Refining our understanding of depressive states and state transitions in response to cognitive behavioural therapy using latent Markov modelling.

BACKGROUND: It is increasingly recognized that existing diagnostic approaches do not capture the underlying heterogeneity and complexity of psychiatric disorders such as depression. This study uses a data-driven approach to define fluid depressive states and explore how patients transition between these states in response to cognitive behavioural therapy (CBT). METHODS: Item-level Patient Health Questionnaire (PHQ-9) data were collected from 9891 patients with a diagnosis of depression, at each CBT treatment session. Latent Markov modelling was used on these data to define depressive states and explore transition probabilities between states. Clinical outcomes and patient demographics were compared between patients starting at different depressive states. RESULTS: A model with seven depressive states emerged as the best compromise between optimal fit and interpretability. States loading preferentially on cognitive/affective v. somatic symptoms of depression were identified. Analysis of transition probabilities revealed that patients in cognitive/affective states do not typically transition towards somatic states and vice-versa. Post-hoc analyses also showed that patients who start in a somatic depressive state are less likely to engage with or improve with therapy. These patients are also more likely to be female, suffer from a comorbid long-term physical condition and be taking psychotropic medication. CONCLUSIONS: This study presents a novel approach for depression sub-typing, defining fluid depressive states and exploring transitions between states in response to CBT. Understanding how different symptom profiles respond to therapy will inform the development and delivery of stratified treatment protocols, improving clinical outcomes and cost-effectiveness of psychological therapies for patients with depression.

Improving the translation of search strategies using the Polyglot Search Translator: a randomized controlled trial.

BACKGROUND: Searching for studies to include in a systematic review (SR) is a time- and labor-intensive process with searches of multiple databases recommended. To reduce the time spent translating search strings across databases, a tool called the Polyglot Search Translator (PST) was developed. The authors evaluated whether using the PST as a search translation aid reduces the time required to translate search strings without increasing errors. METHODS: In a randomized trial, twenty participants were randomly allocated ten database search strings and then randomly assigned to translate five with the assistance of the PST (PST-A method) and five without the assistance of the PST (manual method). We compared the time taken to translate search strings, the number of errors made, and how close the number of references retrieved by a translated search was to the number retrieved by a reference standard translation. RESULTS: Sixteen participants performed 174 translations using the PST-A method and 192 translations using the manual method. The mean time taken to translate a search string with the PST-A method was 31 minutes versus 45 minutes by the manual method (mean difference: 14 minutes). The mean number of errors made per translation by the PST-A method was 8.6 versus 14.6 by the manual method. Large variation in the number of references retrieved makes results for this outcome inconclusive, although the number of references retrieved by the PST-A method was closer to the reference standard translation than the manual method. CONCLUSION: When used to assist with translating search strings across databases, the PST can increase the speed of translation without increasing errors. Errors in search translations can still be a problem, and search specialists should be aware of this.

Quantifying the Association Between Psychotherapy Content and Clinical Outcomes Using Deep Learning.

Importance: Compared with the treatment of physical conditions, the quality of care of mental health disorders remains poor and the rate of improvement in treatment is slow, a primary reason being the lack of objective and systematic methods for measuring the delivery of psychotherapy. Objective: To use a deep learning model applied to a large-scale clinical data set of cognitive behavioral therapy (CBT) session transcripts to generate a quantifiable measure of treatment delivered and to determine the association between the quantity of each aspect of therapy delivered and clinical outcomes. Design, Setting, and Participants: All data were obtained from patients receiving internet-enabled CBT for the treatment of a mental health disorder between June 2012 and March 2018 in England. Cognitive behavioral therapy was delivered in a secure online therapy room via instant synchronous messaging. The initial sample comprised a total of 17 572 patients (90 934 therapy session transcripts). Patients self-referred or were referred by a primary health care worker directly to the service. Exposures: All patients received National Institute for Heath and Care Excellence-approved disorder-specific CBT treatment protocols delivered by a qualified CBT therapist. Main Outcomes and Measures: Clinical outcomes were measured in terms of reliable improvement in patient symptoms and treatment engagement. Reliable improvement was calculated based on 2 severity measures: Patient Health Questionnaire (PHQ-9) and Generalized Anxiety Disorder 7-item scale (GAD-7), corresponding to depressive and anxiety symptoms respectively, completed by the patient at initial assessment and before every therapy session (see eMethods in the Supplement for details). Results: Treatment sessions from a total of 14 899 patients (10 882 women) aged between 18 and 94 years (median age, 34.8 years) were included in the final analysis. We trained a deep learning model to automatically categorize therapist utterances into 1 or more of 24 feature categories. The trained model was applied to our data set to obtain quantifiable measures of each feature of treatment delivered. A logistic regression revealed that increased quantities of a number of session features, including change methods (cognitive and behavioral techniques used in CBT), were associated with greater odds of reliable improvement in patient symptoms (odds ratio, 1.11; 95% CI, 1.06-1.17) and patient engagement (odds ratio, 1.20, 95% CI, 1.12-1.27). The quantity of nontherapy-related content was associated with reduced odds of symptom improvement (odds ratio, 0.89; 95% CI, 0.85-0.92) and patient engagement (odds ratio, 0.88, 95% CI, 0.84-0.92). Conclusions and Relevance: This work demonstrates an association between clinical outcomes in psychotherapy and the content of therapist utterances. These findings support the principle that CBT change methods help produce improvements in patients' presenting symptoms. The application of deep learning to large clinical data sets can provide valuable insights into psychotherapy, informing the development of new treatments and helping standardize clinical practice.

Demographic and clinical predictors of response to internet-enabled cognitive-behavioural therapy for depression and anxiety.

BACKGROUND: Common mental health problems affect a quarter of the population. Online cognitive-behavioural therapy (CBT) is increasingly used, but the factors modulating response to this treatment modality remain unclear. AIMS: This study aims to explore the demographic and clinical predictors of response to one-to-one CBT delivered via the internet. METHOD: Real-world clinical outcomes data were collected from 2211 NHS England patients completing a course of CBT delivered by a trained clinician via the internet. Logistic regression analyses were performed using patient and service variables to identify significant predictors of response to treatment. RESULTS: Multiple patient variables were significantly associated with positive response to treatment including older age, absence of long-term physical comorbidities and lower symptom severity at start of treatment. Service variables associated with positive response to treatment included shorter waiting times for initial assessment and longer treatment durations in terms of the number of sessions. CONCLUSIONS: Knowledge of which patient and service variables are associated with good clinical outcomes can be used to develop personalised treatment programmes, as part of a quality improvement cycle aiming to drive up standards in mental healthcare. This study exemplifies translational research put into practice and deployed at scale in the National Health Service, demonstrating the value of technology-enabled treatment delivery not only in facilitating access to care, but in enabling accelerated data capture for clinical research purposes. DECLARATION OF INTEREST: A.C., S.B., V.T., K.I., S.F., A.R., A.H. and A.D.B. are employees or board members of the sponsor. S.R.C. consults for Cambridge Cognition and Shire. Keywords: Anxiety disorders; cognitive behavioural therapies; depressive disorders; individual psychotherapy.