Research drives innovation and improved practice in psychotherapy. We describe views of members of the Faculty of Medical Psychotherapy of the Royal College of Psychiatrists (RCPsych) regarding their knowledge, experience and perspectives on psychotherapy research. We sent questionnaires to the Faculty membership emailing list.

In total, 172 psychiatrists from all levels of training returned fully complete responses. Respondents considered knowledge of psychotherapy research to be important to clinical work. Many have qualifications and experience in research but lack current opportunities for research involvement and would welcome the Faculty doing more to promote psychotherapy research. Perceived obstacles to research involvement included lack of competence, competing demands and wider organisational factors.

The lack of research opportunities for medical psychotherapists may lead to their underrepresentation in psychotherapy research and a less medically informed research agenda. Providing support at academic, RCPsych and National Health Service organisational levels will allow more clinically relevant research not only in psychotherapy but in other psychiatric disciplines as well.

Although no drugs are licensed for the treatment of personality disorder, pharmacological treatment in clinical practice remains common.

This study aimed to estimate the prevalence of psychotropic drug use and associations with psychological service use among people with personality disorder.

Using data from a large, anonymised mental healthcare database, we identified all adult patients with a diagnosis of personality disorder and ascertained psychotropic medication use between 1 August 2015 and 1 February 2016. Multivariable logistic regression models were constructed, adjusting for sociodemographic, clinical and service use factors, to examine the association between psychological services use and psychotropic medication prescribing.

Of 3366 identified patients, 2029 (60.3%) were prescribed some form of psychotropic medication. Patients using psychological services were significantly less likely to be prescribed psychotropic medication (adjusted odds ratio 0.48, 95% CI 0.39–0.59, P<0.001) such as antipsychotics, benzodiazepines and antidepressants. This effect was maintained following several sensitivity analyses. We found no difference in the risk for mood stabiliser (adjusted odds ratio 0.79, 95% CI 0.57–1.10, P = 0.169) and multi-class psychotropic use (adjusted odds ratio 0.80, 95% CI 0.60–1.07, P = 0.133) between patients who did and did not use psychological services.

Psychotropic medication prescribing is common in patients with personality disorder, but significantly less likely in those who have used psychological services. This does not appear to be explained by differences in demographic, clinical and service use characteristics. There is a need to develop clear prescribing guidelines and conduct research in clinical settings to examine medication effectiveness for this population.

Depression and cardiovascular disease (CVD) are associated with each other but their relationship remains unclear. We aim to determine whether genetic predisposition to depression are causally linked to CVD [including coronary artery disease (CAD), myocardial infarction (MI), stroke and atrial fibrillation (AF)].

Using summary statistics from the largest genome-wide association studies (GWAS) or GWAS meta-analysis of depression (primary analysis: n = 500 199), broad depression (help-seeking behavior for problems with nerves, anxiety, tension or depression; secondary analysis: n = 322 580), CAD (n = 184 305), MI (n = 171 875), stroke (n = 446 696) and AF (n = 1 030 836), genetic correlation was tested between two depression phenotypes and CVD [MI, stroke and AF (not CAD as its correlation was previously confirmed)]. Causality was inferred between correlated traits by Mendelian Randomization analyses.

Both depression phenotypes were genetically correlated with MI (depression: rG = 0.169; p = 9.03 × 10−9; broad depression: rG = 0.123; p = 1 × 10−4) and AF (depression: rG = 0.112; p = 7.80 × 10−6; broad depression: rG = 0.126; p = 3.62 × 10−6). Genetically doubling the odds of depression was causally associated with increased risk of CAD (OR = 1.099; 95% CI 1.031–1.170; p = 0.004) and MI (OR = 1.146; 95% CI 1.070–1.228; p = 1.05 × 10−4). Adjustment for blood lipid levels/smoking status attenuated the causality between depression and CAD/MI. Null causal association was observed for CVD on depression. A similar pattern of results was observed in the secondary analysis for broad depression.

Genetic predisposition to depression may have positive causal roles on CAD/MI. Genetic susceptibility to self-awareness of mood problems may be a strong causal risk factor of CAD/MI. Blood lipid levels and smoking may potentially mediate the causal pathway. Prevention and early diagnosis of depression are important in the management of CAD/MI.

The density of information in digital health records offers new potential opportunities for automated prediction of cost-relevant outcomes.

We investigated the extent to which routinely recorded data held in the electronic health record (EHR) predict priority service outcomes and whether natural language processing tools enhance the predictions. We evaluated three high priority outcomes: in-patient duration, readmission following in-patient care and high service cost after first presentation.

We used data obtained from a clinical database derived from the EHR of a large mental healthcare provider within the UK. We combined structured data with text-derived data relating to diagnosis statements, medication and psychiatric symptomatology. Predictors of the three different clinical outcomes were modelled using logistic regression with performance evaluated against a validation set to derive areas under receiver operating characteristic curves.

In validation samples, the full models (using all available data) achieved areas under receiver operating characteristic curves between 0.59 and 0.85 (in-patient duration 0.63, readmission 0.59, high service use 0.85). Adding natural language processing-derived data to the models increased the variance explained across all clinical scenarios (observed increase in r2 = 12–46%).

EHR data offer the potential to improve routine clinical predictions by utilising previously inaccessible data. Of our scenarios, prediction of high service use after initial presentation achieved the highest performance.