Implementing precision methods in personalizing psychological therapies: Barriers and possible ways forward

Predicting treatment outcome for complex posttraumatic stress disorder using the personalized advantage index

ABSTRACTBackground: Ample studies have demonstrated the effectiveness of psychotherapy for posttraumatic stress disorder (PTSD). However, large individual variation in treatment outcome remains unsolved and treatment options for complex posttraumatic stress disorder (CPTSD) are debated. There is a need for exploring methods for matching patients with treatment they will most likely benefit from.Objective: To develop a personalized advantage index (PAI) based on relevant clinical and demographic predictors of outcome from exposure therapy and skills-training for CPTSD.Method: Data from a previous randomized controlled trial (RCT) in 92 patients with a CPTSD diagnosis was used to compare Prolonged Exposure (PE; n = 32) and Skills Training in Affective and Interpersonal Regulation (STAIR; n = 60). Outcome measures were clinician-assessed and self-reported PTSD symptoms. Predictors of outcome in PE and STAIR were identified separately from sixty-one candidate variables using random forest and bootstrap procedures. Relevant predictors were then used to calculate PAI and retrospectively identify optimal versus suboptimal treatment in a leave-one-out cross-validation approach.Results: In PE, somatoform dissociation, depression, suicidal ideation, and reduced physical health predicted worse outcome. In STAIR, interpersonal problems, total PTSD symptom severity, intrusions, elevated guilt, and psychoticism predicted worse outcome, while being a witness to trauma predicted better outcome. Allocation to optimal treatment according to the PAI was associated with large improvements in clinician-assessed (Cohen's d = 0.83) and moderate improvement in self-rated (Cohen's d = 0.60) PTSD symptoms as compared to allocation to suboptimal teatment.Conclusions: Using the PAI in personalizing psychological treatment for CPTSD is a promising approach to improve treatment benefits. Further research on larger samples and external validation of the PAI is needed.

Clinical prediction instruments available for clinicians treating major depressive disorder: A systematic review

BACKGROUND

Responses to treatment for Major Depressive Disorder (MDD) vary widely, complicating clinical decision-making. Various clinical prediction instruments are available to support this process and potentially improve treatment outcomes. However, a systematic review to guide clinicians in choosing among these instruments is lacking.

OBJECTIVE

To provide an overview of statistically evaluated clinical prediction instruments that are currently available for clinicians to assist in their decision-making processes. This review focuses on instruments accessible online or in print.

METHODS

A systematic search following PRISMA/CHARMS guidelines in Medline, Embase, and PsycINFO databases was conducted from January 1, 2010, to March 1, 2023 (PROSPERO: CRD42021261469). Original studies in English reporting on prediction instruments for adults with MDD, available online or in print, were included. The risk of bias in these studies was evaluated using the Prediction model Risk Of Bias Assessment Tool (PROBAST).

RESULTS

Of the 5879 records screened, 111 full-text records were reviewed for eligibility, resulting in 15 original studies that reported on 16 unique prediction instruments. Most instruments (12 out of 16) were designed for use at the beginning or during treatment, while four specifically assessed outcomes after treatment. All studies had a high risk of bias.

CONCLUSION

This systematic review provides a comprehensive overview of 16 prediction instruments immediately available for clinicians to support decision-making in depression treatment. For use at treatment initiation, we recommend instruments assessing prior treatments and clinical characteristics, such as the Maudsley Staging Method (MSM) or the Dutch Measure for quantification of Treatment Resistance in Depression (DM-TRD).

Comparing three neural networks to predict depression treatment outcomes in psychological therapies

OBJECTIVE

Artificial neural networks have been used in various fields to solve classification and prediction tasks. However, it is unclear if these may be adequate methods to predict psychological treatment outcomes. This study aimed to evaluate the prognostic accuracy of neural networks using psychological treatment outcomes data.

