E-learning has become commonplace in medical education. Incorporation of multimedia, clinical cases, and interactive elements has increased its attractiveness over textbooks. Although there has been an expansion of e-learning in medicine, the feasibility of e-learning in pediatric neurology is unclear. This study evaluates knowledge acquisition and satisfaction using pediatric neurology e-learning compared to conventional learning.

Residents of Canadian pediatrics, neurology, and pediatric neurology programs and medical students from Queens University, Western University, and the University of Ottawa were invited to participate. Learners were randomly assigned two review papers and two ebrain modules in a four-topic crossover design. Participants completed pre-tests, experience surveys, and post-tests. We calculated the median change in score from pre-test to post-test and constructed a mixed-effects model to determine the effect of variables on post-test scores.

In total, 119 individuals participated (53 medical students; 66 residents). Ebrain had a larger positive change than review papers in post-test score from pre-test score for the pediatric stroke learning topic but a smaller positive change for Duchenne muscular dystrophy, childhood absence epilepsy, and acute disseminated encephalomyelitis. Learning topics showed statistical relationship to post-test scores (p = 0.04). Depending on topic, 57–92% (N = 59–66) of respondents favored e-learning over review article learning.

Ebrain users scored higher on post-tests than review paper users. However, the effect is small and it is unclear if it is educationally meaningful. Although the difference in scores may not be substantially different, most learners preferred e-learning. Future projects should focus on improving the quality and efficacy of e-learning modules.

Many male prisoners have significant mental health problems, including anxiety and depression. High proportions struggle with homelessness and substance misuse.

This study aims to evaluate whether the Engager intervention improves mental health outcomes following release.

The design is a parallel randomised superiority trial that was conducted in the North West and South West of England (ISRCTN11707331). Men serving a prison sentence of 2 years or less were individually allocated 1:1 to either the intervention (Engager plus usual care) or usual care alone. Engager included psychological and practical support in prison, on release and for 3–5 months in the community. The primary outcome was the Clinical Outcomes in Routine Evaluation Outcome Measure (CORE-OM), 6 months after release. Primary analysis compared groups based on intention-to-treat (ITT).

In total, 280 men were randomised out of the 396 who were potentially eligible and agreed to participate; 105 did not meet the mental health inclusion criteria. There was no mean difference in the ITT complete case analysis between groups (92 in each arm) for change in the CORE-OM score (1.1, 95% CI –1.1 to 3.2, P = 0.325) or secondary analyses. There were no consistent clinically significant between-group differences for secondary outcomes. Full delivery was not achieved, with 77% (108/140) receiving community-based contact.

Engager is the first trial of a collaborative care intervention adapted for prison leavers. The intervention was not shown to be effective using standard outcome measures. Further testing of different support strategies for prison with mental health problems is needed.

Severe mental illness (SMI) is associated with increased stroke risk, but little is known about how SMI relates to stroke prognosis and receipt of acute care.

To determine the association between SMI and stroke outcomes and receipt of process-of-care quality indicators (such as timely admission to stroke unit).

We conducted a cohort study using routinely collected linked data-sets, including adults with a first hospital admission for stroke in Scotland during 1991–2014, with process-of-care quality indicator data available from 2010. We identified pre-existing schizophrenia, bipolar disorder and major depression from hospital records. We used logistic regression to evaluate 30-day, 1-year and 5-year mortality and receipt of process-of-care quality indicators by pre-existing SMI, adjusting for sociodemographic and clinical factors. We used Cox regression to evaluate further stroke and vascular events (stroke and myocardial infarction).

Among 228 699 patients who had had a stroke, 1186 (0.5%), 859 (0.4%), 7308 (3.2%) had schizophrenia, bipolar disorder and major depression, respectively. Overall, median follow-up was 2.6 years. Compared with adults without a record of mental illness, 30-day mortality was higher for schizophrenia (adjusted odds ratio (aOR) = 1.33, 95% CI 1.16–1.52), bipolar disorder (aOR = 1.37, 95% CI 1.18–1.60) and major depression (aOR = 1.11, 95% CI 1.05–1.18). Each disorder was also associated with marked increased risk of 1-year and 5-year mortality and further stroke and vascular events. There were no clear differences in receipt of process-of-care quality indicators.

Pre-existing SMI was associated with higher risks of mortality and further vascular events. Urgent action is needed to better understand and address the reasons for these disparities.

Discrepancies between the National Institute for Health and Care Excellence (NICE) schizophrenia guideline recommendations and current clinical practice in the UK have been reported.

We aim to assess whether it is cost-effective to improve adherence to the NICE schizophrenia guideline recommendations, compared with current practice.

A previously developed whole-disease model for schizophrenia, using the discrete event simulation method, was adapted to assess the cost and health impacts of adherence to the NICE recommendations. Three scenarios to improve adherence to the clinical guidelines were modelled: universal provision of cognitive–behavioural therapy for patients at clinical high risk of psychosis, universal provision of family intervention for patients with first-episode psychosis and prompt provision of clozapine for patients with treatment-resistant schizophrenia. The primary outcomes were lifetime costs and quality-adjusted life-years gained.

The results suggest full adherence to the guideline recommendations would decrease cost and improve quality-adjusted life-years. Based on the NICE willingness-to-pay threshold of £20 000–£30 000 per quality-adjusted life-year gained, prompt provision of clozapine for patients with treatment-resistant schizophrenia results in the greatest net monetary benefit, followed by universal provision of cognitive–behavioural therapy for patients at clinical high risk of psychosis, and universal provision of family intervention for patients with first-episode psychosis.

Our results suggest that adherence to guideline recommendations would decrease cost and improve quality-adjusted life-years. Greater investment is needed to improve guideline adherence and therefore improve patient quality of life and realise potential cost savings.

