A history of heart interventions moderates the relationship between psychological variables and the presence of chest pain in older women with self-reported coronary heart disease

Objectives: This study examines the hypothesis that a past history of heart interventions will moderate the relationship between psychosocial factors (stressful life events, social support, perceived stress, having a current partner, having a past diagnosis of depression or anxiety over the past 3 years, time pressure, education level, and the mental health index) and the presence of chest pain in a sample of older women. Design: Longitudinal survey over a 3-year period. Methods: The sample was taken from a prospective cohort study of 10,432 women initially aged between 70 and 75 years, who were surveyed in 1996 and then again in 1999. Two groups of women were identified: those reporting to have heart disease but no past history of heart interventions (i.e., coronary artery bypass graft/angioplasty) and those reporting to have heart disease with a past history of heart interventions. Results: Binary logistic regression analysis was used to show that for the women with self-reported coronary heart disease but without a past history of heart intervention, feelings of time pressure as well as the number of stressful life events experienced in the 12 months prior to 1996 were independent risk factors for the presence of chest pain, even after accounting for a range of traditional risk factors. In comparison, for the women with self-reported coronary heart disease who did report a past history of heart interventions, a diagnosis of depression in the previous 3 years was the significant independent risk factor for chest pain even after accounting for traditional risk factors. Conclusion: The results indicate that it is important to consider a history of heart interventions as a moderator of the associations between psychosocial variables and the frequency of chest pain in older women. Statement of Contribution: What is already known on this subject? Psychological factors have been shown to be independent predictors of a range of health outcomes in individuals with coronary heart disease, including the presence of chest pain. Most research has been conducted with men or with small samples of women; however, the evidence does suggest that these relationships exist in women as well as in men. What does this study add? Most studies have looked at overall relationships between psychological variables and health outcomes. The few studies that have looked at moderators have mainly examined gender as a moderator. To our knowledge, this is the first published study to examine a history of heart interventions as a moderator of the relationship between psychological variables and the presence of chest pain.

Psychological symptoms : a review of screening practices for patients with coronary heart disease (CHD)

Background/Aims: Coronary heart disease (CHD) and coronary events have been strongly linked to psychological symptoms in patients during hospitalisation and post-discharge. Within Australia CHD average length of stay is decreasing and symptoms often do not present until discharge. Early screening and treatment of psychological symptoms has been recommended to reduce mortality and identify anxiety and depression. This literature review was undertaken to evaluate and describe current screening practices to identify psychological symptoms in these patients.

HealthMap: A cluster randomised trial of interactive health plans and self-management support to prevent coronary heart disease in people with HIV

Background The leading causes of morbidity and mortality for people in high-income countries living with HIV are now non-AIDS malignancies, cardiovascular disease and other non-communicable diseases associated with ageing. This protocol describes the trial of HealthMap, a model of care for people with HIV (PWHIV) that includes use of an interactive shared health record and self-management support. The aims of the HealthMap trial are to evaluate engagement of PWHIV and healthcare providers with the model, and its effectiveness for reducing coronary heart disease risk, enhancing self-management, and improving mental health and quality of life of PWHIV. Methods/Design The study is a two-arm cluster randomised trial involving HIV clinical sites in several states in Australia. Doctors will be randomised to the HealthMap model (immediate arm) or to proceed with usual care (deferred arm). People with HIV whose doctors are randomised to the immediate arm receive 1) new opportunities to discuss their health status and goals with their HIV doctor using a HealthMap shared health record; 2) access to their own health record from home; 3) access to health coaching delivered by telephone and online; and 4) access to a peer moderated online group chat programme. Data will be collected from participating PWHIV (n = 710) at baseline, 6 months, and 12 months and from participating doctors (n = 60) at baseline and 12 months. The control arm will be offered the HealthMap intervention at the end of the trial. The primary study outcomes, measured at 12 months, are 1) 10-year risk of non-fatal acute myocardial infarction or coronary heart disease death as estimated by a Framingham Heart Study risk equation; and 2) Positive and Active Engagement in Life Scale from the Health Education Impact Questionnaire (heiQ). Discussion The study will determine the viability and utility of a novel technology-supported model of care for maintaining the health and wellbeing of people with HIV. If shown to be effective, the HealthMap model may provide a generalisable, scalable and sustainable system for supporting the care needs of people with HIV, addressing issues of equity of access. Trial registration Universal Trial Number (UTN) U111111506489; ClinicalTrial.gov Id NCT02178930 submitted 29 June 2014

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.

Nurse-led telephone interventions for people with cardiac disease : A review of the research literature

Background: Nurse-led telephone follow-up offers a relatively inexpensive method of delivering education and support for assisting recovery in the early discharge period; however, its efficacy is yet to be determined. Aim: To perform a critical integrative review of the research literature addressing the effectiveness of nurse-led telephone interventions for people with coronary heart disease (CHD). Methods: A literature search of five health care databases; Sciencedirect, Cumulative Index to Nursing and Allied Health Literature, Pubmed, Proquest and Medline to identify journal articles between 1980 and 2009. People with cardiac disease were considered for inclusion in this review. The search yielded 128 papers, of which 24 met the inclusion criteria. Results: A total of 8330 participants from 24 studies were included in the final review. Seven studies demonstrated statistically significant differences in all outcomes measured, used two group experimental research design and valid and reliable instruments. Some positive effects were detected in eight studies in regards to nurse-led telephone interventions for people with cardiac disease and no differences were detected in nine studies. Discussion: Studies with some positive effects generally had stronger research designs, large samples, used valid and reliable instruments and extensive nurse-led educative interventions. Conclusion: The results suggest that people with cardiac disease showed some benefits from nurse-led/delivered telephone interventions. More rigorous research into this area is needed.