An investigation of treatment return after psychological therapy for depression and anxiety.

BACKGROUND: Some patients return for further psychological treatment in routine services, although it is unclear how common this is, as scarce research is available on this topic. AIMS: To estimate the treatment return rate and describe the clinical characteristics of patients who return for anxiety and depression treatment. METHOD: A large dataset (N=21,029) of routinely collected clinical data (2010-2015) from an English psychological therapy service was analysed using descriptive statistics. RESULTS: The return rate for at least one additional treatment episode within 1-5 years was 13.7%. Furthermore, 14.5% of the total sessions provided by the service were delivered to treatment-returning patients. Of those who returned, 58.0% continued to show clinically significant depression and/or anxiety symptoms at the end of their first treatment, while 32.0% had experienced a demonstrable relapse before their second treatment. CONCLUSIONS: This study estimates that approximately one in seven patients return to the same service for additional psychological treatment within 1-5 years. Multiple factors may influence the need for additional treatment, and this may have a major impact on service activity. Future research needs to further explore and better determine the characteristics of treatment returners, prioritise enhancement of first treatment recovery, and evaluate relapse prevention interventions.

Predicting dropout from psychological treatment using different machine learning algorithms, resampling methods, and sample sizes.

Objective: The occurrence of dropout from psychological interventions is associated with poor treatment outcome and high health, societal and economic costs. Recently, machine learning (ML) algorithms have been tested in psychotherapy outcome research. Dropout predictions are usually limited by imbalanced datasets and the size of the sample. This paper aims to improve dropout prediction by comparing ML algorithms, sample sizes and resampling methods. Method: Twenty ML algorithms were examined in twelve subsamples (drawn from a sample of N = 49,602) using four resampling methods in comparison to the absence of resampling and to each other. Prediction accuracy was evaluated in an independent holdout dataset using the F1-Measure. Results: Resampling methods improved the performance of ML algorithms and down-sampling can be recommended, as it was the fastest method and as accurate as the other methods. For the highest mean F1-Score of .51 a minimum sample size of N = 300 was necessary. No specific algorithm or algorithm group can be recommended. Conclusion: Resampling methods could improve the accuracy of predicting dropout in psychological interventions. Down-sampling is recommended as it is the least computationally taxing method. The training sample should contain at least 300 cases.

Predicting patients who will drop out of out-patient psychotherapy using machine learning algorithms.

BACKGROUND: About 30% of patients drop out of cognitive-behavioural therapy (CBT), which has implications for psychiatric and psychological treatment. Findings concerning drop out remain heterogeneous. AIMS: This paper aims to compare different machine-learning algorithms using nested cross-validation, evaluate their benefit in naturalistic settings, and identify the best model as well as the most important variables. METHOD: The data-set consisted of 2543 out-patients treated with CBT. Assessment took place before session one. Twenty-one algorithms and ensembles were compared. Two parameters (Brier score, area under the curve (AUC)) were used for evaluation. RESULTS: The best model was an ensemble that used Random Forest and nearest-neighbour modelling. During the training process, it was significantly better than generalised linear modelling (GLM) (Brier score: d = -2.93, 95% CI (-3.95, -1.90)); AUC: d = 0.59, 95% CI (0.11 to 1.06)). In the holdout sample, the ensemble was able to correctly identify 63.4% of cases of patients, whereas the GLM only identified 46.2% correctly. The most important predictors were lower education, lower scores on the Personality Style and Disorder Inventory (PSSI) compulsive scale, younger age, higher scores on the PSSI negativistic and PSSI antisocial scale as well as on the Brief Symptom Inventory (BSI) additional scale (mean of the four additional items) and BSI overall scale. CONCLUSIONS: Machine learning improves drop-out predictions. However, not all algorithms are suited to naturalistic data-sets and binary events. Tree-based and boosted algorithms including a variable selection process seem well-suited, whereas more advanced algorithms such as neural networks do not.

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.

Predicting personalized process-outcome associations in psychotherapy using machine learning approaches-A demonstration.

Objective: Personalized treatment methods have shown great promise in efficacy studies across many fields of medicine and mental health. Little is known, however, about their utility in process-outcome research. This study is the first to apply personalized treatment methods in the field of process-outcome research, as demonstrated based on the alliance-outcome association. Method: Using a sample of 741 patients, individual regressions were fitted to estimate within-patient effects of the alliance-outcome association. The Boruta algorithm was used to identify patient intake characteristics that moderate the within-patient alliance-outcome association. The nearest neighbor approach was used to identify patients whose relevant pretreatment characteristics were similar to those of a target patient. The alliance-outcome associations of the most similar patients were subsequently used to predict the alliance-outcome association of the target patient. Results: Irrespective of the number of selected nearest neighbors, the correlation between the observed and predicted alliance-outcome associations was low and insignificant. According to the true error of the prediction, the demonstrated approach was unable to improve predictions made with a simple comparison model. Conclusion: The study demonstrated the application of personalized treatment methods in process-outcome research and opens many new paths for future research.

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).