Currently, no separate service exists for patients with young-onset dementia in Cambridgeshire. These patients are managed together with late-onset dementia patients within old age psychiatry services. To inform service design, we sought to characterise young-onset dementia patients in our population. We first analysed service-level data and supplemented this with a detailed case review of 90 patients.

Young-onset dementia remains a relatively rare condition. Only a small proportion of those referred for assessment receive a diagnosis of dementia. Data collected on presenting complaints, comorbidities, medication and Health of the Nation Outcome Scales scores associated young-onset dementia with a greater incidence of depression than late-onset dementia. Outcomes in the two groups did not appear to differ.

The data presented here do not suggest a need to create a separate service. Practitioners should be aware of the increased incidence of depression observed in this group.

We aimed to assess the incidence of obstructive sleep apnoea (OSA) in people with schizophrenia, to explore clinical associates with OSA and how well OSA screening tools perform in this population.

All patients registered in a community outpatient Clozapine clinic, between January 2014 and March 2016, were consecutively approached to participate. Participants were screened for OSA using at home multichannel polysomnography (PSG) and were diagnosed with OSA if the apnoea-hypopnoea index (AHI) was >10 events/hr. Univariate comparison of participants to determine whether AHI > 10 events/hr was associated with demographic factors, anthropometric measures and psychiatric symptoms and cognition was performed. The sensitivity, specificity, positive predictive value and negative predictive value of the commonly used sleep symptoms scales and OSA screening tools were also determined.

Thirty participants were recruited, 24 men and 6 women. Mean age was 38.8 (range: 25–60), and mean body mass index (BMI) was 35.7 (range 19.9–62.1). The proportion of participants with OSA (AHI > 10 events/hr) was 40%, 18 (60%) had no OSA, 4 (13%) had mild OSA (AHI 10.1–20), zero participants had moderate OSA (AHI 20.1–30) and 8 (27%) had severe OSA (AHI > 30). Diagnosis of OSA was significantly associated with increased weight, BMI, neck circumference and systolic blood pressure. Diagnosis of OSA was not significantly associated with Positive and Negative Symptoms Scale, Montgomery Asperger’s Depression Rating Scale, Personal and Social Performance scale or Brief Assessment of Cognition for Schizophrenia scores. All OSA screening tools demonstrated poor sensitivity and specificity for a diagnosis of OSA.

OSA was highly prevalent in this cohort of people with schizophrenia and was associated with traditional anthropometric OSA risk factors.

Recent literature suggests that over 70% of cases of antibody-mediated encephalitis present to psychiatry services with features of psychosis predominantly.

To investigate the seroprevalence of N-Methyl-D-Aspartate receptor antibodies (NMDAr-Ab) in patients with first episode psychosis (FEP)

Following ethical approval, all cases meeting entry criteria were invited to participate. Participants were interviewed with SCID to obtain a DSM diagnosis. NMDAr-Ab were identified in serum by cell based assay using co-transfected Human Embryonic Kidney (HEK)cells. Positive cases were reviewed by clinical neurology. Decision to treat with immunotherapy was made on a case by case basis.

85/115 (72%) of patients with FEP entered the study. 49 (58%) participants were male, mean age (SD) 37 (15.7) years. 42 (52%) were outpatients at the time of assessment. Four cases (5%) were serum NMDAr-Ab positive. 3 of these cases were male, age 48 (16.3) years. All four were admitted as inpatients with normal brain MRI imaging. One case (female, 55) was confirmed as NMDAr-Ab encephalitis based on case presentation, EEG demonstrating bilateral cerebral dysfunction and NMDAr-Ab in CSF. Immunotherapy treatment lead to clinical improvement. In remaining cases, EEG was normal and CSF negative. All 3 of these cases showed clinical improvement following psychiatric treatment as usual.

Our findings support the current estimates as to NMDAr-Ab prevalence in FEP. Increased awareness has lead to rapid treatment of florid cases of NMDAr-Ab encephalitis in our service. Additional seropositive cases are being followed with neuro-cognitive testing for any evidence of decline.

Relatively few studies have assessed the prevalence, correlates, and independent impact on quality of life (QoL) of trichotillomania (TTM) in large samples.

