The promise of machine learning in predicting treatment outcomes in psychiatry

Machine Learning Techniques for Developing Remotely Monitored Central Nervous System Biomarkers Using Wearable Sensors: A Narrative Literature Review

BACKGROUND

Central nervous system (CNS) disorders benefit from ongoing monitoring to assess disease progression and treatment efficacy. Mobile health (mHealth) technologies offer a means for the remote and continuous symptom monitoring of patients. Machine Learning (ML) techniques can process and engineer mHealth data into a precise and multidimensional biomarker of disease activity.

OBJECTIVE

This narrative literature review aims to provide an overview of the current landscape of biomarker development using mHealth technologies and ML. Additionally, it proposes recommendations to ensure the accuracy, reliability, and interpretability of these biomarkers.

METHODS

This review extracted relevant publications from databases such as PubMed, IEEE, and CTTI. The ML methods employed across the selected publications were then extracted, aggregated, and reviewed.

RESULTS

This review synthesized and presented the diverse approaches of 66 publications that address creating mHealth-based biomarkers using ML. The reviewed publications provide a foundation for effective biomarker development and offer recommendations for creating representative, reproducible, and interpretable biomarkers for future clinical trials.

CONCLUSION

mHealth-based and ML-derived biomarkers have great potential for the remote monitoring of CNS disorders. However, further research and standardization of study designs are needed to advance this field. With continued innovation, mHealth-based biomarkers hold promise for improving the monitoring of CNS disorders.

Systematic review of machine learning utilization within outpatient psychodynamic psychotherapy research

Introduction

Although outpatient psychodynamic psychotherapy is effective, there has been no improvement in treatment success in recent years. One way to improve psychodynamic treatment could be the use of machine learning to design treatments tailored to the individual patient's needs. In the context of psychotherapy, machine learning refers mainly to various statistical methods, which aim to predict outcomes (e.g., drop-out) of future patients as accurately as possible. We therefore searched various literature for all studies using machine learning in outpatient psychodynamic psychotherapy research to identify current trends and objectives.

Methods

For this systematic review, we applied the Preferred Reporting Items for systematic Reviews and Meta-Analyses Guidelines.

Results

In total, we found four studies that used machine learning in outpatient psychodynamic psychotherapy research. Three of these studies were published between 2019 and 2021.

Discussion

We conclude that machine learning has only recently made its way into outpatient psychodynamic psychotherapy research and researchers might not yet be aware of its possible uses. Therefore, we have listed a variety of perspectives on how machine learning could be used to increase treatment success of psychodynamic psychotherapies. In doing so, we hope to give new impetus to outpatient psychodynamic psychotherapy research on how to use machine learning to address previously unsolved problems.

Subjective and objective sleep and circadian parameters as predictors of depression-related outcomes: A machine learning approach in UK Biobank

BACKGROUND

Sleep and circadian disruption are associated with depression onset and severity, but it is unclear which features (e.g., sleep duration, chronotype) are important and whether they can identify individuals showing poorer outcomes.

METHODS

Within a subset of the UK Biobank with actigraphy and mental health data (n = 64,353), penalised regression identified the most useful of 51 sleep/rest-activity predictors of depression-related outcomes; including case-control (Major Depression (MD) vs. controls; postnatal depression vs. controls) and within-case comparisons (severe vs. moderate MD; early vs. later onset, atypical vs. typical symptoms; comorbid anxiety; suicidality). Best models (of lasso, ridge, and elastic net) were selected based on Area Under the Curve (AUC).

RESULTS

For MD vs. controls (n_(MD) = 24,229; n_(control) = 40,124), lasso AUC was 0.68, 95 % confidence interval (CI) 0.67-0.69. Discrimination was reasonable for atypical vs. typical symptoms (n_(atypical) = 958; n_(typical) = 18,722; ridge: AUC 0.74, 95 % CI 0.71-0.77) but poor for remaining models (AUCs 0.59-0.67). Key predictors across most models included: difficulty getting up, insomnia symptoms, snoring, actigraphy-measured daytime inactivity and lower morning activity (~8 am). In a distinct subset (n = 310,718), the number of these factors shown was associated with all depression outcomes.

LIMITATIONS

Analyses were cross-sectional and in middle-/older aged adults: comparison with longitudinal investigations and younger cohorts is necessary.

DISCUSSION

Sleep and circadian measures alone provided poor to moderate discrimination of depression outcomes, but several characteristics were identified that may be clinically useful. Future work should assess these features alongside broader sociodemographic, lifestyle and genetic features.

Machine learning v. traditional regression models predicting treatment outcomes for binge-eating disorder from a randomized controlled trial

BACKGROUND

While effective treatments exist for binge-eating disorder (BED), prediction of treatment outcomes has proven difficult, and few reliable predictors have been identified. Machine learning is a promising method for improving the accuracy of difficult-to-predict outcomes. We compared the accuracy of traditional and machine-learning approaches for predicting BED treatment outcomes.

METHODS

Participants were 191 adults with BED in a randomized controlled trial testing 6-month behavioral and stepped-care treatments. Outcomes, determined by independent assessors, were binge-eating (% reduction, abstinence), eating-disorder psychopathology, and weight loss (% loss, ⩾5% loss). Predictors included treatment condition, demographic information, and baseline clinical characteristics. Traditional models were logistic/linear regressions. Machine-learning models were elastic net regressions and random forests. Predictive accuracy was indicated by the area under receiver operator characteristic curve (AUC), root mean square error (RMSE), and R2. Confidence intervals were used to compare accuracy across models.

RESULTS

Across outcomes, AUC ranged from very poor to fair (0.49-0.73) for logistic regressions, elastic nets, and random forests, with few significant differences across model types. RMSE was significantly lower for elastic nets and random forests v. linear regressions but R2 values were low (0.01-0.23).

CONCLUSIONS

Different analytic approaches revealed some predictors of key treatment outcomes, but accuracy was limited. Machine-learning models with unbiased resampling methods provided a minimal advantage over traditional models in predictive accuracy for treatment outcomes.

AI-assisted prediction of differential response to antidepressant classes using electronic health records

Antidepressant selection is largely a trial-and-error process. We used electronic health record (EHR) data and artificial intelligence (AI) to predict response to four antidepressants classes (SSRI, SNRI, bupropion, and mirtazapine) 4 to 12 weeks after antidepressant initiation. The final data set comprised 17,556 patients. Predictors were derived from both structured and unstructured EHR data and models accounted for features predictive of treatment selection to minimize confounding by indication. Outcome labels were derived through expert chart review and AI-automated imputation. Regularized generalized linear model (GLM), random forest, gradient boosting machine (GBM), and deep neural network (DNN) models were trained and their performance compared. Predictor importance scores were derived using SHapley Additive exPlanations (SHAP). All models demonstrated similarly good prediction performance (AUROCs ≥ 0.70, AUPRCs ≥ 0.68). The models can estimate differential treatment response probabilities both between patients and between antidepressant classes for the same patient. In addition, patient-specific factors driving response probabilities for each antidepressant class can be generated. We show that antidepressant response can be accurately predicted from real-world EHR data with AI modeling, and our approach could inform further development of clinical decision support systems for more effective treatment selection.

