Background and aim Postconcussion syndrome (PCS) is a term used to describe the complex, and controversial, constellation of physical, cognitive and emotional symptoms associated with mild brain injury. At the current time, there is a lack of clear, evidence-based treatment strategies. In this systematic review, the authors aimed to evaluate the potential efficacy of cognitive behavioural therapy (CBT) and other psychological treatments in postconcussion symptoms. Methods Four electronic databases were searched up to November 2008 for studies of psychological approaches to treatment or prevention of postconcussion syndrome or symptoms. Results The search identified 7763 citations, and 42 studies were included. This paper reports the results of 17 randomised controlled trials for psychological interventions which fell into four categories: CBT for PCS or specific PCS symptoms; information, reassurance and education; rehabilitation with a psychotherapeutic element and mindfulness/relaxation. Due to heterogeneity of methodology and outcome measures, a meta-analysis was not possible. The largest limitation to our findings was the lack of high-quality studies.

There is a growing body of research investigating the impact on mental health professionals of losing a patient through suicide. However, the nature and extent of the impact is unclear. This systematic review synthesizes both quantitative and qualitative studies in the area. The aim was to review the literature on the impact of losing a patient through suicide with respect to both personal and professional practice responses as well as the support received. A search of the major psychological and medical databases was conducted, using keywords including suicide, patient, practitioner, and impact, which yielded 3,942 records. Fifty‐four studies were included in the final narrative synthesis. Most common personal reactions in qualitative studies included guilt, shock, sadness, anger, and blame. Impact on professional practice included self‐doubt and being more cautious and defensive in the management of suicide risk. As quantitative study methodologies were heterogeneous, it was difficult to make direct comparisons across studies. However, 13 studies (total n = 717 practitioners) utilized the Impact of Event Scale, finding that between 12% and 53% of practitioners recorded clinically significant scores. The need for training that is focused on the impact of suicides, and the value placed upon informal support was often cited. The experience of losing a patient through suicide can have a significant impact on mental health professionals, both in terms of their personal reactions and subsequent changes to professional practice. The negative impact, however, may be moderated by cultural and organisational factors and by the nature of support available.

No abstract

Background Common mental disorders can be effectively treated with psychotherapy, but some patients do not respond well and require timely identification to prevent treatment failure. We aimed to develop and validate a dynamic model to predict psychological treatment outcomes, and to compare the model with currently used methods, including expected treatment response models and machine learning models. MethodsIn this prediction model development and validation study, we obtained data from two UK studies including patients who had accessed therapy via Improving Access to Psychological Therapies (IAPT) services managed by ten UK National Health Service (NHS) Trusts between March, 2012, and June, 2018, to predict treatment outcomes. In study 1, we used data on patient-reported depression (Patient Health Questionnaire 9 [PHQ-9]) and anxiety (Generalised Anxiety Disorder 7 [GAD-7]) symptom measures obtained on a session-by-session basis (Leeds Community Healthcare NHS Trust dataset; n=2317) to train the Oracle dynamic prediction model using iterative logistic regression analysis. The outcome of interest was reliable and clinically significant improvement in depression (PHQ-9) and anxiety (GAD-7) symptoms. The predictive accuracy of the model was assessed in an external test sample (Cumbria Northumberland Tyne and Wear NHS Foundation Trust dataset; n=2036) using the area under the curve (AUC), positive predictive values (PPVs), and negative predictive values (NPVs). In study 2, we retrained the Oracle algorithm using a multiservice sample (South West Yorkshire Partnership NHS Foundation Trust, North East London NHS Foundation Trust, Cheshire and Wirral Partnership NHS Foundation Trust, and Cambridgeshire and Peterborough NHS Foundation Trust; n=42 992) and compared its performance with an expected treatment response model and five machine learning models (Bayesian updating algorithm, elastic net regularisation, extreme gradient boosting, support vector machine, and neural networks based on a multilayer perceptron algorithm) in an external test sample (Whittington Health NHS Trust; Barnet Enfield and Haringey Mental Health Trust; Pennine Care NHS Foundation Trust; and Humber NHS Foundation Trust; n=30 026). Findings The Oracle algorithm trained using iterative logistic regressions generalised well to external test samples, explaining up to 47•3% of variability in treatment outcomes. Prediction accuracy was modest at session one (AUC 0•59 [95% CI 0•55-0•62], PPV 0•63, NPV 0•61), but improved over time, reaching high prediction accuracy (AUC 0•81 [0•77-0•86], PPV 0•79, NPV 0•69) as early as session seven. The performance of the Oracle model was similar to complex (eg, including patient profiling variables) and computationally intensive machine learning models (eg, neural networks based on a multilayer perceptron algorithm, extreme gradient boosting). Furthermore, the predictive accuracy of a more simple dynamic algorithm including only baseline and index-session scores was comparable to more complex algorithms that includ...