Qualitative study of adversity activated development and resilience in adolescents and young adults with congenital heart disease and their parents

What is it like to have a medical condition that few people have ever heard about? How does it feel to have to question whether daily physical activities are dangerous for you, whilst you watch your friends enjoy those activities without a care? Can you imagine that you need to have a complicated heart surgery, with risks such as paralysis or death? Or even imagine facing the painful recovery period and scars after such a surgery? Then imagine that you are a child or teenager dealing with this medical condition when all your friends are simply occupied with school and normal life. Now consider that surgery has been undertaken to extend your lifespan, but the operation is so new that the long-term outcomes are just not known? All you really know is that you might have ‘surgical repairs’ to your heart and symptoms may be relieved or managed by medications or cardiac devices, but you are never going to be cured. What if you had already experienced painful, frightening, lonely and tedious hospitalisations and you were forced to put your life on hold to re-enter that situation, time and time again. This may be your life, as a Congenital Heart Disease or CHD patient. How do such patients cope and in many cases even thrive? This chapter will review current international literature regarding the medical and personal impact of CHD. Our qualitative study of the perspectives of young CHD patients and their parents contributes to the Australian story of CHD, as well as highlighting the potential for CHD related adversity to promote personal development.

Application of data mining techniques in the prediction of coronary artery disease : use of anaesthesia time-series and patient risk factor data

The high morbidity and mortality associated with atherosclerotic coronary vascular disease (CVD) and its complications are being lessened by the increased knowledge of risk factors, effective preventative measures and proven therapeutic interventions. However, significant CVD morbidity remains and sudden cardiac death continues to be a presenting feature for some subsequently diagnosed with CVD. Coronary vascular disease is also the leading cause of anaesthesia related complications. Stress electrocardiography/exercise testing is predictive of 10 year risk of CVD events and the cardiovascular variables used to score this test are monitored peri-operatively. Similar physiological time-series datasets are being subjected to data mining methods for the prediction of medical diagnoses and outcomes. This study aims to find predictors of CVD using anaesthesia time-series data and patient risk factor data. Several pre-processing and predictive data mining methods are applied to this data. Physiological time-series data related to anaesthetic procedures are subjected to pre-processing methods for removal of outliers, calculation of moving averages as well as data summarisation and data abstraction methods. Feature selection methods of both wrapper and filter types are applied to derived physiological time-series variable sets alone and to the same variables combined with risk factor variables. The ability of these methods to identify subsets of highly correlated but non-redundant variables is assessed. The major dataset is derived from the entire anaesthesia population and subsets of this population are considered to be at increased anaesthesia risk based on their need for more intensive monitoring (invasive haemodynamic monitoring and additional ECG leads). Because of the unbalanced class distribution in the data, majority class under-sampling and Kappa statistic together with misclassification rate and area under the ROC curve (AUC) are used for evaluation of models generated using different prediction algorithms. The performance based on models derived from feature reduced datasets reveal the filter method, Cfs subset evaluation, to be most consistently effective although Consistency derived subsets tended to slightly increased accuracy but markedly increased complexity. The use of misclassification rate (MR) for model performance evaluation is influenced by class distribution. This could be eliminated by consideration of the AUC or Kappa statistic as well by evaluation of subsets with under-sampled majority class. The noise and outlier removal pre-processing methods produced models with MR ranging from 10.69 to 12.62 with the lowest value being for data from which both outliers and noise were removed (MR 10.69). For the raw time-series dataset, MR is 12.34. Feature selection results in reduction in MR to 9.8 to 10.16 with time segmented summary data (dataset F) MR being 9.8 and raw time-series summary data (dataset A) being 9.92. However, for all time-series only based datasets, the complexity is high. For most pre-processing methods, Cfs could identify a subset of correlated and non-redundant variables from the time-series alone datasets but models derived from these subsets are of one leaf only. MR values are consistent with class distribution in the subset folds evaluated in the n-cross validation method. For models based on Cfs selected time-series derived and risk factor (RF) variables, the MR ranges from 8.83 to 10.36 with dataset RF_A (raw time-series data and RF) being 8.85 and dataset RF_F (time segmented time-series variables and RF) being 9.09. The models based on counts of outliers and counts of data points outside normal range (Dataset RF_E) and derived variables based on time series transformed using Symbolic Aggregate Approximation (SAX) with associated time-series pattern cluster membership (Dataset RF_ G) perform the least well with MR of 10.25 and 10.36 respectively. For coronary vascular disease prediction, nearest neighbour (NNge) and the support vector machine based method, SMO, have the highest MR of 10.1 and 10.28 while logistic regression (LR) and the decision tree (DT) method, J48, have MR of 8.85 and 9.0 respectively. DT rules are most comprehensible and clinically relevant. The predictive accuracy increase achieved by addition of risk factor variables to time-series variable based models is significant. The addition of time-series derived variables to models based on risk factor variables alone is associated with a trend to improved performance. Data mining of feature reduced, anaesthesia time-series variables together with risk factor variables can produce compact and moderately accurate models able to predict coronary vascular disease. Decision tree analysis of time-series data combined with risk factor variables yields rules which are more accurate than models based on time-series data alone. The limited additional value provided by electrocardiographic variables when compared to use of risk factors alone is similar to recent suggestions that exercise electrocardiography (exECG) under standardised conditions has limited additional diagnostic value over risk factor analysis and symptom pattern. The effect of the pre-processing used in this study had limited effect when time-series variables and risk factor variables are used as model input. In the absence of risk factor input, the use of time-series variables after outlier removal and time series variables based on physiological variable values’ being outside the accepted normal range is associated with some improvement in model performance.