METHOD

Three neural network models (TensorFlow, nnet, and monmlp) and a generalised linear regression model were compared in their ability to predict post-treatment remission of depression symptoms in a large naturalistic sample (n = 69,489) of patients accessing low intensity cognitive behavioural therapy. Prognostic accuracy was evaluated using the area under the curve (AUC) in an external cross-validation design.

RESULTS

The AUC of the neural networks in an external test sample ranged from 0.64 to 0.65 and the AUC of the linear regression model was 0.63.

CONCLUSION

Neural networks can help predict symptom remission in new samples with moderate accuracy, although these models were no more accurate than a simpler inferential statistical linear regression model.

Idiographic Lapse Prediction With State Space Modeling: Algorithm Development and Validation Study

BACKGROUND

Many mental health conditions (eg, substance use or panic disorders) involve long-term patient assessment and treatment. Growing evidence suggests that the progression and presentation of these conditions may be highly individualized. Digital sensing and predictive modeling can augment scarce clinician resources to expand and personalize patient care. We discuss techniques to process patient data into risk predictions, for instance, the lapse risk for a patient with alcohol use disorder (AUD). Of particular interest are idiographic approaches that fit personalized models to each patient.

OBJECTIVE

This study bridges 2 active research areas in mental health: risk prediction and time-series idiographic modeling. Existing work in risk prediction has focused on machine learning (ML) classifier approaches, typically trained at the population level. In contrast, psychological explanatory modeling has relied on idiographic time-series techniques. We propose state space modeling, an idiographic time-series modeling framework, as an alternative to ML classifiers for patient risk prediction.

METHODS

We used a 3-month observational study of participants (N=148) in early recovery from AUD. Using once-daily ecological momentary assessment (EMA), we trained idiographic state space models (SSMs) and compared their predictive performance to logistic regression and gradient-boosted ML classifiers. Performance was evaluated using the area under the receiver operating characteristic curve (AUROC) for 3 prediction tasks: same-day lapse, lapse within 3 days, and lapse within 7 days. To mimic real-world use, we evaluated changes in AUROC when models were given access to increasing amounts of a participant's EMA data (15, 30, 45, 60, and 75 days). We used Bayesian hierarchical modeling to compare SSMs to the benchmark ML techniques, specifically analyzing posterior estimates of mean model AUROC.

RESULTS

Posterior estimates strongly suggested that SSMs had the best mean AUROC performance in all 3 prediction tasks with ≥30 days of participant EMA data. With 15 days of data, results varied by task. Median posterior probabilities that SSMs had the best performance with ≥30 days of participant data for same-day lapse, lapse within 3 days, and lapse within 7 days were 0.997 (IQR 0.877-0.999), 0.999 (IQR 0.992-0.999), and 0.998 (IQR 0.955-0.999), respectively. With 15 days of data, these median posterior probabilities were 0.732, <0.001, and <0.001, respectively.

CONCLUSIONS

The study findings suggest that SSMs may be a compelling alternative to traditional ML approaches for risk prediction. SSMs support idiographic model fitting, even for rare outcomes, and can offer better predictive performance than existing ML approaches. Further, SSMs estimate a model for a patient's time-series behavior, making them ideal for stepping beyond risk prediction to frameworks for optimal treatment selection (eg, administered using a digital therapeutic platform). Although AUD was used as a case study, this SSM framework can be readily applied to risk prediction tasks for other mental health conditions.

Unpacking the chain of change in group CBT and ACT for depression: A protocol for a randomized clinical trial

BACKGROUND

Cognitive behavioral therapy (CBT) and acceptance and commitment therapy (ACT) are among our most effective treatments for depression, yet their efficacy remains modest. Prior research has not been able to improve these efficacy rates in part due to the limited insight into the processes of change in these treatments and which individuals may benefit more or less from different therapeutic processes.

