Epidemiology of heart failure Groenewegen, Amy; Rutten, Frans H.; Mosterd, Arend ...
European journal of heart failure,
August 2020, Volume:
22, Issue:
8
Journal Article
Peer reviewed
Open access
The heart failure syndrome has first been described as an emerging epidemic about 25 years ago. Today, because of a growing and ageing population, the total number of heart failure patients still ...continues to rise. However, the case mix of heart failure seems to be evolving. Incidence has stabilized and may even be decreasing in some populations, but alarming opposite trends have been observed in the relatively young, possibly related to an increase in obesity. In addition, a clear transition towards heart failure with a preserved ejection fraction has occurred. Although this transition is partially artificial, due to improved recognition of heart failure as a disorder affecting the entire left ventricular ejection fraction spectrum, links can be made with the growing burden of obesity‐related diseases and with the ageing of the population. Similarly, evidence suggests that the number of patients with heart failure may be on the rise in low‐income countries struggling under the double burden of communicable diseases and conditions associated with a Western‐type lifestyle. These findings, together with the observation that the mortality rate of heart failure is declining less rapidly than previously, indicate we have not reached the end of the epidemic yet. In this review, the evolving epidemiology of heart failure is put into perspective, to discern major trends and project future directions.
The ‘epidemic’ of heart failure seems to be changing, but precise prevalence estimates of heart failure and left ventricular dysfunction (LVD) in older adults, based on adequate echocardiographic ...assessment, are scarce. Systematic reviews including recent studies on the prevalence of heart failure and LVD are lacking. We aimed to assess the trends in the prevalence of LVD, and heart failure with reduced (HFrEF) and preserved ejection fraction (HFpEF) in the older population at large. A systematic electronic search of the databases Medline and Embase was performed. Studies that reported prevalence estimates in community‐dwelling people ≥60 years old were included if echocardiography was used to establish the diagnosis. In total, 28 articles from 25 different study populations were included. The median prevalence of systolic and ‘isolated’ diastolic LVD was 5.5% (range 3.3–9.2%) and 36.0% (range 15.8–52.8%), respectively. A peak in systolic dysfunction prevalence seems to have occurred between 1995 and 2000. ‘All type’ heart failure had a median prevalence rate of 11.8% (range 4.7–13.3%), with fairly stable rates in the last decade and with HFpEF being more common than HFrEF median prevalence 4.9% (range 3.8–7.4%) and 3.3% (range 2.4–5.8%), respectively. Both LVD and heart failure remain common in the older population at large. The prevalence of diastolic dysfunction is on the rise and currently higher than that of systolic dysfunction. The prevalence of the latter seems to have decreased in the 21st century.
Self‐care is essential in the long‐term management of chronic heart failure. Heart failure guidelines stress the importance of patient education on treatment adherence, lifestyle changes, symptom ...monitoring and adequate response to possible deterioration. Self‐care is related to medical and person‐centred outcomes in patients with heart failure such as better quality of life as well as lower mortality and readmission rates. Although guidelines give general direction for self‐care advice, health care professionals working with patients with heart failure need more specific recommendations. The aim of the management recommendations in this paper is to provide practical advice for health professionals delivering care to patients with heart failure. Recommendations for nutrition, physical activity, medication adherence, psychological status, sleep, leisure and travel, smoking, immunization and preventing infections, symptom monitoring, and symptom management are consistent with information from guidelines, expert consensus documents, recent evidence and expert opinion.
Abstract
Making a firm diagnosis of chronic heart failure with preserved ejection fraction (HFpEF) remains a challenge. We recommend a new stepwise diagnostic process, the ‘HFA–PEFF diagnostic ...algorithm’. Step 1 (P=Pre-test assessment) is typically performed in the ambulatory setting and includes assessment for HF symptoms and signs, typical clinical demographics (obesity, hypertension, diabetes mellitus, elderly, atrial fibrillation), and diagnostic laboratory tests, electrocardiogram, and echocardiography. In the absence of overt non-cardiac causes of breathlessness, HFpEF can be suspected if there is a normal left ventricular ejection fraction, no significant heart valve disease or cardiac ischaemia, and at least one typical risk factor. Elevated natriuretic peptides support, but normal levels do not exclude a diagnosis of HFpEF. The second step (E: Echocardiography and Natriuretic Peptide Score) requires comprehensive echocardiography and is typically performed by a cardiologist. Measures include mitral annular early diastolic velocity (e′), left ventricular (LV) filling pressure estimated using E/e′, left atrial volume index, LV mass index, LV relative wall thickness, tricuspid regurgitation velocity, LV global longitudinal systolic strain, and serum natriuretic peptide levels. Major (2 points) and Minor (1 point) criteria were defined from these measures. A score ≥5 points implies definite HFpEF; ≤1 point makes HFpEF unlikely. An intermediate score (2–4 points) implies diagnostic uncertainty, in which case Step 3 (F1: Functional testing) is recommended with echocardiographic or invasive haemodynamic exercise stress tests. Step 4 (F2: Final aetiology) is recommended to establish a possible specific cause of HFpEF or alternative explanations. Further research is needed for a better classification of HFpEF.
