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Finding the Sweet Spot in Predicting Risk for Hospitalized Patients With Heart Failure

2023; Elsevier BV; Volume: 204; Linguagem: Inglês

10.1016/j.amjcard.2023.07.142

1879-1913

Katherine C. Michelis,

Congenital Heart Disease Studies

Heart failure (HF) remains a major healthcare issue in the United States, with >8 million Americans or 1 in 33 projected to have this diagnosis by 2030.1Heidenreich PA Albert NM Allen LA Bluemke DA Butler J Fonarow GC Ikonomidis JS Khavjou O Konstam MA Maddox TM Nichol G Pham M Piña IL Trogdon JG American Heart Association Advocacy Coordinating Committee, Council on Arteriosclerosis, Thrombosis and Vascular Biology, Council on Cardiovascular Radiology and Intervention, Council on Clinical Cardiology, Council on Epidemiology and Prevention, Stroke CouncilForecasting the impact of heart failure in the United States: a policy statement from the American Heart Association.Circ Heart Fail. 2013; 6: 606-619Crossref PubMed Scopus (1956) Google Scholar HF hospitalizations are associated with substantial morbidity and mortality.2Gheorghiade M Pang PS Acute heart failure syndromes.J Am Coll Cardiol. 2009; 53: 557-573Crossref PubMed Scopus (477) Google Scholar,3Setoguchi S Stevenson LW Schneeweiss S Repeated hospitalizations predict mortality in the community population with heart failure.Am Heart J. 2007; 154: 260-266Crossref PubMed Scopus (441) Google Scholar In recent years, the number of HF hospitalizations in the United States has been increasing, and HF is the second leading cause of hospitalization overall.4Minhas AMK Ijaz SH Jamal S Dani SS Khan MS Greene SJ Fudim M Warraich HJ Shapiro MD Virani SS Nasir K Khan SU. Trends in characteristics and outcomes in primary heart failure hospitalizations among older population in the United States, 2004 to 2018.Circ Heart Fail. 2022; 15e008943Crossref Scopus (4) Google Scholar,5Salah HM Minhas AMK Khan MS Pandey A Michos ED Mentz RJ Fudim M. Causes of hospitalization in the USA between 2005 and 2018.Eur Heart J Open. 2021; 1: oeab001Crossref PubMed Google Scholar Risk stratification is an important aspect of the management of patients hospitalized for HF to guide clinical decision-making, allocation of resources, and discussions regarding prognosis. Several risk prediction models for patients hospitalized with HF have been validated and may improve patient outcomes. Risk scores for patients hospitalized with HF include the Get With the Guidelines-Heart Failure (GWTG-HF) score, which was derived from the American Heart Association GWTG-HF program data and is calculated using 7 variables that are routinely collected upon hospital admission: age, systolic blood pressure, blood urea nitrogen (BUN), serum sodium, heart rate, Black race, and chronic obstructive pulmonary disease.6Peterson PN Rumsfeld JS Liang L Albert NM Hernandez AF Peterson ED Fonarow GC Masoudi FA American Heart Association Get With the Guidelines-Heart Failure ProgramA validated risk score for in-hospital mortality in patients with heart failure from the American Heart Association get with the guidelines program.Circ Cardiovasc Qual Outcomes. 2010; 3: 25-32Crossref PubMed Scopus (355) Google Scholar Of note, age, systolic blood pressure, and BUN contribute most substantially to the overall score for this model. It reliably predicts both in-hospital and long-term, post-discharge mortality in patients hospitalized for HF, including those with preserved left ventricular systolic function.7Suzuki S Yoshihisa A Sato Y Kanno Y Watanabe S Abe S Sato T Oikawa M Kobayashi A Yamaki T Kunii H Nakazato K Ishida T Takeishi Y. Clinical significance of Get With the Guidelines-Heart Failure risk score in patients with chronic heart failure after hospitalization.J Am Heart Assoc. 2018; 7e008316Crossref Scopus (47) Google Scholar The ACUTE HF score is computed using serum creatinine, ejection fraction, age, previous HF hospitalization, previous stroke or transient ischemic attack, degree of mitral regurgitation, and use of noninvasive ventilation and predicts mortality at 30 days, 6 months, and 5 years from hospitalization.8Cameli M Pastore MC De Carli G Henein MY Mandoli GE Lisi E Cameli P Lunghetti S D'Ascenzi F Nannelli C Rizzo L Valente S Mondillo S Acute hf score, a multiparametric prognostic tool for acute heart failure: a real-life study.Int J Cardiol. 2019; 296: 103-108Abstract Full Text Full Text PDF PubMed Scopus (28) Google Scholar Both of these risk scores require the use of a calculation tool. Other risk scores that are easier to calculate manually include the Acute Decompensated Heart Failure National Registry (ADHERE) model, which uses only 3 variables (BUN, serum creatinine, and systolic blood pressure) to classify patients at low, intermediate, or high risk of in-hospital mortality.9Fonarow GC Adams Jr, KF Abraham WT Yancy CW Boscardin WJ ADHERE Scientific Advisory Committee, Study Group, and Investigators. Risk stratification for in-hospital mortality in acutely decompensated heart failure: classification and regression tree analysis.JAMA. 2005; 293: 572-580Crossref PubMed Scopus (1366) Google Scholar The Atrial fibrillation; Hemoglobin; Elderly; Abnormal renal parameters; Diabetes mellitus (AHEAD) score is also relatively user-friendly, with each 1-point increase corresponding to a roughly 10 percentage point increase in 1-year mortality for patients hospitalized for HF.10Spinar J Jarkovsky J Spinarova L Mebazaa A Gayat E Vitovec J Linhart A Widimsky P Miklik R Zeman K Belohlavek J Malek F Felsoci M Kettner J Ostadal P Cihalik C Vaclavik J Taborsky M Dusek L Littnerova S Parenica J. Ahead score–long-term risk classification in acute heart failure.Int J Cardiol. 