Individual treatment selection for patients with post-traumatic stress disorder: External validation of a personalised advantage index.

OBJECTIVE: To test the predictive accuracy and generalisability of a personalised advantage index (PAI) model designed to support treatment selection for Post-Traumatic Stress Disorder (PTSD). METHOD: A PAI model developed by Deisenhofer et al. (2018) was used to predict treatment outcomes in a statistically independent dataset including archival records for N = 152 patients with PSTD who accessed either trauma-focussed cognitive behavioural therapy or eye movement desensitisation and reprocessing in routine care. Outcomes were compared between patients who received their PAI-indicated optimal treatment versus those who received their suboptimal treatment. RESULTS: The model did not yield treatment specific predictions and patients who had received their PAI-indicated optimal treatment did not have better treatment outcomes in this external validation sample. CONCLUSION: This PAI model did not generalise to an external validation sample.

Love yourself as a therapist, doubt yourself as an institution? Therapist and institution effects on outcome, treatment length, and dropout.

OBJECTIVE: Research suggests that some therapists achieve better outcomes than others. However, an overlooked area of study is how institution differences impact patient outcomes independent of therapist variance. This study aimed to examine the role of institution and therapist differences in adult outpatient psychotherapy. METHOD: The study included 1428 patients who were treated by 196 therapists at 10 clinics. Two- and three-level hierarchical linear regression models were employed to investigate the effects of therapists and institutions on three dependent patient variables: (1) symptom change, (2) treatment duration, and (3) dropout. Level three explanatory variables were tested. RESULTS: The results showed that therapist effects (TE) were significant for all three types of treatment outcome (7.8%-18.2%). When a third level (institution) was added to the model, the differences between therapists decreased, and significant institution effects (IE) were found: 6.3% for symptom change, 10.6% for treatment duration, and 6.5% for dropout. The exploratory analyses found no predictors able to explain the systematic variation at the institution level. DISCUSSION: TE on psychotherapy outcomes remain a relevant factor but may have been overestimated in previous studies due to not properly distinguishing them from differences at the institution level.

Movement-based patient-therapist attunement in psychological therapy and its association with early change.

Objective: Attunement is a novel measure of nonverbal synchrony reflecting the duration of the present moment shared by two interaction partners. This study examined its association with early change in outpatient psychotherapy. Methods: Automated video analysis based on motion energy analysis (MEA) and cross-correlation of the movement time-series of patient and therapist was conducted to calculate movement synchrony for N = 161 outpatients. Movement-based attunement was defined as the range of connected time lags with significant synchrony. Latent change classes in the HSCL-11 were identified with growth mixture modeling (GMM) and predicted by pre-treatment covariates and attunement using multilevel multinomial regression. Results: GMM identified four latent classes: high impairment, no change (Class 1); high impairment, early response (Class 2); moderate impairment (Class 3); and low impairment (Class 4). Class 2 showed the strongest attunement, the largest early response, and the best outcome. Stronger attunement was associated with a higher likelihood of membership in Class 2 (b = 0.313, p = .007), Class 3 (b = 0.251, p = .033), and Class 4 (b = 0.275, p = .043) compared to Class 1. For highly impaired patients, the probability of no early change (Class 1) decreased and the probability of early response (Class 2) increased as a function of attunement. Conclusions: Among patients with high impairment, stronger patient-therapist attunement was associated with early response, which predicted a better treatment outcome. Video-based assessment of attunement might provide new information for therapists not available from self-report questionnaires and support therapists in their clinical decision-making.

Progress feedback narrows the gap between more and less effective therapists: A therapist effects meta-analysis of clinical trials.

OBJECTIVE: Some psychotherapists are more effective than others, which means that patients' treatment outcomes partly depend on therapist effects (TEs). This study investigated whether the use of progress feedback influences TE. METHOD: Data from N = 4,549 participants and 131 therapists across six clinical trials of progress feedback were analyzed. All trials used the Outcome-Questionnaire-45 (OQ-45) outcome measure and assigned psychotherapy patients to a usual psychological care condition or feedback condition. We examined whether feedback utilization moderated TE using multilevel modeling and random-effects meta-analysis. RESULTS: TE explained a small proportion (intracluster correlation coefficient [ICC] = .011) of variability in posttreatment OQ-45 scores in the pooled multistudy sample, after controlling for intake severity. Feedback utilization was associated with a statistically significant reduction of the magnitude of the TE (ICC = .009) by approximately 18.2%. Secondary analyses of OQ-45 subscales indicated that TEs were statistically significant in relation to symptom distress, but not interpersonal relations or social role. Feedback was associated with better treatment outcomes and narrower variability between therapists. CONCLUSIONS: Feedback-informed treatment reduces the gap between more and less effective therapists, leading to more equitable and effective psychological care. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

For whom should psychotherapy focus on problem coping? A machine learning algorithm for treatment personalization.