Can Epigenetics Predict Drug Efficiency in Mental Disorders?

Psychiatric disorders affect millions of individuals and their families worldwide, and the costs to society are substantial and are expected to rise due to a lack of effective treatments. Personalized medicine-customized treatment tailored to the individual-offers a solution. Although most mental diseases are influenced by genetic and environmental factors, finding genetic biomarkers that predict treatment efficacy has been challenging. This review highlights the potential of epigenetics as a tool for predicting treatment efficacy and personalizing medicine for psychiatric disorders. We examine previous studies that have attempted to predict treatment efficacy through epigenetics, provide an experimental model, and note the potential challenges at each stage. While the field is still in its infancy, epigenetics holds promise as a predictive tool by examining individual patients' epigenetic profiles in conjunction with other indicators. However, further research is needed, including additional studies, replication, validation, and application beyond clinical settings.

Multimodal predictions of treatment outcome in major depression: A comparison of data-driven predictors with importance ratings by clinicians

BACKGROUND

Reliable prediction models of treatment outcome in Major Depressive Disorder (MDD) are currently lacking in clinical practice. Data-driven outcome definitions, combining data from multiple modalities and incorporating clinician expertise might improve predictions.

METHODS

We used unsupervised machine learning to identify treatment outcome classes in 1060 MDD inpatients. Subsequently, classification models were created on clinical and biological baseline information to predict treatment outcome classes and compared to the performance of two widely used classical outcome definitions. We also related the findings to results from an online survey that assessed which information clinicians use for outcome prognosis.

RESULTS

Three and four outcome classes were identified by unsupervised learning. However, data-driven outcome classes did not result in more accurate prediction models. The best prediction model was targeting treatment response in its standard definition and reached accuracies of 63.9 % in the test sample, and 59.5 % and 56.9 % in the validation samples. Top predictors included sociodemographic and clinical characteristics, while biological parameters did not improve prediction accuracies. Treatment history, personality factors, prior course of the disorder, and patient attitude towards treatment were ranked as most important indicators by clinicians.

LIMITATIONS

Missing data limited the power to identify biological predictors of treatment outcome from certain modalities.

CONCLUSIONS

So far, the inclusion of available biological measures in addition to psychometric and clinical information did not improve predictive value of the models, which was overall low. Optimized biomarkers, stratified predictions and the inclusion of clinical expertise may improve future prediction models.

Individual differences in computational psychiatry: a review of current challenges

Bringing precision to the understanding and treatment of mental disorders requires instruments for studying clinically relevant individual differences. One promising approach is the development of computational assays: integrating computational models with cognitive tasks to infer latent patient-specific disease processes in brain computations. While recent years have seen many methodological advancements in computational modelling and many cross-sectional patient studies, much less attention has been paid to basic psychometric properties (reliability and construct validity) of the computational measures provided by the assays. In this review, we assess the extent of this issue by examining emerging empirical evidence. We find that many computational measures suffer from poor psychometric properties, which poses a risk of invalidating previous findings and undermining ongoing research efforts using computational assays to study individual (and even group) differences. We provide recommendations for how to address these problems and, crucially, embed them within a broader perspective on key developments that are needed for translating computational assays to clinical practice.

Machine learning methods to predict outcomes of pharmacological treatment in psychosis

In recent years, machine learning (ML) has been a promising approach in the research of treatment outcome prediction in psychosis. In this study, we reviewed ML studies using different neuroimaging, neurophysiological, genetic, and clinical features to predict antipsychotic treatment outcomes in patients at different stages of schizophrenia. Literature available on PubMed until March 2022 was reviewed. Overall, 28 studies were included, among them 23 using a single-modality approach and 5 combining data from multiple modalities. The majority of included studies considered structural and functional neuroimaging biomarkers as predictive features used in ML models. Specifically, functional magnetic resonance imaging (fMRI) features contributed to antipsychotic treatment response prediction of psychosis with good accuracies. Additionally, several studies found that ML models based on clinical features might present adequate predictive ability. Importantly, by examining the additive effects of combining features, the predictive value might be improved by applying multimodal ML approaches. However, most of the included studies presented several limitations, such as small sample sizes and a lack of replication tests. Moreover, considerable clinical and analytical heterogeneity among included studies posed a challenge in synthesizing findings and generating robust overall conclusions. Despite the complexity and heterogeneity of methodology, prognostic features, clinical presentation, and treatment approaches, studies included in this review suggest that ML tools may have the potential to predict treatment outcomes of psychosis accurately. Future studies need to focus on refining feature characterization, validating prediction models, and evaluate their translation in real-world clinical practice.

Predicting psychotic relapse following randomised discontinuation of paliperidone in individuals with schizophrenia or schizoaffective disorder: an individual participant data analysis

BACKGROUND

Predicting relapse for individuals with psychotic disorders is not well established, especially after discontinuation of antipsychotic treatment. We aimed to identify general prognostic factors of relapse for all participants (irrespective of treatment continuation or discontinuation) and specific predictors of relapse for treatment discontinuation, using machine learning.

METHODS

For this individual participant data analysis, we searched the Yale University Open Data Access Project's database for placebo-controlled, randomised antipsychotic discontinuation trials with participants with schizophrenia or schizoaffective disorder (aged ≥18 years). We included studies in which participants were treated with any antipsychotic study drug and randomly assigned to continue the same antipsychotic drug or to discontinue it and receive placebo. We assessed 36 prespecified baseline variables at randomisation to predict time to relapse, using univariate and multivariate proportional hazard regression models (including multivariate treatment group by variable interactions) with machine learning to categorise the variables as general prognostic factors of relapse, specific predictors of relapse, or both.

FINDINGS

We identified 414 trials, of which five trials with 700 participants (304 [43%] women and 396 [57%] men) were eligible for the continuation group and 692 participants (292 [42%] women and 400 [58%] men) were eligible for the discontinuation group (median age 37 [IQR 28-47] years for continuation group and 38 [28-47] years for discontinuation group). Out of the 36 baseline variables, general prognostic factors of increased risk of relapse for all participants were drug-positive urine; paranoid, disorganised, and undifferentiated types of schizophrenia (lower risk for schizoaffective disorder); psychiatric and neurological adverse events; higher severity of akathisia (ie, difficulty or inability to sit still); antipsychotic discontinuation; lower social performance; younger age; lower glomerular filtration rate; benzodiazepine comedication (lower risk for anti-epileptic comedication). Out of the 36 baseline variables, predictors of increased risk specifically after antipsychotic discontinuation were increased prolactin concentration, higher number of hospitalisations, and smoking. Both prognostic factors and predictors with increased risk after discontinuation were oral antipsychotic treatment (lower risk for long-acting injectables), higher last dosage of the antipsychotic study drug, shorter duration of antipsychotic treatment, and higher score on the Clinical Global Impression (CGI) severity scale The predictive performance (concordance index) for participants who were not used to train the model was 0·707 (chance level is 0·5).

INTERPRETATION

Routinely available general prognostic factors of psychotic relapse and predictors specific for treatment discontinuation could be used to support personalised treatment. Abrupt discontinuation of higher dosages of oral antipsychotics, especially for individuals with recurring hospitalisations, higher scores on the CGI severity scale, and increased prolactin concentrations, should be avoided to reduce the risk of relapse.