Dalcetrapib restoring belief in modulating CETP as a beneficial mechanism in cardiovascular disease

Abstract Background: As low HDL cholesterol levels are a risk factor for cardiovascular disease, raising HDL cholesterol substantially by inhibiting or modulating cholesteryl ester transfer protein (CETP) may be useful in coronary artery disease. The first CETP inhibitor that went into clinical trial, torcetrapib, was shown to increase the levels of HDL cholesterol, but it also increased cardiovascular outcomes, probably due to an increase in blood pressure and aldosterone secretion, by an off-target mechanism/s. Objective/methods: Dalcetrapib is a new CETP modulator that increases the levels of HDL cholesterol, but does not increase blood pressure or aldosterone secretion. The objective was to evaluate a paper describing the effects of dalcetrapib on carotid and aortic wall thickness in subjects with, or at high risk, of coronary artery disease; the dal-PLAQUE study. Results: dal-PLAQUE showed that dalcetrapib reduced the progression of atherosclerosis and may also reduce the vascular inflammation associated with this, in subjects with, or with high risk of, coronary heart disease, who were already taking statins. Conclusions: These results suggest that modulating CETP with dalcetrapib may be a beneficial mechanism in cardiovascular disease. The results of the dal-HEART series, which includes dal-PLAQUE 1 and 2, and dal-OUTCOMES, when complete, will provide more definitive information about the benefit, or not, of dalcetrapib in coronary artery disease.

Extremely cold and hot temperatures increase the risk of ischaemic heart disease mortality : epidemiological evidence from China

Objective: To examine the effects of extremely cold and hot temperatures on ischaemic heart disease (IHD) mortality in five cities (Beijing, Tianjin, Shanghai, Wuhan and Guangzhou) in China; and to examine the time relationships between cold and hot temperatures and IHD mortality for each city. Design: A negative binomial regression model combined with a distributed lag non-linear model was used to examine city-specific temperature effects on IHD mortality up to 20 lag days. A meta-analysis was used to pool the cold effects and hot effects across the five cities. Patients: 16 559 IHD deaths were monitored by a sentinel surveillance system in five cities during 2004–2008. Results: The relationships between temperature and IHD mortality were non-linear in all five cities. The minimum-mortality temperatures in northern cities were lower than in southern cities. In Beijing, Tianjin and Guangzhou, the effects of extremely cold temperatures were delayed, while Shanghai and Wuhan had immediate cold effects. The effects of extremely hot temperatures appeared immediately in all the cities except Wuhan. Meta-analysis showed that IHD mortality increased 48% at the 1st percentile of temperature (extremely cold temperature) compared with the 10th percentile, while IHD mortality increased 18% at the 99th percentile of temperature (extremely hot temperature) compared with the 90th percentile. Conclusions: Results indicate that both extremely cold and hot temperatures increase IHD mortality in China. Each city has its characteristics of heat effects on IHD mortality. The policy for response to climate change should consider local climate–IHD mortality relationships.