OBJECTIVE: We aimed to develop and test an algorithm for individual patient predictions of problem coping experiences (PCE) (i.e., patients' understanding and ability to deal with their problems) effects in cognitive-behavioral therapy. Method: In an outpatient sample with a variety of diagnoses (n=1010), we conducted Dynamic Structural Equation Modelling to estimate within-patient cross-lagged PCE effects on outcome during the first ten sessions. In a randomly selected training sample (2/3 of the cases), we tried different machine learning algorithms (i.e., ridge regression, LASSO, elastic net, and random forest) to predict PCE effects (i.e., the degree to which PCE was a time-lagged predictor of symptoms), using baseline demographic, diagnostic, and clinically-relevant patient features. Then, we validated the best algorithm on a test sample (1/3 of the cases). RESULTS: The random forest algorithm performed best, explaining 14.7% of PCE effects variance in the training set. The results remained stable in the test set, explaining 15.4% of PCE effects variance. CONCLUSIONS: The results show the suitability to perform individual predictions of process effects, based on patients' initial information. If the results are replicated, the algorithm might have the potential to be implemented in clinical practice by integrating it into monitoring and therapist feedback systems.

Are some therapists better at facilitating and consolidating sudden gains than others?

BACKGROUND: Changes during psychotherapy often include sudden symptom improvements, called sudden gains (SGs), which have been identified as being superior to gradual symptom change with regard to treatment success. This study investigates the role of therapists in initiating and/or consolidating SGs. METHODS: The analyses are based on a sample of patients (N = 1937) who were seen by 155 therapists and received individual psychotherapy at a university outpatient clinic. First, the therapist effect (TE) on SG was investigated using multilevel modeling (MLM). Second, MLM was used to explore the relative importance of patient and therapist variability in SGs as they relate to outcome. RESULTS: The TE on SGs accounted for 1.8% of variance, meaning that therapists are accountable for inter-individual differences in their patients' likelihood to experience SGs. Furthermore, results revealed a significant effect of SGs on outcome for both levels, while therapist differences regarding the consolidation of SGs were not significant. CONCLUSIONS: The analyses indicated that some therapists are better in facilitating and initiating SGs. The process of triggering SGs seems to be a therapist skill or competence, which opens up an additional pathway to positive outcomes that could be used to improve clinical training.

Prospective evaluation of a clinical decision support system in psychological therapy.

OBJECTIVE: Thus far, most applications in precision mental health have not been evaluated prospectively. This article presents the results of a prospective randomized-controlled trial investigating the effects of a digital decision support and feedback system, which includes two components of patient-specific recommendations: (a) a clinical strategy recommendation and (b) adaptive recommendations for patients at risk for treatment failure. METHOD: Therapist-patient dyads (N = 538) in a cognitive behavioral therapy outpatient clinic were randomized to either having access to a decision support system (intervention group; n = 335) or not (treatment as usual; n = 203). First, treatment strategy recommendations (problem-solving, motivation-oriented, or a mix of both strategies) for the first 10 sessions were evaluated. Second, the effect of psychometric feedback enhanced with clinical problem-solving tools on treatment outcome was investigated. RESULTS: The prospective evaluation showed a differential effect size of about 0.3 when therapists followed the recommended treatment strategy in the first 10 sessions. Moreover, the linear mixed models revealed therapist symptom awareness and therapist attitude and confidence as significant predictors of an outcome as well as therapist-rated usefulness of feedback as a significant moderator of the feedback-outcome and the not on track-outcome associations. However, no main effects were found for feedback. CONCLUSIONS: The results demonstrate the importance of prospective studies and the high-quality implementation of digital decision support tools in clinical practice. Therapists seem to be able to learn from such systems and incorporate them into their clinical practice to enhance patient outcomes, but only when implementation is successful. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

[Convergence of Scales to Measure Social Anxiety: An IRT Linking Approach]. / Konvergenz von Skalen zur Erfassung sozialer Ängste: Ein IRT-Linking Ansatz.

