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Regional Variation of Mortality in Heart Failure With Reduced and Preserved Ejection Fraction Across Asia: Outcomes in the ASIAN-HF Registry

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Background Data comparing outcomes in heart failure (HF) across Asia are limited. We examined regional variation in mortality among patients with HF enrolled in the ASIAN‐HF (Asian Sudden Cardiac Death in Heart Failure) registry with separate analyses for those with reduced ejection fraction (EF; <40%) versus preserved EF (≥50%). Methods and Results The ASIAN‐HF registry is a prospective longitudinal study. Participants with symptomatic HF were recruited from 46 secondary care centers in 3 Asian regions: South Asia (India), Southeast Asia (Thailand, Malaysia, Philippines, Indonesia, Singapore), and Northeast Asia (South Korea, Japan, Taiwan, Hong Kong, China). Overall, 6480 patients aged >18 years with symptomatic HF were recruited (mean age: 61.6±13.3 years; 27% women; 81% with HF and reduced rEF). The primary outcome was 1‐year all‐cause mortality. Striking regional variations in baseline characteristics and outcomes were observed. Regardless of HF type, Southeast Asians had the highest burden of comorbidities, particularly diabetes mellitus and chronic kidney disease, despite being younger than Northeast Asian participants. One‐year, crude, all‐cause mortality for the whole population was 9.6%, higher in patients with HF and reduced EF (10.6%) than in those with HF and preserved EF (5.4%). One‐year, all‐cause mortality was significantly higher in Southeast Asian patients (13.0%), compared with South Asian (7.5%) and Northeast Asian patients (7.4%; P<0.001). Well‐known predictors of death accounted for only 44.2% of the variation in risk of mortality. Conclusions This first multinational prospective study shows that the outcomes in Asian patients with both HF and reduced or preserved EF are poor overall and worst in Southeast Asian patients. Region‐specific risk factors and gaps in guideline‐directed therapy should be addressed to potentially improve outcomes. Clinical Trial Registration URL: https://www.clinicaltrials.gov/. Unique identifier: NCT01633398.
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Regional Variation of Mortality in Heart Failure With Reduced and
Preserved Ejection Fraction Across Asia: Outcomes in the ASIAN-HF
Registry
Michael R. MacDonald, MB ChB, BSc (Hons); Wan Ting Tay, MAppStat; Tiew-Hwa Katherine Teng, MPH, PhD; Inder Anand, MD, PhD;
Lieng Hsi Ling, MBBS, MD; Jonathan Yap, MBBS, MPH; Jasper Tromp, MD, PhD; Gurpreet S. Wander, MD, DM; Ajay Naik, MD, DM;
Tachapong Ngarmukos, MD; Bambang B. Siswanto, MD, PhD; Chung-Lieh Hung, MD; ASIAN-F investigators;* A. Mark Richards, MD, PhD;
Carolyn S. P. Lam, MBBS, PhD
Background-Data comparing outcomes in heart failure (HF) across Asia are limited. We examined regional variation in mortality
among patients with HF enrolled in the ASIAN-HF (Asian Sudden Cardiac Death in Heart Failure) registry with separate analyses for
those with reduced ejection fraction (EF; <40%) versus preserved EF (50%).
Methods and Results-The ASIAN-HF registry is a prospective longitudinal study. Participants with symptomatic HF were
recruited from 46 secondary care centers in 3 Asian regions: South Asia (India), Southeast Asia (Thailand, Malaysia, Philippines,
Indonesia, Singapore), and Northeast Asia (South Korea, Japan, Taiwan, Hong Kong, China). Overall, 6480 patients aged >18 years
with symptomatic HF were recruited (mean age: 61.613.3 years; 27% women; 81% with HF and reduced rEF). The primary
outcome was 1-year all-cause mortality. Striking regional variations in baseline characteristics and outcomes were observed.
Regardless of HF type, Southeast Asians had the highest burden of comorbidities, particularly diabetes mellitus and chronic kidney
disease, despite being younger than Northeast Asian participants. One-year, crude, all-cause mortality for the whole population was
9.6%, higher in patients with HF and reduced EF (10.6%) than in those with HF and preserved EF (5.4%). One-year, all-cause
mortality was signicantly higher in Southeast Asian patients (13.0%), compared with South Asian (7.5%) and Northeast Asian
patients (7.4%; P<0.001). Well-known predictors of death accounted for only 44.2% of the variation in risk of mortality.
Conclusions-This rst multinational prospective study shows that the outcomes in Asian patients with both HF and reduced or
preserved EF are poor overall and worst in Southeast Asian patients. Region-specic risk factors and gaps in guideline-directed
therapy should be addressed to potentially improve outcomes.
Clinical Trial Registration-URL: https://www.clinicaltrials.gov/. Unique identier: NCT01633398. (J Am Heart Assoc. 2020;9:
e012199. DOI: 10.1161/JAHA.119.012199.)
Key Words: epidemiology heart failure mortality
Heart failure (HF) is a rapidly growing, global public health
problem that imposes a severe burden of morbidity and
mortality.
1
Worldwide, >38 million people have HF,
2
at an
estimated cost of US$100 billion in 2012.
3
With explosive
population growth in the past century, Asia is currently home to
4.4 billion people (60% of the worlds population), and its
population is projected to reach 5.2 billion by 2050. Exponen-
tial population growth, urbanization, sedentary lifestyle, and an
From the Changi General Hospital, Singapore (M.R.M.); National Heart Centre Singapore, Singapore (W.T.T., T.-H.K.T., J.Y., J.T., C.S.P.L.); School of Population & Global
Health, University of Western Australia, Perth, Australia (T.-H.K.T.); Veterans Affairs Medical Center, Minneapolis, MN (I.A.); Cardiovascular Research Institute, National
University Heart Centre, Singapore (L.H.L., A.M.R.); Department of Cardiology, University Medical Center Groningen, Groningen, The Netherlands (J.T.C., C.S.P.L.);
Dayanand Medical College and Hospital, Ludhiana, India (G.S.W.); Care Institute of Medical Sciences, Ahmedabad, India (A.N.); Ramathibodi Hospital, Bangkok, Thailand
(T.N.); National Cardiovascular Center Universitas Indonesia, Jakarta, Indonesia (B.B.S.); Mackay Memorial Hospital, Taipei, Taiwan (C.-L.H.); University of Otago, New
Zealand (A.M.R.); Duke-National University of Singapore Medical School, Singapore (C.S.P.L.).
*A complete list of the ASIAN-HF investigators can be found in the Appendix at the end of the article.
Correspondence to: Carolyn S. Lam, MBBS, PhD, National Heart Centre Singapore, 5 Hospital Dr, Singapore 169609, Singapore. E-mail: carolyn.lam@duke-nus.edu.sg
Received February 12, 2019; accepted August 28, 2019.
ª2019 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley. This is an open access article under the terms of the Creative Commons
Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for
commercial purposes.
DOI: 10.1161/JAHA.119.012199 Journal of the American Heart Association 1
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aging population together with increasing rates of obesity,
hypertension, and diabetes mellitus, all of which predispose to
HF, have contributed to a tsunamiof cardiovascular disease,
including HF, in Asia. Asia is highly heterogeneous, with
countries at different stages of economic development.
Population-based data suggest that socioeconomically
deprived persons are likely to develop HF at an earlier age
than afuent individuals.
4
Limited data from Southeast Asian
countries also suggest a higher prevalence of HF compared
with other Asian countries.
5
This is of increasing concern,
especially as the burden of HF is predicted to be highest in the
poorest countries least equipped to deal with the onslaught.
2
Most outcomes data in HF come from European and North
American populations, with scant information from Asia,
restricted to a few individual Asian countries.
6,7
In afuent
Western populations, 5-year mortality rates in HF patients
from population-based studies rival those of major cancers at
5060%.
2
The limited data from low- and middle-income
countries suggest their mortality rates may be even higher.
6,8
Furthermore, studies have suggested that Asian patients
present with HF on average at least a decade earlier than their
white counterparts, with two thirds presenting with multimor-
bidity.
9
The INTER-CHF (International Congestive Heart Fail-
ure) study recently reported marked regional differences in
mortality in patients with HF that persist even after adjust-
ment for known cardiac and noncardiac predictors, suggest-
ing distinctive heterogeneity of the Asian HF phenotype.