A sonographer's guide to the assessment of heart disease

The book is mostly designed for students of echocardiography, teachers of echocardiography and cardiac sonographers working in routine clinical practice, but will also be very useful to echocardiologists and cardiac registrars. The goal of the text is to provide a comprehensive review of transthoracic echocardiography in the assessment of various cardiac pathologies. Refresher notes on cardiac anatomy and the relevant cardiac physiology and pathophysiology are included to expand the cardiac sonographer’s knowledge in this area and further their understanding of various diseases, disease processes and associated findings.

The contribution of major depression to the global burden of ischemic heart disease : a comparative risk assessment

Background Cardiovascular disease and mental health both hold enormous public health importance, both ranking highly in results of the recent Global Burden of Disease Study 2010 (GBD 2010). For the first time, the GBD 2010 has systematically and quantitatively assessed major depression as an independent risk factor for the development of ischemic heart disease (IHD) using comparative risk assessment methodology. Methods A pooled relative risk (RR) was calculated from studies identified through a systematic review with strict inclusion criteria designed to provide evidence of independent risk factor status. Accepted case definitions of depression include diagnosis by a clinician or by non-clinician raters adhering to Diagnostic and Statistical Manual of Mental Disorders (DSM) or International Classification of Diseases (ICD) classifications. We therefore refer to the exposure in this paper as major depression as opposed to the DSM-IV category of major depressive disorder (MDD). The population attributable fraction (PAF) was calculated using the pooled RR estimate. Attributable burden was calculated by multiplying the PAF by the underlying burden of IHD estimated as part of GBD 2010. Results The pooled relative risk of developing IHD in those with major depression was 1.56 (95% CI 1.30 to 1.87). Globally there were almost 4 million estimated IHD disability-adjusted life years (DALYs), which can be attributed to major depression in 2010; 3.5 million years of life lost and 250,000 years of life lived with a disability. These findings highlight a previously underestimated mortality component of the burden of major depression. As a proportion of overall IHD burden, 2.95% (95% CI 1.48 to 4.46%) of IHD DALYs were estimated to be attributable to MDD in 2010. Eastern Europe and North Africa/Middle East demonstrate the highest proportion with Asia Pacific, high income representing the lowest. Conclusions The present work comprises the most robust systematic review of its kind to date. The key finding that major depression may be responsible for approximately 3% of global IHD DALYs warrants assessment for depression in patients at high risk of developing IHD or at risk of a repeat IHD event.

Letter: Brain natriuretic peptide is a potentially useful screening tool for the detection of cardiovascular disease in patients with rheumatoid arthritis

Patients with rheumatoid arthritis (RA) have a significantly higher risk of coronary heart disease, despite being less likely to report symptoms of angina, and are more likely to experience unrecognised myocardial infarction and sudden cardiac death than non-RA controls.1 Furthermore, left ventricular diastolic dysfunction has been described in up to 40% of patients with RA.2...

Coronary calcium scoring: Calcium location needs to be integrated!

Coronary calcium scoring (CCS) has been a topic of great interest lately. In a large population-based study comprising 6,722 patients, Detrano et al. (1) have effectively shown that CCS can be a strong predictor of incident coronary heart disease among different racial groups. Henneman et al. (2) have, however, reported that CCS does not reliably exclude the presence of (significant) atherosclerosis. This topic is quite controversial as there is significant evidence from Detrano's work that higher CCS is associated with an increased risk of acute coronary events. We think that the location of calcium within the coronary arteries should also be considered. Li et al. (3,4) have shown that the position of the calcium in the plaque is a better determinant of plaque vulnerability than the total calcium load. Using a biomechanical model, predicted maximum stress was found to increase by 47.5% when calcium deposits were located in the thin fibrous cap. The presence of calcium deposits in the lipid core or remote from the fibrous cap resulted in no increase in maximum stress. It was also noted that the presence of calcification within the lipid core may even stabilize the plaque. Integration of calcium location in CCS will, therefore, enable better assessment of severity of atherosclerosis and prediction of future cardiovascular events.