Additional file 2: of Patient empowerment and general self-efficacy in patients with coronary heart disease: a cross-sectional study

Questionnaire measuring Self-rated health (PDF 110 kb

Multidrug and optimal heart failure therapy prescribing in older general practice populations: a clinical data linkage study.

OBJECTIVE: To investigate multidrug therapy in the cardiovascular disease (CVD) population and whether it was associated with suboptimal drug prescribing in heart failure (HF). DESIGN: A population-based cross-sectional clinical data linkage study. SETTING: The clinical database populations were registered with three general practices in North Staffordshire that are part of a research network. PARTICIPANTS: 3155 patients aged 50 years and over were selected on the basis of a CVD-related prescription and a CVD consultation code applied to their electronic medical record in a 2-year time period. All available diagnostic data were linked to all drugs prescribed data during this time period. Two study groups were: (1) HF and (2) non-HF CVD (reference group). EXPOSURE: A standard drug formulary system was used to define four multidrug count categories based on the number of different British National Formulary drug chapters prescribed at the same time. PRIMARY AND SECONDARY OUTCOME MEASURES: Optimal HF therapy was defined as the prescribing of ACE inhibitor (ACEi) or a combination of ACEi and β-blocker in the 2-year time window. An additional three specific CVD drug categories that are indicated in HF were also measured. RESULTS: The HF group, compared with the reference group, had higher non-CVD multidrug therapy (26% with 7 or more counts compared with 14% in the non-HF CVD reference group). For the first-choice optimal drug treatment for HF with ACEi (64%) or ACEi and β-blocker combined therapy (23%), the multidrug-adjusted associations between the HF group and the reference group were OR 3.89; 95% CI 2.8 to 5.5 and 1.99; 1.4 to 2.9, respectively. These estimates were not influenced by adjustment for sociodemographic factors and multidrug counts. CONCLUSIONS: Multidrug therapy prescribing is much higher in the HF group than in a comparable CVD group but did not influence optimal drug prescribing

Clinical and demographic characteristics of the patient-partner dyads at baseline.

<p>^a Lung disease was significantly (<i>p</i> <0.05) more common in the partner control group compared to the partner intervention group.</p><p>Clinical and demographic characteristics of the patient-partner dyads at baseline.</p

Description of the modules in the intervention.

<p>Content of each of the three modules utilised in the intervention.</p><p>Description of the modules in the intervention.</p

Readmissions and reason for readmissions during the 24- month’s follow-up period.

<p>Student’s t- test was used for continuous variables, presented as mean ± standard deviation (SD). Categorical variables are given as percent (%) and are compared using Chi-square test.</p><p>Readmissions and reason for readmissions during the 24- month’s follow-up period.</p

Flowchart for the participating dyad from enrolment until 24 months.

<p>Flowchart for the participating dyad from enrolment until 24 months.</p

Additional file 1: of Guided Internet-delivered cognitive behavioural therapy in patients with non-cardiac chest pain – a pilot randomized controlled study

CONSORT 2010 checklist of information to include when reporting a randomized trial. (DOC 217 kb

The challenge of multimorbidity in nurse education: an international perspective.

The rise in prevalence of chronic diseases has become a global healthcare priority and a system wide approach has been called for to manage this growing epidemic. Whilst healthcare reform to tackle the scale of chronic disease and other long term conditions is still in its infancy, there is an emerging recognition that in an ageing society, people often suffer from more than one chronic disease at the same time. Multimorbidity poses new and distinct challenges and was the focus of a global conference held by the Organization of Economic Cooperation and Development (OECD) in 2011. Health education was raised as requiring radical redesign to equip graduates with the appropriate skills to face the challenges ahead. We wanted to explore how different aspects of multimorbidity were addressed within pre-registration nurse education and held an international (United Kingdom-Sweden) nurse workshop in Linköping, Sweden in April 2013, which included nurse academics and clinicians. We also sent questionnaire surveys to final year student nurses from both countries. This paper explores the issues of multimorbidity from a patient, healthcare and nurse education perspective and presents the preliminary discussions from the workshop and students' survey

Hypothetical HF health model.

<p>Model based on a revised version of Wilson and Cleary’s health-related quality of life conceptual model [<a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1002540#pmed.1002540.ref016" target="_blank">16</a>,<a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1002540#pmed.1002540.ref017" target="_blank">17</a>]. The arrows represent direct relationships for patient and environmental factors as well as 4 of the 5 health domains: bio-physiological status (comorbidities), symptoms, functional status, and general health perception. Only arrows between adjacent domains are displayed, but it is postulated that each domain may have other direct relationships with any of the proceeding domains, and patient and environmental factors are related to every domain. ACEi, angiotensin converting enzyme inhibitor; ARB, angiotensin II receptor blocker; EQ-VAS, EuroQol–5 dimension visual analogue scale; HF, heart failure.</p

Cardiovascular comorbidities in heart failure and patient health pathway.

<p>In the regression graph an arrow is present between a response and an explanatory variable if there is a significant association (<i>P</i> < 0.01), controlling for all remaining regressors. The strength of this association is shown as OR (95% CI), if the response variable is binary, and mean difference (95% CI) in the response variable for a 1-unit increase in the explanatory variable, if the response variable is continuous. Significant interactions and non-linear relationships are also indicated. Reduced ejection fraction defined as <40%. Pain and anxiety or depression defined as ‘any problems’. Shortness of breath and fatigue defined as ‘marked or severe’, and functional limitation as ‘any’ limitation in usual activities. Patient-rated health was measured by EuroQol visual analogue scale, ranging from 0 (worst health imaginable) to 100 (best health imaginable). AF, atrial fibrillation; IHD, ischemic heart disease; OR, odds ratio.</p