6
Adding to this complexity, HF subtypes (ie, with reduced
ejection fraction [rEF] or preserved ejection fraction [pEF]) are
heterogeneous syndromes. Epidemiological data on outcomes
in these HF subtypes in Asia are scarce, limited to single-
center or national data.
10
The degree of regional variation in
HF outcomes and the underlying reasons for such variations
are uncertain. Research into factors underpinning regional
disparities is necessary to enable targeted action to improve
population-level outcomes. We therefore examined the
regional variation in all-cause and cause-specic mortality
among all patients with HF enrolled in the ASIAN-HF (Asian
Sudden Cardiac Death in Heart Failure) registry with separate
subanalyses in HF patients with rEF (HFrEF) and with pEF
(HFpEF).
Methods
Study Population
The study data and materials used to conduct the research
cannot be made available to other researchers for purposes of
reproducing the results or replicating the procedure because
of the legal restrictions imposed by multinational jurisdictions.
ASIAN-HF is a prospective observational multinational registry
of Asian patients aged >18 years with symptomatic HF (at
least 1 episode of decompensated HF in the previous
6 months, resulting in a hospital admission or treatment in
an outpatient clinic) recruited from 46 medical centers across
11 Asian regions (China, Hong Kong, India, Indonesia, Japan,
Korea, Malaysia, Philippines, Singapore, Taiwan, and Thai-
land), following informed consent.
11
Investigation sites (46 in
total with >220 investigators; Appendix) covered a broad
spectrum of medical, cardiology, and HF specialty units,
admitting patients with acute HF and conducting outpatient
follow-up of patients with chronic HF. Site selection in ASIAN-
HF was based on the size of the country, the geographic
location of the site within the country, the patient population
served, HF patient volume, and availability of expertise in
echocardiography. A mix of private and public hospitals and
tertiary, university, and cardiovascular specialty hospitals in
capital and provincial cities were included. Ethics approvals
conforming to the Declaration of Helsinki were obtained from
the relevant human ethics committees at all sites. Patients
were excluded if they had severe valve disease as the primary
cause of HF, had life-threatening comorbidity with life
expectancy of <1 year, were unable or unwilling to give
consent, or were concurrently participating in a clinical
therapeutic trial. Asian patients were recruited in 2 stages:
those with HF and rEF (HFrEF; EF <40% on baseline
echocardiography) were enrolled between October 1, 2012,
and December 31, 2015, with overlapping recruitment of
those with HF and pEF (HFpEF; EF 50% on baseline
echocardiography) between September 9, 2013, and October
31, 2016. Recruitment of patients with HFpEF started later
than the recruitment of patients with HFrEF, for funding
reasons; however, the delay was only 1 year (October 1,
2012, versus September 9, 2013). For the majority of the
recruitment period (until October 6, 2016), there was overlap
in recruitment of both types of HF. We do not anticipate that
there were substantial shifts in epidemiology or treatment of
Clinical Perspective
What Is New?
This study describes large interregional differences in the
characteristics and outcomes of patients with heart failure
in Asia.
Heart failure with reduced ejection fraction carries higher
mortality than heart failure with preserved ejection fraction
in Asia, and patients from Southeast Asia have the poorest
outcomes.
What Are the Clinical Implications?
The results of this study will allow us to target region-
specic risk factors and gaps in guideline-directed therapy
to improve patient outcomes.
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patients with HFrEF or HFpEF during this year that would have
biased regional patterns of multimorbidity, although this
cannot be entirely excluded. All patients included in the
ASIAN-HF registry were required to have a validated clinical
diagnosis of HF (based on symptoms and signs according to
The Framingham Heart Study criteria with decompensation
within 6 months) and left ventricular (LV) EF <40% and 50%
for HFrEF and HFpEF, respectively.
12
A previous hospitaliza-
tion for HF was dened as any previous hospitalization for HF
during 6 months before inclusion requiring intravenous or oral
diuretic treatment. Patients treated for an episode of decom-
pensation in an outpatient clinic were generally treated with
additional or high dosages of oral diuretics for short periods,
failing which intravenous diuretics would be administered,
usually in a hospital setting. Criteria for recruitment of
patients were standardized across sites. Patients with HFpEF
and a previously recorded LVEF <50% were excluded. In
addition, 99.5% of HFpEF patients had structural or functional
abnormalities meeting the 2016 European Society of Cardi-
ology criteria for diastolic dysfunction (E/e13, Emedial/
lateral <9 ms, left atrial enlargement or LV hypertrophy.
1,13
The collection and processing of echocardiographic data
have been reported previously.
11
Echocardiography was
performed at each center according to internationally
accepted guidelines.
14
Left atrial size, LV diastolic function,
stroke volume, and cardiac output were documented in
addition to LVEF and LV dimensions. The Cardiovascular
Imaging Laboratory of the National University Health System,
Singapore, provided oversight and imaging protocol guidelines
and quality assurance of the echocardiograms. Echocardio-
graphic measurements were performed at the site level with
standardized protocols provided by the echocardiography
laboratory in Singapore.
Demographics (including socioeconomic status), clinical
signs and symptoms, functional status, date of HF diagnosis,
duration of HF, prior cardiovascular procedures or investiga-
tions, clinical and lifestyle risk factors, medical history,
comorbidities, quality of life, and blood chemistry were
documented at recruitment. Standard 12-lead ECG and
transthoracic echocardiography were also recorded at base-
line. Coronary artery disease was dened as the angiograph-
ically documented presence of signicant coronary obstruction,
history of myocardial infarction, or prior revascularization.
Hypertension was dened as any past or current history of
hypertension, and diabetes mellitus was dened as having a
prior diagnosis of diabetes mellitus. Estimated glomerular
ltration rate was calculated using the MDRD (Modication of
Diet in Renal Disease) study equation, and chronic kidney
disease (CKD) was dened as estimated glomerular ltration
rate <60 mL/min per 1.73 m
2
. Atrial brillation (AF), peripheral
arterial vascular disease, previous stroke, and chronic obstruc-
tive pulmonary disease were identied by medical history.
Geographic regions were dened according to United
Nations classication as follows: Northeast Asia included
South Korea, Japan, Taiwan, Hong Kong, and China; South
Asia included India; and Southeast Asia included Thailand,
Malaysia, Philippines, Indonesia, and Singapore. National
income level as dened by the World Bank was used to
categorize countries as high,middle, and low income.
9
All data were captured prospectively in an electronic
database, with registry operations and data management
handled by Quintiles Outcomes as the contract research
organization appointed by the academic executive committee.
Outcomes
The primary outcome of interest was all-cause mortality at
1 year. In all, 5875 (90.7%) patients had outcome data
available, whereas 605 (9.3%) patients were lost to follow-up.
The cause of death was adjudicated by an independent
committee, using the US Food and Drug Administrations
standardized event denitions.
15
Two members of the end point
committee independently reviewed the death data collected
from the case report forms; the third member reviewed cases in
which adjudicated causes of death were discordant between
the rst 2 members. After the third review, if there was still
discordance, the events were adjudicated at a meeting of the
end point committee. As such, ascertainment of mortality does
not vary between sites.
Causes of death were classied as cardiovascular,noncar-
diovascular,orunknown/presumed cardiovascular in those with
insufcient information on cause of death. Specic mode of
cardiovascular death was further classied as sudden death,HF
death,acute myocardial infarction,stroke,cardiovascular hem-
orrhage,orprocedural or other cardiovascular death.
Statistical Analyses
Standard descriptive statistics were used to describe baseline
characteristics by nation and geographic region for patients
with HFrEF and HFpEF separately. Differences in the baseline
characteristics were assessed with ANOVA and v
2
tests for
continuous and categorical factors, respectively. Univariable
and multivariable Cox proportional hazards models were used
to assess factors associated with risk of mortality at 1 year,
with geographical region as the key factor of interest. We tested
for interaction in the univariable models with the specic main
effects by geographical region and HF type on 1-year all-cause
mortality outcomes and presented stratied results only if
interactions were signicant. Factors associated with mortality
(P<0.1) in univariable models and/or clinically important
factors (demographics, clinical signs, concomitant comorbidi-
ties, pharmacotherapy, or device therapy) were included
for adjustment of the multivariable models. South Asia was
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used as the reference region because it had the lowest death
rate and the least heterogeneity. Analyses were then repeated
and stratied by geographical region (in the presence of
signicant interaction) to assess differences in the risk factors
for 1-year mortality. The Cox proportional hazards assumption
was conrmed using log-log plots and the Schoenfeld residuals
test. The overall assessment of explained risk and relative
importance of a subset of factors in the Cox proportional
hazards models was calculated using the explained risk statistic
and interpreted similarly to the coefcient of determination R
2
in the normal linear model.
