• Richard M Nowak, Brian P Reed, Salvatore DiSomma, Prabath Nanayakkara, Michele Moyer, Scott Millis, and Phillip Levy.
• Department of Emergency Medicine, Henry Ford Health System, Detroit, MI, USA. Electronic address: rnowak1@hfhs.org.
• Am J Emerg Med. 2017 Apr 1; 35 (4): 536-542.

BackgroundThere is little known about the baseline hemodynamic (HD) profiles (beyond pulse/blood pressure) of patients presenting to the Emergency department (ED) with acute heart failure (AHF). Assessing these baseline parameters could help differentiate underlying HD phenotypes which could be used to develop specific phenotypic specific approaches to patient care.MethodsPatients with suspected AHF were enrolled in the PREMIUM (Prognostic Hemodynamic Profiling in the Acutely Ill Emergency Department Patient) multinational registry and continuous HD monitoring was initiated on ED presentation using noninvasive finger cuff technology (Nexfin, BMEYE, Edwards Lifesciences, Irvine, California). Individuals with clinically suspected and later confirmed AHF were included in this analysis and initial 15minute averages for available HD parameters were calculated. K-means clustering was performed to identify out of 23 HD variables a set that provided the greatest level of inter-cluster discrimination and intra-cluster cohesions.ResultsA total of 127 patients had confirmed AHF. The final model, using mean normalized patient baseline HD values was able to differentiate these individuals into 3 distinct phenotypes. Cluster 1: normal cardiac index (CCI) and systemic vascular resistance index (SVRI); cluster 2: very low CI and markedly increased SVRI: and cluster 3: low CI and an elevated SVRI. These clusters were not differentiated using clinically available ED information.ConclusionsThree distinct clusters were defined using novel noninvasive presenting HD monitoring technology in this cohort of ED AHF patients. Further studies are needed to determine whether phenotypic specific therapies based on these clusters can improve outcomes.Copyright © 2016 Elsevier Inc. All rights reserved.

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