OBJECTIVE: The Liebowitz Social Anxiety Scale (LSAS) and the Social Phobia Inventory (SPIN) are established measures in the investigation of social anxiety. Furthermore, the subscale Interpersonal Sensitivity of the Brief Symptom Inventory (BSI-53) is frequently used to screen social anxiety. All three scales claim to capture the same construct, which raises the question of the convergence of these scales. To make research findings comparable by a cross-questionnaire factor (common factor), an item response theory (IRT) linking approach is used in the present study. METHODS: 64 German-speaking psychiatric patients and 295 healthy subjects completed the three questionnaires. Different IRT models, including Graded Response Models (GRM), were constructed, and their model fit compared. Regression analyses were performed based on the best-fit model. The common factor was predicted from the questionnaire total values. RESULTS: The relationship between the different scales was best explained by a bifactor GRM with one common factor and three domain-specific factors (RMSEA=0.036, CFI=0.977, WRMR=1.061). Based on the results of the regression analyses, three equations were derived for the transformation of questionnaire's total values. CONCLUSION: The IRT linking approach allows the derivation of a general factor of social anxiety, taking into account commonalities and differences between the instruments used. This has advantages for both research and practice. A replication of this study as well as the implementation of further instruments are recommended to verify the validity of this approach and to generalize the results.

Personalization of Treatment for Patients with Childhood-Abuse-Related Posttraumatic Stress Disorder.

BACKGROUND: Differences in effectiveness among treatments for posttraumatic stress disorder (PTSD) are typically small. Given the variation between patients in treatment response, personalization offers a new way to improve treatment outcomes. The aim of this study was to identify predictors of psychotherapy outcome in PTSD and to combine these into a personalized advantage index (PAI). METHODS: We used data from a recent randomized controlled trial comparing prolonged exposure (PE; n = 48), intensified PE (iPE; n = 51), and skills training (STAIR), followed by PE (n = 50) in 149 patients with childhood-abuse-related PTSD (CA-PTSD). Outcome measures were clinician-assessed and self-reported PTSD symptoms. Predictors were identified in the exposure therapies (PE and iPE) and STAIR+PE separately using random forests and subsequent bootstrap procedures. Next, these predictors were used to calculate PAI and to retrospectively determine optimal and suboptimal treatment in a leave-one-out cross-validation approach. RESULTS: More depressive symptoms, less social support, more axis-1 diagnoses, and higher severity of childhood sexual abuse were predictors of worse treatment outcomes in PE and iPE. More emotion regulation difficulties, lower general health status, and higher baseline PTSD symptoms were predictors of worse treatment outcomes in STAIR+PE. Randomization to optimal treatment based on these predictors resulted in more improvement than suboptimal treatment in clinician assessed (Cohens' d = 0.55) and self-reported PTSD symptoms (Cohens' d = 0.47). CONCLUSION: Personalization based on PAI is a promising tool to improve therapy outcomes in patients with CA-PTSD. Further studies are needed to replicate findings in prospective studies.

Dynamic prediction of psychological treatment outcomes: development and validation of a prediction model using routinely collected symptom data.

