Richard M Nowak1, Brian P Reed2, Salvatore DiSomma3, Prabath Nanayakkara4, Michele Moyer5, Scott Millis6, Phillip Levy7. 1. Department of Emergency Medicine, Henry Ford Health System, Detroit, MI, USA. Electronic address: rnowak1@hfhs.org. 2. Department of Biostatistics, Wayne State University, Detroit, MI, USA. Electronic address: bpreed@med.wayne.edu. 3. Department of Medical-Surgery Sciences and Translational Medicine, University Sapienza, Rome, Italy. Electronic address: Salvatore.Disomma@uniroma1.it. 4. Section of Acute Medicine, Department of Internal Medicine, VU Medical Center, Amsterdam, Netherlands. Electronic address: P.Nanayakkara@vumc.nl. 5. Department of Emergency Medicine, Henry Ford Health System, Detroit, MI, USA. Electronic address: mmoyer1@hfhs.org. 6. Department of Biostatistics, Wayne State University, Detroit, MI, USA. Electronic address: smillis@med.wayne.edu. 7. Departments of Emergency Medicine and Physiology and Cardiovascular Research Institute, Wayne State University, Detroit, MI, USA. Electronic address: plevy@med.wayne.edu.
Abstract
BACKGROUND: There 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. METHODS: Patients 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. RESULTS: A 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. CONCLUSIONS: Three 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.
BACKGROUND: There 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. METHODS:Patients 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. RESULTS: A 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. CONCLUSIONS: Three 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.
Authors: Amitava Banerjee; Suliang Chen; Ghazaleh Fatemifar; Mohamad Zeina; R Thomas Lumbers; Johanna Mielke; Simrat Gill; Dipak Kotecha; Daniel F Freitag; Spiros Denaxas; Harry Hemingway Journal: BMC Med Date: 2021-04-06 Impact factor: 11.150
Authors: Nicholas Eric Harrison; Sarah Meram; Xiangrui Li; Morgan B White; Sarah Henry; Sushane Gupta; Dongxiao Zhu; Peter Pang; Phillip Levy Journal: PLoS One Date: 2022-03-31 Impact factor: 3.240
Authors: Szymon Urban; Mikołaj Błaziak; Maksym Jura; Gracjan Iwanek; Agata Zdanowicz; Mateusz Guzik; Artur Borkowski; Piotr Gajewski; Jan Biegus; Agnieszka Siennicka; Maciej Pondel; Petr Berka; Piotr Ponikowski; Robert Zymliński Journal: Biomedicines Date: 2022-06-27