synthesises recent developments in neural predictors of response to targeted brain stimulation in depression. It then proposes a framework to evaluate the clinical potential of putative 'biomarkers'.

RECENT FINDINGS

Largely, developments in identifying putative predictors emerge from two approaches: data-driven, including machine learning algorithms applied to resting state or structural neuroimaging data, and theory-driven, including task-based neuroimaging. Theory-driven approaches can also yield mechanistic insight into the cognitive processes altered by the intervention.

SUMMARY

A pragmatic framework for discovery and testing of biomarkers of brain stimulation response in depression is proposed, involving (1) identification of a cognitive-neural phenotype; (2) confirming its validity as putative biomarker, including out-of-sample replicability and within-subject reliability; (3) establishing the association between this phenotype and treatment response and/or its modifiability with particular brain stimulation interventions via an early-phase randomised controlled trial RCT; and (4) multi-site RCTs of one or more treatment types measuring the generalisability of the biomarker and confirming the superiority of biomarker-selected patients over randomly allocated groups.

Pattern classification as decision support tool in antipsychotic treatment algorithms

Pattern classification aims to establish a new approach in personalized treatment. The scope is to tailor treatment on individual characteristics during all phases of care including prevention, diagnosis, treatment, and clinical outcome. In psychotic disorders, this need results from the fact that a third of patients with psychotic symptoms do not respond to antipsychotic treatment and are described as having treatment-resistant disorders. This, in addition to the high variability of treatment responses among patients, enhances the need of applying advanced classification algorithms to identify antipsychotic treatment patterns. This review comprehensively summarizes advancements and challenges of pattern classification in antipsychotic treatment response to date and aims to introduce clinicians and researchers to the challenges of including pattern classification into antipsychotic treatment decision algorithms.