METHOD

One hundred adults with a diagnosis of major depressive disorder will be randomized to receive eight weeks of group CBT (n = 50) or ACT (n = 50). We will use intensive longitudinal sampling methods to examine therapeutic skills use, theorized treatment mechanisms, and treatment outcomes throughout the course of treatment. The primary aim is to examine the mediational effect of theorized treatment mechanisms on the association between therapeutic skills use and treatment outcomes. Our secondary aim is to examine the combined moderating effects of treatment modality and client characteristics on the association between therapeutic skills use and activation of mechanisms of change.

CONCLUSION

This work has the potential to inform precision mental health care by closing in on the question of "what works and for whom" as it relates to data-driven psychotherapeutic interventions for depression.

TRIAL REGISTRATION NUMBER

The study is registered at www.

CLINICALTRIALS

gov (NCT06245096).

Prescriptive Predictors of Mindfulness Ecological Momentary Intervention for Social Anxiety Disorder: Machine Learning Analysis of Randomized Controlled Trial Data

BACKGROUND

Shame and stigma often prevent individuals with social anxiety disorder (SAD) from seeking and attending costly and time-intensive psychotherapies, highlighting the importance of brief, low-cost, and scalable treatments. Creating prescriptive outcome prediction models is thus crucial for identifying which clients with SAD might gain the most from a unique scalable treatment option. Nevertheless, widely used classical regression methods might not optimally capture complex nonlinear associations and interactions.

OBJECTIVE

Precision medicine approaches were thus harnessed to examine prescriptive predictors of optimization to a 14-day fully self-guided mindfulness ecological momentary intervention (MEMI) over a self-monitoring app (SM).

METHODS

This study involved 191 participants who had probable SAD. Participants were randomly assigned to MEMI (n=96) or SM (n=95). They completed self-reports of symptoms, risk factors, treatment, and sociodemographics at baseline, posttreatment, and 1-month follow-up (1MFU). Machine learning (ML) models with 17 predictors of optimization to MEMI over SM, defined as a higher probability of SAD remission from MEMI at posttreatment and 1MFU, were evaluated. The Social Phobia Diagnostic Questionnaire, structurally equivalent to the Diagnostic and Statistical Manual SAD criteria, was used to define remission. These ML models included random forest and support vector machines (radial basis function kernel) and 10-fold nested cross-validation that separated model training, minimal tuning in inner folds, and model testing in outer folds.

RESULTS

ML models outperformed logistic regression. The multivariable ML models using the 10 most important predictors achieved good performance, with the area under the receiver operating characteristic curve (AU-ROC) values ranging from .71 to .72 at posttreatment and 1MFU. These prerandomization and early-stage prescriptive predictors consistently identified which participants had the highest probability of optimization of MEMI over SM after 14 days and 6 weeks from baseline. Significant predictors included 4 strengths (higher trait mindfulness, lower SAD severity, presence of university education, no current psychotropic medication use), 2 weaknesses (higher generalized anxiety severity and clinician-diagnosed depression or anxiety disorder), and 1 sociodemographic variable (Chinese ethnicity). Emotion dysregulation and current psychotherapy predicted remission with inconsistent signs across time points.

CONCLUSIONS

The AU-ROC values indicated moderately meaningful effect sizes in identifying prescriptive predictors within multivariable models for clients with SAD. Focusing on the identified notable client strengths, weaknesses, and Chinese ethnicity may enhance our ability to predict future responses to scalable treatments. Estimating the likelihood of SAD remission with a "prescriptive predictor calculator" for each client may help clinicians and policymakers allocate scarce treatment resources effectively. Clients with high remission probability may benefit from receiving the MEMI as a vigilant waitlist strategy before intensive therapist-led psychotherapy. These efforts may aid in creating actionable treatment selection tools to optimize care for clients with SAD in routine health care settings that use stratified care principles.

TRIAL REGISTRATION

OSF Registries 10.17605/OSF.IO/M3KXZ; https://osf.io/m3kxz.

Developing a multivariable prediction model to support personalized selection among five major empirically-supported treatments for adult depression. Study protocol of a systematic review and individual participant data network meta-analysis

BACKGROUND

Various treatments are recommended as first-line options in practice guidelines for depression, but it is unclear which is most efficacious for a given person. Accurate individualized predictions of relative treatment effects are needed to optimize treatment recommendations for depression and reduce this disorder's vast personal and societal costs.

