Predicting post-treatment symptom severity for adults receiving psychological therapy in routine care for generalised anxiety disorder: a machine learning approach.

Approximately half of generalised anxiety disorder (GAD) patients do not recover from first-line treatments, and no validated prediction models exist to inform individuals or clinicians of potential treatment benefits. This study aimed to develop and validate an accurate and explainable prediction model of post-treatment GAD symptom severity. Data from adults receiving treatment for GAD in eight Improving Access to Psychological Therapies (IAPT) services (n=15,859) were separated into training, validation and holdout datasets. Thirteen machine learning algorithms were compared using 10-fold cross-validation, against two simple clinically relevant comparison models. The best-performing model was tested on the holdout dataset and model-specific explainability measures identified the most important predictors. A Bayesian Additive Regression Trees model out-performed all comparison models (MSE=16.54 [95 % CI=15.58; 17.51]; MAE=3.19; R²=0.33, including a single predictor linear regression model: MSE=20.70 [95 % CI=19.58; 21.82]; MAE=3.94; R²=0.14). The five most important predictors were: PHQ-9 anhedonia, GAD-7 annoyance/irritability, restlessness and fear items, then the referral-assessment waiting time. The best-performing model accurately predicted post-treatment GAD symptom severity using only pre-treatment data, outperforming comparison models that approximated clinical judgement and remaining within the GAD-7 error of measurement and minimal clinically important differences. This model could inform treatment decision-making and provide desired information to clinicians and patients receiving treatment for GAD.

Identifying trajectories of change in sleep disturbance during psychological treatment for depression.

BACKGROUND: Sleep disturbance may impact response to psychological treatment for depression. Understanding how sleep disturbance changes during the course of psychological treatment, and identifying the risk factors for sleep disturbance response may inform clinical decision-making. METHOD: This analysis included 18,915 patients receiving high-intensity psychological therapy for depression from one of eight London-based Improving Access to Psychological Therapies (IAPT) services between 2011 and 2020. Distinct trajectories of change in sleep disturbance were identified using growth mixture modelling. The study also investigated associations between identified trajectory classes, pre-treatment patient characteristics, and eventual treatment outcomes from combined PHQ-9 and GAD-7 metrics used by the services. RESULTS: Six distinct trajectories of sleep disturbance were identified: two demonstrated improvement, while one showed initial deterioration and the other three groups displayed only limited change in sleep disturbance, each with varying baseline sleep disturbance. Associations with trajectory class membership were found based on: gender, ethnicity, unemployment status, antidepressant medication use, long-term health condition status, severity of depressive symptom, and functional impairment. Groups that showed improvement in sleep had the best eventual outcomes from depression treatment, followed by groups that consistently slept well. LIMITATION: Single item on sleep disturbance used, no data on treatment adherence. CONCLUSIONS: These findings reveal heterogeneity in the course of sleep disturbance during psychological treatment for depression. Closer monitoring of changes in sleep disturbance during treatment might inform treatment planning. This includes decisions about when to incorporate sleep management interventions, and whether to change or augment therapy with interventions to reduce sleep disturbance.