16
Patients who were lost to follow-
up or had incomplete data on adjusted risk factors were
excluded from survival analysis. All analyses were 2-tailed, and
P<0.05 was considered statistically signicant. Analyses were
performed in Stata v14 (StataCorp), and explained risk
statistics were calculated in R software v3.4.1.
Results
Overall, 6480 patients (mean age: 61.613.3 years; 27%
women; 81% HFrEF) were enrolled from 46 centers across 11
Asian regions. All patients had a documented episode of
decompensated HF requiring hospitalization or equivalent
outpatient treatment within the 6 months before enrollment.
In addition, 42% of patients were enrolled as inpatients
recruited after initial treatment of acute symptoms and
following stabilization. Vital status was available for 5851
(90.3%) patients who completed 1 year of follow-up. Table 1
describes the baseline characteristics of the entire cohort by
HF group and geographic region. Detailed baseline charac-
teristics at a country level are listed in the Appendix.
Demographics
Demographics varied considerably by geographical region.
Regardless of ejection fraction, Northeast Asian participants
were the oldest, followed by Southeast then South Asian
participants. Patients with HFrEF were on average 8 years
younger than the patients with HFpEF (Table 1). Among the
patients with HFrEF, older cohorts were more likely to be
female; 25.2% of patients in Northeast Asia were female, in
comparison to 17.7% in Southeast Asia. Approximately half of
the patients with HFpEF were women in all 3 regions. The
Philippines had the youngest cohort (20% female), aged
55.8 years, in stark contrast to the patients from Hong Kong,
who were almost 17 years older (46.8% female) at 72.9 years.
Comorbidity
Prevalence of comorbidities was high throughout Asia, but
marked regional variation was evident. Regardless of HF
group, Southeast Asia had the highest prevalence of diabetes
mellitus, stroke, hypertension, CKD, and coronary artery
disease despite a relatively low mean age. In both HFrEF and
HFpEF, ischemic etiology of HF was twice as common in
Southeast Asia as in Northeast and South Asia. In keeping
with the higher prevalence of comorbidities, Southeast Asian
patients also had the highest body mass index (kg/m
2
)at
25.7 and 28.2 in patients with HFrEF and HFpEF, respectively.
Between countries, patients from the Philippines had the
highest body mass index at 26.9, and those from Japan had
the lowest at 23.0. Across all regions, HFpEF patients had
higher body mass indexes than those with HFrEF. South
Asians had distinctly lower prevalence of AF (4.2% [versus
22.8% average prevalence in other geographical regions] in
HFrEF and 7.2% [versus 23.6% average prevalence in other
regions] in HFpEF) and chronic obstructive pulmonary
disease.
Pharmacological and Device Therapy
Despite the lack of evidence-based HF therapies for HFpEF
patients, angiotensin-converting enzyme inhibitors (ACEIs),
angiotensin receptor blockers (ARBs), and b-blockers were
usually prescribed in both HFrEF and HFpEF patients
(Table 1). Southeast Asia had the highest rates of ACEI and
ARB use (77.3% and 70.2%, respectively) and b-blocker use
(81.8% and 78.9%, respectively), whereas mineralocorticoid
receptor antagonists were most frequently prescribed in
Northeast Asia for 61.7% and 26.6% of HFrEF and HFpEF
patients, respectively. Marked variation in pharmacological
treatment was observed at the country levelfor example,
Indonesia had the lowest usage (61.7%) of b-blockers but the
highest (85.8%) of ACEIs/ARBs; Japan had highest usage of b-
blockers (89.5%) but moderate usage (78.4%) of ACEIs/ARBs.
There was wide variability in device implantation across
countries and regions, with Japan having the highest rate.
Outcomes
One-year outcome data were available for 93% of patients in
South Asia, 86% in Southeast Asia, and 93% in Northeast Asia.
One-year crude all-cause mortality for the whole population
was 9.6%, with a higher mortality rate for patients with HFrEF
than HFpEF (10.6% and 5.4%, respectively; P<0.001). In
almost all countries, participants recruited as inpatients had
almost twice (13.4% versus 6.8%) the 1-year mortality of those
recruited as outpatients. Figures 1 through 3 and Tables 2
and 3 detail 1-year outcomes.
Among patients with HFrEF, Southeast Asia had the
highest crude all-cause mortality at 13.6% compared with
8.9% and 8.3% in Northeast Asia and South Asia, respectively.
The high mortality in Southeast Asia was largely driven by a
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Table 1. Baseline Characteristics by Geographic Region and HF Group
Overall HFrEF HFpEF
Northeast
Asia South Asia
Southeast
Asia PValue
Northeast
Asia South Asia
Southeast
Asia PValue
Northeast
Asia South Asia
Southeast
Asia PValue
n 2201 1688 2591 1658 1436 2182 543 252 409
Demographic variables
Age, y, mean (SD) 64.9 (14.4) 59.0 (12.6) 60.6 (12.2) <0.001 62.7 (14.5) 58.3 (12.5) 59.3 (11.9) <0.001 71.6 (11.7) 63.4 (12.5) 67.4 (11.6) <0.001
Female 692 (31.4) 467 (27.7) 591 (22.8) <0.001 417 (25.2) 349 (24.3) 387 (17.7) <0.001 275 (50.6) 118 (46.8) 204 (49.9) 0.600
Enrolled as inpatient 965 (43.8) 496 (29.4) 1286 (49.6) <0.001 840 (50.7) 393 (27.4) 1057 (48.4) <0.001 125 (23.0) 103 (40.9) 229 (56.0) <0.001
Clinical variables
NYHA class III/IV 809 (43.1) 499 (35.5) 595 (24.0) <0.001 690 (46.7) 454 (37.2) 524 (25.2) <0.001 119 (30.1) 45 (24.6) 71 (17.7) <0.001
Body mass index,
kg/m
2
, mean (SD)
24.2 (4.7) 25.4 (5.1) 26.0 (5.8) <0.001 23.8 (4.5) 25.0 (4.8) 25.7 (5.6) <0.001 25.7 (5.4) 28.1 (6.1) 28.2 (6.2) <0.001
Heart rate, bpm,
mean (SD)