AIMS

We describe the protocol for a systematic review and individual participant data (IPD) network meta-analysis (NMA) to inform personalized treatment selection among five major empirically-supported depression treatments.

METHOD

We will use the METASPY database to identify randomized clinical trials that compare two or more of five treatments for adult depression: antidepressant medication, cognitive therapy, behavioral activation, interpersonal psychotherapy, and psychodynamic therapy. We will request IPD from identified studies. We will conduct an IPD-NMA and develop a multivariable prediction model that estimates individualized relative treatment effects from demographic, clinical, and psychological participant characteristics. Depressive symptom level at treatment completion will constitute the primary outcome. We will evaluate this model using a range of measures for discrimination and calibration, and examine its potential generalizability using internal-external cross-validation.

CONCLUSIONS

We describe a state-of-the-art method to predict personalized treatment effects based on IPD from multiple trials. The resulting prediction model will need prospective evaluation in mental health care for its potential to inform shared decision-making. This study will result in a unique database of IPD from randomized clinical trials around the world covering five widely used depression treatments, available for future research.

Subtyping Service Receipt in Personality Disorder Services in South London: Observational Validation Study Using Latent Profile Analysis

BACKGROUND

Personality disorders (PDs) are typically associated with higher mental health service use; however, individual patterns of engagement among patients with complex needs are poorly understood.

OBJECTIVE

The study aimed to identify subgroups of individuals based on patterns of service receipt in secondary mental health services and examine how routinely collected information is associated with these subgroups.

METHODS

A sample of 3941 patients diagnosed with a personality disorder and receiving care from secondary services in South London was identified using health care records covering an 11-year period from 2007 to 2018. Basic demographic information, service use, and treatment data were included in the analysis. Service use measures included the number of contacts with clinical teams and instances of did-not-attend.

RESULTS

Using a large sample of 3941 patients with a diagnosis of PD, latent profile analysis identified 2 subgroups characterized by low and high service receipt, denoted as profile 1 (n=2879, 73.05%) and profile 2 (n=1062, 26.95%), respectively. A 2-profile solution (P<.01) was preferred over a 3-profile solution, which was nonsignificant. In unconditional (t3941,3939=19.53; P<.001; B=7.27; 95% CI 6.54-8) and conditional (t3941,3937=-3.31; P<.001; B=-74.94; 95% CI -119.34 to -30.56) models, cluster membership was significantly related to receipt of nursing contacts, over and above other team contacts.

CONCLUSIONS

These results suggest that routinely collected data may be used to classify likely engagement subtypes among patients with complex needs. The algorithm identified factors associated with service use and has the potential to inform clinical decision-making to improve treatment for individuals with complex needs.

Ecological Momentary Assessment in psychotherapy research: A systematic review

Ecological Momentary Assessment (EMA) stands as a valuable method to capture real-time data on individuals' daily experiences and behaviors. In recent years, the utilization of EMA as a measurement method has substantially increased with the majority of studies emphasizing its clinical utility. However, a comprehensive overview of its use in psychotherapy research is lacking. This study addresses that gap by systematically reviewing EMA's application in psychotherapy research. In total, 168 studies met the inclusion criteria and were classified according to clinical utilization. Six areas of clinical EMA application were identified: prediction of therapy outcome (n = 8), prediction of psychopathology (n = 40), prediction of biopsychosocial states (n = 44), evaluation of therapy outcome (n = 21), acquisition of further clinical insights into specific disorders (n = 68) and adaptation of treatment processes (n = 18). Despite studies consistently highlighting EMA's potential in tailoring psychotherapeutic treatments, its limited use in this area warrants further research. Drawing from our findings, we discuss future research directions for the direct application of EMA in psychotherapeutic settings.

