Predicting response to cognitive therapy and interpersonal therapy, with or without antidepressant medication, for major depression: a pragmatic trial in routine practice

Developing an individualized treatment rule for Veterans with major depressive disorder using electronic health records

Efforts to develop an individualized treatment rule (ITR) to optimize major depressive disorder (MDD) treatment with antidepressant medication (ADM), psychotherapy, or combined ADM-psychotherapy have been hampered by small samples, small predictor sets, and suboptimal analysis methods. Analyses of large administrative databases designed to approximate experiments followed iteratively by pragmatic trials hold promise for resolving these problems. The current report presents a proof-of-concept study using electronic health records (EHR) of n = 43,470 outpatients beginning MDD treatment in Veterans Health Administration Primary Care Mental Health Integration (PC-MHI) clinics, which offer access not only to ADMs but also psychotherapy and combined ADM-psychotherapy. EHR and geospatial databases were used to generate an extensive baseline predictor set (5,865 variables). The outcome was a composite measure of at least one serious negative event (suicide attempt, psychiatric emergency department visit, psychiatric hospitalization, suicide death) over the next 12 months. Best-practices methods were used to adjust for nonrandom treatment assignment and to estimate a preliminary ITR in a 70% training sample and to evaluate the ITR in the 30% test sample. Statistically significant aggregate variation was found in overall probability of the outcome related to baseline predictors (AU-ROC = 0.68, S.E. = 0.01), with test sample outcome prevalence of 32.6% among the 5% of patients having highest predicted risk compared to 7.1% in the remainder of the test sample. The ITR found that psychotherapy-only was the optimal treatment for 56.0% of patients (roughly 20% lower risk of the outcome than if receiving one of the other treatments) and that treatment type was unrelated to outcome risk among other patients. Change in aggregate treatment costs of implementing this ITR would be negligible, as 16.1% fewer patients would be prescribed ADMs and 2.9% more would receive psychotherapy. A pragmatic trial would be needed to confirm the accuracy of the ITR.

Randomized trial of brief interpersonal psychotherapy and cognitive behavioral therapy for depression delivered both in-person and by telehealth

BACKGROUND

Expert consensus guidelines recommend Cognitive Behavioral Therapy (CBT) and Interpersonal Psychotherapy (IPT), interventions that were historically delivered face-to-face, as first-line treatments for Major Depressive Disorder (MDD). Despite the ubiquity of telehealth following the COVID-19 pandemic, little is known about differential outcomes with CBT versus IPT delivered in-person (IP) or via telehealth (TH) or whether working alliance is affected.

METHODS

Adults meeting DSM-5 criteria for MDD were randomly assigned to either 8 sessions of IPT or CBT (group). Mid-trial, COVID-19 forced a change of therapy delivery from IP to TH (study phase). We compared changes in Hamilton Rating Scale for Depression (HRSD-17) and Working Alliance Inventory (WAI) scores for individuals by group and phase: CBT-IP (n = 24), CBT-TH (n = 11), IPT-IP (n = 25) and IPT-TH (n = 17).

RESULTS

HRSD-17 scores declined significantly from pre to post treatment (pre: M = 17.7, SD = 4.4 vs. post: M = 11.7, SD = 5.9; p < .001; d = 1.45) without significant group or phase effects. WAI scores did not differ by group or phase. Number of completed therapy sessions was greater for TH (M = 7.8, SD = 1.2) relative to IP (M = 7.2, SD = 1.6) (Mann-Whitney U = 387.50, z = -2.24, p = .025).

LIMITATIONS

Participants were not randomly assigned to IP versus TH. Sample size is small.

CONCLUSIONS

This study provides preliminary evidence supporting the efficacy of both brief IPT and CBT, delivered by either TH or IP, for depression. It showed that working alliance is preserved in TH, and delivery via TH may improve therapy adherence. Prospective, randomized controlled trials are needed to definitively test efficacy of brief IPT and CBT delivered via TH versus IP.

Development of a model to predict combined antidepressant medication and psychotherapy treatment response for depression among veterans

BACKGROUND

Although research shows that more depressed patients respond to combined antidepressants (ADM) and psychotherapy than either alone, many patients do not respond even to combined treatment. A reliable prediction model for this could help treatment decision-making. We attempted to create such a model using machine learning methods among patients in the US Veterans Health Administration (VHA).

METHODS

A 2018-2020 national sample of VHA patients beginning combined depression treatment completed self-report assessments at baseline and 3 months (n = 658). A learning model was developed using baseline self-report, administrative, and geospatial data to predict 3-month treatment response defined by reductions in the Quick Inventory of Depression Symptomatology Self-Report and/or in the Sheehan Disability Scale. The model was developed in a 70 % training sample and tested in the remaining 30 % test sample.