78.0 (16.3) 81.4 (16.3) 78.4 (15.6) <0.001 78.8 (16.9) 81.5 (15.8) 79.2 (15.7) <0.001 75.7 (13.9) 81.1 (19.1) 74.5 (14.5) <0.001
Systolic BP, mm Hg,
mean (SD)
120.2 (20.7) 118.3 (20.2) 123.4 (21.9) <0.001 116.8 (19.5) 115.9 (18.6) 121.3 (20.9) <0.001 130.5 (20.8) 132.3 (23.5) 134.6 (23.5) 0.021
Diastolic BP, mm Hg,
mean (SD)
71.1 (13.2) 74.1 (11.0) 72.4 (13.1) <0.001 70.9 (13.2) 73.7 (10.8) 72.6 (13.1) <0.001 71.8 (13.2) 76.4 (12.0) 71.3 (13.0) <0.001
eGFR, mean (SD) 67.0 (26.6) 72.1 (31.9) 59.8 (26.4) <0.001 68.1 (26.7) 72.4 (31.5) 60.4 (25.5) <0.001 63.4 (26.0) 70.0 (35.6) 57.3 (29.8) <0.001
Duration of HF
<1 y 977 (44.4) 766 (45.7) 1208 (50.4) 733 (44.2) 662 (46.1) 981 (48.3) 244 (44.9) 104 (43.3) 227 (62.0)
15 y 594 (27.0) 648 (38.7) 727 (30.3) 429 (25.9) 538 (37.5) 647 (31.8) 165 (30.4) 110 (45.8) 80 (21.9)
510 y 353 (16.0) 165 (9.8) 329 (13.7) 265 (16.0) 147 (10.2) 291 (14.3) 88 (16.2) 18 (7.5) 38 (10.4)
10 y 276 (12.6) 97 (5.8) 134 (5.6) <0.001 230 (13.9) 89 (6.2) 113 (5.6) <0.001 46 (8.5) 8 (3.3) 21 (5.7) <0.001
Ischemic etiology of HF 667 (30.3) 587 (34.9) 1586 (62.0) <0.001 534 (32.2) 536 (37.3) 1400 (65.0) <0.001 133 (24.5) 51 (20.9) 186 (46.0) <0.001
Coronary artery disease 777 (35.3) 788 (47.1) 1410 (55.1) <0.001 633 (38.2) 734 (51.1) 1261 (58.5) <0.001 144 (26.5) 54 (22.9) 149 (36.7) <0.001
AF 694 (31.5) 77 (4.6) 508 (19.8) <0.001 501 (30.2) 60 (4.2) 380 (17.6) <0.001 193 (35.5) 17 (7.2) 128 (31.3) <0.001
Hypertension 1206 (54.8) 639 (38.2) 1717 (67.0) <0.001 797 (48.1) 544 (37.9) 1378 (64.0) <0.001 409 (75.3) 95 (40.3) 339 (82.9) <0.001
Prior stroke 165 (7.5) 30 (1.8) 239 (9.3) <0.001 119 (7.2) 26 (1.8) 193 (9.0) <0.001 46 (8.5) 4 (1.7) 46 (11.2) <0.001
PAD 82 (3.7) 24 (1.4) 97 (3.8) <0.001 70 (4.2) 22 (1.5) 87 (4.0) <0.001 12 (2.2) 2 (0.8) 10 (2.5) 0.350
COPD 235 (10.7) 79 (4.7) 228 (8.9) <0.001 181 (10.9) 68 (4.7) 185 (8.6) <0.001 54 (9.9) 11 (4.7) 43 (10.5) 0.029
Diabetes mellitus 739 (33.6) 601 (35.9) 1316 (51.3) <0.001 525 (31.7) 533 (37.1) 1062 (49.3) <0.001 214 (39.4) 68 (28.8) 254 (62.1) <0.001
Renal artery stenosis 19 (0.9) 6 (0.4) 30 (1.2) 0.019 16 (1.0) 6 (0.4) 25 (1.2) 0.064 3 (0.6) 0 (0.0) 5 (1.2) 0.170
Cancer 152 (6.9) 8 (0.5) 56 (2.2) <0.001 108 (6.5) 5 (0.3) 49 (2.3) <0.001 44 (8.1) 3 (1.3) 7 (1.7) <0.001
Continued
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Table 1. Continued
Overall HFrEF HFpEF
Northeast
Asia South Asia
Southeast
Asia PValue
Northeast
Asia South Asia
Southeast
Asia PValue
Northeast
Asia South Asia
Southeast
Asia PValue
Smoking, ever 983 (44.7) 291 (17.4) 1362 (53.2) <0.001 850 (51.3) 280 (19.5) 1231 (57.1) <0.001 133 (24.5) 11 (4.7) 131 (32.1) <0.001
Alcohol, ever 727 (33.0) 245 (14.7) 725 (28.3) <0.001 633 (38.2) 234 (16.3) 650 (30.2) <0.001 94 (17.3) 11 (4.7) 75 (18.4) <0.001
CKD 771 (40.3) 381 (37.0) 1160 (54.0) <0.001 564 (39.0) 336 (36.5) 931 (52.6) <0.001 207 (44.3) 45 (41.3) 229 (60.4) <0.001
ECG rhythm
Sinus rhythm 1084 (54.4) 1228 (78.8) 1812 (74.0) <0.001 854 (53.6) 1079 (80.0) 1556 (75.6) <0.001 230 (57.4) 149 (71.0) 256 (65.8) <0.001
AF/flutter 408 (20.5) 61 (3.9) 362 (14.8) 280 (17.5) 45 (3.3) 272 (13.2) 128 (31.9) 16 (7.6) 90 (23.1)
Other rhythms/unknown 501 (25.1) 269 (17.3) 273 (11.2) 458 (28.8) 224 (16.6) 230 (11.2) 43 (10.7) 45 (21.4) 43 (11.1)
Left bundle-branch block 200 (10.0) 283 (18.1) 244 (10.0) <0.001 190 (11.9) 274 (20.3) 230 (11.2) <0.001 10 (2.5) 9 (4.3) 14 (3.6) 0.440
Medication or device use
ACEI 862 (40.0) 639 (40.9) 1336 (54.1) <0.001 780 (47.4) 616 (43.7) 1177 (56.4) <0.001 82 (16.2) 23 (14.9) 159 (41.5) <0.001
ARB 741 (34.4) 517 (33.1) 613 (24.8) <0.001 472 (28.7) 459 (32.6) 492 (23.6) <0.001 269 (53.1) 58 (37.7) 121 (31.6) <0.001
ACEI or ARB 1546 (71.8) 1134 (72.6) 1882 (76.2) 0.002 1203 (73.0) 1053 (74.7) 1613 (77.3) 0.011 343 (67.7) 81 (52.6) 269 (70.2) <0.001
b-Blocker 1682 (78.1) 990 (63.3) 2010 (81.3) <0.001 1353 (82.1) 914 (64.9) 1708 (81.8) <0.001 329 (64.9) 76 (49.4) 302 (78.9) <0.001
Mineralocorticoid
receptor antagonist
1151 (53.4) 868 (55.5) 1203 (48.7) <0.001 1016 (61.7) 832 (59.0) 1150 (55.1) <0.001 135 (26.6) 36 (23.4) 53 (13.8) <0.001
Loop diuretic 1519 (70.5) 1242 (79.5) 2092 (84.7) <0.001 1241 (75.3) 1157 (82.1) 1788 (85.6) <0.001 278 (54.8) 85 (55.2) 304 (79.4) <0.001
Diuretic 1586 (73.6) 1268 (81.1) 2106 (85.2) <0.001 1256 (76.3) 1169 (83.0) 1795 (86.0) <0.001 330 (65.1) 99 (64.3) 311 (81.2) <0.001
Digoxin 523 (24.3) 500 (32.0) 532 (21.5) <0.001 486 (29.5) 488 (34.6) 489 (23.4) <0.001 37 (7.3) 12 (7.8) 43 (11.2) 0.110
Ivabradine 34 (1.6) 316 (20.2) 144 (5.8) <0.001 34 (2.1) 303 (21.5) 134 (6.4) <0.001 0 (0.0) 13 (8.4) 10 (2.6) <0.001
Device therapy
None 1702 (77.4) 1559 (93.2) 2339 (91.3) <0.001 1209 (73.0) 1336 (93.0) 1952 (90.6) <0.001 493 (90.8) 223 (94.1) 387 (94.9) 0.015
ICD only 133 (6.0) 46 (2.7) 103 (4.0) 115 (6.9) 38 (2.6) 101 (4.7) 18 (3.3) 8 (3.4) 2 (0.5)
Pacemaker only 72 (3.3) 16 (1.0) 48 (1.9) 45 (2.7) 12 (0.8) 30 (1.4) 27 (5.0) 4 (1.7) 18 (4.4)
Biventricular pacer only 57 (2.6) 17 (1.0) 19 (0.7) 55 (3.3) 15 (1.0) 19 (0.9) 2 (0.4) 2 (0.8) 0 (0.0)
Biventricular pacer and ICD 236 (10.7) 35 (2.1) 54 (2.1) 233 (14.1) 35 (2.4) 53 (2.5) 3 (0.6) 0 (0.0) 1 (0.2)
Sociodemographics
Education
None/primary education 664 (30.8) 460 (27.5) 630 (35.6) <0.001 398 (24.5) 386 (27.1) 556 (35.1) <0.001 266 (49.8) 74 (29.8) 74 (39.8) <0.001
Secondary education 697 (32.3) 485 (29.0) 729 (41.2) 578 (35.6) 430 (30.2) 655 (41.4) 119 (22.3) 55 (22.2) 74 (39.8)
Preuniversity 374 (17.3) 209 (12.5) 175 (9.9) 294 (18.1) 177 (12.4) 157 (9.9) 80 (15.0) 32 (12.9) 18 (9.7)
Continued
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21.4% mortality rate in Indonesia and 14.3% in the Philippines,
despite these 2 countries having the youngest populations.
