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Exercise Stress Echocardiography–Based Phenotyping of Heart Failure With Preserved Ejection Fraction - 01/08/24

Doi : 10.1016/j.echo.2024.05.003 
Yuki Saito, MD, PhD a, b, Yuto Omae, PhD c, Tomonari Harada, MD, PhD a, Hidemi Sorimachi, MD, PhD a, Naoki Yuasa, MD a, Kazuki Kagami, MD, PhD a, d, Fumitaka Murakami, MD a, Ayami Naito, MD a, d, Yuta Tani, PT a, Toshimitsu Kato, MD, PhD a, Naoki Wada, MD, PhD e, Yasuo Okumura, MD, PhD b, Hideki Ishii, MD, PhD a, Masaru Obokata, MD, PhD a,
a Department of Cardiovascular Medicine, Gunma University Graduate School of Medicine, Maebashi, Japan 
b Division of Cardiology, Department of Medicine, Nihon University School of Medicine, Tokyo, Japan 
c Department of Industrial Engineering and Management, College of Industrial Technology, Nihon University, Chiba, Japan 
d Division of Cardiovascular Medicine, National Defense Medical College, Tokorozawa, Japan 
e Department of Rehabilitation Medicine, Gunma University Graduate School of Medicine, Maebashi, Japan 

Reprint requests: Masaru Obokata, MD, PhD, Department of Cardiovascular Medicine, Gunma University Graduate School of Medicine, 3-39-22 Showa-machi, Maebashi, Gunma 371-8511, Japan.Department of Cardiovascular MedicineGunma University Graduate School of Medicine3-39-22 Showa-machiMaebashiGunma371-8511Japan

Abstract

Background

Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous syndrome requiring improved phenotypic classification. Previous studies have identified subphenotypes of HFpEF, but the lack of exercise assessment is a major limitation. The aim of this study was to identify distinct pathophysiologic clusters of HFpEF based on clinical characteristics, and resting and exercise assessments.

Methods

A total of 265 patients with HFpEF underwent ergometry exercise stress echocardiography with simultaneous expired gas analysis. Cluster analysis was performed by the K-prototype method with 21 variables (10 clinical and resting echocardiographic variables and 11 exercise echocardiographic parameters). Pathophysiologic features, exercise tolerance, and prognosis were compared among phenogroups.

Results

Three distinct phenogroups were identified. Phenogroup 1 (n = 112 [42%]) was characterized by preserved biventricular systolic reserve and cardiac output augmentation. Phenogroup 2 (n = 58 [22%]) was characterized by a high prevalence of atrial fibrillation, increased pulmonary arterial and right atrial pressures, depressed right ventricular systolic functional reserve, and impaired right ventricular–pulmonary artery coupling during exercise. Phenogroup 3 (n = 95 [36%]) was characterized by the smallest body mass index, ventricular and vascular stiffening, impaired left ventricular diastolic reserve, and worse exercise capacity. Phenogroups 2 and 3 had higher rates of composite outcomes of all-cause mortality or heart failure events than phenogroup 1 (log-rank P = .02).

Conclusion

Exercise echocardiography–based cluster analysis identified three distinct phenogroups of HFpEF, with unique exercise pathophysiologic features, exercise capacity, and clinical outcomes.

Il testo completo di questo articolo è disponibile in PDF.

Graphical abstract

Central Illustration Description of characteristics of phenogroups. BMI, Body mass index. Other abbreviations as in Figure 1 and Figure 2.



Central Illustration : 

Description of characteristics of phenogroups. BMI, Body mass index; CO, cardiac output; LV, left ventricular; LVEDV, left ventricular end-diastolic volume; PA, pulmonary artery; RV, right ventricular. Other abbreviations as in Figure 1 and Figure 2


Central IllustrationDescription of characteristics of phenogroups. BMI, Body mass index; CO, cardiac output; LV, left ventricular; LVEDV, left ventricular end-diastolic volume; PA, pulmonary artery; RV, right ventricular. Other abbreviations as in Figures 1 and 2

Il testo completo di questo articolo è disponibile in PDF.

Highlights

Exercise echocardiography–based cluster analysis revealed three HFpEF phenogroups.
The phenogroups had different exercise capacities and clinical outcomes.
The data showed the importance of exercise assessment to better phenotype HFpEF.

Il testo completo di questo articolo è disponibile in PDF.

Keywords : Exercise, Heart failure with preserved ejection fraction, Phenotyping, Machine learning, Stress echocardiography

Abbreviations : AF, CO, Ea, EDV, HF, HFpEF, ICC, LA, LV, LVEF, NCD, PA, PASP, RAP, RHC, RV, SV, TAC, TAPSE, TR, TV, Vo2


Mappa


 Dr. Obokata has received research grants from the Fukuda Foundation for Medical Technology, the Mochida Memorial Foundation for Medical and Pharmaceutical Research, Nippon Shinyaku, the Takeda Science Foundation, the Japanese Circulation Society, the Japanese College of Cardiology, AMI, Nippon Boehringer-Ingelheim, JSPS KAKENHI (21K16078), and AMED (23jm0210104h0002). Dr. Harada has received research grants from Bayer Academic Support. Dr. Ishii has received scholarship funds or donations from Abbott Medical Japan, Boehringer Ingelheim Japan, Bristol Myers Squibb and Pfizer Japan Inc.


© 2024  American Society of Echocardiography. Pubblicato da Elsevier Masson SAS. Tutti i diritti riservati.
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Vol 37 - N° 8

P. 759-768 - agosto 2024 Ritorno al numero
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