2016; 202: 21-26Abstract Full Text Full Text PDF PubMed Scopus (55) Google Scholar The AHEAD-U score additionally incorporates uric acid, resulting in improved risk classification for all-cause and cardiovascular mortality.11Chen YJ Sung SH Cheng HM Huang WM Wu CL Huang CJ Hsu PF Yeh JS Guo CY Yu WC Chen CH. Performance of AHEAD score in an Asian cohort of acute heart failure with either preserved or reduced left ventricular systolic function.J Am Heart Assoc. 2017; 6e004297Crossref Scopus (30) Google Scholar The HANBAH score was derived in a Taiwanese cohort and predicts both short-term and long-term mortality for patients hospitalized for HF. This score is calculated by scoring each of the following as 1 point: Hemoglobin level <13.0 g/L for men and 70 years; serum sodium (N) level 26 mg/100 ml for men and >28 mg/100 ml for women; Atrial fibrillation; and High-density lipoprotein level <25 mg/100 ml.12Guo CY Chan CH Chou YC Sung SH Cheng HM. A statistical predictive model consistent within a 5-year follow-up period for patients with acute heart failure.J Chin Med Assoc. 2020; 83: 1008-1013Crossref PubMed Scopus (7) Google Scholar In the previous issue of the American Journal of Cardiology, Kaneko et al13Kaneko T Kagiyama N Nakamura Y Dotare T Sunayama T Ishiwata S Maeda D Iso T Kato T Suda S Hiki M Matsue Y Kasai T Minamino T. Usefulness of HANBAH score in Japanese patients with acute heart failure.Am J Cardiol. 2023; 203: 45-52Abstract Full Text Full Text PDF PubMed Scopus (1) Google Scholar externally validate the HANBAH score in a cohort of 744 Japanese patients hospitalized with HF from 2015 to 2019 and demonstrate a significant association with all-cause death, a composite of death and HF hospitalization, and in-hospital death, even after multivariable adjustment. Additionally, the authors analyzed the area under receiver operator characteristic curves to compare prognostic performance between the HANBAH score and other validated HF risk scores. They found that the HANBAH score performed better than the AHEAD and AHEAD-U scores and was similar to the multi-domain ACUTE HF score for all end points. As noted by the authors, one disadvantage of the ACUTE HF score is that ejection fraction and severity of valvular lesions are not always known at admission. Therefore, the HANBAH score has the potential to be a powerful tool for clinicians caring for patients hospitalized for HF. There are several important considerations, however. First, although summing points to calculate the HANBAH score is straightforward, remembering the specific cut-off values for each parameter may be challenging, especially for time-pressured clinicians. Second, because the HANBAH score has been both derived and validated in Asian cohorts, it may be less prognostic in patients with different racial characteristics. Of note, though, there is considerable overlap between the variables included in the HANBAH score versus other HF risk scores that have been validated in larger and more racially diverse patient populations. Finally, and perhaps most importantly, is the question of whether clinicians will actually use it. The REVeAL-HF (Risk EValuation And its Impact on ClinicAL Decision Making and Outcomes in Heart Failure) study demonstrated that alerting clinicians to 1-year mortality estimates for patients admitted with HF did not significantly affect rates of 30-day hospital readmissions or mortality.14Ahmad T Desai NR Yamamoto Y Biswas A Ghazi L Martin M Simonov M Dhar R Hsiao A Kashyap N Allen LA Velazquez EJ Wilson FP Alerting clinicians to 1-year mortality risk in patients hospitalized with heart failure: the reveal-hf randomized clinical trial.JAMA Cardiol. 2022; 7: 905-912Crossref PubMed Scopus (13) Google Scholar The authors of REVeAL HF, however, hypothesized that although clinicians are not necessarily good at predicting risk in hospitalized HF patients,15Yamokoski LM Hasselblad V Moser DK Binanay C Conway GA Glotzer JM Hartman KA Stevenson LW Leier CV. Prediction of rehospitalization and death in severe heart failure by physicians and nurses of the escape trial.J Card Fail. 2007; 13: 8-13Abstract Full Text Full Text PDF PubMed Scopus (78) Google Scholar they may ignore risk estimates because of “algorithm aversion.” Despite these limitations, the HANBAH score is a relatively simple and now well-validated risk score that factors in multiple clinical domains for patients admitted with HF. As we seek to continually improve our care of patients with HF, incorporating prognostic information from risk scores such as the HANBAH score into our practice can hopefully facilitate efforts to deliver better, more personalized care. The author has no competing interests to declare.