FUNDING

German Research Foundation and Berlin Institute of Health.

Moderators of LGBQ-affirmative cognitive behavioral therapy: ESTEEM is especially effective among Black and Latino sexual minority men

OBJECTIVE

Lesbian, gay, bisexual, and queer (LGBQ)-affirmative cognitive behavioral therapy (CBT) focused on minority stress processes can address gay and bisexual men's transdiagnostic mental and behavioral health concerns. Identifying moderators of treatment outcomes may inform the mechanisms of LGBQ-affirmative CBT and subpopulations who may derive particular benefit.

METHOD

Data were from a clinical trial in which gay and bisexual men with mental and behavioral health concerns were randomized to receive Effective Skills to Empower Effective Men (ESTEEM; an LGBQ-affirmative transdiagnostic CBT; n = 100) or one of two control conditions (n = 154): LGBQ-affirmative community mental health treatment (CMHT) or HIV counseling and testing (HCT). The preregistered outcome was a comorbidity index of depression, anxiety, alcohol/drug problems, and human immunodeficiency virus (HIV) transmission risk behavior at 8-month follow-up (i.e., 4 months postintervention). A two-step exploratory machine learning process was employed for 20 theoretically informed baseline variables identified by study therapists as potential moderators of ESTEEM efficacy. Potential moderators included demographic factors, pretreatment comorbidities, clinical facilitators, and minority stress factors.

RESULTS

Racial/ethnic minority identification, namely as Black or Latino, was the only statistically significant moderator of treatment efficacy (B = -3.23, 95% CI [-5.03, -1.64]), t(197) = -3.88, p < .001. Racially/ethnically minoritized recipients (d = -0.71, p < .001), but not White/non-Latino recipients (d = 0.22, p = .391), had greater reductions in comorbidity index scores in ESTEEM compared to the control conditions. This moderation was driven by improvements in anxiety and alcohol/drug use problems.

DISCUSSION

Black and Latino gay and bisexual men experiencing comorbid mental and behavioral health risks might particularly benefit from a minority stress-focused LGBQ-affirmative CBT. Future research should identify mechanisms for this moderation to inform targeted treatment delivery and dissemination. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Precision Medicine Approaches to Mental Health Care

Developing a more comprehensive understanding of the physiological underpinnings of mental illness, precision medicine has the potential to revolutionize psychiatric care. With recent breakthroughs in next-generation multi-omics technologies and data analytics, it is becoming more feasible to leverage multimodal biomarkers, from genetic variants to neuroimaging biomarkers, to objectify diagnostics and treatment decisions in psychiatry and improve patient outcomes. Ongoing work in precision psychiatry will parallel progress in precision oncology and cardiology to develop an expanded suite of blood- and neuroimaging-based diagnostic tests, empower monitoring of treatment efficacy over time, and reduce patient exposure to ineffective treatments. The emerging model of precision psychiatry has the potential to mitigate some of psychiatry's most pressing issues, including improving disease classification, lengthy treatment duration, and suboptimal treatment outcomes. This narrative-style review summarizes some of the emerging breakthroughs and recurring challenges in the application of precision medicine approaches to mental health care.

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.

Prediction of Chinese clients' satisfaction with psychotherapy by machine learning

Background

Effective psychotherapy should satisfy the client, but that satisfaction depends on many factors. We do not fully understand the factors that affect client satisfaction with psychotherapy and how these factors synergistically affect a client's psychotherapy experience.

Aims

This study aims to use machine learning to predict Chinese clients' satisfaction with psychotherapy and analyze potential outcome contributors.

Methods

In this cross-sectional investigation, a self-compiled online questionnaire was delivered through the WeChat app. The information of 791 participants who had received psychotherapy was used in the study. A series of features, for example, the participants' demographic features and psychotherapy-related features, were chosen to distinguish between participants satisfied and dissatisfied with the psychotherapy they received. With our dataset, we trained seven supervised machine-learning-based algorithms to implement prediction models.

Results

Among the 791 participants, 619 (78.3%) reported being satisfied with the psychotherapy sessions that they received. The occupation of the clients, the location of psychotherapy, and the form of access to psychotherapy are the three most recognizable features that determined whether clients are satisfied with psychotherapy. The machine-learning model based on the CatBoost achieved the highest prediction performance in classifying satisfied and psychotherapy clients with an F1 score of 0.758.

Conclusion

This study clarified the factors related to clients' satisfaction with psychotherapy, and the machine-learning-based classifier accurately distinguished clients who were satisfied or unsatisfied with psychotherapy. These results will help provide better psychotherapy strategies for specific clients, so they may achieve better therapeutic outcomes.

Electroencephalography for predicting antidepressant treatment success: A systematic review and meta-analysis

BACKGROUND

Patients suffering from major depressive disorder (MDD) regularly experience non-response to treatment for their depressive episode. Personalized clinical decision making could shorten depressive episodes and reduce patient suffering. Although no clinical tools are currently available, machine learning analysis of electroencephalography (EEG) shows promise in treatment response prediction.

METHODS

With a systematic review and meta-analysis, we evaluated the accuracy of EEG for individual patient response prediction. Importantly, we included only prediction studies that used cross-validation. We used a bivariate model to calculate prediction success, as expressed by area-under the curve, sensitivity and specificity. Furthermore, we analyzed prediction success for separate antidepressant interventions.

RESULTS

15 studies with 12 individual patient samples and a total of 479 patients were included. Research methods varied considerably between studies. Meta-analysis of results from this heterogeneous set of studies resulted in an area under the curve of 0.91, a sensitivity of 83 % (95 % CI 74-89 %), and a specificity of 86 % (95 % CI 81-90 %). Classification performance did not significantly differ between treatments. Although studies were all internally validated, no externally validated studies have been reported. We found substantial risk of bias caused by methodological shortcomings such as non-independent feature selection, though performance of non-biased studies was comparable.

LIMITATIONS

Sample sizes were relatively small and no study used external validation, increasing the risk of overestimation of accuracy.

CONCLUSIONS

Electroencephalography can predict the response to antidepressant treatment with high accuracy. However, future studies with more rigorous validation are needed to produce a clinical tool to guide interventions in MDD.

PROSPERO REGISTRATION NUMBER

CRD42021268169.

The Precision in Psychiatry (PIP) study: Testing an internet-based methodology for accelerating research in treatment prediction and personalisation

BACKGROUND

Evidence-based treatments for depression exist but not all patients benefit from them. Efforts to develop predictive models that can assist clinicians in allocating treatments are ongoing, but there are major issues with acquiring the volume and breadth of data needed to train these models. We examined the feasibility, tolerability, patient characteristics, and data quality of a novel protocol for internet-based treatment research in psychiatry that may help advance this field.

METHODS

A fully internet-based protocol was used to gather repeated observational data from patient cohorts receiving internet-based cognitive behavioural therapy (iCBT) (N = 600) or antidepressant medication treatment (N = 110). At baseline, participants provided > 600 data points of self-report data, spanning socio-demographics, lifestyle, physical health, clinical and other psychological variables and completed 4 cognitive tests. They were followed weekly and completed another detailed clinical and cognitive assessment at week 4. In this paper, we describe our study design, the demographic and clinical characteristics of participants, their treatment adherence, study retention and compliance, the quality of the data gathered, and qualitative feedback from patients on study design and implementation.