▶ The caregiving effect is the welfare effect of providing informal care, i.e., the effect of the burden of caregiving. The family effect is a direct influence of the health of a patient on others’ ...well-being, i.e., the effects of caring about other people. ▶ TThe caregiving effect can be present only in caregivers while the family effect in the broader group of significant other, regardless of their caregiving status. However, both effects are usually disregarded in economic evaluations which treat patients as isolated individuals. ▶ Using a sample of Dutch informal caregivers we found that both effects exist and may be comparable in size. Our results, while explorative, indicate that economic evaluations adopting a societal perspective should include both the family and the caregiving effects measured in the relevant individuals.
Besides patients’ health and well-being, healthcare interventions may affect the well-being of significant others. Such ‘spill over effects’ in significant others may be distinguished in two distinct effects: (i) the
caregiving effect and (ii)
the family effect. The first refers to the welfare effects of providing informal care, i.e., the effects of caring
for someone who is ill. The second refers to a direct influence of the health of a patient on others’ well-being, i.e., the effects of caring
about other people. Using a sample of Dutch informal caregivers we found that both effects exist and may be comparable in size. Our results, while explorative, indicate that economic evaluations adopting a societal perspective should include both the family and the caregiving effects measured in the relevant individuals.
Welfarism vs. extra-welfarism Brouwer, Werner B.F.; Culyer, Anthony J.; van Exel, N. Job A. ...
Journal of health economics,
03/2008, Volume:
27, Issue:
2
Journal Article
Peer reviewed
Open access
‘Extra-welfarism’ has received some attention in health economics, yet there is little consensus on what distinguishes it from more conventional ‘welfarist economics’. In this paper, we seek to ...identify the characteristics of each in order to make a systematic comparison of the ways in which they evaluate alternative social states. The focus, though this is not intended to be exclusive, is on health. Specifically, we highlight four areas in which the two schools differ: (i) the outcomes considered relevant in an evaluation; (ii) the sources of valuation of the relevant outcomes; (iii) the basis of weighting of relevant outcomes and (iv) interpersonal comparisons. We conclude that these differences are substantive.
Background Timely recognition of patients with acute coronary syndromes (ACS) is important for successful treatment. Previous research has suggested that women with ACS present with different ...symptoms compared with men. This review assessed the extent of sex differences in symptom presentation in patients with confirmed ACS. Methods and Results A systematic literature search was conducted in PubMed, Embase, and Cochrane up to June 2019. Two reviewers independently screened title-abstracts and full-texts according to predefined inclusion and exclusion criteria. Methodological quality was assessed using the Newcastle-Ottawa Scale. Pooled odds ratios (OR) with 95% CI of a symptom being present were calculated using aggregated and cumulative meta-analyses as well as sex-specific pooled prevalences for each symptom. Twenty-seven studies were included. Compared with men, women with ACS had higher odds of presenting with pain between the shoulder blades (OR 2.15; 95% CI, 1.95-2.37), nausea or vomiting (OR 1.64; 95% CI, 1.48-1.82) and shortness of breath (OR 1.34; 95% CI, 1.21-1.48). Women had lower odds of presenting with chest pain (OR 0.70; 95% CI, 0.63-0.78) and diaphoresis (OR 0.84; 95% CI, 0.76-0.94). Both sexes presented most often with chest pain (pooled prevalences, men 79%; 95% CI, 72-85, pooled prevalences, women 74%; 95% CI, 72-85). Other symptoms also showed substantial overlap in prevalence. The presence of sex differences has been established since the early 2000s. Newer studies did not materially change cumulative findings. Conclusions Women with ACS do have different symptoms at presentation than men with ACS, but there is also considerable overlap. Since these differences have been shown for years, symptoms should no longer be labeled as "atypical" or "typical."
Cardiovascular conditions were shown to be predictive of clinical deterioration in hospitalised patients with coronavirus disease 2019 (COVID-19). Whether this also holds for outpatients managed in ...primary care is yet unknown. The aim of this study was to determine the incremental value of cardiovascular vulnerability in predicting the risk of hospital referral in primary care COVID-19 outpatients.
Analysis of anonymised routine care data extracted from electronic medical records from three large Dutch primary care registries.
Primary care.
Consecutive adult patients seen in primary care for COVID-19 symptoms in the 'first wave' of COVID-19 infections (March 1 2020 to June 1 2020) and in the 'second wave' (June 1 2020 to April 15 2021) in the Netherlands.
A multivariable logistic regression model was fitted to predict hospital referral within 90 days after first COVID-19 consultation in primary care. Data from the 'first wave' was used for derivation (n = 5,475 patients). Age, sex, the interaction between age and sex, and the number of cardiovascular conditions and/or diabetes (0, 1, or ≥2) were pre-specified as candidate predictors. This full model was (i) compared to a simple model including only age and sex and its interaction, and (ii) externally validated in COVID-19 patients during the 'second wave' (n = 16,693).
The full model performed better than the simple model (likelihood ratio test p<0.001). Older male patients with multiple cardiovascular conditions and/or diabetes had the highest predicted risk of hospital referral, reaching risks above 15-20%, whereas on average this risk was 5.1%. The temporally validated c-statistic was 0.747 (95%CI 0.729-0.764) and the model showed good calibration upon validation.
For patients with COVID-19 symptoms managed in primary care, the risk of hospital referral was on average 5.1%. Older, male and cardiovascular vulnerable COVID-19 patients are more at risk for hospital referral.
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