RESULTS

30.0 % of patients responded to treatment. The prediction model had a test sample AUC-ROC of 0.657. A strong gradient was found in probability of treatment response from 52.7 % in the highest predicted quintile to 14.4 % in the lowest predicted quintile. The most important predictors were episode characteristics (symptoms, comorbidities, history), personality/psychological resilience, recent stressors, and treatment characteristics.

LIMITATIONS

Restrictions in sample definition, a low recruitment rate, and reliance on patient self-report rather than clinician assessments to determine treatment response limited the generalizability of results.

CONCLUSIONS

A machine learning model could help depressed patients and providers predict likely response to combined ADM-psychotherapy. Parallel information about potential harms and costs of alternative treatments would be needed, though, to inform optimal treatment selection.

Are depressed patients' coping strategies associated with psychotherapy treatment outcomes?

BACKGROUND

In theory, depression is thought to be associated with deficits in adaptive and excesses in maladaptive coping strategies. This study aimed to investigate associations between coping strategies and depression treatment outcomes.

METHOD

Participants (N = 126) completed measures of adaptive and maladaptive coping strategies before and after accessing evidence-based psychotherapies for depression. The primary outcome was self-reported depression severity measured with the Patient Health Questionnaire (PHQ-9). Hierarchical regression was used to investigate associations between coping strategies and post-treatment depression symptoms, controlling for therapeutic alliance and relevant demographics.

RESULTS

Lower pre-treatment engagement coping and higher rumination predicted higher post-treatment depression, but both of these effects became non-significant after controlling for baseline depression severity. Similarly, correlations between change in rumination and change in depression were no longer significant after controlling for baseline severity.

CONCLUSIONS

Deficits in adaptive (engagement) and excesses in maladaptive (rumination) coping strategies may simply be proxy indicators (epiphenomena) of depression severity.

PRACTITIONER POINTS

Lower pre-treatment engagement coping predicted higher post-treatment depression Higher pre-treatment rumination predicted higher post-treatment depression Change in rumination during treatment correlated with change in depression symptoms However, none of the above associations remained statistically significant after controlling for baseline depression severity.

A randomised controlled trial of a family-group cognitive-behavioural (FGCB) preventive intervention for the children of parents with depression: short-term effects on symptoms and possible mechanisms

OBJECTIVE

Parental depression is one of the biggest risk factors for youth depression. This parallel randomized controlled trial evaluates the effectiveness of the German version of the family-group-cognitive-behavioral (FGCB) preventive intervention for children of depressed parents.

METHODS

Families with (i) a parent who has experienced depression and (ii) a healthy child aged 8-17 years (mean = 11.63; 53% female) were randomly allocated (blockwise; stratified by child age and parental depression) to the 12-session intervention (EG; n = 50) or no intervention (CG; usual care; n = 50). Self-reported (unblinded) outcomes were assessed immediately after the intervention (6 months). We hypothesized that CG children would show a greater increase in self-reported symptoms of depression (DIKJ) and internalising/externalising disorder (YSR/CBCL) over time compared to the EG. Intervention effects on secondary outcome variables emotion regulation (FEEL-KJ), attributional style (ASF-KJ), knowledge of depression and parenting style (ESI) were also expected. Study protocol (Belinda Platt, Pietsch, Krick, Oort, & Schulte-Körne, 2014) and trial registration (NCT02115880) reported elsewhere.

RESULTS

We found significant intervention effects on self-reported internalising ([Formula: see text] = 0.05) and externalising ([Formula: see text] = 0.08) symptoms but did not detect depressive symptoms or parent-reported psychopathology. Parental depression severity did not modify these effects. Both groups showed equally improved knowledge of depression ([Formula: see text] = 0.06). There were no intervention effects on emotion regulation, attributional style or parenting style.

CONCLUSION

The German version of the FGCB intervention is effective in reducing symptoms of general psychopathology. There was no evidence that the mechanisms targeted in the intervention changed within the intervention period.

What factors indicate prognosis for adults with depression in primary care? A protocol for meta-analyses of individual patient data using the Dep-GP database