RESULTS

Participant retention was 92% at week 3 and 84% for the final assessment. The relatively short study duration of 4 weeks was sufficient to reveal early treatment effects; there were significant reductions in 11 transdiagnostic psychiatric symptoms assessed, with the largest improvement seen for depression. Most participants (66%) reported being distracted at some point during the study, 11% failed 1 or more attention checks and 3% consumed an intoxicating substance. Data quality was nonetheless high, with near perfect 4-week test retest reliability for self-reported height (ICC = 0.97).

CONCLUSIONS

An internet-based methodology can be used efficiently to gather large amounts of detailed patient data during iCBT and antidepressant treatment. Recruitment was rapid, retention was relatively high and data quality was good. This paper provides a template methodology for future internet-based treatment studies, showing that such an approach facilitates data collection at a scale required for machine learning and other data-intensive methods that hope to deliver algorithmic tools that can aid clinical decision-making in psychiatry.

Improving treatment outcomes for borderline personality disorder: what can we learn from biomarker studies of psychotherapy?

PURPOSE OF REVIEW

Borderline personality disorder (BPD) is a severe and common psychiatric disorder and though evidence-based psychotherapies are effective, rates of treatment nonresponse are as high as 50%. Treatment studies may benefit from interdisciplinary approaches from neuroscience and genetics research that could generate novel insights into treatment mechanisms and tailoring interventions to the individual.

RECENT FINDINGS

We provide a timely update to the small but growing body of literature investigating neurobiological and epigenetic changes and using biomarkers to predict outcomes from evidence-based psychotherapies for BPD. Using a rapid review methodology, we identified eight new studies, updating our earlier 2018 systematic review. Across all studies, neuroimaging ( n  = 18) and genetics studies ( n  = 4) provide data from 735 participants diagnosed with BPD (mean sample size across studies = 33.4, range 2-115).

SUMMARY

We report further evidence for psychotherapy-related alterations of neural activation and connectivity in regions and networks relating to executive control, emotion regulation, and self/interpersonal functioning in BPD. Emerging evidence also shows epigenetic changes following treatment. Future large-scale multisite studies may help to delineate multilevel treatment targets to inform intervention design, selection, and monitoring for the individual patient via integration of knowledge generated through clinical, neuroscience, and genetics research.

The development and internal evaluation of a predictive model to identify for whom Mindfulness-Based Cognitive Therapy (MBCT) offers superior relapse prevention for recurrent depression versus maintenance antidepressant medication

Depression is highly recurrent, even following successful pharmacological and/or psychological intervention. We aimed to develop clinical prediction models to inform adults with recurrent depression choosing between antidepressant medication (ADM) maintenance or switching to Mindfulness-Based Cognitive Therapy (MBCT). Using data from the PREVENT trial (N=424), we constructed prognostic models using elastic net regression that combined demographic, clinical and psychological factors to predict relapse at 24 months under ADM or MBCT. Only the ADM model (discrimination performance: AUC=.68) predicted relapse better than baseline depression severity (AUC=.54; one-tailed DeLong's test: z=2.8, p=.003). Individuals with the poorest ADM prognoses who switched to MBCT had better outcomes compared to those who maintained ADM (48% vs. 70% relapse, respectively; superior survival times [z=-2.7, p=.008]). For individuals with moderate-to-good ADM prognosis, both treatments resulted in similar likelihood of relapse. If replicated, the results suggest that predictive modeling can inform clinical decision-making around relapse prevention in recurrent depression.

Resting-State Brain Activity Dysfunctions in Schizophrenia and Their Associations with Negative Symptom Domains: An fMRI Study

The aim of the present study was to examine the neurobiological correlates of the two negative symptom domains of schizophrenia, the Motivational Deficit domain (including avolition, anhedonia, and asociality) and the Expressive Deficit domain (including blunted affect and alogia), focusing on brain areas that are most commonly found to be associated with negative symptoms in previous literature. Resting-state (rs) fMRI data were analyzed in 62 subjects affected by schizophrenia (SZs) and 46 healthy controls (HCs). The SZs, compared to the HCs, showed higher rs brain activity in the right inferior parietal lobule and the right temporoparietal junction, and lower rs brain activity in the right dorsolateral prefrontal cortex, the bilateral anterior dorsal cingulate cortex, and the ventral and dorsal caudate. Furthermore, in the SZs, the rs brain activity in the left orbitofrontal cortex correlated with negative symptoms (r = -0.436, p = 0.006), in particular with the Motivational Deficit domain (r = -0.424, p = 0.002), even after controlling for confounding factors. The left ventral caudate correlated with negative symptoms (r = -0.407, p = 0.003), especially with the Expressive Deficit domain (r = -0.401, p = 0.003); however, these results seemed to be affected by confounding factors. In line with the literature, our results demonstrated that the two negative symptom domains might be underpinned by different neurobiological mechanisms.

Sex and gender differences in clinical and functional indices in subjects with schizophrenia and healthy controls: Data from the baseline and 4-year follow-up studies of the Italian Network for Research on Psychoses

Gender differences in clinical and psychosocial aspects of schizophrenia have been widely reported. Findings have not always been consistent, and some of them need further research. In a large sample of community dwelling persons with schizophrenia, we investigated gender differences in clinical, cognitive and functional indices, as well as their changes over a 4-year follow-up and their impact on real-life functioning. Gender differences in personal resources, cognitive and functional indices were explored also in a sample of healthy controls. Men with respect to women had an earlier age of illness onset, a worse premorbid adjustment in the academic domain, more severe avolition, expressive deficit and positive symptoms, lower prevalence of comorbidity for affective disorders, less frequent use of two coping strategies ('religion' and 'use of emotional support') and more frequent positive history of substance and alcohol abuse. In addition, men were more impaired in verbal learning, while women in reasoning/problem solving. Some patterns of gender differences observed in healthy controls were not confirmed in patients. Men's disadvantages in the clinical picture did not translate into a worse outcome. This finding may be related to the complex interplay of several factors acting as predictors or mediators of outcome.

Burning down the house: reinventing drug discovery in psychiatry for the development of targeted therapies

Despite advances in neuroscience, limited progress has been made in developing new and better medications for psychiatric disorders. Available treatments in psychiatry rely on a few classes of drugs that have a broad spectrum of activity across disorders with limited understanding of mechanism of action. While the added value of more targeted therapies is apparent, a dearth of pathophysiologic mechanisms exists to support targeted treatments, and where mechanisms have been identified and drugs developed, results have been disappointing. Based on serendipity and early successes that led to the current drug armamentarium, a haunting legacy endures that new drugs should align with outdated and overinclusive diagnostic categories, consistent with the idea that "one size fits all". This legacy has fostered clinical trial designs focused on heterogenous populations of patients with a single diagnosis and non-specific outcome variables. Disturbingly, this approach likely contributed to missed opportunities for drugs targeting the hypothalamic-pituitary-adrenal axis and now inflammation. Indeed, cause-and-effect data support the role of inflammatory processes in neurotransmitter alterations that disrupt specific neurocircuits and related behaviors. This pathway to pathology occurs across disorders and warrants clinical trial designs that enrich for patients with increased inflammation and use primary outcome variables associated with specific effects of inflammation on brain and behavior. Nevertheless, such trial designs have not been routinely employed, and results of anti-inflammatory treatments have been underwhelming. Thus, to accelerate development of targeted therapeutics including in the area of inflammation, regulatory agencies and the pharmaceutical industry must embrace treatments and trials focused on pathophysiologic pathways that impact specific symptom domains in subsets of patients, agnostic to diagnosis. Moreover, closer collaboration among basic and clinical investigators is needed to apply neuroscience knowledge to reveal disease mechanisms that drive psychiatric symptoms. Together, these efforts will support targeted treatments, ultimately leading to new and better therapeutics in psychiatry.