Japan, with a relatively elderly population, had the lowest 1-
year mortality rate at 4.4%.
Overall, the most common cause of death among patients
with HFrEF (54%) was known cardiovascular death, with a
further 34% of deaths having a presumed cardiovascular or
unknown cause, whereas 12% of deaths were noncardiovas-
cular. Sudden death and HF death accounted for at least 80%
of cardiovascular deaths. Among patients with HFpEF,
cardiovascular and noncardiovascular deaths accounted for
half and a quarter of deaths, respectively.
Associations With Mortality
Table 4 demonstrates the variables associated with 1-year
mortality in the entire ASIAN-HF cohort. In unadjusted
analyses, multiple variables were associated with death. After
adjustment, clinical and demographic features associated
with death within 1 year included enrollment as an inpatient
(hazard ratio [HR]: 1.49; 95% CI, 1.191.86), HFpEF (HR: 0.54;
95% CI, 0.370.80), New York Heart Association (NYHA) class
III/IV (HR: 1.99; 95% CI, 1.602.47), body mass index (HR:
0.95; 95% CI, 0.930.97), systolic blood pressure (HR: 0.90;
95% CI, 0.850.95), AF (HR: 1.33; 95% CI, 1.051.67), and
CKD (HR: 1.58; 95% CI, 1.271.97). Treatment with an ACEI/
ARB and treatment with a b-blocker were both independently
associated with better survival, with HRs of 0.61 (95% CI,
0.500.76) and 0.66 (95% CI, 0.520.83), respectively.
However, use of mineralocorticoid receptor antagonists was
not associated with outcomes, and use of diuretics was
associated with worse outcomes (HR: 1.96; 95% CI, 1.37
2.82). There were a number of interactions with regional
status in the univariable models. AF was associated with poor
prognosis in South Asian patients (HR: 2.79; 95% CI, 1.31
5.94) and Northeast Asian patients (HR: 1.55; 95% CI, 1.07
2.25) but not in Southeast Asian patients, in whom it was not
signicantly associated with mortality. There was also a
signicant interaction between enrollment as an inpatient and
region. Inpatient enrollment was associated with higher
mortality than outpatient enrollment in Northeast and South
Asia but not in Southeast Asia. Southeast Asia had almost
double the adjusted risk of mortality of the other 2 regions
(HR: 1.94; 95% CI, 1.422.66). The relationship between
geographical region and outcomes was not signicantly
affected by HF type (P
interaction
=0.075), such that Southeast
Asia had higher risk of mortality compared with South and
Northeast Asia for both HFrEF and HFpEF.
Table 5 shows the contribution of each demographic,
clinical, medication/device, and regional variable that con-
tributed to the relative amount of the risk of death at 1 year.
These known variables accounted for only 44.8% of the risk of
Table 1. Continued
Overall HFrEF HFpEF
Northeast
Asia South Asia
Southeast
Asia PValue
Northeast
Asia South Asia
Southeast
Asia PValue
Northeast
Asia South Asia
Southeast
Asia PValue
Degree or higher 347 (16.1) 451 (26.9) 199 (11.3) 288 (17.8) 402 (28.2) 189 (11.9) 59 (11.0) 49 (19.8) 10 (5.4)
Decline to respond 74 (3.4) 69 (4.1) 35 (2.0) 64 (3.9) 31 (2.2) 25 (1.6) 10 (1.9) 38 (15.3) 10 (5.4)
Marital status <0.001 <0.001 <0.001
Single 206 (9.6) 28 (1.7) 146 (8.3) 165 (10.2) 26 (1.8) 139 (8.8) 41 (7.7) 2 (0.8) 7 (3.8)
Married 1621 (75.2) 1570 (93.8) 1417 (80.2) 1246 (76.8) 1336 (93.7) 1274 (80.6) 375 (70.2) 234 (94.4) 143 (76.9)
Separated/divorced 82 (3.8) 9 (0.5) 77 (4.4) 66 (4.1) 6 (0.4) 70 (4.4) 16 (3.0) 3 (1.2) 7 (3.8)
Widowed 204 (9.5) 60 (3.6) 119 (6.7) 115 (7.1) 53 (3.7) 93 (5.9) 89 (16.7) 7 (2.8) 26 (14.0)
Decline to answer 43 (2.0) 7 (0.4) 8 (0.5) 30 (1.8) 5 (0.4) 5 (0.3) 13 (2.4) 2 (0.8) 3 (1.6)
Data presented are mean (SD) or n (%). ACEI indicates angiotensin-converting enzyme inhibitor; AF, atrial brillation; ARB, angiotensin receptor blocker; BP, blood pressure; CKD, chronic kidney disease; COPD, chronic obstructive pulmonary
disease; eGFR, estimated glomerular ltration rate; HF, heart failure; HFpEF, heart failure with preserved ejection fraction; HFrEF, heart failure with reduced ejection fraction; ICD, implantable cardioverter-debrillator; NYHA, New York Heart
Association; PAD, peripheral arterial disease.
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Outcomes in the ASIAN-HF Registry MacDonald et al
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death and consisted of 3.9% demographic, 21.9% clinical,
7.0% medication/device, and 5.8% regional variables. The
relative contributions were similar in HFrEF, comprising 3.8%
demographic, 18.2% clinical, 7.8% medication/device, and
5.3% regional variables. In HFpEF, however, known variables
accounted for 49.0% of the risk of death, mostly contributed
by demographics and region.
Discussion
The ASIAN-HF study is the rst multinational prospective
registry to present outcomes data for patients with HF by
regions and HF subtypes across Asia. Crude 1-year all-cause
mortality was 9.6% in the overall cohort. However, we
observed signicant regional differences in patient character-
istics, treatment, and mortality. Asian patients with HFrEF had
worse 1-year outcomes than those with HFpEF, despite being
signicantly younger. Patients from the lowest income
countries presented at the youngest ages but had the poorest
outcomes. Southeast Asia had the highest rate of comorbidi-
ties, and patients from this region were almost twice as likely
to die during the rst year of follow-up in comparison to
patients from South and Northeast Asia. Despite the consid-
erable regional variations in demographics, comorbidity, and
treatment, these measured factors explain less than half of
the interregional variation in mortality, with unmeasured
factors accounting for the bulk of the varying mortality risk.
Notably, region was as or more important than demographics
in predicting mortality in both HFrEF and HFpEF patients.
Few other studies have explored the outcomes of patients
with HF in Asia at national and regional levels.
10
The INTER-
CHF study was a prospective HF cohort study conducted in
108 centers worldwide.
6
In addition to enrolling patients from
the Middle East, Africa, and South America, INTER-CHF also
enrolled 2660 patients from 4 Asian countries: China, India,
Malaysia, and the Philippines. Unlike ASIAN-HF, INTER-CHF
did not report outcomes in the different HF subtypes. The 1-
year mortality rate in Southeast Asia (Malaysia and Philip-
pines) in INTER-CHF was 15%, which is comparable to the
mortality in our own Southeast Asian cohort (covering 5
countries) at 13%. However, the INTER-CHF mortality rates for
China (7.3%) and India (23.3%) do not accord with our data;
this is attributed to marked differences in patient character-
istics of both studies. In ASIAN-HF, we found almost double
the mortality rate in China (13.6%) and a far lower mortality
0%
2%
4%
6%
8%
10%
12%
14%
16%
South Asia Northeast
Asia
Southeast
Asia
South Asia Northeast
Asia
Southeast
Asia
South Asia Northeast
Asia
Southeast
Asia
Overall HFrEF HFpEF
1 year mortality rates
CV
Non CV
Unknown
Figure 1. One-year mortality and cause of death by region and heart failure group. CV indicates
cardiovascular; HFrEF, heart failure with reduced ejection fraction; HFpEF, heart failure with preserved
ejection fraction.
Figure 2. KaplanMeier curves for mortality by region.
