Using the Personalized Advantage Index for Individual Treatment Allocation to Blended Treatment or Treatment as Usual for Depression in Secondary Care

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.

Predicting optimal treatment allocation for cognitive analytic-guided self-help versus cognitive behavioural-guided self-help

OBJECTIVES

Given the ubiquity in routine services of low-intensity guided self-help (GSH) psychological interventions, better patient selection for these brief interventions would be organizationally efficient. This study therefore sought to define who would respond best to two different types of GSH for anxiety to enable better future treatment matching.

METHODS

The study used outcome data from a patient preference trial (N = 209) comparing cognitive analytic therapy-guided self-help (CAT-GSH) with cognitive behavioural therapy-guided self-help (CBT-GSH). Elastic Net regularization and Boruta random forest variable selection methods were applied. Regression models calculated the patient advantage index (PAI) to designate which GSH was likely the most effective for each patient. Outcomes were compared for those receiving their PAI-indicated optimal and non-optimal GSH.

RESULTS

Lower baseline depression and anxiety severity predicted better outcomes for both types of GSH. Patient preference status was not associated with outcome during either GSH. Sixty-three % received their model indicating optimal GSH and these had significantly higher rates of reliable and clinically significant reductions in anxiety at both post-treatment (35.9% vs. 16.6%) and follow-up (36.6% vs. 19.2%). No single patient with a large PAI had a reliable and clinically significant reduction in anxiety at post-treatment or follow-up when they did not receive their optimal GSH.

CONCLUSIONS

Treatment matching algorithms have the potential to support evidenced-based treatment selection for GSH. Treatment selection and supporting patient choice needs to be integrated. Future research needs to investigate the use of the PAI for GSH treatment matching, but with larger and more balanced samples.

Types and mechanisms of idiographic change during guided self-help for anxiety

OBJECTIVES

To compare idiographic change during two formats of guided self-help (GSH); cognitive-behavioural therapy guided self-help (CBT-GSH) and cognitive analytic therapy guided self-help (CAT-GSH).

DESIGN

Qualitative inductive thematic analysis.

METHODS

Semi-structured interviews with N = 17 participants with a reliable change outcome on the GAD-7 after completing GSH for anxiety. Changes were categorised and themes extracted.

RESULTS

No differences between CAT-GSH and CBT-GSH were found regarding types of change reported. The five overarching themes found were personal qualities of success, enlightenment through understanding, specific tools and techniques, changes to relationships and tailoring support. Four themes maximally differentiated between the two different types of GSH; CAT-GSH enabled relational insight and change whilst CBT-GSH enabled better understanding of anxiety, new coping techniques and supportive relationships.

CONCLUSIONS

Both common and model-specific factors contribute to patient change during GSH. Whilst all forms of GSH are grounded in the psychoeducational approach, separate theoretical foundations and associated methods facilitate different types of ideographic change.

Individual-Specific Animated Profiles of Mental Health

How important is the timing of the pretreatment evaluation? If we consider mental health to be a relatively fixed condition, the specific timing (e.g., day, hour) of the evaluation is immaterial and often determined on the basis of technical considerations. Indeed, the fundamental assumption underlying the vast majority of psychotherapy research and practice is that mental health is a state that can be captured in a one-dimensional snapshot. If this fundamental assumption, underlying 80 years of empirical research and practice, is incorrect, it may help explain why for decades psychotherapy failed to rise above the 50% efficacy rate in the treatment of mental-health disorders, especially depression, a heterogeneous disorder and the leading cause of disability worldwide. Based on recent studies suggesting within-individual dynamics, this article proposes that mental health and its underlying therapeutic mechanisms have underlying intrinsic dynamics that manifest across dimensions. Computational psychotherapy is needed to develop individual-specific pretreatment animated profiles of mental health. Such individual-specific animated profiles are expected to improve the ability to select the optimal treatment for each patient, devise adequate treatment plans, and adjust them on the basis of ongoing evaluations of mental-health dynamics, creating a new understanding of therapeutic change as a transition toward a more adaptive animated profile.

