Connie M. Lewis

HFSA and AAHFN Joint Position Statement: Advocating for a Full Scope of Nursing Practice and Leadership in Heart Failure

The Heart Failure Society of America (HFSA) and theAmerican Association of Heart Failure Nurses (AAHFN)share a common core mission to improve outcomes ofpatients with heart failure. A recent report underscoredthe importance of increasing advocacy efforts to enablenurses to practice to the full extent of their education andtraining and engage in full partnership with physiciansand other health professionals in redesigning health care.

Perceived barriers and facilitators to patients receiving 60 minutes of heart failure education: A survey of AAHFN members

Since its inception, the American Association of Heart Failure Nurses (AAHFN) has assisted heart failure nurses providing appropriate education for their patients. In the most recent strategic plan the Board of Directors specifically targeted ways to enhance and expand patient education resources that our members can use in their clinical practice. This is particularly important as the recognition of 60 min, of inpatient education has been set as a goal by programs that measure outcomes and recognize quality.1,2 The goal of 60 min was supported by research that found a reduction in early readmission in those patients with a total of at least 1 h of inpatient education.3 To provide a baseline assessment as part of our quality improvement efforts, AAHFN devised a survey for membership that would: 1. Assess heart failure (HF) patient provision of 60 min of patient education (60MPE) and preparation for self-care, 2. Identify the barriers to patient education, 3. Assess the difficulty of teaching various topics, and 4. Examine institutional and nurse variables that promote 60MPE. A brief report of the results of the first question was published in a recent AAHFN publication.4 This paper provides a more detailed report of the survey.

HFSA and AAHFN joint position statement: Advocating for a full scope of nursing practice and leadership in heart failure

CHRISTOPHER S. LEE, RN, PhD, BARRY H. GREENBERG, MD, ANN S. LARAMEE, APRN, MS, SUSAN E. AMMON, RN, MS, FNP, MARILYN PRASUN, PhD, CCNS-BC, MARIE GALVAO, MSN, ANP-BC, CHFN, LYNN V. DOERING, DNSC, M. EUGENE SHERMAN, MD, LYNNE WARNER STEVENSON, MD, DOUGLAS D. GREGORY, PHD, PAUL A. HEIDENREICH, MD, MS, NAVIN K. KAPUR, MD, JOHN B. O’CONNELL, MD, ANNE L. TAYLOR, MD, JOSEPH A. HILL, MD, PhD, LINDA BAAS, RN, PhD, ACNP, CHFN, ASHLEY GIBBS, RN, MSN, ANP/GNP-BC, CHFN, KISMET RASMUSSON, FNP-BC, CHFN, CONNIE LEWIS, MSN, ACNP-BC, NP-C, CCRN, CHFN, PEGGY KIRKWOOD, RN, MSN, ACNPC, AACC, CHFN, JUANITA REIGLE, RN, MSN, ACNP-BC, CHFN, LISA RATHMAN, MSN, CRNP, CHFN, AND CYNTHIA BITHER, RN, MSN, APN-C, ACNP-C

Centers for Medicare and Medicaid Services’ readmission reports inaccurately describe an institution's decompensated heart failure admissions

Hospitals typically use Center for Medicare and Medicaid Services’ (CMS) Hospital Readmission Reduction Program (HRRP) administrative reports as the standard of heart failure (HF) admission quantification. We aimed to evaluate the HF admission population identified by CMS HRRP definition of HF hospital admissions compared with a clinically based HF definition. We evaluated all hospital admissions at an academic medical center over 16 months in patients with Medicare fee‐for service benefits and age ≥65 years. We compared the CMS HRRP HF definition against an electronic HF identification algorithm. Admissions identified solely by the CMS HF definition were manually reviewed by HF providers. Admissions confirmed with having decompensated HF as the primary problem by manual review or by the HF ID algorithm were deemed “HF positive,” whereas those refuted were “HF negative.” Of the 1672 all‐cause admissions evaluated, 708 (42%) were HF positive. The CMS HF definition identified 440 admissions: sensitivity (54%), specificity (94%), positive predictive value (87%), negative predictive value (74%). The CMS HF definition missed 324 HF admissions because of inclusion/exclusion criteria (15%) and decompensated HF being a secondary diagnosis (85%). The CMS HF definition falsely identified 56 admissions as HF. The most common admission reasons in this cohort included elective pacemaker or defibrillator implantations (n = 13), noncardiac dyspnea (n = 9), left ventricular assist device complications (n = 8), and acute coronary syndrome (n = 6). The CMS HRRP HF report is a poor representation of an institutions HF admissions because of limitations in administrative coding and the HRRP HF report inclusion/exclusion criteria.

