Self-management of coronary heart disease in angina patients after percutaneous coronary intervention: A mixed methods study.

After having elective percutaneous coronary intervention (PCI) patients are expected to self-manage their coronary heart disease (CHD) by modifying their risk factors, adhering to medication and effectively managing any recurring angina symptoms but that may be ineffective. Objective: Explore how patients self-manage their coronary heart disease (CHD) after elective PCI and identify any factors that may infl uence that. Design and method: This mixed methods study recruited a convenience sample of patients (n=93) approximately three months after elective PCI. Quantitative data were collected using a survey and were subject to univariate, bivariate and multi-variate analysis. Qualitative data from participant interviews was analysed using thematic analysis. Findings: After PCI, 74% of participants managed their angina symptoms inappropriately. Younger participants and those with threatening perceptions of their CHD were more likely to know how to effectively manage their angina symptoms. Few patients adopted a healthier lifestyle after PCI. Qualitative analysis revealed that intentional non-adherence to some medicines was an issue. Some participants felt unsupported by healthcare providers and social networks in relation to their self-management. Participants reported strong emotional responses to CHD and this had a detrimental effect on their self-management. Few patients accessed cardiac rehabilitation.

MLCut: exploring multi-level cuts in dendrograms for biological data.

Choosing a single similarity threshold for cutting dendrograms is not sufficient for performing hierarchical clustering analysis of heterogeneous data sets. In addition, alternative automated or semi-automated methods that cut dendrograms in multiple levels make assumptions about the data in hand. In an attempt to help the user to find patterns in the data and resolve ambiguities in cluster assignments, we developed MLCut: a tool that provides visual support for exploring dendrograms of heterogeneous data sets in different levels of detail. The interactive exploration of the dendrogram is coordinated with a representation of the original data, shown as parallel coordinates. The tool supports three analysis steps. Firstly, a single-height similarity threshold can be applied using a dynamic slider to identify the main clusters. Secondly, a distinctiveness threshold can be applied using a second dynamic slider to identify “weak-edges” that indicate heterogeneity within clusters. Thirdly, the user can drill-down to further explore the dendrogram structure - always in relation to the original data - and cut the branches of the tree at multiple levels. Interactive drill-down is supported using mouse events such as hovering, pointing and clicking on elements of the dendrogram. Two prototypes of this tool have been developed in collaboration with a group of biologists for analysing their own data sets. We found that enabling the users to cut the tree at multiple levels, while viewing the effect in the original data, is a promising method for clustering which could lead to scientific discoveries.