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Outcomes in the ASIAN-HF Registry MacDonald et al
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rate in India (7.7%). The mortality variation in China may well
be explained by the differing proportions of patients with
HFrEF and inpatients in each study; in INTER-CHF, 27% of
Chinese patients had HFrEF and 35% were recruited as
inpatients. In contrast, the corresponding proportions of
Chinas participants in ASIAN-HF were 98.8% with HFrEF and
91.3% recruited as inpatients. Higher mortality in the INTER-
CHF population from India may be partly explained by a sicker
population, with patients more likely to be enrolled as
inpatients, to have more NYHA III/IV class HF, and to use
fewer ACEIs/ARBs and b-blockers compared with ASIAN-HF
participants.
The SWEDE-HF (Swedish Heart Failure) registry had
enrollment criteria similar to the ASIAN-HF registry, so it is
a useful comparator to represent non-Asian populations.
Crude 1-year mortality rates for patients with EF <40%
(n=23 400) and EF 50% (n=9640) were 15% and 17%,
respectively. These rates are higher than those in the ASIAN-
HF cohort but may partly reect the older age of the Swedish
cohort, with a mean age of 72 years for HFrEF and 77 years
for HFpEF patients, in comparison to mean ages of 60 and
Table 2. One-Year Mortality Rates
Overall HFrEF HFpEF
All-Cause Death
Cardio-
vascular
Death
Non
Cardio-
vascular
Death
Unknown/
Presumed
Cardio-
vascular
Deaths All-Cause Death
Cardio-
vascular
Death
Non
Cardio-
vascular
Death
Unknown/
Presumed
Cardio-
vascular
Deaths All-Cause Death
Cardiovascular
Death
Non
Cardio-
vascular
Death
Unknown/
Presumed
Cardio-
vascular
Deaths
N n (%) n (%) n (%) n (%) N n (%) n (%) n (%) n (%) N n (%) n (%) n (%) n (%)
ASIAN-HF 5851 560 (9.6) 341 (60.9) 74 (13.2) 145 (25.9) 4737 500 (10.6) 270 (54.0) 60 (12.0) 170 (34.0) 1114 60 (5.4) 32 (53.3) 14 (23.3) 14 (23.3)
By geographical region
South Asia 1570 117 (7.5) 64 (54.7) 8 (6.8) 45 (38.5) 1328 110 (8.3) 61 (55.5) 7 (6.4) 42 (38.2) 242 7 (2.9) 3 (42.9) 1 (14.3) 3 (42.9)
Northeast Asia 2049 152 (7.4) 104 (68.4) 21 (13.8) 27 (17.8) 1554 138 (8.9) 97 (70.3) 17 (12.3) 24 (17.4) 495 14 (2.8) 7 (50.0) 4 (28.6) 3 (21.4)
Southeast Asia 2232 291 (13.0) 173 (59.4) 45 (15.5) 73 (25.1) 1855 252 (13.6) 151 (59.9) 36 (14.3) 65 (25.8) 377 39 (10.3) 22 (56.4) 9 (23.1) 8 (20.5)
By enrollment status
Inpatient 2472 331 (13.4) 215 (64.9) 42 (12.7) 74 (22.4) 2062 297 (14.4) 198 (66.7) 33 (11.1) 66 (22.2) 410 34 (8.3) 17 (50.0) 9 (26.5) 8 (23.5)
Outpatient 3379 229 (6.8) 126 (55.0) 32 (14.0) 71 (31.0) 2675 203 (7.6) 111 (54.7) 27 (13.3) 65 (32.0) 704 26 (3.7) 15 (57.7) 5 (19.2) 6 (23.1)
ASIAN-HF indicates Asian Sudden Cardiac Death in Heart Failure; HFpEF, heart failure with preserved ejection fraction; HFrEF, heart failure with reduced ejection fraction.
Figure 3. KaplanMeier curves for mortality by heart failure
group. A, Heart failure with reduced ejection fraction (HFrEF). B,
Heart failure with preserved ejection fraction (HFpEF).
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68 years, respectively, in ASIAN-HF participants. Other
notable differences in the populations were a far higher
proportion of patients with AF in the Swedish cohort, at 51%
in those with HFrEF and 63% in those with HFpEF. In addition,
the Swedish cohort had a higher proportion of female
participants, with 29% of HFrEF patients and 55% of HFPEF
patients being female.
17
We previously described the interregional differences in
clinical and echo characteristics and outcomes of patients
with HFpEF in the ASIAN-HF population. We found that in
patients with HFpEF, Southeast Asian patients had the highest
rate of comorbidities and LV hypertrophy and the poorest
outcomes. We extend this study in a number of ways by (1)
presenting mortality rates for HFrEF and comparing these with
HFpEF, (2) investigating differences in adjudicated causes of
death in patients with HFrEF and HFpEF, and (3) studying
differences in predictors of clinical outcomes between
patients with HFrEF and HFpEF from Asia. A similar pattern
of comorbidity clustering occurs in HFrEF patients, with
particularly high rates of comorbidities and mortality in
Southeast Asia. Mortality rates are almost double in HFrEF
patients despite their being signicantly younger.
After multivariable analysis we found that the relationship
between certain key factors and mortality was modied by
regional status. Enrollment as an inpatient was associated
with signicantly worse outcomes in Northeast and South
Asia but not in Southeast Asia. This may reect that inpatients
in Southeast Asia may not be as sick as inpatients in
Northeast and South Asia, or perhaps be due to dispropor-
tionately higher enrollment of inpatients from Singapore and
Malaysia, where there is good tertiary care. Interestingly, in
patients from South Asia, AF was associated with a far greater
risk of death than in other regions. This result is particularly
intriguing, given the markedly lower prevalence of AF in the
South Asian population, and further study is warranted.
At a national level, Indonesia had the highest rate of all-
cause death at 22.6%, with the second highest in Hong Kong
at 19.8%an interesting nding, given that Indonesia had the
youngest population in the cohort (mean age: 57 years) and
Hong Kong had the oldest (mean age: 79 years). It is notable
that Indonesia also had the highest rates of coronary artery
disease (62%) and smoking (66.4%) and a high prevalence of
CKD but the lowest uptake of implantable cardioverter-
debrillators and cardiac resynchronization therapydebril-
lators and b-blockers. The nding of poor outcomes in young
patients in Asia was reported previously from a subgroup
analysis of the PARADIGM-HF trial that compared patient
characteristics and outcomes among different global geo-
graphic regions, including 1487 patients with HFrEF from the
Asia Pacic region.
18
Patients in the Asia Pacic region were
on average 10.5 years younger than their western European
counterparts at enrollment and had one of the highest rates
of all-cause mortality and cardiovascular death globally,
before and after adjustment for clinical characteristics.
Poorer outcomes in younger Southeast Asian populations
may reect later presentation and more advanced disease
but also the lower life expectancy seen in lower income
countries such as Indonesia and the Philippines, which have
vast geographic spread and less developed healthcare
infrastructure.
We found marked divergence in both drug and device
utilization across Asia. Device utilization in Northeast Asia
was more than 3-fold that seen in Southeast and South Asia,
with particularly high rates of device usage in Japan. The
disparity in device utilization across geographical regions was
recently described in a pan-Asian analysis from ASIAN-HF.
19
Table 3. One-Year Cause-Specic Mortality Rates
Overall HFrEF HFpEF
South Asia
Northeast
Asia
Southeast
Asia South Asia
Northeast
Asia
Southeast
Asia South Asia
Northeast
Asia
Southeast
Asia
No. of cardiovascular deaths 64 104 173 61 97 151 3 7 22
Specific cause of cardiovascular death
Sudden death 41 (64.0) 43 (41.3) 49 (28.3) 41 (67.2) 41 (42.3) 47 (31.1) 0 (0.0) 2 (28.6) 2 (9.1)
HF death 18 (28.1) 52 (50.0) 41 (23.7) 17 (27.9) 47 (48.4) 39 (25.8) 1 (33.3) 5 (71.4) 2 (9.1)
AMI death 4 (6.3) 5 (4.8) 14 (8.1) 3 (4.9) 5 (5.2) 11 (7.3) 1 (33.3) 0 (0.0) 3 (13.6)
Stroke death 1 (1.6) 2 (1.9) 7 (4.0) 0 (0.0) 2 (2.1) 6 (4.0) 1 (33.3) 0 (0.0) 1 (4.6)
Cardiovascular hemorrhage death 0 (0) 1 (1.0) 4 (2.3) 0 (0.0) 1 (1.0) 2 (1.3) 0 (0.0) 0 (0.0) 2 (9.1)
Procedure death 0 (0) 1 (1.0) 2 (1.2) 0 (0.0) 1 (1.0) 2 (1.3) 0 (0.0) 0 (0.0) 0 (0.0)
Other cardiovascular death 0 (0) 0 (0) 56 (32.4) 0 (0.0) 0 (0.0) 44 (29.1) 0 (0.0) 0 (0.0) 12 (55.5)
Data are shown as n (%) except as noted. AMI indicates acute myocardial infarction; HF, heart failure; HFpEF, heart failure with preserved ejection fraction; HFrEF, heart failure with reduced
ejection fraction.
