Heart Failure Epidemiology Analysis and Forecast to 2032

Heart Failure (HF) is defined as a clinical syndrome with symptoms and/or signs caused by a structural and/or functional cardiac abnormality and corroborated by elevated natriuretic peptide levels and/or objective evidence of pulmonary or systemic congestion (Bozkurt et al., 2021). HF is a lifelong condition in which the heart muscle cannot pump enough blood to meet the body's needs for blood and oxygen. Sudden or acute HF often occurs due to allergic reactions, blood clots in the lungs, severe infections, use of certain medicines, and viruses that attack the heart muscle (Mayo Clinic, 2023). Additionally, the disease can occur in newborns, infants, toddlers, and teenagers due to congenital heart defects, and certain medical disorders (American Heart Association, 2023d). Major signs and symptoms include dyspnea, persistent coughing or wheezing, edema, fatigue even after rest, lack of appetite, nausea, confusion, increased heart rate, and weight changes (American Heart Association, 2023e). The symptoms of HF can range from mild to severe and may come and go, however, with the disease progression signs and symptoms get worse. The clinical presentation of acute HF is characterized by systemic congestion due to extracellular fluid accumulation, initiated by increased biventricular cardiac filling pressures (Ishihara et al., 2016).

Left ventricular ejection fraction (LVEF) is the central measure of left ventricular systolic function, calculated as the fraction of chamber volume ejected in systole in relation to the volume of the blood in the ventricle at the end of diastole. Based on limitations of physical activity, the New York Heart Association (NYHA) functional classification is the mostly used classification system which places HF patients in four classes (I-IV). Additionally, the American College of Cardiology Foundation (ACCF)/American Heart Association (AHA) staging system has defined HF into four stages i.e., A, B, C, and D (American Heart Association, 2023b). Although patients are diagnosed with HF based on NYHA class, however ACCF/AHA staging shows the progression of the disease.

Scope

This report provides an overview of the risk factors, comorbidities, and the global and historical epidemiological trends for HF in the seven major markets (7MM: US, France, Germany, Italy, Spain, UK, and Japan).
The report includes a 10-year epidemiology forecast for the diagnosed incident cases and diagnosed prevalent cases of HF. The diagnosed incident cases and the diagnosed prevalent cases of HF are segmented by age (0-18 years, 19-44 years, 45-49 years, 50-59 years, 60-69 years, 70-79 years, and 80 years and above) and sex. The report also includes the diagnosed incident cases and diagnosed prevalent cases of HF by ejection fraction (HF-PEF = LVEF =50%; HF-mrEF = LVEF = 40-49%; HF-REF = LVEF < 40%). Diagnosed incident cases of HF are further segmented based on acute HF hospitalizations, acute HF hospitalizations based on presentation, hospital length of stay, and re-admissions within 30 days post-discharge.
Additionally, diagnosed prevalent cases of HF are segmented based on NYHA classes (class I-IV), diagnosed prevalent cases of HF-PEF, HF-mrEF, and HF-REF segmented based on NYHA classes, and diagnosed prevalent cases of HF by ACCF/AHA stages (stage B, C, and D). Although not covered in this report, the diagnosed prevalent cases of HF segmented by comorbidities such as coronary artery disease, hypertension, previous myocardial infarction, renal dysfunction or failure, anemia, diabetes mellitus, and atrial fibrillation can be found in the model.
This epidemiology forecast for HF is supported by data obtained from peer-reviewed articles and population-based studies. The forecast methodology was kept consistent across the 7MM to allow for a meaningful comparison of the forecast diagnosed incident cases and diagnosed prevalent cases of HF across these markets.

Reasons to Buy

The heart failure epidemiology series will allow you to:

Develop business strategies by understanding the trends shaping and driving the global heart failure market.

Quantify patient populations in the global heart failure market to improve product design, pricing, and launch plans.

Organize sales and marketing efforts by identifying the age groups that present the best opportunities for heart failure therapeutics in each of the markets covered.