BACKGROUND: Common mental disorders can be effectively treated with psychotherapy, but some patients do not respond well and require timely identification to prevent treatment failure. We aimed to develop and validate a dynamic model to predict psychological treatment outcomes, and to compare the model with currently used methods, including expected treatment response models and machine learning models. METHODS: In this prediction model development and validation study, we obtained data from two UK studies including patients who had accessed therapy via Improving Access to Psychological Therapies (IAPT) services managed by ten UK National Health Service (NHS) Trusts between March, 2012, and June, 2018, to predict treatment outcomes. In study 1, we used data on patient-reported depression (Patient Health Questionnaire 9 [PHQ-9]) and anxiety (Generalised Anxiety Disorder 7 [GAD-7]) symptom measures obtained on a session-by-session basis (Leeds Community Healthcare NHS Trust dataset; n=2317) to train the Oracle dynamic prediction model using iterative logistic regression analysis. The outcome of interest was reliable and clinically significant improvement in depression (PHQ-9) and anxiety (GAD-7) symptoms. The predictive accuracy of the model was assessed in an external test sample (Cumbria Northumberland Tyne and Wear NHS Foundation Trust dataset; n=2036) using the area under the curve (AUC), positive predictive values (PPVs), and negative predictive values (NPVs). In study 2, we retrained the Oracle algorithm using a multiservice sample (South West Yorkshire Partnership NHS Foundation Trust, North East London NHS Foundation Trust, Cheshire and Wirral Partnership NHS Foundation Trust, and Cambridgeshire and Peterborough NHS Foundation Trust; n=42 992) and compared its performance with an expected treatment response model and five machine learning models (Bayesian updating algorithm, elastic net regularisation, extreme gradient boosting, support vector machine, and neural networks based on a multilayer perceptron algorithm) in an external test sample (Whittington Health NHS Trust; Barnet Enfield and Haringey Mental Health Trust; Pennine Care NHS Foundation Trust; and Humber NHS Foundation Trust; n=30 026). FINDINGS: The Oracle algorithm trained using iterative logistic regressions generalised well to external test samples, explaining up to 47·3% of variability in treatment outcomes. Prediction accuracy was modest at session one (AUC 0·59 [95% CI 0·55-0·62], PPV 0·63, NPV 0·61), but improved over time, reaching high prediction accuracy (AUC 0·81 [0·77-0·86], PPV 0·79, NPV 0·69) as early as session seven. The performance of the Oracle model was similar to complex (eg, including patient profiling variables) and computationally intensive machine learning models (eg, neural networks based on a multilayer perceptron algorithm, extreme gradient boosting). Furthermore, the predictive accuracy of a more simple dynamic algorithm including only baseline and index-session scores was comparable to more complex algorithms that included additional predictors modelling sample-level and individual-level variability. Overall, the Oracle algorithm significantly outperformed the expected treatment response model (mean AUC 0·80 vs 0·70, p<0·0001]). INTERPRETATION: Dynamic prediction models using sparse and readily available symptom measures are capable of predicting psychotherapy outcomes with high accuracy. FUNDING: University of Sheffield.

The influence of extra-therapeutic social support on the association between therapeutic bond and treatment outcome.

Objective: Both good therapeutic bond as well as extra-therapeutic social support seem to enhance treatment outcomes. Some features of the therapeutic bond are similar to experiences in extra-therapeutic relationships (e.g., feelings of trust or belongingness). Patients with a lack of social support might benefit particularly from a good therapeutic bond, because a well-formed bond can partly substitute relationship needs. This study replicates former research (main effects of bond and social support) and investigates the hypothesized interaction between both constructs. Method: Data from 1206 adult patients receiving cognitive-behavioral outpatient therapy were analyzed. Patients rated early therapeutic bond, their impairment, as well as their social support. Multilevel regression analyses were applied to test for main effects and interactions between bond and social support predicting therapy outcome post treatment. Results: Consistent with prior research, both therapeutic bond and social support predicted therapy outcome. Among patients with high social support, the impact of the therapeutic bond was minimal, while patients with low social support benefited most from a good therapeutic bond. Conclusions: Results suggest that both the therapeutic bond and social support play a role in therapy outcomes and that good therapeutic bond quality might be especially important if a patient lacks social support.

Interpersonal clarification effects in Cognitive-Behavioral Therapy for depression and how they are moderated by the therapeutic alliance.