Individual Prediction of Optimal Treatment Allocation Between Electroconvulsive Therapy or Ketamine using the Personalized Advantage Index

Introduction

Electroconvulsive therapy (ECT) and ketamine are two effective treatments for depression with similar efficacy; however, individual patient outcomes may be improved by models that predict optimal treatment assignment. Here, we adapt the Personalized Advantage Index (PAI) algorithm using machine learning to predict optimal treatment assignment between ECT and ketamine using medical record data from a large, naturalistic patient cohort. We hypothesized that patients who received a treatment predicted to be optimal would have significantly better outcomes following treatment compared to those who received a non-optimal treatment.

Methods

Data on 2526 ECT and 235 mixed IV ketamine and esketamine patients from McLean Hospital was aggregated. Depressive symptoms were measured using the Quick Inventory of Depressive Symptomatology (QIDS) before and during acute treatment. Patients were matched between treatments on pretreatment QIDS, age, inpatient status, and psychotic symptoms using a 1:1 ratio yielding a sample of 470 patients (n=235 per treatment). Random forest models were trained and predicted differential patientwise minimum QIDS scores achieved during acute treatment (min-QIDS) scores for ECT and ketamine using pretreatment patient measures. Analysis of Shapley Additive exPlanations (SHAP) values identified predictors of differential outcomes between treatments.

Results

Twenty-seven percent of patients with the largest PAI scores who received a treatment predicted optimal had significantly lower min-QIDS scores compared to those who received a non-optimal treatment (mean difference=1.6, t=2.38, q<0.05, Cohen's D=0.36). Analysis of SHAP values identified prescriptive pretreatment measures.

Conclusions

Patients assigned to a treatment predicted to be optimal had significantly better treatment outcomes. Our model identified pretreatment patient factors captured in medical records that can provide interpretable and actionable guidelines treatment selection.

Das Beste aus zwei Welten: Eine systematische Übersicht zu Faktoren der Implementierung einer “Blended Therapy” (Gemischte Therapie) in der Psychotherapeutischen Routineversorgung

<b><i>Ziel:</i></b> Diese Arbeit gibt einen Überblick über bisherige wissenschaftliche Empfehlungen zur Implementierung der blended therapy, d.h. der Kombination von Therapien in Präsenz und via digitaler Medien. Die Empfehlungen um­fassen die Themen: (1) wahrgenommene Barrieren von Psychotherapeut*innen, (2) Format der blended therapy und (3) Indikationen für Patient*innen. In diesem Rahmen werden Faktoren aufgezeigt, die Akteur*innen im Gesundheitssystem als Orientierung dienen können, welche Determinanten bei der Implementierung in die Routineversorgung zu berücksichtigen sind. <b><i>Methodik:</i></b> Die systematische Suche erfolgte in den Datenbanken PsycArticles, PsycInfo, PSYNDEX und PubMed. <b><i>Ergebnisse:</i></b> Die Publikationen umfassen vier quantitative, neun qualitative sowie drei Mixed-Methods-Studien. Die bisherigen Arbeiten kommen zu dem Ergebnis, dass Psychotherapeut*innen frühzeitig in den Implementierungsprozess einbezogen und langfristige supportive Maßnahmen für die Umstrukturierung der bisherigen Arbeitsroutine geschaffen werden sollten. Das Format der blended therapy sollte nicht standardisiert angewandt, sondern vielmehr individuell an die Patient*innen angepasst werden. Hinsichtlich möglicher Indikationen werden verschiedene Einflussfaktoren der Patient*innen diskutiert, die bisher jedoch nur unzureichend untersucht wurden. <b><i>Diskussion:</i></b> Bisherige Studien weisen größtenteils homogene Charakteristika und Ergebnisse auf. Forschungslücken bestehen hinsichtlich der Frage, inwieweit sich die Ergebnisse auf andere Therapieschulen, Berufsgruppen und Störungsbilder übertragen lassen. <b><i>Schlussfolgerung:</i></b> Das Review veranschaulicht, dass es wichtiger Vorarbeit hinsichtlich der Anwendungsunterstützung für Psychotherapeut*innen sowie weiterer Forschungsaktivität bedarf, um die Implementierung von blended therapy im Sinne einer möglichen Verbesserung der psychotherapeutischen Versorgung voranzutreiben.