Predicting treatment outcome in depression: an introduction into current concepts and challenges

Improving response and remission rates in major depressive disorder (MDD) remains an important challenge. Matching patients to the treatment they will most likely respond to should be the ultimate goal. Even though numerous studies have investigated patient-specific indicators of treatment efficacy, no (bio)markers or empirical tests for use in clinical practice have resulted as of now. Therefore, clinical decisions regarding the treatment of MDD still have to be made on the basis of questionnaire- or interview-based assessments and general guidelines without the support of a (laboratory) test. We conducted a narrative review of current approaches to characterize and predict outcome to pharmacological treatments in MDD. We particularly focused on findings from newer computational studies using machine learning and on the resulting implementation into clinical decision support systems. The main issues seem to rest upon the unavailability of robust predictive variables and the lacking application of empirical findings and predictive models in clinical practice. We outline several challenges that need to be tackled on different stages of the translational process, from current concepts and definitions to generalizable prediction models and their successful implementation into digital support systems. By bridging the addressed gaps in translational psychiatric research, advances in data quantity and new technologies may enable the next steps toward precision psychiatry.

Predicting early dropout in online versus face-to-face guided self-help: A machine learning approach

BACKGROUND

Early dropout hinders the effective adoption of brief psychological interventions and is associated with poor treatment outcomes. This study examined if attendance and depression treatment outcomes could be improved by matching patients to either face-to-face or computerized low-intensity psychological interventions.

METHODS

Archival clinical records were analysed for 85,664 patients who accessed face-to-face or computerized guided self-help (GSH). The primary outcome was early dropout (attending ≤3 sessions). Supervised machine learning analyses were applied in a training sample (n = 55,529). The trained algorithm was cross-validated in an independent test sample (n = 30,135). The clinical utility of the model was evaluated using logistic regression, chi-square tests, and sensitivity analyses in a balanced subsample.

RESULTS

Patients who received their model-indicated treatment modality were 12% more likely to receive an adequate dose of treatment OR = 1.12 (95% CI = 1.02 to 1.24), p = .02, and the strength of this effect was larger in the balanced subsample (OR = 2.10, 95% CI = 1.65 to 2.68, p < .001). Patients had better treatment outcomes when matched to their model-indicated treatment modality.

CONCLUSIONS

Machine learning approaches may enable services to optimally match patients to the treatment modality that maximizes attendance.

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.

Expectations for Artificial Intelligence (AI) in Psychiatry

PURPOSE OF REVIEW

Artificial intelligence (AI) is often presented as a transformative technology for clinical medicine even though the current technology maturity of AI is low. The purpose of this narrative review is to describe the complex reasons for the low technology maturity and set realistic expectations for the safe, routine use of AI in clinical medicine.

RECENT FINDINGS

For AI to be productive in clinical medicine, many diverse factors that contribute to the low maturity level need to be addressed. These include technical problems such as data quality, dataset shift, black-box opacity, validation and regulatory challenges, and human factors such as a lack of education in AI, workflow changes, automation bias, and deskilling. There will also be new and unanticipated safety risks with the introduction of AI. The solutions to these issues are complex and will take time to discover, develop, validate, and implement. However, addressing the many problems in a methodical manner will expedite the safe and beneficial use of AI to augment medical decision making in psychiatry.

Metaverse as a possible tool for reshaping schema modes in treating personality disorders

Personality disorders (PD) are usually treated with face-to-face sessions and/or digital mental health services. Among many schools of therapies, schema therapy stands out because rather than simply targeting the symptoms of PD, it cordially targets the cause of PD and heals the early maladaptive schema, thus is exceptionally good at soothing emotional disturbances before enacting cognitive restructuring, resulting in long-term efficacy. However, according to Piaget's genetic epistemology, the unmet needs lie in the fact that the schemata that determine the adaptive behavior can only be formed in the interaction with the real world that the patient is living in and reconsolidated by the feedback from the object world upon the patient's newly-formed behavior. Therefore, in order to reshape the patient's schema modes to support adaptive behavior and regain emotional regulation capabilities of the healthy adult, one may have to reconstruct the object world surrounding the patient. Metaverse, the bestowed successor to the Internet with the cardinal feature of "the sense of full presence," can become a powerful tool to reconstruct a new object world for the patient with the prescription of a psychotherapist, so as to transform the treatment techniques in schema therapy into the natural autobiographical experiences of patients in the new object world, thus gradually reshape the patient's schema modes that can ultimately result in an adaptive, and more inclusive, interaction with the real world. This work describes the underlying theory, the mechanism, the process, and ethical considerations of such promising technology for the not-too-far future.

A common factors perspective on mindfulness-based interventions

Mindfulness-based interventions (MBIs) have entered mainstream Western culture in the past four decades. There are now dozens of MBIs with varying degrees of empirical support and a variety of mindfulness-specific psychological mechanisms have been proposed to account for the beneficial effects of MBIs. Although it has long been acknowledged that non-specific or common factors might contribute to MBI efficacy, relatively little empirical work has directly investigated these aspects. In this Perspective, I suggest that situating MBIs within the broader psychotherapy research literature and emphasizing the commonalities rather than differences between MBIs and other treatments might help guide future MBI research. To that end, I summarize the evidence for MBI efficacy and several MBI-specific psychological mechanisms, contextualize MBI findings within the broader psychotherapy literature from a common factors perspective, and propose suggestions for future research based on innovations and challenges occurring within psychotherapy research.

Psychiatric diagnosis and treatment in the 21st century: paradigm shifts versus incremental integration

Psychiatry has always been characterized by a range of different models of and approaches to mental disorder, which have sometimes brought progress in clinical practice, but have often also been accompanied by critique from within and without the field. Psychiatric nosology has been a particular focus of debate in recent decades; successive editions of the DSM and ICD have strongly influenced both psychiatric practice and research, but have also led to assertions that psychiatry is in crisis, and to advocacy for entirely new paradigms for diagnosis and assessment. When thinking about etiology, many researchers currently refer to a biopsychosocial model, but this approach has received significant critique, being considered by some observers overly eclectic and vague. Despite the development of a range of evidence-based pharmacotherapies and psychotherapies, current evidence points to both a treatment gap and a research-practice gap in mental health. In this paper, after considering current clinical practice, we discuss some proposed novel perspectives that have recently achieved particular prominence and may significantly impact psychiatric practice and research in the future: clinical neuroscience and personalized pharmacotherapy; novel statistical approaches to psychiatric nosology, assessment and research; deinstitutionalization and community mental health care; the scale-up of evidence-based psychotherapy; digital phenotyping and digital therapies; and global mental health and task-sharing approaches. We consider the extent to which proposed transitions from current practices to novel approaches reflect hype or hope. Our review indicates that each of the novel perspectives contributes important insights that allow hope for the future, but also that each provides only a partial view, and that any promise of a paradigm shift for the field is not well grounded. We conclude that there have been crucial advances in psychiatric diagnosis and treatment in recent decades; that, despite this important progress, there is considerable need for further improvements in assessment and intervention; and that such improvements will likely not be achieved by any specific paradigm shifts in psychiatric practice and research, but rather by incremental progress and iterative integration.