Recognizing individual variability in misophonia: Identifying symptom-based subgroups with Gaussian mixture modeling

Misophonia is characterized by intense emotional and physiological reactions to everyday sounds such as chewing and tapping. While previous researchers have focused on defining and characterizing the disorder, limited attention has been given to the variability in symptom presentations across individuals. In this study, we sought to identify distinct subgroups of individuals with misophonia by applying a Gaussian finite mixture model to explore the heterogeneity of symptom profiles. Sixty treatment-seeking participants completed the Duke Misophonia Interview, which assessed the presence and severity of various behavioral, affective, and cognitive symptoms. Items from this measure served as model indicators. Two clusters were found: anticipatory and reactive. The anticipatory group reported heightened awareness of potential triggers, preemptive anticipatory distress, and increased avoidance behaviors, while the reactive group primarily displayed emotional and physiological responses during the occurrence of sounds. Notably, both groups reported similar frequencies of misophonic triggers, but the anticipatory group demonstrated greater internalizing symptoms, such as intrusive thoughts or rumination about misophonic sounds and social isolation. Our findings support the need for tailored interventions that address subgroup-specific symptom patterns. Future researchers should aim to include larger sample sizes and develop more comprehensive models to capture the full spectrum of misophonia symptoms, including externalizing behaviors.

Enhancing psychological assessment and treatment of chronic pain: A research agenda for personalized and process-based approaches

The heterogeneity of chronic pain and stagnating improvements in treatment effectiveness have prompted calls for a shift toward personalized and process-based approaches to the assessment and treatment of chronic pain. As this opens a new line of research, several fundamental questions arise. We begin by defining key terms and reviewing attempts to personalize treatment to date. Despite progress in personalization, long-term effects remain unclear. Existing studies are limited by group-based approaches that overlook individual variability. Future research should use idiographic methods and process-based therapy to tailor interventions to individual needs. A person- and process-oriented research agenda is needed that combines ambulatory assessment, network modeling, and single-case designs to advance personalized treatments for chronic pain and improve clinical decision-making.

Elucidating therapist differences: Therapists' interpersonal skills and their effect on treatment outcome

OBJECTIVE

Therapists differ in their average treatment outcomes. However, it remains unclear which characteristics differentiate more from less effective therapists. This study examined the association between therapist interpersonal skills and treatment outcome as well as the moderating effect of initial impairment.

METHOD

Interpersonal skills were assessed with the Therapy-Related Interpersonal Behaviors (TRIB) scale, a group-discussion based rating system, in 99 incoming therapy trainees. The trainees treated n = 1031 outpatients with psychological therapies, whose treatment outcomes were assessed with the Symptom-Checklist 90 Revised (SCL-90-R). Linear mixed models were conducted to predict outcome by therapists' interpersonal skills beyond initial impairment, number of sessions, therapist age, gender, and theoretical orientation. The moderating effect of initial impairment was calculated as cross-level interaction.

RESULTS

The therapist effect (TE) in this sample was 5.6%. Interpersonal skills were a significant predictor of outcome (b = -0.124, p < .001) and explained 1.3% of variance beyond all control variables. The TE in the final model was VPC = .036 indicating that 26.79% of the TE were attributable to interpersonal skills. The impairment-skills interaction was significant (b = -0.172, p < .001). The effect of interpersonal skills on outcome increased with more severe initial impairment. Results were replicated in a second outcome measure (Outcome Questionnaire 30).

CONCLUSIONS

Interpersonal skills were found to be important characteristics to differentiate between more and less effective therapists, especially when treating severely distressed patients. Considering them in therapist selection and matching, outcome prediction, and clinical training could improve the effectiveness of psychological therapies.