Heart failure nurses play a pivotal role in linking clinical research to clinical practice: Translational research

There are approximately 6 million Americans living with heart failure and more than 670,000 patients with newly diagnosed heart failure annually who depend on heart failure nurses to combine the science and art of nursing/medicine to improve patient outcomes and quality of life, while decreasing readmissions, length of stay, and cost. Translational research is often described as the translation of the evidence from clinical trials into real-world practice.We know from numerous studies that it may take 1 or 2 decades for original research to be implemented into everyday practice. Because “patients are the heart of whatwedo,” heart failure nurseshave to recognize their significant roles in research. Few of us are involved in basicscienceresearch,althoughmanyplay integral roles innursing research initiatives and clinical trials. All of us should participate in the challenge to narrow the gap between interpreting results from clinical studies and changing our clinical practice to reflect the dissemination of that knowledge. We play a pivotal role in assessing that our patients are receiving the lifesavingmedical and device therapies that are appropriate. A goal of translational research is to rapidly diffuse available knowledge, interventions, and innovations into daily practice while understanding the barriers to practice. Fontanarosa and colleagues stated that “effective translation of the new knowledge, mechanisms, and techniques generated by advances in basic science and treatment of disease is essential for improving health.” Heart failure nurses from novice to expert and from research roles to clinic practice, academics, and administrative roles are uniquely positioned to make a difference in the health care of our patients. We can have a positive influence on the health care delivery systems, the decisions of policy makers, and the public awareness of the challenges our patients with heart failure and their families andcaregivers faceonaday-today basis. We must not be complacent. We are the best advocate for our patients. We canmake a difference.

Validation of an automated electronic algorithm and “dashboard” to identify and characterize decompensated heart failure admissions across a medical center

Background We aim to validate the diagnostic performance of the first fully automatic, electronic heart failure (HF) identification algorithm and evaluate the implementation of an HF Dashboard system with 2 components: real‐time identification of decompensated HF admissions and accurate characterization of disease characteristics and medical therapy. Methods We constructed an HF identification algorithm requiring 3 of 4 identifiers: B‐type natriuretic peptide >400 pg/mL; admitting HF diagnosis; history of HF International Classification of Disease, Ninth Revision, diagnosis codes; and intravenous diuretic administration. We validated the diagnostic accuracy of the components individually (n = 366) and combined in the HF algorithm (n = 150) compared with a blinded provider panel in 2 separate cohorts. We built an HF Dashboard within the electronic medical record characterizing the disease and medical therapies of HF admissions identified by the HF algorithm. We evaluated the HF Dashboards performance over 26 months of clinical use. Results Individually, the algorithm components displayed variable sensitivity and specificity, respectively: B‐type natriuretic peptide >400 pg/mL (89% and 87%); diuretic (80% and 92%); and International Classification of Disease, Ninth Revision, code (56% and 95%). The HF algorithm achieved a high specificity (95%), positive predictive value (82%), and negative predictive value (85%) but achieved limited sensitivity (56%) secondary to missing provider‐generated identification data. The HF Dashboard identified and characterized 3147 HF admissions over 26 months. Conclusions Automated identification and characterization systems can be developed and used with a substantial degree of specificity for the diagnosis of decompensated HF, although sensitivity is limited by clinical data input.

American Association of Heart Failure Nurses Best Practices paper: Literature synthesis and guideline review for dietary sodium restriction.