Blending Internet-based and tele group treatment: Acceptability, effects, and mechanisms of change of cognitive behavioral treatment for depression

The current COV-19 pandemic increases the need for remote treatment. Among several provision strategies, tele groups have been tested as an efficient option. Still, the number of studies is comparably low, with a clear lack of studies investigating supposed treatment mechanisms. Sixty-one mildly to moderately depressed participants from Salzburg, Bavaria, and Upper Austria were randomized to the intervention or a waiting list control group (RCT). The seven-week treatment comprised preparatory online modules, followed by personalized feedback and a subsequent tele group session. Large treatment effects were observed for depression (CES-D: d = 0.99, p < .001; PHQ-9: d = 0.87, p = .002), together with large effects for cognitive behavioral skills (cognitive style, and behavioral activation, d = 0.88-0.97). Changes in skills mediated treatment outcomes for CES-D and PHQ-9, suggesting comparable mechanisms as in face-to-face therapy. Two typical moderators, therapeutic alliance, and group cohesion, however, failed to predict outcome (p = .289), or only exhibited statistical tendencies (p = .049 to .071). Client satisfaction, system usability, and treatment adherence were high. Blending Internet-based and tele group interventions offers additional options for low-threshold care that is less dependent on population density, commuting distances, or constraints due to the current COV-19 crisis. Results indicate that the blended intervention is clinically effective by fostering core CBT skills. While findings suggest the notion that working alliance and group cohesion can be established online, their relevancy for outcomes of blended treatment needs to be further investigated.

Component analysis of a synchronous and asynchronous blended care CBT intervention for symptoms of depression and anxiety: Pragmatic retrospective study

BACKGROUND

Depression and anxiety are leading causes of disability worldwide. Though effective treatments exist, depression and anxiety remain undertreated. Blended care psychotherapy, combining the scalability of online interventions with the personalization and engagement of a live therapist, is a promising approach for increasing access to evidence-based care.

OBJECTIVES

To evaluate the effectiveness and individual contribution of two components - i) digital tools and ii) video-based therapist-led sessions - in a blended care CBT-based intervention under real world conditions.

METHODS

A retrospective cohort design was used to analyze N = 1372 US-based individuals who enrolled in blended care psychotherapy. Of these, at baseline, 761 participants had depression symptoms in the clinical range (based on PHQ-9), and 1254 had anxiety symptoms in the clinical range (based on GAD-7). Participants had access to the program as a mental health benefit offered by their employer. The CBT-based blended care psychotherapy program consisted of regular video sessions with therapists, complemented by digital lessons and digital exercises assigned by the clinician and completed in between sessions. Depression and anxiety levels and clients' treatment engagement were tracked throughout treatment. A 3-level individual growth curve model incorporating time-varying covariates was utilized to examine symptom trajectories of PHQ-9 scores (for those with clinical range of depression at baseline) and GAD-7 scores (for those with clinical range of anxiety at baseline).

RESULTS

On average, individuals exhibited a significant decline in depression and anxiety symptoms during the initial weeks of treatment (P < .001), and a continued decline over subsequent weeks at a slower rate (P < .001). Engaging in a therapy session in a week was associated with lower GAD-7 (b = -0.81) and PHQ-9 (b = -1.01) scores in the same week, as well as lower GAD-7 (b = -0.58) and PHQ-9 (b = -0.58) scores the following week (all P < .01). Similarly, engaging with digital lessons was independently associated with lower GAD-7 (b = -0.19) and PHQ-9 (b = -0.18) scores during the same week, and lower GAD-7 (b = -0.25) and PHQ-9 (b = -0.27) the following week (all P < .01).

CONCLUSIONS

Therapist-led video sessions and digital lessons had separate contributions to improvements in symptoms of depression and anxiety over the course of treatment. Future research should investigate whether clients' characteristics are related to differential effects of therapist-led and digital components of care.

Clinical Effectiveness of Blended CBT Compared vs Face-to-Face CBT for adult depression: a Randomised Controlled Non-Inferiority Trial (Preprint)

Background

Internet-based cognitive behavioral therapy (iCBT) has been demonstrated to be cost- and clinically effective. There is a need, however, for increased therapist contact for some patient groups. Combining iCBT with traditional face-to-face (FtF) consultations in a blended format may produce a new treatment format (B-CBT) with multiple benefits from both traditional CBT and iCBT, such as individual adaptation, lower costs than traditional therapy, wide geographical and temporal availability, and possibly lower threshold to implementation.