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Table 4. Variables Associated With All-Cause Mortality At 1 Year
Univariable Analysis Multivariable Analysis Interaction With Region
HR (95% CI) PValue HR (95% CI) PValue P
interaction
Northeast Asia South Asia Southeast Asia
Demographic variables
Age, per 10 y 1.13 (1.061.21) <0.001 1.07 (0.981.18) 0.148 0.002 1.08 (0.931.26) 0.94 (0.771.15) 1.17 (1.021.33)
Female 0.81 (0.670.99) 0.038 0.97 (0.741.27) 0.834 ... ... ... ...
Enrolled as inpatient 2.12 (1.792.51) <0.001 1.47 (1.171.84) 0.001 0.001 1.88 (1.212.93) 1.86 (1.113.12) 1.20 (0.891.62)
HF hospitalization in
past 6 mo
1.87 (1.592.21) <0.001 1.47 (1.201.80) <0.001 0.062 ... ... ...
Clinical variables
HFpEF 0.51 (0.390.67) <0.001 0.55 (0.370.83) 0.004 ... ... ... ...
NYHA class III/IV 2.26 (1.912.68) <0.001 1.93 (1.542.42) <0.001 ... ... ... ...
Body mass index, kg/m
2
0.94 (0.930.96) <0.001 0.95 (0.930.97) <0.001 ... ... ... ...
Heart rate, per 5 bpm 1.02 (1.001.05) 0.074 1.02 (0.991.05) 0.201 ... ... ... ...
Systolic BP, per
10 mm Hg
0.89 (0.860.93) <0.001 0.91 (0.860.97) 0.002 ... ... ... ...
Diastolic BP, per
10 mm Hg
0.87 (0.810.93) <0.001 ... ... ... ... ... ...
Duration of HF, y 1.20 (1.111.31) <0.001 1.16 (1.041.29) 0.006 ... ... ... ...
Ischemic etiology
of HF
1.64 (1.381.95) <0.001 ... ... ... ... ... ...
Coronary artery
disease
1.53 (1.301.81) <0.001 1.14 (0.911.42) 0.265 ... ... ... ...
AF 1.44 (1.191.74) <0.001 1.35 (1.061.71) 0.013 0.019 1.55 (1.072.25) 2.79 (1.315.94) 1.15 (0.841.57)
Hypertension 1.11 (0.941.31) 0.218 ... ... ... ... ... ...
Prior stroke 1.38 (1.031.85) 0.029 0.99 (0.701.40) 0.975 ... ... ... ...
PAD 2.28 (1.633.20) <0.001 1.41 (0.922.17) 0.117 ... ... ... ...
COPD 1.33 (1.011.74) 0.039 0.88 (0.631.24) 0.479 ... ... ... ...
Diabetes mellitus 1.37 (1.161.61) <0.001 1.06 (0.851.31) 0.621 ... ... . .. ...
Renal artery stenosis 2.34 (1.294.26) 0.005 ... ... ... ... . .. ...
Cancer 1.12 (0.731.73) 0.607 ... ... ... ... ... ...
Smoking, ever 1.33 (1.131.57) 0.001 0.99 (0.781.25) 0.930 ... ... ... ...
Alcohol, ever 1.05 (0.871.27) 0.596 ... ... ... ... ... ...
CKD 1.97 (1.642.37) <0.001 1.57 (1.261.97) <0.001 ... ... ... ...
Left bundle-branch
block
0.96 (0.741.25) 0.758 ... ... ... ... ... ...
Medications/device use
ACEI or ARB 0.55 (0.460.65) <0.001 0.61 (0.490.75) <0.001 ... ... ... ...
b-Blocker 0.63 (0.520.75) <0.001 0.66 (0.520.83) <0.001 ... ... ... . ..
Mineralocorticoid
receptor antagonist
0.89 (0.751.05) 0.161 0.90 (0.721.11) 0.325 ... ... ... ...
Diuretic 2.01 (1.542.64) <0.001 1.87 (1.302.69) 0.001 ... ... ... ...
Digoxin 1.35 (1.131.62) 0.001 ... ... ... ... . .. ...
ICD/CRT-D 0.92 (0.691.22) 0.558 0.75 (0.531.08) 0.119 ... ... ... ...
ACEI indicates angiotensin-converting enzyme inhibitor; AF, atrial brillation; ARB, angiotensin receptor blocker; BP, blood pressure; CKD, chronic kidney disease; COPD, chronic
obstructive pulmonary disease; CRT-D, cardiac resynchronization therapydebrillator; HF, heart failure; HFpEF, heart failure with preserved ejection fraction; HR, hazard ratio; ICD,
implantable cardioverter-debrillator; NYHA, New York Heart Association; PAD, peripheral arterial disease.
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The ASTRONAUT (Aliskiren Trial on Acute Heart Failure
Outcomes) study
20
previously reported low rates of guideline-
directed medical therapy and very low rates of implantable
cardioverter-debrillator utilization in a small group of patients
(n=439) from the Asia Pacic region (India, Singapore, Israel,
Philippines, Turkey, Taiwan). Implantable cardioverter-debril-
lator usage was 5.7% in Asia Pacic in comparison to 38% in
North America, and patients from the Asia Pacic region had the
highest 1-year all-cause mortality rate at 26.7%, in comparison
to 7.3% in North America. The largest driver of regional
differences in mortality was sudden cardiac death, with a 10.3%
1-year rate of sudden cardiac death in Asia compared with 1.6%
in North America.
The clear interregional differences in mortality seen in
ASIAN-HF were only partly explained by differences in patient
demographics, comorbidities, and treatment. These measured
variables account for less than half of the variation in mortality
risk after multivariable adjustment. The majority of regional
variation in mortality arises from unmeasured factors that likely
include differences in healthcare infrastructure, delayed pre-
sentation to healthcare facilities, access to and delivery of
health care, and the quality of healthcare, as shown by the
poorest countries having the greatest mortality. Other impor-
tant variables such as genetic, cultural, and environmental
factors may also be operative. Consequently, efforts at
unraveling and addressing these unmeasured factors, including
improvements in national and regional healthcare infrastruc-
ture and organization, may have great potential for enhancing
survival outcomes in Asian HF patients. With pharmacological
management and device therapy explaining only 7% of variation
in the risk of mortality in the fully adjusted model in chronic
patients with HF, primary prevention of HF through attention to
its key risk factors or antecedents (eg. coronary artery disease,
hypertension, diabetes mellitus, and CKD) is key to reducing the
burden of HF in Asia.
Limitations
There is a potential bias in site selection (with a clear bias to
reputable, academically inclined centers with expertise in
echocardiography and resources to devote to research
activities) and willingness of patients to participate in a
prospective registry. Priority was given to sites that could
provide high-quality data with as little missing data as
possible. Therefore, the experience of our centers likely
represents the best practice achievable in our multinational
observational registry. Our results may therefore underesti-
mate the true outcome burden of HF across Asia. Underre-
porting bias is also a possibility. To minimize this, every effort
was made to ensure protocol standardization and adherence,
including on-site investigator training, regular monitoring
(both in person and remote), and centralized database
management. Variation in reporting of cause of death across
the different sites precluded the ascertainment of the cause
of death in a signicant proportion of cases, with subsequent
substantial percentage variation in the category of death with
unknown cause. However, in the absence of ofcial pan-Asian
population-based data, ASIAN-HF provides the best informa-
tion available on HF to date across a broad swathe of Asia.
Conclusions
These rst multinational prospective outcomes data on
patients with HF across 11 Asian regions show that patients
from Southeast Asia (particularly low-income countries with
the youngest patients) had the poorest outcomes regardless
of LVEF. Region-specic risk factors and gaps in guideline-
directed therapy should be addressed, along with exploration
of potential regional differences in healthcare systems,
access to device therapy, and genetic and environmental
factors.