About the Publisher

1 Heart Failure: Executive Summary

1.1 Catalyst
1.2 Related reports
1.3 Upcoming reports

2 Epidemiology

2.1 Disease background
2.2 Risk factors and comorbidities
2.3 Global and historical trends
2.4 7MM forecast methodology
2.4.1 Sources
2.4.2 Forecast assumptions and methods
2.4.3 Forecast assumptions and methods: diagnosed incident cases of HF - 7MM
2.4.4 Forecast assumptions and methods: diagnosed incident cases of HF by EF
2.4.5 Forecast assumptions and methods: hospitalizations for acute HF
2.4.6 Forecast assumptions and methods: acute HF hospitalizations based on presentation
2.4.7 Forecast assumptions and methods: hospital LoS days for acute HF hospitalizations
2.4.8 Forecast assumptions and methods: re-admissions within 30 days post-discharge after acute HF hospitalization
2.4.9 Forecast assumptions and methods: diagnosed prevalent cases of HF - 7MM
2.4.10 Forecast assumptions and methods: diagnosed prevalent cases of HF by EF
2.4.11 Forecast assumptions and methods: diagnosed prevalent cases of HF by NYHA classes
2.4.12 Forecast assumptions and methods: diagnosed prevalent cases of HF-PEF (LVEF =50%) by NYHA class
2.4.13 Forecast assumptions and methods: diagnosed prevalent cases of HF-mrEF (LVEF = 40-49%) by NYHA class
2.4.14 Forecast assumptions and methods: diagnosed prevalent cases of HF-REF (LVEF < 40%) by NYHA class
2.4.15 Forecast assumptions and methods: diagnosed prevalent cases of HF by ACCF/AHA stages
2.5 Epidemiological forecast for heart failure (2022-32)
2.5.1 Diagnosed incident cases of HF
2.5.2 Age-specific diagnosed incident cases of HF
2.5.3 Sex-specific diagnosed incident cases of HF
2.5.4 Diagnosed incident cases of HF by EF
2.5.5 Hospitalizations for acute HF
2.5.6 Acute HF hospitalizations based on presentation
2.5.7 Hospital LoS for acute HF
2.5.8 Re-admissions within 30 days post-discharge after acute HF
2.5.9 Diagnosed prevalent cases of HF
2.5.10 Age-specific diagnosed prevalent cases of HF
2.5.11 Sex-specific diagnosed prevalent cases of HF
2.5.12 Diagnosed prevalent cases of HF by EF
2.5.13 Diagnosed prevalent cases of HF by NYHA classes
2.5.14 Diagnosed prevalent cases of HF-PEF (LVEF =50%) by NYHA class
2.5.15 Diagnosed prevalent cases of HF-mrEF (LVEF = 40-49%) by NYHA class
2.5.16 Diagnosed prevalent cases of HF-REF (LVEF < 40%) by NYHA class
2.5.17 Diagnosed prevalent cases of HF by ACCF/AHA stages
2.6 Discussion
2.6.1 Epidemiological forecast insight
2.6.2 COVID-19 impact
2.6.3 Limitations of the analysis
2.6.4 Strengths of the analysis

3 Appendix

3.1 Bibliography
3.2 About the Authors
3.2.1 Epidemiologist
3.2.2 Reviewers
3.2.3 Vice President of Disease Intelligence and Epidemiology

Contact the Publisher

List of Tables

Table 1: Summary of newly added data types
Table 2: Summary of updated data types
Table 3: Risk factors and comorbidities for HF

List of Figures

Figure 1: 7MM, diagnosed incident cases of HF, both sexes, N, all ages, 2022 and 2032
Figure 2: 7MM, diagnosed prevalent cases of HF, both sexes, N, ages =40 years, 2022 and 2032
Figure 3: 7MM, diagnosed incidence of HF (cases per 100,000 population), men and women, all ages, 2022
Figure 4: 7MM, diagnosed prevalence of HF (%), men and women, all ages, 2022
Figure 5: 7MM, sources used and not used to forecast the diagnosed incident cases of HF
Figure 6: 7MM, sources used to forecast the diagnosed incident cases of HF by EF
Figure 7: 7MM, sources used to forecast the hospitalizations for acute HF
Figure 8: 7MM, sources used to forecast the hospitalizations for acute HF by presentation
Figure 9: 7MM, sources used to forecast the hospital LoS for acute HF
Figure 10: 7MM, sources used to forecast the re-admissions within 30 days post-discharge after acute HF hospitalization
Figure 11: 7MM, sources used to forecast the diagnosed prevalent cases of HF
Figure 12: 7MM, sources used to forecast the diagnosed prevalent cases of HF by EF
Figure 13: 7MM, sources used to forecast the diagnosed prevalent cases of HF by NYHA classes
Figure 14: 7MM, sources used to forecast the diagnosed prevalent cases of HF-PEF, HF-mrEF, and HF-REF by NYHA class
Figure 15: 7MM, sources used to forecast the diagnosed prevalent cases of HF by ACCF/AHA stages
Figure 16: 7MM, diagnosed incident cases of HF, N, both sexes, all ages, 2022
Figure 17: 7MM, diagnosed incident cases of HF by age, N, both sexes, 2022
Figure 18: 7MM, diagnosed incident cases of HF by sex, N, all ages, 2022
Figure 19: 7MM, diagnosed incident cases of HF by EF, N, both sexes, all ages, 2022
Figure 20: 7MM, hospitalizations for acute HF, N, both sexes, all ages, 2022
Figure 21: 7MM, acute HF hospitalizations based on presentation, N, both sexes, all ages, 2022
Figure 22: 7MM, hospital LoS for acute HF, days, both sexes, all ages, 2022
Figure 23: 7MM, re-admissions within 30 days post-discharge after acute HF hospitalization, N, both sexes, all ages, 2022
Figure 24: 7MM, diagnosed prevalent cases of HF, N, both sexes, all ages, 2022
Figure 25: 7MM, diagnosed prevalent cases of HF by age, N, both sexes, 2022
Figure 26: 7MM, diagnosed prevalent cases of HF by sex, N, all ages, 2022
Figure 27: 7MM, diagnosed prevalent cases of HF by EF, N, both sexes, all ages, 2022
Figure 28: 7MM, diagnosed prevalent cases of HF by NYHA classes, N, both sexes, all ages, 2022
Figure 29: 7MM, diagnosed prevalent cases of HF-PEF (LVEF =50%) by NYHA class, N, both sexes, all ages, 2022
Figure 30: 7MM, diagnosed prevalent cases of HF-mrEF (LVEF = 40-49%) by NYHA class, N, both sexes, all ages, 2022
Figure 31: 7MM, diagnosed prevalent cases of HF-REF (LVEF < 40%) by NYHA class, N, both sexes, all ages, 2022
Figure 32: 7MM, diagnosed prevalent cases of HF by ACCF/AHA stages, N, both sexes, all ages, 2022

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Scope

Reasons to Buy

The heart failure epidemiology series will allow you to:

Table of Contents

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