Next-Generation Cognitive-Behavioral Therapy for Depression: Integrating Digital Tools, Teletherapy, and Personalization for Enhanced Mental Health Outcomes

Background and Objectives: This systematic review aims to present the latest developments in next-generation CBT interventions of digital support tools, teletherapies, and personalized treatment modules in enhancing accessibility, improving treatment adherence, and optimizing therapeutic outcomes for depression. Materials and Methods: This review analyzed 81 PRISMA-guided studies on the efficacy, feasibility, and applicability of NG-CBT approaches. Other important innovations include web-based interventions, AI-operated chatbots, and teletherapy platforms, each of which serves as a critical challenge in delivering mental health care. Key messages have emerged regarding technological readiness, patient engagement, and the changing role of therapists within the digital context of care. Results: Findings indicate that NG-CBT interventions improve treatment accessibility and engagement while maintaining clinical effectiveness. Personalized digital tools enhance adherence, and teletherapy platforms provide scalable and cost-effective alternatives to traditional therapy. Conclusions: Such developments promise great avenues for decreasing the global burden of depression and enhancing the quality of life through novel, accessible, and high-quality therapeutic approaches.

The current clinical approach to feeding and eating disorders aimed to increase personalization of management

Feeding and eating disorders (FEDs) are a heterogeneous grouping of disorders at the mind-body interface, with typical onset from childhood into emerging adulthood. They occur along a spectrum of disordered eating and compensatory weight management behaviors, and from low to high body weight. Psychiatric comorbidities are the norm. In contrast to other major psychiatric disorders, first-line treatments for FEDs are mainly psychological and/or nutrition-focused, with medications playing a minor adjunctive role. Patients, carers and clinicians all have identified personalization of treatment as a priority. Yet, for all FEDs, the evidence base supporting this personalization is limited. Importantly, disordered eating and related behaviors can have serious physical consequences and may put the patient's life at risk. In these cases, immediate safety and risk management considerations may at least for a period need to be prioritized over other efforts at personalization of care. This paper systematically reviews several key domains that may be relevant to the characterization of the individual patient with a FED aimed at personalization of management. These domains include symptom profile, clinical subtypes, severity, clinical staging, physical complications and consequences, antecedent and concomitant psychiatric conditions, social functioning and quality of life, neurocognition, social cognition and emotion, dysfunctional cognitive schemata, personality traits, family history, early environmental exposures, recent environmental exposures, stigma, and protective factors. Where possible, validated assessment measures for use in clinical practice are identified. The limitations of the current evidence are pointed out, and possible directions for future research are highlighted. These also include novel and emerging approaches aimed at providing more fine-grained and sophisticated ways to personalize treatment of FEDs, such as those that utilize neurobiological markers. We additionally outline remote measurement technologies designed to delineate patients' illness and recovery trajectories and facilitate development of novel intervention approaches.

Using Progress Feedback to Enhance Treatment Outcomes: A Narrative Review

We face increasing demand for greater access to effective routine mental health services, including telehealth. However, treatment outcomes in routine clinical practice are only about half the size of those reported in controlled trials. Progress feedback, defined as the ongoing monitoring of patients' treatment response with standardized measures, is an evidence-based practice that continues to be under-utilized in routine care. The aim of the current review is to provide a summary of the current evidence base for the use of progress feedback, its mechanisms of action and considerations for successful implementation. We reviewed ten available meta-analyses, which report small to medium overall effect sizes. The results suggest that adding feedback to a wide range of psychological and psychiatric interventions (ranging from primary care to hospitalization and crisis care) tends to enhance the effectiveness of these interventions. The strongest evidence is for patients with common mental health problems compared to those with very severe disorders. Effect sizes for not-on-track cases, a subgroup of cases that are not progressing well, are found to be somewhat stronger, especially when clinical support tools are added to the feedback. Systematic reviews and recent studies suggest potential mechanisms of action for progress feedback include focusing the clinician's attention, altering clinician expectations, providing new information, and enhancing patient-centered communication. Promising approaches to strengthen progress feedback interventions include advanced systems with signaling technology, clinical problem-solving tools, and a broader spectrum of outcome and progress measures. An overview of methodological and implementation challenges is provided, as well as suggestions for addressing these issues in future studies. We conclude that while feedback has modest effects, it is a small and affordable intervention that can potentially improve outcomes in psychological interventions. Further research into mechanisms of action and effective implementation strategies is needed.

