2013 ACCF/AHA Guideline for the Management of Heart Failure: Executive Summary

International Society for Heart and Lung Transplantation: Practice guidelines for management of heart failure in children

The Complex Relationship Between Ischemic Heart Disease and COPD Exacerbations

Heart failure

Adherence to Guideline-Recommended Adjunctive Heart Failure Therapies Among Outpatient Cardiology Practices (Findings from IMPROVE HF)

Treatment of Diastolic Heart Failure

The "2022 AHA/ACC/HFSA Guideline for the Management of Heart Failure" replaces the "2013 ACCF/AHA Guideline for the Management of Heart Failure" and the "2017 ACC/AHA/HFSA Focused Update of the 2013 ACCF/AHA Guideline for the Management of Heart Failure." The 2022 guideline is intended to provide patient-centric recommendations for clinicians to prevent, diagnose, and manage patients with heart failure. A comprehensive literature search was conducted from May 2020 to December 2020, encompassing studies, reviews, and other evidence conducted on human subjects that were published in English from MEDLINE (PubMed), EMBASE, the Cochrane Collaboration, the Agency for Healthcare Research and Quality, and other relevant databases. Additional relevant clinical trials and research studies, published through September 2021, were also considered. This guideline was harmonized with other American Heart Association/American College of Cardiology guidelines published through December 2021. Structure: Heart failure remains a leading cause of morbidity and mortality globally. The 2022 heart failure guideline provides recommendations based on contemporary evidence for the treatment of these patients. The recommendations present an evidence-based approach to managing patients with heart failure, with the intent to improve quality of care and align with patients' interests. Many recommendations from the earlier heart failure guidelines have been updated with new evidence, and new recommendations have been created when supported by published data. Value statements are provided for certain treatments with high-quality published economic analyses.

ACC/AHA guidelines for the evaluation and management of chronic heart failure in the adult: executive summary: A report of the american college of cardiology/american heart association task force on practice guidelines (committee to revise the 1995 guidelines for the evaluation and management of heart failure) developed in collaboration with the international society for heart and lung transplantation endorsed by the

Application of mechanical cardiac support now requires consideration of a wider range of goals beyond bridging to transplantation to include destination therapy and perhaps bridging to recovery.1,2 Responsible dissemination of the technology requires identification of patient populations from which to select candidates most likely to benefit. At this early stage, benefit is most apparent against a high background mortality from end-stage heart failure. Heart failure affects an estimated 5 million patients in the United States. Of those, ≈60% have heart failure with left ventricular dilation and reduced ejection fraction. Trials demonstrating benefit of therapies for heart failure have focused primarily on mild–moderate heart failure with reduced ejection fraction, generally with annual mortality in the range of 8% to 18%.3 Advanced heart failure has been defined as symptoms limiting daily activity (New York Heart Association class III and IV) despite attempted therapy with angiotensin-converting enzyme inhibitors, β-blockers, digoxin, and diuretics,4 a description that applies to ≈300 000 to 800 000 patients in the United States. Although often labeled as “refractory,” many patients enjoy improved quality of life and decreased hospitalizations after referral to experienced heart failure centers, where aggressive medical strategies focus on relief of congestion. Surgical approaches include complex revascularization, valvular repair/replacement, or ventricular reconstruction. When technically successful, biventricular pacing can improve functional status for many of the 25% to 40% of patients with marked ventricular asynchrony.5 If early stabilization allows institution of β-adrenergic–blocking agents, prognosis is further improved.6 Dedicated heart failure management programs that facilitate patient education, compliance, and fluid balance have been integral to benefits observed with these therapies. The highest-risk heart failure populations are best identified after optimization of current therapies. Low left ventricular ejection fraction is not sufficient description of either function or prognosis once heart failure has become advanced. Neither …

published online Mar 26, 2009; J. Am. Coll. Cardiol. Lynne Warner Stevenson, and Clyde W. Yancy Ganiats, Marvin A. Konstam, Donna M. Mancini, Peter S. Rahko, Marc A. Silver, Abraham, Donald E. Casey, Arthur M. Feldman, Gary S. Francis, Theodore G. Silver, Lynne Warner Stevenson, Clyde W. Yancy, Mariell Jessup, William T. Konstam, Donna M. Mancini, Keith Michl, John A. Oates, Peter S. Rahko, Marc A. M. Feldman, Gary S. Francis, Theodore G. Ganiats, Mariell Jessup, Marvin A. Transplantation, Sharon Ann Hunt, William T. Abraham, Marshall H. Chin, Arthur Force on Practice Guidelines, International Society for Heart and Lung American College of Cardiology Foundation/American Heart Association Task Diagnosis and Management of Heart Failure in Adults 2009 Focused Update Incorporated Into the ACC/AHA 2005 Guidelines for the This information is current as of March 27, 2009 http://content.onlinejacc.org/cgi/content/full/j.jacc.2008.11.013v1 located on the World Wide Web at: The online version of this article, along with updated information and services, is