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.

A machine learning approach for predicting suicidal ideation in post stroke patients

Currently, the identification of stroke patients with an increased suicide risk is mainly based on self‐report questionnaires, and this method suffers from a lack of objectivity. This study developed and validated a suicide ideation (SI) prediction model using clinical data and identified SI predictors. Significant variables were selected through traditional statistical analysis based on retrospective data of 385 stroke patients; the data were collected from October 2012 to March 2014. The data were then applied to three boosting models (Xgboost, CatBoost, and LGBM) to identify the comparative and best performing models. Demographic variables that showed significant differences between the two groups were age, onset, type, socioeconomic, and education level. Additionally, functional variables also showed a significant difference with regard to ADL and emotion (p < 0.05). The CatBoost model (0.900) showed higher performance than the other two models; and depression, anxiety, self-efficacy, and rehabilitation motivation were found to have high importance. Negative emotions such as depression and anxiety showed a positive relationship with SI and rehabilitation motivation and self-efficacy displayed an inverse relationship with SI. Machine learning-based SI models could augment SI prevention by helping rehabilitation and medical professionals identify high-risk stroke patients in need of SI prevention intervention.

Unveiling the Associations between EEG Indices and Cognitive Deficits in Schizophrenia-Spectrum Disorders: A Systematic Review

Cognitive dysfunctions represent a core feature of schizophrenia-spectrum disorders due to their presence throughout different illness stages and their impact on functioning. Abnormalities in electrophysiology (EEG) measures are highly related to these impairments, but the use of EEG indices in clinical practice is still limited. A systematic review of articles using Pubmed, Scopus and PsychINFO was undertaken in November 2021 to provide an overview of the relationships between EEG indices and cognitive impairment in schizophrenia-spectrum disorders. Out of 2433 screened records, 135 studies were included in a qualitative review. Although the results were heterogeneous, some significant correlations were identified. In particular, abnormalities in alpha, theta and gamma activity, as well as in MMN and P300, were associated with impairments in cognitive domains such as attention, working memory, visual and verbal learning and executive functioning during at-risk mental states, early and chronic stages of schizophrenia-spectrum disorders. The review suggests that machine learning approaches together with a careful selection of validated EEG and cognitive indices and characterization of clinical phenotypes might contribute to increase the use of EEG-based measures in clinical settings.

Technology Matters: Machine learning approaches to personalised child and adolescent mental health care

There has been much interest in the potential for machine learning and artificial intelligence to enhance health care. In this article, we discuss the potential applications of the technology to child and adolescent mental health services (CAMHS). We also outline the four key criteria that are likely to be necessary for automated prediction to be translated into clinical benefit. These relate to the choice of task to be automated, the nature of the available data, the methods applied and the context of the system to be implemented.

Predicting remission after internet-delivered psychotherapy in patients with depression using machine learning and multi-modal data

This study applied supervised machine learning with multi-modal data to predict remission of major depressive disorder (MDD) after psychotherapy. Genotyped adult patients (n = 894, 65.5% women, age 18-75 years) diagnosed with mild-to-moderate MDD and treated with guided Internet-based Cognitive Behaviour Therapy (ICBT) at the Internet Psychiatry Clinic in Stockholm were included (2008-2016). Predictor types were demographic, clinical, process (e.g., time to complete online questionnaires), and genetic (polygenic risk scores). Outcome was remission status post ICBT (cut-off ≤10 on MADRS-S). Data were split into train (60%) and validation (40%) given ICBT start date. Predictor selection employed human expertise followed by recursive feature elimination. Model derivation was internally validated through cross-validation. The final random forest model was externally validated against a (i) null, (ii) logit, (iii) XGBoost, and (iv) blended meta-ensemble model on the hold-out validation set. Feature selection retained 45 predictors representing all four predictor types. With unseen validation data, the final random forest model proved reasonably accurate at classifying post ICBT remission (Accuracy 0.656 [0.604, 0.705], P vs null model = 0.004; AUC 0.687 [0.631, 0.743]), slightly better vs logit (bootstrap D = 1.730, P = 0.084) but not vs XGBoost (D = 0.463, P = 0.643). Transparency analysis showed model usage of all predictor types at both the group and individual patient level. A new, multi-modal classifier for predicting MDD remission status after ICBT treatment in routine psychiatric care was derived and empirically validated. The multi-modal approach to predicting remission may inform tailored treatment, and deserves further investigation to attain clinical usefulness.

Schizophrenia: A Narrative Review of Etiopathogenetic, Diagnostic and Treatment Aspects

Although schizophrenia is currently conceptualized as being characterized as a syndrome that includes a collection of signs and symptoms, there is strong evidence of heterogeneous and complex underpinned etiological, etiopathogenetic, and psychopathological mechanisms, which are still under investigation. Therefore, the present viewpoint review is aimed at providing some insights into the recently investigated schizophrenia research fields in order to discuss the potential future research directions in schizophrenia research. The traditional schizophrenia construct and diagnosis were progressively revised and revisited, based on the recently emerging neurobiological, genetic, and epidemiological research. Moreover, innovative diagnostic and therapeutic approaches are pointed to build a new construct, allowing the development of better clinical and treatment outcomes and characterization for schizophrenic individuals, considering a more patient-centered, personalized, and tailored-based dimensional approach. Further translational studies are needed in order to integrate neurobiological, genetic, and environmental studies into clinical practice and to help clinicians and researchers to understand how to redesign a new schizophrenia construct.