Advancing the personalized advantage index (PAI): a systematic review and application in two large multi-site samples in anxiety disorders

BACKGROUND

The Personalized Advantage Index (PAI) shows promise as a method for identifying the most effective treatment for individual patients. Previous studies have demonstrated its utility in retrospective evaluations across various settings. In this study, we explored the effect of different methodological choices in predictive modelling underlying the PAI.

METHODS

Our approach involved a two-step procedure. First, we conducted a review of prior studies utilizing the PAI, evaluating each study using the Prediction model study Risk Of Bias Assessment Tool (PROBAST). We specifically assessed whether the studies adhered to two standards of predictive modeling: refraining from using leave-one-out cross-validation (LOO CV) and preventing data leakage. Second, we examined the impact of deviating from these methodological standards in real data. We employed both a traditional approach violating these standards and an advanced approach implementing them in two large-scale datasets, PANIC-net (n = 261) and Protect-AD (n = 614).

RESULTS

The PROBAST-rating revealed a substantial risk of bias across studies, primarily due to inappropriate methodological choices. Most studies did not adhere to the examined prediction modeling standards, employing LOO CV and allowing data leakage. The comparison between the traditional and advanced approach revealed that ignoring these standards could systematically overestimate the utility of the PAI.

CONCLUSION

Our study cautions that violating standards in predictive modeling may strongly influence the evaluation of the PAI's utility, possibly leading to false positive results. To support an unbiased evaluation, crucial for potential clinical application, we provide a low-bias, openly accessible, and meticulously annotated script implementing the PAI.

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.

Effectiveness of Attachment-Based Family Therapy for Suicidal Adolescents and Young Adults: A Systematic Review and Meta-Analysis

Background

Suicide is a leading cause of death among adolescents and young adults. While only few evidence-based treatments with limited efficacy are available, family processes have recently been posed as a possible alternative target for intervention. Here, we review the evidence for Attachment-Based Family Therapy (ABFT), a guideline-listed treatment targeting intrafamilial ruptures and building protective caregiver-child relationships.

Method

PubMed, PsycINFO, Embase, and Scopus were searched for prospective trials on ABFT in youth published up until November 6th, 2023, and including measures of suicidality. Results were independently screened by two researchers following PRISMA guidelines. Risk of bias was assessed using the Cochrane RoB-2 framework. A random effects meta-analysis was conducted on suicidal ideation and depressive symptoms post-intervention scores in randomized-controlled trials (RCTs).

Results

Seven articles reporting on four RCTs (n = 287) and three open trials (n = 45) were identified. Mean age of participants was M pooled = 15.2 years and the majority identified as female (~80%). Overall, ABFT was not significantly more effective in reducing youth suicidal ideation, gpooled = 0.40, 95% CI [-0.12, 0.93], nor depressive symptoms, gpooled = 0.33, 95% CI [-0.18, 0.84], compared to investigated controls (Waitlist, (Enhanced) Treatment as Usual, Family-Enhanced Nondirective Supportive Therapy).

Conclusion

Evidence is strongly limited, with few available trials, small sample sizes, high sample heterogeneity, attrition rates, and risk of bias. While not generally superior to other treatments, ABFT might still be a clinically valid option in specific cases and should be further investigated. Clinicians are currently recommended to apply caution when considering ABFT as stand-alone intervention for suicidal youth and to decide on a case-by-case basis.

The future of the eating disorder field: Inclusive, aware of systems, and personalized

Eating disorders are serious psychiatric illnesses associated with large amounts of suffering, high morbidity, and high mortality rates, signifying a clear need for rapid advancements in the underlying science. Relative to other fields of clinical psychological science, the eating disorder field is new. However, despite the fields' late beginnings, there is growing science in several important areas. The current paper discusses the current literature in three primary areas of importance: (a) diversity and inclusion, (b) systemic and social factors, and (c) treatment personalization. We discuss how these areas have huge potential to push both eating disorder and clinical psychological science in general forward, to improve our underlying understanding of psychological illness, and to enhance treatment access and effectiveness. We call for more research in these areas and end with our vision for the field for the next decade, including areas in need of significant future research.