Background: Pre-treatment severity is a key indicator of prognosis for those with depression. Knowledge is limited on how best to encompass severity of disorders. A number of non-severity related factors such as social support and life events are also indicators of prognosis. It is not clear whether this holds true after adjusting for pre-treatment severity as a) a depressive symptom scale score, and b) a broader construct encompassing symptom severity and related indicators: "disorder severity". In order to investigate this, data from the individual participants of clinical trials which have measured a breadth of "disorder severity" related factors are needed. Aims: 1) To assess the association between outcomes for adults seeking treatment for depression and the severity of depression pre-treatment, considered both as i) depressive symptom severity only and ii) "disorder severity" which includes depressive symptom severity and comorbid anxiety, chronicity, history of depression, history of previous treatment, functional impairment and health-related quality of life. 2) To determine whether i) social support, ii) life events, iii) alcohol misuse, and iv) demographic factors (sex, age, ethnicity, marital status, employment status, level of educational attainment, and financial wellbeing) are prognostic indicators of outcomes, independent of baseline "disorder severity" and the type of treatment received. Methods: Databases were searched for randomised clinical trials (RCTs) that recruited adults seeking treatment for depression from their general practitioners and used the same diagnostic and screening instrument to measure severity at baseline - the Revised Clinical Interview Schedule; outcome measures could differ between studies. Chief investigators of all studies meeting inclusion criteria were contacted and individual patient data (IPD) were requested. Conclusions: In total 15 RCTs met inclusion criteria. The Dep-GP database will include the 6271 participants from the 13 studies that provided IPD. This protocol outlines how these data will be analysed. Registration: PROSPERO CRD42019129512 (01/04/2019).

What factors indicate prognosis for adults with depression in primary care? A protocol for meta-analyses of individual patient data using the Dep-GP database

Background: Pre-treatment severity is a key indicator of prognosis for those with depression. Knowledge is limited on how best to encompass severity of disorders. A number of non-severity related factors such as social support and life events are also indicators of prognosis. It is not clear whether this holds true after adjusting for pre-treatment severity as a) a depressive symptom scale score, and b) a broader construct encompassing symptom severity and related indicators: "disorder severity". In order to investigate this, data from the individual participants of clinical trials which have measured a breadth of "disorder severity" related factors are needed. Aims: 1) To assess the association between outcomes for adults seeking treatment for depression and the severity of depression pre-treatment, considered both as i) depressive symptom severity only and ii) "disorder severity" which includes depressive symptom severity and comorbid anxiety, chronicity, history of depression, history of previous treatment, functional impairment and health-related quality of life. 2) To determine whether i) social support, ii) life events, iii) alcohol misuse, and iv) demographic factors (sex, age, ethnicity, marital status, employment status, level of educational attainment, and financial wellbeing) are prognostic indicators of outcomes, independent of baseline "disorder severity" and the type of treatment received. Methods: Databases were searched for randomised clinical trials (RCTs) that recruited adults seeking treatment for depression from their general practitioners and used the same diagnostic and screening instrument to measure severity at baseline - the Revised Clinical Interview Schedule; outcome measures could differ between studies. Chief investigators of all studies meeting inclusion criteria were contacted and individual patient data (IPD) were requested. Conclusions: In total 15 RCTs met inclusion criteria. The Dep-GP database will include the 6271 participants from the 13 studies that provided IPD. This protocol outlines how these data will be analysed. Registration: PROSPERO CRD42019129512 (01/04/2019).

What factors indicate prognosis for adults with depression in primary care? A protocol for meta-analyses of individual patient data using the Dep-GP database

Background: Pre-treatment severity is a key indicator of prognosis for those with depression. Knowledge is limited on how best to encompass severity of disorders. A number of non-severity related factors such as social support and life events are also indicators of prognosis. It is not clear whether this holds true after adjusting for pre-treatment severity as a) a depressive symptom scale score, and b) a broader construct encompassing symptom severity and related indicators: "disorder severity". In order to investigate this, data from the individual participants of clinical trials which have measured a breadth of "disorder severity" related factors are needed. Aims: 1) To assess the association between outcomes for adults seeking treatment for depression and the severity of depression pre-treatment, considered both as i) depressive symptom severity only and ii) "disorder severity" which includes depressive symptom severity and comorbid anxiety, chronicity, history of depression, history of previous treatment, functional impairment and health-related quality of life. 2) To determine whether i) social support, ii) life events, iii) alcohol misuse, and iv) demographic factors (sex, age, ethnicity, marital status, employment status, level of educational attainment, and financial wellbeing) are prognostic indicators of outcomes, independent of baseline "disorder severity" and the type of treatment received. Methods: Databases were searched for randomised clinical trials (RCTs) that recruited adults seeking treatment for depression from their general practitioners and used the same diagnostic and screening instrument to measure severity at baseline - the Revised Clinical Interview Schedule; outcome measures could differ between studies. Chief investigators of all studies meeting inclusion criteria were contacted and individual patient data (IPD) were requested. Conclusions: In total 13 RCTs were found to meet inclusion criteria. The Dep-GP database was formed from the 6271 participants. This protocol outlines how these data will be analysed. Registration: PROSPERO CRD42019129512 (01/04/2019).