Consecutive participants (N = 7639) were recruited from a cross-sectional web-based study. Sociodemographic data were collected and several validated self-reported mental health measures were completed (Minnesota Impulsive Disorders Interview, Hypomania checklist, Fagerström Test for Nicotine Dependence, Alcohol Use Disorders Identification Test, Early Trauma Inventory Self Report–Short Form, and the Symptom Checklist-90–Revised Inventory). Health-related QoL was assessed with the World Health Organization QoL abbreviated scale (WHOQOL-Bref). Multivariable models adjusted associations to potential confounders.

The sample was predominantly composed of young females (71.3%; mean age: 27.2 ± 7.9 years). The prevalence of probable TTM was 1.4% (95% confidence intervals [CI]: 1.2-1.7), and was more common among females. Participants with probable TTM had a greater likelihood of having co-occurring probable depression (adjusted odds ratio [ORadj] = 1.744; 95% CI: 1.187-2.560), tobacco (ORadj = 2.250; 95% CI: 1.191-4.250), and alcohol (ORadj = 1.751; 95% CI: 1.169-2.621) use disorders. Probable TTM was also independently associated with suicidal ideation (ORadj = 1.917; 95% CI: 1.224-3.003) and exposure to childhood sexual abuse (ORadj = 1.221; 95% CI: 1.098-1.358). In addition, a positive screen for TTM had more impaired physical and mental QoL.

TTM was associated with a positive screen for several psychiatric comorbidities as well as impaired physical and psychological QoL. Efforts towards the recognition and treatment of TTM across psycho-dermatology services are warranted.

Disasters are high-acuity, low-frequency events which require medical providers to respond in often chaotic settings. Due to this infrequency, skills can atrophy, so providers must train and drill to maintain them. Historically, drilling for disaster response has been costly, and thus infrequent. Virtual Reality Environments (VREs) have been demonstrated to be acceptable to trainees, and useful for training Disaster Medicine skills. The improved cost of virtual reality training can allow for increased frequency of simulation and training.

The problem addressed was to create a novel Disaster Medicine VRE for training and drilling.

A VRE was created using SecondLife (Linden Lab; San Francisco, California USA) and adapted for use in Disaster Medicine training and drilling. It is easily accessible for the end-users (trainees), and is adaptable for multiple scenario types due to the presence of varying architecture and objects. Victim models were created which can be role played by educators, or can be virtual dummies, and can be adapted for wide ranging scenarios. Finally, a unique physiologic simulator was created which allows for dummies to mimic disease processes, wounds, and treatment outcomes.

The VRE was created and has been used extensively in an academic setting to train medical students, as well as to train and drill disaster responders.

This manuscript presents a new VRE for the training and drilling of Disaster Medicine scenarios in an immersive, interactive experience for trainees.

To define optimal thromboprophylaxis strategy after stent implantation in superior or total cavopulmonary connections.

Stent thrombosis is a rare complication of intravascular stenting, with a perceived higher risk in single-ventricle patients.

All patients who underwent stent implantation within superior or total cavopulmonary connections (caval vein, innominate vein, Fontan, or branch pulmonary arteries) were included. Cohort was divided into aspirin therapy alone versus advanced anticoagulation, including warfarin, enoxaparin, heparin, or clopidogrel. Primary endpoint was in-stent or downstream thrombus, and secondary endpoints included bleeding complications.

A total of 58 patients with single-ventricle circulation underwent 72 stent implantations. Of them 14 stents (19%) were implanted post-superior cavopulmonary connection and 58 (81%) post-total cavopulmonary connection. Indications for stenting included vessel/conduit stenosis (67%), external compression (18%), and thrombotic occlusion (15%). Advanced anticoagulation was prescribed for 32 (44%) patients and aspirin for 40 (56%) patients. Median follow up was 1.1 (25th–75th percentile, 0.5–2.6) years. Echocardiograms were available in 71 patients (99%), and advanced imaging in 44 patients (61%). Thrombosis was present in two patients on advanced anticoagulation (6.3%) and none noted in patients on aspirin (p = 0.187). Both patients with in-stent thrombus underwent initial stenting due to occlusive left pulmonary artery thrombus acutely post-superior cavopulmonary connection. There were seven (22%) significant bleeding complications for advanced anticoagulation and none for aspirin (p < 0.001).

Antithrombotic strategy does not appear to affect rates of in-stent thrombus in single-ventricle circulations. Aspirin alone may be sufficient for most patients undergoing stent implantation, while pre-existing thrombus may warrant advanced anticoagulation.