Psychiatric disorders, including eating disorders (EDs), have clinical outcomes that range widely in severity and chronicity. The ability to predict such outcomes is extremely limited. Machine-learning (ML) approaches that model complexity may optimize the prediction of multifaceted psychiatric behaviors. However, the investigations of many psychiatric concerns have not capitalized on ML to improve prognosis. This study conducted the first comparison of an ML approach (elastic net regularized logistic regression) to traditional regression to longitudinally predict ED outcomes.

Females with heterogeneous ED diagnoses completed demographic and psychiatric assessments at baseline (n = 415) and Year 1 (n = 320) and 2 (n = 277) follow-ups. Elastic net and traditional logistic regression models comprising the same baseline variables were compared in ability to longitudinally predict ED diagnosis, binge eating, compensatory behavior, and underweight BMI at Years 1 and 2.

Elastic net models had higher accuracy for all outcomes at Years 1 and 2 [average Area Under the Receiving Operating Characteristics Curve (AUC) = 0.78] compared to logistic regression (average AUC = 0.67). Model performance did not deteriorate when the most important predictor was removed or an alternative ML algorithm (random forests) was applied. Baseline ED (e.g. diagnosis), psychiatric (e.g. hospitalization), and demographic (e.g. ethnicity) characteristics emerged as important predictors in exploratory predictor importance analyses.

ML algorithms can enhance the prediction of ED symptoms for 2 years and may identify important risk markers. The superior accuracy of ML for predicting complex outcomes suggests that these approaches may ultimately aid in advancing precision medicine for serious psychiatric disorders.

Psychiatric disorders are associated with increased risk of ischaemic heart disease (IHD) and stroke, but it is not known whether the associations or the role of sociodemographic factors have changed over time.

To investigate the association between psychiatric disorders and IHD and stroke, by time period and sociodemographic factors.

We used Scottish population-based records from 1991 to 2015 to create retrospective cohorts with a hospital record for psychiatric disorders of interest (schizophrenia, bipolar disorder or depression) or no record of hospital admission for mental illness. We estimated incidence and relative risks of IHD and stroke in people with versus without psychiatric disorders by calendar year, age, gender and area-based deprivation level.

In all cohorts, incidence of IHD (645 393 events) and stroke (276 073 events) decreased over time, but relative risks decreased for depression only. In 2015, at the mean age at event onset, relative risks were 2- to 2.5-fold higher in people with versus without a psychiatric disorder. Age at incidence of outcome differed by cohort, gender and socioeconomic status. Relative but not absolute risks were generally higher in women than men. Increasing deprivation conveys a greater absolute risk of IHD for people with bipolar disorder or depression.

Despite declines in absolute rates of IHD and stroke, relative risks remain high in those with versus without psychiatric disorders. Cardiovascular disease monitoring and prevention approaches may need to be tailored by psychiatric disorder and cardiovascular outcome, and be targeted, for example, by age and deprivation level.

Infants with single ventricle physiology have arterial oxygen saturations between 75 and 85%. Home monitoring with daily pulse oximetry is associated with improved interstage survival. They are typically sent home with expensive, bulky, hospital-grade pulse oximeters. This study evaluates the accuracy of both the currently used Masimo LNCS and a relatively inexpensive, portable, and equipped with Bluetooth technology study device, by comparing with the gold standard co-oximeter.

Prospective, observational study.

Single institution, paediatric cardiac critical care unit, and neonatal ICU.

none.

Twenty-four infants under 12 months of age with baseline oxygen saturation less than 90% due to cyanotic CHD.

Pulse oximetry with WristOx2 3150 with infant sensors 8008 J (study device) and Masimo LCNS saturation sensor connected to a Philips monitor (hospital device) were measured simultaneously and compared to arterial oxy-haemoglobin saturation measured by co-oximetry. Statistical analysis evaluated the performances of each and compared to co-oximetry with Schuirmann’s TOST equivalence tests, with equivalence defined as an absolute difference of 5% saturation or less. Neither the study nor the hospital device met the predefined standard for equivalence when compared with co-oximetry. The study device reading was on average 4.0% higher than the co-oximeter, failing to show statistical equivalence (p = 0.16). The hospital device was 7.4% higher than the co-oximeter and also did not meet the predefined standard for equivalence (p = 0.97).

Both devices tended to overestimate oxygen saturation in this patient population when compared to the gold standard, co-oximetry. The study device is at least as accurate as the hospital device and offers the advantage of being more portable with Bluetooth technology that allows reliable, efficient data transmission. Currently FDA-approved, smaller portable pulse oximeters can be considered for use in home monitoring programmes.

Many studies have identified changes in the brain associated with obsessive–compulsive disorder (OCD), but few have examined the relationship between genetic determinants of OCD and brain variation.

We present the first genome-wide investigation of overlapping genetic risk for OCD and genetic influences on subcortical brain structures.

Using single nucleotide polymorphism effect concordance analysis, we measured genetic overlap between the first genome-wide association study (GWAS) of OCD (1465 participants with OCD, 5557 controls) and recent GWASs of eight subcortical brain volumes (13 171 participants).

We found evidence of significant positive concordance between OCD risk variants and variants associated with greater nucleus accumbens and putamen volumes. When conditioning OCD risk variants on brain volume, variants influencing putamen, amygdala and thalamus volumes were associated with risk for OCD.

These results are consistent with current OCD neurocircuitry models. Further evidence will clarify the relationship between putamen volume and OCD risk, and the roles of the detected variants in this disorder.

The authors have declared that no competing interests exist.

The authors have no competing financial interests to declare in relation to the current work. Sarah McAllister was supported by a King's Undergraduate Research Fellowship.