BACKGROUND: Although a wide body of research links depression to interpersonal deficits, Cognitive-Behavioral Therapy (CBT), considered the gold standard in the treatment of this condition, has not been developed to specifically address interpersonal difficulties. However, cognitive changes on a relational level occurring during CBT might play an important role in the treatment of depression. Interpersonal clarification refers to the process of better understanding the nature of one's interpersonal patterns during therapy. The aim of this study is to analyze the effects of interpersonal clarification in CBT and how they are moderated by the therapeutic alliance. METHODS: A sample of 621 patients diagnosed with depression were treated with CBT by 126 therapists in a university outpatient clinic. Patients completed measures of interpersonal problems and depression severity at baseline, measures of symptomatic evolution before each session and process measures (assessing interpersonal clarification and alliance) after each session. Multilevel models separating between-patient (BP) and within-patient (WP) effects of interpersonal clarification, and including BP and WP alliance effects as covariates and moderators of the interpersonal clarification effects were conducted. RESULTS: Analyses showed both significant BP and WP effects interpersonal clarification, even when adjusting for alliance effects. Furthermore, significant interactive effects were found between outcome of WP interpersonal clarification with both BP alliance and WP alliance. LIMITATIONS: Interpersonal clarification was measured with one single-item and adherence to CBT was not explicitly measured. CONCLUSIONS: The results present preliminary evidence for considering interpersonal clarification a meaningful change process in CBT for depression, especially in the context of a stronger therapeutic alliance.

Working Towards the Development and Implementation of Precision Mental Healthcare: An Example.

Leonard Bickman's (2020) Festschrift paper in the special issue "The Future of Children's Mental Health Services" on improving mental health services is an impressive reflection of his career, highlighting his major insights and the development of mental health services research as a whole. Five major difficulties in this field's current research and practice are attentively delineated: poor diagnostics, measurement problems, disadvantages of randomized controlled trials (RCTs), lack of feedback and personalized treatments. Dr. Bickman recommends possible solutions based on his extensive experience and empirical findings. We agree with his thoughts and illustrate how we, challenged with the same problems, have attempted to develop clinically informed research and evidence-based clinical practice. A comprehensive feedback system that deals with the aforementioned problems is briefly described. It includes pre-treatment recommendations for treatment strategies and an empirically informed dropout prediction based on a variety of data sources. In addition to treatment recommendations, continuous feedback as well as individualized treatment adaptation tools are provided during ongoing therapy. New projects are being implemented to further improve the system by including new data assessment strategies and sources, e.g., ecological momentary assessment (EMA) and automated video analysis.

Patterns of early change in interpersonal problems and their relationship to nonverbal synchrony and multidimensional outcome.

Early change is an increasing area of investigation in psychotherapy research. In this study, we analyzed patterns of early change in interpersonal problems and their relationship to nonverbal synchrony and multiple outcome measures for the first time. We used growth mixture modeling to identify different latent classes of early change in interpersonal problems with 212 patients who underwent cognitive-behavioral treatment including interpersonal and emotion-focused elements. Furthermore, videotaped sessions were analyzed using motion energy analysis, providing values for the calculation of nonverbal synchrony to predict early change in interpersonal problems. The relationship between early change patterns and symptoms as well as overall change in interpersonal problems was also investigated. Three latent subgroups were identified: 1 class with slow improvement (n = 145), 1 class with fast improvement (n = 12), and 1 early deterioration class (n = 55). Lower levels of early nonverbal synchrony were significantly related to fast improvement in interpersonal change patterns. Furthermore, such patterns predicted treatment outcome in symptoms and interpersonal problems. The results suggest that nonverbal synchrony is associated with early change patterns in interpersonal problems, which are also predictive of treatment outcome. Limitations of the applied methods as well as possible applications in routine care are discussed. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Assessing treatment integrity in personalized CBT: the inventory of therapeutic interventions and skills.

The third wave of cognitive behavioral therapy (CBT) has increased the heterogeneity of today's CBT practice, while developments in patient-focused research are paving the road to the empirical personalization of CBT. This paper presents the development and psychometric properties of a therapy video rating instrument, which was designed to adequately assess the treatment integrity (adherence and competence) of modern, personalized CBT. The Inventory of Therapeutic Interventions and Skills (ITIS) was developed based on two existing CBT adherence and competence scales and augmented with third wave content and overarching therapeutic strategies. The instrument was then applied by graduate students and post-graduate clinicians to rate N = 185 therapy videos from N = 70 patients treated at a university outpatient clinic. Descriptive results, inter-rater reliability, item structure, and associations with session outcome and alliance were examined. Average inter-rater reliability was excellent for Interventions items and good for Skills items. Intercorrelations were low between Interventions items, but higher and significant between Skills items, which loaded on a single factor. Several ITIS items were shown to be predictive of session outcome and alliance, even after controlling for the nested data structure. Implications of these results for future research and clinical training are discussed.