ACC/AHA guidelines for the evaluation and management of chronic heart failure in the adult: executive summary: A report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines (Committee to Revise the 1995 Guidelines for the Evaluation and Management of Heart Failure) Developed in Collaboration with the International Society for Heart and Lung Transplantation Endorsed by the

More than 4000 patients have been evaluated in randomized controlled trials of cardiac resynchronization therapy (CRT). These studies have demonstrated that CRT with or without an implantable cardioverter-defibrillator (ICD) consistently improves quality of life, functional status, exercise capacity, and cardiac structure and function and reduces morbidity and mortality in heart failure patients with ventricular dyssynchrony. The magnitude of benefit seen with CRT is comparable to or exceeds that seen with evidence-based drug therapies for heart failure but occurs in patients who are already receiving such medications. Thus, CRT has been added to the list of evidence-based therapies that make heart failure patients feel better and live longer (the Table). Consequently, a strong ethical mandate exists for the use of CRT in heart failure. This mandate is reflected in our current practice guidelines for the management of chronic heart failure, which state that all eligible patients should receive CRT unless contraindicated.1,2 End of debate! CRT should be a routine part of any evidence-based treatment regimen for heart failure. View this table: Major Benefits of Evidence-Based Heart Failure Therapies Of course, things are never quite so simple, so let us take a look at the evidence supporting this clinical mandate for CRT and address patient selection, some of the limitations of CRT, and some of the unanswered questions about the use of CRT in heart failure. None of this discussion will lessen the role of CRT in the treatment of heart failure; rather, it will guide the selection of appropriate patients and speculate on the future application of CRT to an even broader group of heart failure patients. Response by Greenberg and Mehra p 2698 Approximately one third of patients with systolic heart failure exhibit ventricular dyssynchrony, defined as a QRS duration >120 ms on the surface ECG.3,4 Ventricular dyssynchrony produces suboptimal ventricular …

2009;53;1343-1382; originally published online Mar 26, 2009; J. Am. Coll. Cardiol. Rahko, Marc A. Silver, Lynne Warner Stevenson, and Clyde W. Yancy Francis, Theodore G. Ganiats, Marvin A. Konstam, Donna M. Mancini, Peter S. Mariell Jessup, William T. Abraham, Donald E. Casey, Arthur M. Feldman, Gary S. Heart and Lung Transplantation Developed in Collaboration With the International Society for Guidelines Cardiology Foundation/American Heart Association Task Force on Practice Management of Heart Failure in Adults: A Report of the American College of 2009 Focused Update: ACCF/AHA Guidelines for the Diagnosis and This information is current as of August 30, 2010 http://content.onlinejacc.org/cgi/content/full/53/15/1343 located on the World Wide Web at: The online version of this article, along with updated information and services, is

To Infuse or Not?

2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: The Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC). Developed with the special contribution of the Heart Failure Association (HFA) of the ESC.