Objective

The primary aim of this study is to compare directly the clinical effectiveness of B-CBT with FtF-CBT for adult major depressive disorder.

Methods

A 2-arm randomized controlled noninferiority trial compared B-CBT for adult depression with treatment as usual (TAU). The trial was researcher blinded (unblinded for participants and clinicians). B-CBT comprised 6 sessions of FtF-CBT alternated with 6-8 web-based CBT self-help modules. TAU comprised 12 sessions of FtF-CBT. All participants were aged 18 or older and met the diagnostic criteria for major depressive disorder and were recruited via a national iCBT clinic. The primary outcome was change in depression severity on the 9-item Patient Health Questionnaire (PHQ-9). Secondary analyses included client satisfaction (8-item Client Satisfaction Questionnaire [CSQ-8]), patient expectancy (Credibility and Expectancy Questionnaire [CEQ]), and working (Working Alliance Inventory [WAI] and Technical Alliance Inventory [TAI]). The primary outcome was analyzed by a mixed effects model including all available data from baseline, weekly measures, 3-, 6, and 12-month follow-up.

Results

A total of 76 individuals were randomized, with 38 allocated to each treatment group. Age ranged from 18 to 71 years (SD 13.96) with 56 (74%) females. Attrition rate was 20% (n=15), which was less in the FtF-CBT group (n=6, 16%) than in the B-CBT group (n=9, 24%). As many as 53 (70%) completed 9 or more sessions almost equally distributed between the groups (nFtF-CBT=27, 71%; nB-CBT=26, 68%). PHQ-9 reduced 11.38 points in the FtF-CBT group and 8.10 in the B-CBT group. At 6 months, the mean difference was a mere 0.17 points. The primary analyses confirmed large and significant within-group reductions in both groups (FtF-CBT: β=–.03; standard error [SE] 0.00; P<.001 and B-CBT: β=–.02; SE 0.00; P<.001). A small but significant interaction effect was observed between groups (β=.01; SE 0.00; P=.03). Employment status influenced the outcome differently between groups, where the B-CBT group was seen to profit more from not being full-time employed than the FtF group.

Conclusions

With large within-group effects in both treatment arms, the study demonstrated feasibility of B-CBT in Denmark. At 6 months’ follow-up, there appeared to be no difference between the 2 treatment formats, with a small but nonsignificant difference at 12 months. The study seems to demonstrate that B-CBT is capable of producing treatment effects that are close to FtF-CBT and that completion rates and satisfaction rates were comparable between groups. However, the study was limited by small sample size and should be interpreted with caution.

Trial Registration

ClinicalTrials.gov NCT02796573; https://clinicaltrials.gov/ct2/show/NCT02796573

International Registered Report Identifier (IRRID)

RR2-10.1186/s12888-016-1140-y

Implementing Digital Trainings within Medical Rehabilitations: Improvement of Mental Health and Synergetic Outcomes with Healthcare Service

The need for new technologies in healthcare services has been stressed. However, little is known about the effectiveness of digital interventions integrated in psychosomatic rehabilitation processes. Data from 724 patients from psychosomatic rehabilitation clinics were analyzed with regard to the effectiveness of digital trainings indicated by a change in symptoms related to depression, anxiety, stress, and loneliness from pre– to post–rehabilitation. Rehabilitation satisfaction was examined in association with reaching rehabilitation goals and satisfaction with communication. A mixed repeated measures analyses of covariance, analyses of covariance, and hierarchical stepwise regression analyses were performed. Results indicated a superior effectiveness for the intervention group receiving all offered digital treatments in addition to the regular face-to-face rehabilitation program with regard to symptoms of depression (F (2674) = 3.93, p < 0.05, η_p² = 0.01), anxiety (F (2678) = 3.68, p < 0.05, η_p² = 0.01) post-rehabilitation, with large effect sizes for both depression (d = 1.28) and anxiety (d = 1.08). In addition, rehabilitation satisfaction was positively associated with reaching rehabilitation goals and perceived communication with healthcare workers. Digital interventions appeared effective in supporting mental health of psychosomatic rehabilitation patients’ post-rehabilitation. These findings support the inclusion of multidisciplinary and interdisciplinary digital and face-to-face treatment programs and call for more implementations of new technologies in a context of complexity to improve health and healthcare service.