What works best for whom? Cognitive Behavior Therapy and Mindfulness-Based Cognitive Therapy for depressive symptoms in patients with diabetes

Objective

Cognitive Behavior Therapy (CBT) and Mindfulness-Based Cognitive Therapy (MBCT) have shown to be effective interventions for treating depressive symptoms in patients with diabetes. However, little is known about which intervention works best for whom (i.e., moderators of efficacy). The aim of this study was to identify variables that differentially predicted response to either CBT or MBCT (i.e., prescriptive predictors).

Methods

The sample consisted of 91 adult outpatients with type 1 or type 2 diabetes and comorbid depressive symptoms (i.e., BDI-II ≥ 14) who were randomized to either individual 8-week CBT (n = 45) or individual 8-week MBCT (n = 46). Patients were followed for a year and depressive symptoms were measured at pre-treatment, post-treatment, and at 9-months follow-up. The predictive effect of demographics, depression related characteristics, and disease specific characteristics on change in depressive symptoms was assessed by means of hierarchical regression analyses.

Results

Analyses showed that education was the only factor that differentially predicted a decrease in depressive symptoms directly after the interventions. At post-treatment, individuals with higher educational attainment responded better to MBCT, as compared to CBT. Yet, this effect was not apparent at 9-months follow-up.

Conclusions

This study did not identify variables that robustly differentially predicted treatment effectiveness of CBT and MBCT, indicating that both CBT and MBCT are accessible interventions that are effective for treating depressive symptoms in broad populations with diabetes. More research is needed to guide patient-treatment matching in clinical practice.

Prognostic and prescriptive predictors of improvement in a naturalistic study on inpatient and day hospital treatment of depression

BACKGROUND

The study aimed to identify prognostic (associated with general outcome) and prescriptive (associated with differential outcome in two different settings) predictors of improvement in a naturalistic multi-center study on inpatient and day hospital treatment in major depressive disorder (MDD).

METHODS

250 inpatients and 250 day hospital patients of eight psychosomatic hospitals were assessed at admission, discharge and a 3-months follow-up. Primary outcome was defined as a reduction of depressive symptomatology from admission to discharge and from discharge to follow-up (QIDS-C, total score). Percent improvement scores at discharge and at follow-up were entered as dependent variables into two General Linear Models with a set of predictor variables and the respective interaction terms with treatment setting. The selection of predictor sets was guided by statistical methods of variable preselection (LASSO).

RESULTS

Three variables were associated with less improvement from admission to discharge: the number of additional axis-I diagnoses, axis-II co-morbidity (SCID) and lower motivation (expert assessment). Social support (F-SozU) predicted symptom course between discharge and 3-month follow-up. Patients with no absent / sick days prior to admission showed a less favorable symptom course after discharge when treated as inpatients.

CONCLUSIONS

Patients with co-morbidity show less improvement during the active treatment phase. Motivation can be considered a prerequisite for symptom reduction, whereas social support seems to be an important factor for the maintenance of treatment gains. The lack in prescriptive predictors found may point to the fact that inpatient and day hospital treatment have comparable effects for most subgroups of patients with MDD.

Predicting Optimal Outcomes in Cognitive Therapy or Interpersonal Psychotherapy for Depressed Individuals Using the Personalized Advantage Index Approach

Introduction

Although psychotherapies for depression produce equivalent outcomes, individual patients respond differently to different therapies. Predictors of outcome have been identified in the context of randomized trials, but this information has not been used to predict which treatment works best for the depressed individual. In this paper, we aim to replicate a recently developed treatment selection method, using data from an RCT comparing the effects of cognitive therapy (CT) and interpersonal psychotherapy (IPT).

Methods

134 depressed patients completed the pre- and post-treatment BDI-II assessment. First, we identified baseline predictors and moderators. Second, individual treatment recommendations were generated by combining the identified predictors and moderators in an algorithm that produces the Personalized Advantage Index (PAI), a measure of the predicted advantage in one therapy compared to the other, using standard regression analyses and the leave-one-out cross-validation approach.

Results

We found five predictors (gender, employment status, anxiety, personality disorder and quality of life) and six moderators (somatic complaints, cognitive problems, paranoid symptoms, interpersonal self-sacrificing, attributional style and number of life events) of treatment outcome. The mean average PAI value was 8.9 BDI points, and 63% of the sample was predicted to have a clinically meaningful advantage in one of the therapies. Those who were randomized to their predicted optimal treatment (either CT or IPT) had an observed mean end-BDI of 11.8, while those who received their predicted non-optimal treatment had an end-BDI of 17.8 (effect size for the difference = 0.51).

Discussion

Depressed patients who were randomized to their predicted optimal treatment fared much better than those randomized to their predicted non-optimal treatment. The PAI provides a great opportunity for formal decision-making to improve individual patient outcomes in depression. Although the utility of the PAI approach will need to be evaluated in prospective research, this study promotes the development of a treatment selection approach that can be used in regular mental health care, advancing the goals of personalized medicine.