Heart failure with preserved ejection fraction (HFpEF) accounts for approximately 50% of diagnoses of heart failure (HF) and frequently leads to hospitalization. Clinical algorithms developed for diagnosis have been applied to stratify risk for HF hospitalization or death.1Heidenreich P.A. Bozkurt B. Aguilar D. et al.2022 AHA/ACC/HFSA guideline for the Management of heart failure: executive summary: a report of the American College of Cardiology/American Heart Association Joint Committee on clinical practice guidelines.J Am Coll Cardiol. 2022; 79: 1757-1780Crossref PubMed Scopus (392) Google Scholar, 2Selvaraj S. Myhre P.L. Vaduganathan M. et al.Application of diagnostic algorithms for heart failure with preserved ejection fraction to the community.JACC Heart Fail. 2020; 8: 640-653Crossref PubMed Scopus (80) Google Scholar, 3Verbrugge F.H. Reddy Y.N.V. Sorimachi H. et al.Diagnostic scores predict morbidity and mortality in patients hospitalized for heart failure with preserved ejection fraction.Eur J Heart Fail. 2021; 23: 954-963Crossref PubMed Scopus (29) Google Scholar Deep learning has been applied to the automated interpretation of echocardiograms, but limited information exists regarding the potential of the learning models for predicting clinical outcomes.4Lau E.S. Di Achille P. Kopparapu K. et al.Deep learning-enabled assessment of left heart structure and function predicts cardiovascular outcomes.J Am Coll Cardiol. 2023; 82: 1936-1948Crossref Scopus (8) Google Scholar An artificial intelligence (AI) model was recently developed to identify patients with HFpEF using a single apical four-chamber video clip from a standard transthoracic echocardiographic examination.5Akerman A.P. Porumb M. Scott C.G. et al.Automated echocardiographic detection of heart failure with preserved ejection fraction using artificial intelligence.JACC: Advances. 2023; 2100452Crossref Scopus (20) Google Scholar A convolutional neural network was applied to the video clip. The model comprised a series of three-dimensional convolutional layers designed to operate on two-dimensional videos over two in-plane spatial dimensions within the image frames and across the time dimension. The present study was conducted to assess the association between the model output and other HF biomarkers, risk for HF hospitalization and cardiac mortality, and to compare its performance with two clinical scores: H2FPEF (heavy, hypertensive, atrial fibrillation, pulmonary hypertension, elder, and filling pressure)6Reddy Y.N.V. Carter R.E. Obokata M. et al.A simple, evidence-based approach to help guide diagnosis of heart failure with preserved ejection fraction.Circulation. 2018; 138: 861-870Crossref PubMed Scopus (713) Google Scholar and HFA-PEFF (Heart Failure Association pretest assessment, echocardiography and natriuretic peptide score, functional testing, and final etiology).7Pieske B. Tschope C. de Boer R.A. et al.How to diagnose heart failure with preserved ejection fraction: the HFA-PEFF diagnostic algorithm: a consensus recommendation from the Heart Failure Association (HFA) of the European Society of Cardiology (ESC).Eur Heart J. 2019; 40: 3297-3317Crossref PubMed Scopus (951) Google Scholar This retrospective, multisite study was approved by our institutional review board. The model was developed to classify patients with HFpEF vs individuals without HFpEF (control subjects). Patients with HFpEF were defined according to guidelines and included a diagnosis by the treating physician1Heidenreich P.A. Bozkurt B. Aguilar D. et al.2022 AHA/ACC/HFSA guideline for the Management of heart failure: executive summary: a report of the American College of Cardiology/American Heart Association Joint Committee on clinical practice guidelines.J Am Coll Cardiol. 2022; 79: 1757-1780Crossref PubMed Scopus (392) Google Scholar within 1 year of an echocardiographic examination demonstrating elevated left ventricular filling pressure. Control subjects were patients undergoing clinically indicated echocardiography who lacked these features (Supplemental Appendix). All patients had left ventricular ejection fractions ≥50%. The present analysis used the second version of the AI model (Supplemental Appendix). In the previously described independent test population consisting of 646 patients with HFpEF and 638 control subjects, the updated model produced 95 uncertain outputs (7.4%); in the remaining 607 patients and 582 control subjects, sensitivity was 89.8% (95% CI, 87.5%-92.5%), specificity was 86.3% (95% CI, 83.6%-89.7%), negative predictive value was 89.0% (95% CI, 87.0%-91.7%), and positive predictive value was 87.2% (95% CI, 84.7%-89.8%). Incident HF hospitalization was obtained from electronic health record chart review using standardized definitions, using the first event after the echocardiographic examination. Mortality was obtained from the National Death Index, and causes of cardiac deaths were manually reviewed. End points were adjudicated by investigators blinded to AI analysis results. Cardiac mortality and HF hospitalization were plotted accounting for death as a competing risk. The method of Fine and Gray was used to estimate the hazard ratios (HRs) adjusted for differences in age and sex. Among 1,284 patients followed for a median of 3.4 years (interquartile range, 1.7-6.5 years), there were 252 HF hospitalizations and 540 deaths. Figure 1 demonstrates the risk for HF hospitalization on the basis