A critical challenge facing emergency department (ED) physicians is how best to manage patients presenting with symptoms of heart failure. Currently, most patients being evaluated for heart failure are admitted to the hospital, yet not all of these patients warrant such intensive treatment, and up to 50% of these admissions could be avoided. Improving the ability of the emergency physician to effectively and safely manage low-risk patients is essential to avoid unnecessary hospitalizations. We propose developing a decision tool derived from prospectively gathered ED data that will predict risk for inpatient or outpatient death and serious in-hospital or out-of-hospital complications. Further, the proposed project will validate the usefulness and generalizability of this decision tool in three different ED environments across racially and socioeconomically diverse patient populations. To develop our decision tool, over 100 variables routinely available to the emergency physician within the first two hours of ED presentation will be considered for inclusion in a statistical risk model. Unlike exisitng models using inpatient data, these measures are representative of actual clinical practice and routinely used to decide a patient's disposition. We will collect standardized data during a patient's evaluation for heart failure. Relying on chart review or large dataset analyses can lead to missing and inconsistent data. We will include all patients evaluated for heart failure regardless of final diagnosis, thus avoiding selection bias inherent in models based on patients with a definitive diagnosis. A fundamental innovation we propose is a tool using 5-day outcomes for primary analyses, and 30-day outcomes for secondary analyses. This overcomes the limitation of 30-day outcome models that are highly dependent on unpredictable, post-visit patient and provider behavior. Another novel aspect of the proposed project is the combining of expertise in emergency medicine, cardiology, and biostatistics to accurately assign post-treatment outcomes to acute presentations. Results will be translated into an algorithm that will be disseminated worldwide. This is the first step toward achieving our broad objective of appropriate allocation of hospital resources to reduce costs of heart failure care. In collaboration with outcomes and effectiveness researchers, we plan to conduct further studies to test the efficacy of our risk model.

National Institute of Health (NIH)
National Heart, Lung, and Blood Institute (NHLBI)
Research Project (R01)
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Study Section
Clinical and Integrative Cardiovascular Sciences Study Section (CICS)
Program Officer
Mascette, Alice
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Vanderbilt University Medical Center
Emergency Medicine
Schools of Medicine
United States
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Self, Wesley H; Storrow, Alan B; Hartmann, Oliver et al. (2016) Plasma bioactive adrenomedullin as a prognostic biomarker in acute heart failure. Am J Emerg Med 34:257-62
Collins, Sean P; Jenkins, Cathy A; Harrell Jr, Frank E et al. (2015) Identification of Emergency Department Patients With Acute Heart Failure at Low Risk for 30-Day Adverse Events: The STRATIFY Decision Tool. JACC Heart Fail 3:737-47
McNaughton, Candace; Rothman, Russell; Storrow, Alan et al. (2014) Measuring numeracy and health literacy in the emergency department. Acad Emerg Med 21:944-5
Storrow, Alan B; Jenkins, Cathy A; Self, Wesley H et al. (2014) The burden of acute heart failure on U.S. emergency departments. JACC Heart Fail 2:269-77
Pang, Peter S; Collins, Sean P; Sauser, Kori et al. (2014) Assessment of dyspnea early in acute heart failure: patient characteristics and response differences between likert and visual analog scales. Acad Emerg Med 21:659-66
McNaughton, Candace D; Collins, Sean P; Kripalani, Sunil et al. (2013) Low numeracy is associated with increased odds of 30-day emergency department or hospital recidivism for patients with acute heart failure. Circ Heart Fail 6:40-6
Collins, Sean P; Lindsell, Christopher J; Storrow, Alan B et al. (2012) Early changes in clinical characteristics after emergency department therapy for acute heart failure syndromes: identifying patients who do not respond to standard therapy. Heart Fail Rev 17:387-94
Henry-Okafor, Queen; Collins, Sean P; Jenkins, Cathy A et al. (2012) Relationship between Uric Acid Levels and Diagnostic and Prognostic Outcomes in Acute Heart Failure. Open Biomark J 5:9-15
Collins, Sean P; Hart, Kimberly W; Lindsell, Christopher J et al. (2012) Elevated urinary neutrophil gelatinase-associated lipocalcin after acute heart failure treatment is associated with worsening renal function and adverse events. Eur J Heart Fail 14:1020-9
Collins, Sean P; Lindsell, Christopher J; Yealy, Donald M et al. (2012) A comparison of criterion standard methods to diagnose acute heart failure. Congest Heart Fail 18:262-71

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