Predicting treatment response using EEG in major depressive disorder: A machine-learning meta-analysis

Selecting a course of treatment in psychiatry remains a trial-and-error process, and this long-standing clinical challenge has prompted an increased focus on predictive models of treatment response using machine learning techniques. Electroencephalography (EEG) represents a cost-effective and scalable potential measure to predict treatment response to major depressive disorder. We performed separate meta-analyses to determine the ability of models to distinguish between responders and non-responders using EEG across treatments, as well as a performed subgroup analysis of response to transcranial magnetic stimulation (rTMS), and antidepressants (Registration Number: CRD42021257477) in Major Depressive Disorder by searching PubMed, Scopus, and Web of Science for articles published between January 1960 and February 2022. We included 15 studies that predicted treatment responses among patients with major depressive disorder using machine-learning techniques. Within a random-effects model with a restricted maximum likelihood estimator comprising 758 patients, the pooled accuracy across studies was 83.93% (95% CI: 78.90-89.29), with an Area-Under-the-Curve (AUC) of 0.850 (95% CI: 0.747-0.890), and partial AUC of 0.779. The average sensitivity and specificity across models were 77.96% (95% CI: 60.05-88.70), and 84.60% (95% CI: 67.89-92.39), respectively. In a subgroup analysis, greater performance was observed in predicting response to rTMS (Pooled accuracy: 85.70% (95% CI: 77.45-94.83), Area-Under-the-Curve (AUC): 0.928, partial AUC: 0.844), relative to antidepressants (Pooled accuracy: 81.41% (95% CI: 77.45-94.83, AUC: 0.895, pAUC: 0.821). Furthermore, across all meta-analyses, the specificity (true negatives) of EEG models was greater than the sensitivity (true positives), suggesting that EEG models thus far better identify non-responders than responders to treatment in MDD. Studies varied widely in important features across models, although relevant features included absolute and relative power in frontal and temporal electrodes, measures of connectivity, and asymmetry across hemispheres. Predictive models of treatment response using EEG hold promise in major depressive disorder, although there is a need for prospective model validation in independent datasets, and a greater emphasis on replicating physiological markers. Crucially, standardization in cut-off values and clinical scales for defining clinical response and non-response will aid in the reproducibility of findings and the clinical utility of predictive models. Furthermore, several models thus far have used data from open-label trials with small sample sizes and evaluated performance in the absence of training and testing sets, which increases the risk of statistical overfitting. Large consortium studies are required to establish predictive signatures of treatment response using EEG, and better elucidate the replicability of specific markers. Additionally, it is speculated that greater performance was observed in rTMS models, since EEG is assessing neural networks more likely to be directly targeted by rTMS, comprising electrical activity primarily near the surface of the cortex. Prospectively, there is a need for models that examine the comparative effectiveness of multiple treatments across the same patients. However, this will require a thoughtful consideration towards cumulative treatment effects, and whether washout periods between treatments should be utilised. Regardless, longitudinal cross-over trials comparing multiple treatments across the same group of patients will be an important prerequisite step to both facilitate precision psychiatry and identify generalizable physiological predictors of response between and across treatment options.

Trajectories of symptoms in digital interventions for depression and anxiety using Routine Outcome Monitoring data: A secondary analysis study (Preprint)

BACKGROUND

Research suggests there is heterogeneity in treatment response for internet-delivered cognitive behavioral therapy (iCBT) users, but few studies have investigated the trajectory of individual symptom change across iCBT treatment. Large patient data sets using routine outcome measures allows the investigation of treatment effects over time as well as the relationship between outcomes and platform use. Understanding trajectories of symptom change, as well as associated characteristics, may prove important for tailoring interventions or identifying patients who may not benefit from the intervention.

OBJECTIVE

We aimed to identify latent trajectories of symptom change during the iCBT treatment course for depression and anxiety and to investigate the patients' characteristics and platform use for each of these classes.

METHODS

This is a secondary analysis of data from a randomized controlled trial designed to examine the effectiveness of guided iCBT for anxiety and depression in the UK Improving Access to Psychological Therapies (IAPT) program. This study included patients from the intervention group (N=256) and followed a longitudinal retrospective design. As part of the IAPT's routine outcome monitoring system, patients were prompted to complete the Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder-7 (GAD-7) after each supporter review during the treatment period. Latent class growth analysis was used to identify the underlying trajectories of symptom change across the treatment period for both depression and anxiety. Differences in patient characteristics were then evaluated between these trajectory classes, and the presence of a time-varying relationship between platform use and trajectory classes was investigated.

RESULTS

Five-class models were identified as optimal for both PHQ-9 and GAD-7. Around two-thirds (PHQ-9: 155/221, 70.1%; GAD-7: 156/221, 70.6%) of the sample formed various trajectories of improvement classes that differed in baseline score, the pace of symptom change, and final clinical outcome score. The remaining patients were in 2 smaller groups: one that saw minimal to no gains and another with consistently high scores across the treatment journey. Baseline severity, medication status, and program assigned were significantly associated (P<.001) with different trajectories. Although we did not find a time-varying relationship between use and trajectory classes, we found an overall effect of time on platform use, suggesting that all participants used the intervention significantly more in the first 4 weeks (P<.001).

CONCLUSIONS

Most patients benefit from treatment, and the various patterns of improvement have implications for how the iCBT intervention is delivered. Identifying predictors of nonresponse or early response might inform the level of support and monitoring required for different types of patients. Further work is necessary to explore the differences between these trajectories to understand what works best for whom and to identify early on those patients who are less likely to benefit from treatment.

Second-Generation Antipsychotics' Effectiveness and Tolerability: A Review of Real-World Studies in Patients with Schizophrenia and Related Disorders

Despite methodological limitations, real-world studies might support clinicians by broadening the knowledge of antipsychotics' (APs) effectiveness and tolerability in different clinical scenarios and complement clinical trials. We conducted an extensive literature search in the PubMed database to evaluate the effectiveness and tolerability profiles of second-generation antipsychotics (SGAs) from real-world studies to aid clinicians and researchers in selecting the proper treatment for patients with schizophrenia and related disorders. The present review evidenced that SGAs demonstrated superior effectiveness over first-generation antipsychotics (FGAs) in relapse-free survival and psychiatric hospitalization rate and for treating negative symptoms. Persistence and adherence to therapy were higher in SGAs than FGAs. Most studies concluded that switching to long-acting injectables (LAIs) was significantly associated with a lower treatment failure rate than monotherapy with oral SGAs. Considerable improvements in general functionality, subjective well-being, and total score on global satisfaction tests, besides improved personal and social performance, were reported in some studies on patients treated with LAI SGAs. Clozapine was also associated with the lowest rates of treatment failure and greater effectiveness over the other SGAs, although with more severe side effects. Effectiveness on primary negative symptoms and cognitive deficits was rarely measured in these studies. Based on the data analyzed in the present review, new treatments are needed with better tolerability and improved effectiveness for negative, affective, and cognitive symptoms.

Creating sparser prediction models of treatment outcome in depression: a proof-of-concept study using simultaneous feature selection and hyperparameter tuning

BACKGROUND

Predicting treatment outcome in major depressive disorder (MDD) remains an essential challenge for precision psychiatry. Clinical prediction models (CPMs) based on supervised machine learning have been a promising approach for this endeavor. However, only few CPMs have focused on model sparsity even though sparser models might facilitate the translation into clinical practice and lower the expenses of their application.

METHODS

In this study, we developed a predictive modeling pipeline that combines hyperparameter tuning and recursive feature elimination in a nested cross-validation framework. We applied this pipeline to a real-world clinical data set on MDD treatment response and to a second simulated data set using three different classification algorithms. Performance was evaluated by permutation testing and comparison to a reference pipeline without nested feature selection.

RESULTS

Across all models, the proposed pipeline led to sparser CPMs compared to the reference pipeline. Except for one comparison, the proposed pipeline resulted in equally or more accurate predictions. For MDD treatment response, balanced accuracy scores ranged between 61 and 71% when models were applied to hold-out validation data.

CONCLUSIONS

The resulting models might be particularly interesting for clinical applications as they could reduce expenses for clinical institutions and stress for patients.

