CHD associated with syndromic diagnoses: peri-operative risk factors and early outcomes

CHD is frequently associated with a genetic syndrome. These syndromes often present specific cardiovascular and non-cardiovascular co-morbidities that confer significant peri-operative risks affecting multiple organ systems. Although surgical outcomes have improved over time, these co-morbidities continue to contribute substantially to poor peri-operative mortality and morbidity outcomes. Peri-operative morbidity may have long-standing ramifications on neurodevelopment and overall health. Recognising the cardiovascular and non-cardiovascular risks associated with specific syndromic diagnoses will facilitate expectant management, early detection of clinical problems, and improved outcomes--for example, the development of syndrome-based protocols for peri-operative evaluation and prophylactic actions may improve outcomes for the more frequently encountered syndromes such as 22q11 deletion syndrome

Patient preferences for management of high blood pressure in the UK:A discrete choice experiment

Background: With a variety of potentially effective hypertension management options, it is important to determine how patients value different models of care, and the relative importance of factors in their decision-making process. Aim: To explore patient preferences for the management of hypertension in the UK. Design and setting: Online survey of patients who have hypertension in the UK including an unlabelled discrete choice experiment (DCE). Method: A DCE was developed to assess patient preferences for the management of hypertension based on four attributes: model of care, frequency of blood pressure (BP) measurement, reduction in 5-year cardiovascular risk, and costs to the NHS. A mixed logit model was used to estimate preferences, willingness-to-pay was modelled, and a scenario analysis was conducted to evaluate the impact of changes in attribute levels on the uptake of different models of care. Results: One hundred and sixty-seven participants completed the DCE (aged 61.4 years, 45.0% female, 82.0% >5 years since diagnosis). All four attributes were significant in choice (P<0.05). Reduction in 5-year cardiovascular risk was the main driver of patient preference as evidenced in the scenario and willingness-to-pay analyses. GP management was significantly preferred over self-management. Patients preferred scenarios with more frequent BP measurement, and lower costs to the NHS. Conclusion: Participants had similar preferences for GP management, pharmacist management, and telehealth, but a negative preference for self-management. When introducing new models of care for hypertension to patients, discussion of the potential benefits in terms of risk reduction should be prioritised to maximise uptake

Deep learning networks find unique mammographic differences in previous negative mammograms between interval and screen-detected cancers: a case-case study.

BackgroundTo determine if mammographic features from deep learning networks can be applied in breast cancer to identify groups at interval invasive cancer risk due to masking beyond using traditional breast density measures.MethodsFull-field digital screening mammograms acquired in our clinics between 2006 and 2015 were reviewed. Transfer learning of a deep learning network with weights initialized from ImageNet was performed to classify mammograms that were followed by an invasive interval or screen-detected cancer within 12 months of the mammogram. Hyperparameter optimization was performed and the network was visualized through saliency maps. Prediction loss and accuracy were calculated using this deep learning network. Receiver operating characteristic (ROC) curves and area under the curve (AUC) values were generated with the outcome of interval cancer using the deep learning network and compared to predictions from conditional logistic regression with errors quantified through contingency tables.ResultsPre-cancer mammograms of 182 interval and 173 screen-detected cancers were split into training/test cases at an 80/20 ratio. Using Breast Imaging-Reporting and Data System (BI-RADS) density alone, the ability to correctly classify interval cancers was moderate (AUC = 0.65). The optimized deep learning model achieved an AUC of 0.82. Contingency table analysis showed the network was correctly classifying 75.2% of the mammograms and that incorrect classifications were slightly more common for the interval cancer mammograms. Saliency maps of each cancer case found that local information could highly drive classification of cases more than global image information.ConclusionsPre-cancerous mammograms contain imaging information beyond breast density that can be identified with deep learning networks to predict the probability of breast cancer detection

Selecting the Best of the Best: Associations between Anthropometric and Fitness Assessment Results and Success in Police Specialist Selection

International Journal of Exercise Science 11(4): 785-796, 2018. To successfully complete specialist police selection, officers must be physically fit. The aim of this study was to investigate the relationship between performance on selected anthropometric and fitness tests and successful selection into a specialist police unit. Thirty-two male police officers (mean age = 29.48±4.99 years) participated in a Barrier Fitness Assessment (BFA), followed by a Specialist Selection Course (SSC). The BFA spanned two consecutive days of testing (pull-ups, push-ups, seven-stage sit-ups, a timed loaded pack march, a Multi-Stage Fitness Test, an agility run, a lift and carry task and a 300m swim assessment). The SSC occurred 4 weeks later and consisted of 8 days of intense police training. Officers who successfully completed the SSC were graded based on their performance and this determined their ultimate selection. Data were categorized into four participant groups: Group 1 - Did not complete the BFA; Group 2 - Completed the BFA but not the SSC; Group 3 - Completed the SSC and were not selected; and Group 4 - Completed the SSC and were selected. A Spearman’s rank order correlation analysis was conducted to assess the strengths of the relationships between selection stage achieved and scores on each of the predictor variables, with significance set at 0.05. Height (p=0.011), body weight (p=0.011), pull-ups (p=0.021) and push-ups (p=0.016), seven-stage sit-up scores (p=0.042) and lift and carry speed (p=0.010) were significantly and positively correlated with level of selection success. Results suggest that candidates wishing to attempt selection into specialist police units would benefit from being tall and training to optimize musculoskeletal strength and muscular endurance