Nell Hodgson Woodruff School of Nursing, Emory University, 1520 Clifton Rd, NE #366, Atlanta, GA 30322, USA b School of Nursing & Health Studies, Georgetown University, USA Medstar, Georgetown University Hospital, USA Advanced Heart Failure Center, University of Cincinnati, USA Northeast Georgia Medical Center, Gainesville, GA, USA College of Nursing, University of Kentucky, USA Vanderbilt University Medical Center, USA Memorial Medical Center, Springfield, IL, USA

Customizing national models for a medical center's population to rapidly identify patients at high risk of 30-day all-cause hospital readmission following a heart failure hospitalization

Background: Nationally‐derived models predicting 30‐day readmissions following heart failure (HF) hospitalizations yield insufficient discrimination for institutional use. Objective: Develop a customized readmission risk model from Medicare‐employed and institutionally‐customized risk factors and compare the performance against national models in a medical center. Methods: Medicare patients age ≥ 65 years hospitalized for HF (n = 1,454) were studied in a derivation cohort and in a separate validation cohort (n = 243). All 30‐day hospital readmissions were documented. The primary outcome was risk discrimination (c‐statistic) compared to national models. Results: A customized model demonstrated improved discrimination (c‐statistic 0.72; 95% CI 0.69 – 0.74) compared to national models (c‐statistics of 0.60 and 0.61) with a c‐statistic of 0.63 in the validation cohort. Compared to national models, a customized model demonstrated superior readmission risk profiling by distinguishing a high‐risk (38.3%) from a low‐risk (9.4%) quartile. Conclusions: A customized model improved readmission risk discrimination from HF hospitalizations compared to national models.

HEART FAILURE READMISSION RISK PREDICTION: EVALUATION ON DIFFERENT APPROACHES FOR PATIENT LEVEL PROFILING OF READMISSION

Two heart failure (HF) readmission models utilize a hierarchy generalized linear model (HGLM) to predicted a patient’s readmission risk. An administrative claims based model is employed by the Center for Medicare and Medicaid Services (CMS) in the Hospital Readmission Reduction Program (HRRP),

Implications for heart failure prevention and treatment of cardiotoxicity in the cancer patient.

Cancer treatment is more effective than ever before. The life expectancy has significantly improved with chemotherapeutic agents, radiation, and targeted therapies that inhibit specific molecules called tyrosine kinase inhibitors. These advances have not been without a price. Cardiotoxicity is an increasing concern not only in the oncology community but also in the cardiology, specifically heart failure specialist. There are three main cardiovascular systems affected by cancer treatment: vascular, structure, and function. The vascular compromise can result in hypertension, thrombosis, and ischemia. Cardiac structure changes may present as valvular heart disease, conduction disturbances, or pericardial disease.1 The conduction disturbances are often late cardiac effects that may present as atrial fibrillation, bradycardia, heart block, or ventricular tachycardia. The pericardial effusions are most often seen during breast and lung cancer treatments. Heart failure, myocardial dysfunction, may occur acutely or late. The most typical sign of chronic cardiotoxicity is asymptomatic systolic and/or diastolic left ventricular dysfunction that leads to severe heart failure and that may ultimately lead to death.1 We are well aware of anthracycline-induced heart failure. Abundant literature has documented anthracycline-induced cardiotoxicity is a dose and cumulative dose effect.2 There is also compelling data that heart failure may develop 10e20 years after anthracycline exposure.3 Radiation is also a contributing factor in the development of cardiac toxicities. Radiation to any vascular location places the patient at high risk for early and complex atherosclerosis, with mediastinal irradiation being a major risk for the development of coronary artery disease.1 Heart failure, myocardial fibrosis, pericardial disease, conduction system disturbances, and valvular disease may also be late consequences of radiation.4,5 Tyrosine kinase inhibitors (TKI) are associated with severe hypertension and heart failure. Some data suggest that TKI-induced heart failure may be reversible with heart failure evidence-based therapies, beta blockers and angiotensin-converting enzyme inhibitors.6,7