Who will respond to intensive PTSD treatment? A machine learning approach to predicting response prior to starting treatment

Despite the established effectiveness of evidence-based PTSD treatments, not everyone responds the same. Specifically, some individuals respond early while others respond minimally throughout treatment. Our ability to predict these trajectories at baseline has been limited. Predicting which individuals will respond to a certain type of treatment can significantly reduce short- and long-term costs and increase the ability to preemptively match individuals with treatments to which they are most likely to respond. In the present study, we examined whether veterans' responses to a 3-week Cognitive Processing Therapy-based intensive PTSD treatment program could be accurately predicted prior to the first session. Using a sample of 432 veterans, and a wide range of demographic and clinical data collected during intake, we assessed six machine learning and statistical methods and their ability to predict fast and minimal responders prior to treatment initiation. For fast response classification, gradient boosted models (GBM) had the highest AUC-PR (0.466). For minimal response classification, elastic net (EN) had the highest mean CV AUC-PR (0.628). Using the best performing classifiers, we were able to predict both fast and minimal responders prior to starting treatment with relatively high AUC-ROC of 0.765 (GBM) and 0.826 (EN), respectively. These results may inform treatment modifications, although the accuracy may not be sufficient for clinicians to base inclusion/exclusion decisions entirely on the classifiers. Future research should evaluate whether these classifiers can be expanded to predict to which treatment type(s) an individual is most likely to respond based on various clinical, circumstantial, and biological features.

Updated perspectives on the clinical significance of negative symptoms in patients with schizophrenia

INTRODUCTION

Negative symptoms in schizophrenia are associated with poor response to available treatments, poor quality of life, and functional outcome. Therefore, they represent a substantial burden for people with schizophrenia, their families, and health-care systems.

AREAS COVERED

In this manuscript, we will provide an update on the conceptualization, assessment, and treatment of this complex psychopathological dimension of schizophrenia.

EXPERT OPINION

Despite the progress in the conceptualization of negative symptoms and in the development of state-of-the-art assessment instruments made in the last decades, these symptoms are still poorly recognized, and not always assessed in line with current conceptualization. Every effort should be made to disseminate the current knowledge on negative symptoms, on their assessment instruments and available treatments whose efficacy is supported by research evidence. Longitudinal studies should be promoted to evaluate the natural course of negative symptoms, improve our ability to identify the different sources of secondary negative symptoms, provide effective interventions, and target primary and persistent negative symptoms with innovative treatment strategies. Further research is needed to identify pathophysiological mechanisms of primary negative symptoms and foster the development of new treatments.

Patterns and correlates of patient-reported helpfulness of treatment for common mental and substance use disorders in the WHO World Mental Health Surveys

Patient-reported helpfulness of treatment is an important indicator of quality in patient-centered care. We examined its pathways and predictors among respondents to household surveys who reported ever receiving treatment for major depression, generalized anxiety disorder, social phobia, specific phobia, post-traumatic stress disorder, bipolar disorder, or alcohol use disorder. Data came from 30 community epidemiological surveys - 17 in high-income countries (HICs) and 13 in low- and middle-income countries (LMICs) - carried out as part of the World Health Organization (WHO)'s World Mental Health (WMH) Surveys. Respondents were asked whether treatment of each disorder was ever helpful and, if so, the number of professionals seen before receiving helpful treatment. Across all surveys and diagnostic categories, 26.1% of patients (N=10,035) reported being helped by the very first professional they saw. Persisting to a second professional after a first unhelpful treatment brought the cumulative probability of receiving helpful treatment to 51.2%. If patients persisted with up through eight professionals, the cumulative probability rose to 90.6%. However, only an estimated 22.8% of patients would have persisted in seeing these many professionals after repeatedly receiving treatments they considered not helpful. Although the proportion of individuals with disorders who sought treatment was higher and they were more persistent in HICs than LMICs, proportional helpfulness among treated cases was no different between HICs and LMICs. A wide range of predictors of perceived treatment helpfulness were found, some of them consistent across diagnostic categories and others unique to specific disorders. These results provide novel information about patient evaluations of treatment across diagnoses and countries varying in income level, and suggest that a critical issue in improving the quality of care for mental disorders should be fostering persistence in professional help-seeking if earlier treatments are not helpful.

Clinical prediction models in psychiatry: a systematic review of two decades of progress and challenges

Recent years have seen the rapid proliferation of clinical prediction models aiming to support risk stratification and individualized care within psychiatry. Despite growing interest, attempts to synthesize current evidence in the nascent field of precision psychiatry have remained scarce. This systematic review therefore sought to summarize progress towards clinical implementation of prediction modeling for psychiatric outcomes. We searched MEDLINE, PubMed, Embase, and PsychINFO databases from inception to September 30, 2020, for English-language articles that developed and/or validated multivariable models to predict (at an individual level) onset, course, or treatment response for non-organic psychiatric disorders (PROSPERO: CRD42020216530). Individual prediction models were evaluated based on three key criteria: (i) mitigation of bias and overfitting; (ii) generalizability, and (iii) clinical utility. The Prediction model Risk Of Bias ASsessment Tool (PROBAST) was used to formally appraise each study's risk of bias. 228 studies detailing 308 prediction models were ultimately eligible for inclusion. 94.5% of developed prediction models were deemed to be at high risk of bias, largely due to inadequate or inappropriate analytic decisions. Insufficient internal validation efforts (within the development sample) were also observed, while only one-fifth of models underwent external validation in an independent sample. Finally, our search identified just one published model whose potential utility in clinical practice was formally assessed. Our findings illustrated significant growth in precision psychiatry with promising progress towards real-world application. Nevertheless, these efforts have been inhibited by a preponderance of bias and overfitting, while the generalizability and clinical utility of many published models has yet to be formally established. Through improved methodological rigor during initial development, robust evaluations of reproducibility via independent validation, and evidence-based implementation frameworks, future research has the potential to generate risk prediction tools capable of enhancing clinical decision-making in psychiatric care.

An exploratory application of machine learning methods to optimize prediction of responsiveness to digital interventions for eating disorder symptoms

OBJECTIVE

Digital interventions show promise to address eating disorder (ED) symptoms. However, response rates are variable, and the ability to predict responsiveness to digital interventions has been poor. We tested whether machine learning (ML) techniques can enhance outcome predictions from digital interventions for ED symptoms.

METHOD

Data were aggregated from three RCTs (n = 826) of self-guided digital interventions for EDs. Predictive models were developed for four key outcomes: uptake, adherence, drop-out, and symptom-level change. Seven ML techniques for classification were tested and compared against the generalized linear model (GLM).

RESULTS

The seven ML methods used to predict outcomes from 36 baseline variables were poor for the three engagement outcomes (AUCs = 0.48-0.52), but adequate for symptom-level change (R²  = .15-.40). ML did not offer an added benefit to the GLM. Incorporating intervention usage pattern data improved ML prediction accuracy for drop-out (AUC = 0.75-0.93) and adherence (AUC = 0.92-0.99). Age, motivation, symptom severity, and anxiety emerged as influential outcome predictors.

CONCLUSION

A limited set of routinely measured baseline variables was not sufficient to detect a performance benefit of ML over traditional approaches. The benefits of ML may emerge when numerous usage pattern variables are modeled, although this validation in larger datasets before stronger conclusions can be made.