The next generation of developmental psychopathology research: Including broader perspectives and becoming more precise

The current Special Issue marks a major milestone in the history of developmental psychopathology; as the final issue edited by Cicchetti, we have an opportunity to reflect on the remarkable progress of the discipline across the last four decades, as well as challenges and future directions for the field. With contemporary issues in mind, including rising rates of psychopathology, health disparities, and international conflict, as well as rapid growth and accessibility of digital and mobile technologies, the discipline of developmental psychopathology is poised to advance multidisciplinary, developmentally- and contextually- informed research, and to make substantial progress in supporting the healthy development of individuals around the world. We highlight key future directions and challenges for the next generation of developmental psychopathology research including further investigation of culture at multiple levels of analysis, incorporation of macro-level influences into developmental psychopathology research, methods advances to address heterogeneity in translational research, precision mental health, and the extension of developmental psychopathology research across the lifespan.

The neuroscience of mental illness: Building toward the future

Mental illnesses arise from dysfunction in the brain. Although numerous extraneural factors influence these illnesses, ultimately, it is the science of the brain that will lead to novel therapies. Meanwhile, our understanding of this complex organ is incomplete, leading to the oft-repeated trope that neuroscience has yet to make significant contributions to the care of individuals with mental illnesses. This review seeks to counter this narrative, using specific examples of how neuroscientific advances have contributed to progress in mental health care in the past and how current achievements set the stage for further progress in the future.

Using the 'Leapfrog' Design as a Simple Form of Adaptive Platform Trial to Develop, Test, and Implement Treatment Personalization Methods in Routine Practice

The route for the development, evaluation and dissemination of personalized psychological therapies is complex and challenging. In particular, the large sample sizes needed to provide adequately powered trials of newly-developed personalization approaches means that the traditional treatment development route is extremely inefficient. This paper outlines the promise of adaptive platform trials (APT) embedded within routine practice as a method to streamline development and testing of personalized psychological therapies, and close the gap to implementation in real-world settings. It focuses in particular on a recently-developed simplified APT design, the 'leapfrog' trial, illustrating via simulation how such a trial may proceed and the advantages it can bring, for example in terms of reduced sample sizes. Finally it discusses models of how such trials could be implemented in routine practice, including potential challenges and caveats, alongside a longer-term perspective on the development of personalized psychological treatments.

Reading tea leaves or tracking true constructs? An assessment of personality-based latent profiles in eating disorders

Background

Eating disorder (ED) subtyping studies have often extracted an undercontrolled, an overcontrolled and a resilient profile based on trait impulsivity and perfectionism. However, the extent to which methodological choices impact the coherence and distinctness of resulting subtypes remains unclear.

Objective

In this paper, we aimed to assess the robustness of these findings by extracting personality-based subtypes on a sample of ED patients (N = 221) under different analytic conditions.

Methods

We ran four latent profile analyses (LPA), varying the extent to which we constrained variances and covariances during model parametrization. We then performed a comparative analysis also including state ED symptom measures as indicators. Finally, we used cross-method validation via k-means clustering to further assess the robustness of our profiles.

Results

Our results demonstrated a four-profile model based on variances in impulsivity and perfectionism to fit the data well. Across model solutions, the profiles with the most and least state and trait disturbances were replicated most stably, while more nuanced variations in trait variables resulted in less consistent profiles. Inclusion of ED symptoms as indicator variables increased subtype differentiation and similarity across profiles. Validation cluster analyses aligned most with more restrictive LPA models.

Conclusion

These results suggest that ED subtypes track true constructs, since subtypes emerged method-independently. We found analytic methods to constrain the theoretical and practical conclusions that can be drawn. This underscores the importance of objective-driven analytic design and highlights its relevance in applying research findings in clinical practice.