of HF categorical AI output (top) and quartiles of continuous probability output (bottom). After adjustment for age and sex, positive AI output was associated with a higher risk for HF hospitalization than negative output (HR, 3.76; 95% CI, 2.71-5.21; P < .001) and likewise for uncertain output (HR, 2.79; 95% CI, 1.60-4.62; P < .001). Cardiac deaths (n = 135) were attributable to HF in 63 patients (47%), to coronary artery disease in 55 (41%), to valve disease in five (4%), to arrhythmia in five (4%), and to other causes in seven (5%). Again adjusting for age and sex, cardiac mortality was higher in patients with positive output (HR, 5.55; 95% CI, 3.28-9.37; P < .001); patients with an uncertain output tended to have a higher mortality (HR, 2.22; 95% CI, 0.94-5.24; P = .07). Patients with higher continuous probability outputs demonstrated incrementally higher risk for cardiac mortality (fourth quartile vs first quartile: HR, 11.65; 95% CI, 4.65-29.20; P < .0001). Figure 2 demonstrates the risk for HF hospitalization on the basis of clinical H2FPEF score6Reddy Y.N.V. Carter R.E. Obokata M. et al.A simple, evidence-based approach to help guide diagnosis of heart failure with preserved ejection fraction.Circulation. 2018; 138: 861-870Crossref PubMed Scopus (713) Google Scholar (top). The clinical score differentiated high and low risk for HF hospitalization, but 776 of 1,284 patients (60%) were indeterminate. Application of the AI model to the nondiagnostic H2FPEF outputs (bottom) allowed the classification of all but 68 of the 776 patients (8.8%). The AI model demonstrated a similar relationship between output and risk for HF hospitalization in patients with and those without diagnostic H2FPEF output. Findings were similar when patients were stratified according to HFA-PEFF score. HFA-PEFF score, brain natriuretic peptide, and N-terminal pro–brain natriuretic peptide also differed according to the AI model prediction (Table). Few patients underwent exercise testing; differences in exercise capacity were not significantly different.Figure 2Risk for HF hospitalization was higher in patients with positive H2FPEF outputs (top), but many (n = 776) had nondiagnostic outputs. Application of the AI model was able to reclassify 708 (91%) of the nondiagnostic H2FPEF outputs (bottom).View Large Image Figure ViewerDownload Hi-res image Download (PPT)TableAdditional testing within 1 year of the qualifying echocardiographic studyAI model predictionPNegative (n = 564)Positive (n = 625)Uncertain (n = 95)H2FpEFF category, n (%)<.0001∗Chi-Square P value. Prediction negative161 (28.5)6 (1.0)6 (6.3) Prediction positive50 (8.9)264 (42.2)21 (22.1) Nondiagnostic353 (62.6)355 (56.8)68 (71.6)HFA-PEFF category, n (%)<.0001∗Chi-Square P value. Prediction negative292 (51.8)27 (4.3)26 (27.4) Prediction positive20 (3.5)207 (33.1)11 (11.6) Nondiagnostic252 (44.7)391 (62.6)58 (61.1)BNP, pg/mL<.0001†Kruskal-Wallis P value. Median (IQR)63.0 (22-148)352 (200-636)115 (27-211) n4911410NT-proBNP, pg/mL<.0001†Kruskal-Wallis P value. Median (IQR)210 (74-572)1,941 (697-5,866)675 (206-3,306) n8728426Exercise test workload, METs Mean ± SD9.1 ± 2.87.4 ± 3.211.1 ± 2.2.13‡Analysis of variance P value. n4983Exercise test FAC, % Mean ± SD100.8 ± 28.986.0 ± 28.0128.0 ± 25.5.11‡Analysis of variance P value. n4373BNP, Brain natriuretic peptide; FAC, Functional aerobic capacity; IQR, interquartile range; METs, metabolic equivalents; NT-proBNP, N-terminal pro–brain natriuretic peptide.∗ Chi-Square P value.† Kruskal-Wallis P value.‡ Analysis of variance P value. Open table in a new tab BNP, Brain natriuretic peptide; FAC, Functional aerobic capacity; IQR, interquartile range; METs, metabolic equivalents; NT-proBNP, N-terminal pro–brain natriuretic peptide. In this study we assessed the ability of a novel, HFpEF AI model using a single echocardiographic video clip to identify patients at increased risk for HF hospitalization and cardiac mortality. In summary, (1) positive model output was associated with higher risks for HF hospitalization and cardiac mortality, (2) patients with uncertain outputs demonstrated intermediate risks for these end points, (3) HF hospitalization and cardiac mortality risk were incrementally associated with higher model probability output scores, and (4) the AI model reclassified HF hospitalization risk in nondiagnostic clinical scores, including 91% for H2FPEF outputs and 92% for HFA-PEFF. This is the first AI echocardiographic model to produce outputs discriminating a specific disease (HFpEF) that are incrementally associated with risk for HF hospitalization and cardiac mortality. Prospective studies are required to confirm these retrospective results, to externally validate the AI model's outputs in other echocardiographic laboratories, and to understand the implications for patient management. Studies using a broad representation of HFpEF phenotypes should be undertaken to understand the generalizability of this model in a naturally heterogeneous clinical syndrome. Given her role as JASE Editor-in-Chief, Patricia A. Pellikka, MD, had no involvement in the peer review of this article and has no access to information regarding its peer review. Full responsibility for the editorial process for this article was delegated to Federico M. Asch, MD. Drs. Akerman, Porumb, Hawkes, Woodward, and Upton are employed by Ultromics. Download .pdf (.13 MB) Help with pdf files Supplemental Appendix