Culture-based studies, which focus on individual organisms, have implicated stethoscopes as potential vectors of nosocomial bacterial transmission. However, the full bacterial communities that contaminate in-use stethoscopes have not been investigated.

We used bacterial 16S rRNA gene deep-sequencing, analysis, and quantification to profile entire bacterial populations on stethoscopes in use in an intensive care unit (ICU), including practitioner stethoscopes, individual-use patient-room stethoscopes, and clean unused individual-use stethoscopes. Two additional sets of practitioner stethoscopes were sampled before and after cleaning using standardized or practitioner-preferred methods.

Bacterial contamination levels were highest on practitioner stethoscopes, followed by patient-room stethoscopes, whereas clean stethoscopes were indistinguishable from background controls. Bacterial communities on stethoscopes were complex, and community analysis by weighted UniFrac showed that physician and patient-room stethoscopes were indistinguishable and significantly different from clean stethoscopes and background controls. Genera relevant to healthcare-associated infections (HAIs) were common on practitioner stethoscopes, among which Staphylococcus was ubiquitous and had the highest relative abundance (6.8%–14% of contaminating bacterial sequences). Other HAI-related genera were also widespread although lower in abundance. Cleaning of practitioner stethoscopes resulted in a significant reduction in bacterial contamination levels, but these levels reached those of clean stethoscopes in only a few cases with either standardized or practitioner-preferred methods, and bacterial community composition did not significantly change.

Stethoscopes used in an ICU carry bacterial DNA reflecting complex microbial communities that include nosocomially important taxa. Commonly used cleaning practices reduce contamination but are only partially successful at modifying or eliminating these communities.

An estimated 293,300 healthcare-associated cases of Clostridium difficile infection (CDI) occur annually in the United States. To date, research has focused on developing risk prediction models for CDI that work well across institutions. However, this one-size-fits-all approach ignores important hospital-specific factors. We focus on a generalizable method for building facility-specific models. We demonstrate the applicability of the approach using electronic health records (EHR) from the University of Michigan Hospitals (UM) and the Massachusetts General Hospital (MGH).

We utilized EHR data from 191,014 adult admissions to UM and 65,718 adult admissions to MGH. We extracted patient demographics, admission details, patient history, and daily hospitalization details, resulting in 4,836 features from patients at UM and 1,837 from patients at MGH. We used L2 regularized logistic regression to learn the models, and we measured the discriminative performance of the models on held-out data from each hospital.

Using the UM and MGH test data, the models achieved area under the receiver operating characteristic curve (AUROC) values of 0.82 (95% confidence interval [CI], 0.80–0.84) and 0.75 ( 95% CI, 0.73–0.78), respectively. Some predictive factors were shared between the 2 models, but many of the top predictive factors differed between facilities.

A data-driven approach to building models for estimating daily patient risk for CDI was used to build institution-specific models at 2 large hospitals with different patient populations and EHR systems. In contrast to traditional approaches that focus on developing models that apply across hospitals, our generalizable approach yields risk-stratification models tailored to an institution. These hospital-specific models allow for earlier and more accurate identification of high-risk patients and better targeting of infection prevention strategies.

Infect Control Hosp Epidemiol 2018;39:425–433

Predicting recurrent Clostridium difficile infection (rCDI) remains difficult. METHODS. We employed a retrospective cohort design. Granular electronic medical record (EMR) data had been collected from patients hospitalized at 21 Kaiser Permanente Northern California hospitals. The derivation dataset (2007–2013) included data from 9,386 patients who experienced incident CDI (iCDI) and 1,311 who experienced their first CDI recurrences (rCDI). The validation dataset (2014) included data from 1,865 patients who experienced incident CDI and 144 who experienced rCDI. Using multiple techniques, including machine learning, we evaluated more than 150 potential predictors. Our final analyses evaluated 3 models with varying degrees of complexity and 1 previously published model.

Despite having a large multicenter cohort and access to granular EMR data (eg, vital signs, and laboratory test results), none of the models discriminated well (c statistics, 0.591–0.605), had good calibration, or had good explanatory power.

Our ability to predict rCDI remains limited. Given currently available EMR technology, improvements in prediction will require incorporating new variables because currently available data elements lack adequate explanatory power.

Infect Control Hosp Epidemiol 2017;38:1196–1203