Table 5. Explained Risk Analysis
1-y Mortality
Overall* HFrEF* HFpEF
Value (%) SE (%) Value (%) SE (%) Value (%) SE (%)
Demographic variables+clinical variables+
medication or device use+region
44.8 2.9 42.7 3.2 49.0 7.9
Only demographic variables 3.9 1.6 3.8 1.7 20.4 9.7
Only clinical variables 21.9 3.2 18.2 3.2 11.5 7.8
Only medications/device use 7.0 2.0 7.8 2.2 ... ...
Only region 5.8 1.9 5.3 2.0 21.8 8.8
HFpEF indicates heart failure with preserved ejection fraction; HFrEF, heart failure with reduced ejection fraction.
*Adjusted for demographic variables (age, sex, inpatient enrollment), clinical variables (New York Heart Association class, body mass index, heart rate, systolic blood pressure, duration of
heart failure, coronary artery disease, atrial brillation, prior stroke, peripheral arterial disease, chronic obstructive pulmonary disease, diabetes mellitus, smoking, chronic kidney disease),
medication or device use (angiotensin-converting enzyme inhibitor or angiotensin receptor blocker, b-blocker, mineralocorticoid receptor antagonist, diuretic, implantable cardioverter-
debrillator/cardiac resynchronization therapydebrillator) and regional variables.
Adjusted for demographic (age, inpatient enrollment), clinical (New York Heart Association class, body mass index, duration of heart failure), and regional variables.
DOI: 10.1161/JAHA.119.012199 Journal of the American Heart Association 12
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ORIGINAL RESEARCH
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Appendix
ASIAN-HF Executive Committee
Professor A. Mark Richards (as Chairman), Cardiovascular
Research Institute, National University of Singapore, Singapore.
Email: mdcarthu@nus.edu.sg. Professor Carolyn S. P. Lam (as
Principal Investigator), National Heart Centre Singapore, Duke-
NUS Medical School, Singapore. Email: carolyn.lam@duke-
nus.edu.sg. Professor Inder Anand (as Director, Publications
Committee), University of Minnesota Medical School, VA
Medical Center Minneapolis and San Diego, USA. Email:
anand001@umn.edu. Dr Chung-Lieh Hung, Mackay Memorial
Hospital, Taipei, Taiwan. Email: jotaro3791@gmail.com. Profes-
sor Lieng Hsi Ling (as Director, Echo Core Laboratory),
Cardiovascular Research Institute, National University of Singa-
pore, Singapore. Email: lieng_hsi_ling@nuhs.edu.sg. Dr Houng
Bang Liew, Queen Elizabeth II Hospital, Clinical Research Center,
Sabah, Malaysia. Email: hbliew22@gmail.com. Dr Calambur
Narasimhan, Care Hospital, Hyderabad, India. Email: calam-
bur@hotmail.com. Dr Tachapong Ngarmukos, Ramathibodi
Hospital, Mahidol University, Bangkok, Thailand. Email: tachapo-
nis.nga@mahidol.ac.th. Dr Sang Weon Park, SeJong General
Hospital, Seoul, South Korea. Email: swparkmd@gmail.com. Dr
Eugenio Reyes, Manila Doctors Hospital, Manila, Philippines.
Email: eugenereyes@yahoo.com. Professor Bambang B. Sis-
wanto, National Cardiovascular Center Universitas Indonesia,
Jakarta, Indonesia. Email: bambbs@gmail.com. Professor
Wataru Shimizu, Department of Cardiovascular Medicine, Nip-
pon Medical School, Tokyo, Japan. Email: wshimizu@nms.ac.jp.
Professor Shu Zhang, Fuwai Cardiovascular Hospital, Beijing,
Peoples Republic of China. Email: zsfuwai@vip.163.com.
Country and Site Investigators
China
Fuwai Hospital: Shu Zhang (Country PI), Xiaohan Fan, Keping
Chen. Ruijin Hospital, Shanghai Jiaotong university: Liqun Wu,
Yucai Xie, Qi Jin, Tianyou Ling. The First Afliated Hospital With
Nanjing Medical University: Xinli Li, Fang Zhou, Yanli Zhou,
Dongjie Xu, Haifeng Zhang. Zhongshan Hospital Fudan Univer-
sity: Yangang Su, Xueying Chen, Shengmei Qin, Jingfeng Wang,
Xue Gong, Zhaodi Wu.
Hong Kong
Chinese University of Hong Kong: Cheuk Man Yu (Country PI).
India
CARE Hospital: Calambur Narasimhan (Country PI), B K S
Sastry, Arun Gopi, K Raghu, C Sridevi, Daljeet Kaur. Care
Institute of Medical Sciences: Ajay Naik, Keyur Parikh, Anish
Chandarana, Urmil Shah, Milan Chag, Hemang Baxi, Satya
Gupta, Jyoti Bhatia, Vaishali Khakhkhar, Vineet Sankhla, Tejas
Patel, Vipul Kapoor. Hero Dayanand Medical College Heart
Institute: Gurpreet Singh Wander, Rohit Tandon. Medanta-The
Medicity: Vijay Chopra, Manoj Kumar, Hatinder Jeet Singh Sethi,
Rashmi Verma, Sanjay Mittal. Sir Ganga Ram Hospital: Jitendra
Sawhney, Manish Kr. Sharma. Westfort Hi-Tech Hospital Ltd:
Mohanan Padinhare Purayil.
Indonesia
Rumah Sakit Jantung dan Pembuluh Darah Harapan Kita:
Bambang Budi Siswanto (Country PI). RS Dr Hasan Sadikin:
Pintoko Tedjokusumo, Erwan Martanto, Erwinanto. R S Khusus
Jantung Binawaluya: Muhammad Munawar, Jimmy Agung Pam-
budi. RS Siloam Karawaci: Antonia Lukito, Ingrid Pardede, Alvin
Thengker, Vito Damay, Siska Suridanda Danny, Rarsari Surarso.
Japan
Nippon Medical School: Wataru Shimizu (Country PI), National
Cerebral and Cardiovascular Center: Takashi Noda, Ikutaro
Nakajima, Mitsuru Wada, Kohei Ishibashi. Kinki University
Hospital Cardiovascular Center: Takashi Kurita, Ryoubun
Yasuoka. Nippon Medical School Hospital: Kuniya Asai, Kohji
Murai, Yoshiaki Kubota, Yuki Izumi. Toho University Omori
Medical Center: Takanori Ikeda, Shinji Hisatake, Takayuki
Kabuki, Shunsuke Kiuchi, Tokyo Womens Medical University:
Nobuhisa Hagiwara, Atsushi Suzuki, Dr Tsuyoshi Suzuki.
Korea
SeJong General Hospital: Sang-Weon Park (Country PI), Suk
Keun Hong, SookJin Lee, Lim Dal Soo, Dong-Hyeok Kim. Korea
University Anam Hospital: Jaemin Shim, Seong-Mi Park,
Seung-Young Roh, Young Hoon Kim, Mina Kim, Jong-Il Choi.
Korea University Guro Hospital: Jin Oh Na, Seung Woon Rha,
Hong Seog Seo, Dong Joo Oh, Chang Gyu Park, Eung Ju Kim,
Sunki Lee,
Severance Hospital, Yonsei University Health System: Boy-
oung Joung, Jae-Sun Uhm, Moon Hyoung Lee, In-Jeong Cho, Hui-
Nam Park. Chonnam National University Hospital: Hyung-Wook
Park, Jeong-Gwan Cho, Namsik Yoon, KiHong Lee, Kye Hun Kim.
Korea University Ansan Hospital: Seong Hwan Kim.
Malaysia
Hospital Queen Elizabeth II: Houng Bang Liew (Country PI),
Sahrin Saharudin, Boon Cong Beh, Yu Wei Lee, Chia How Yen,
Mohd Khairi Othman, Amie-Anne Augustine, Mohd Hariz Mohd
Asnawi, Roberto Angelo Mojolou, You Zhuan Tan, Aida
Nurbaini Arbain, Chii Koh Wong. Institut Jantung Negara:
DOI: 10.1161/JAHA.119.012199 Journal of the American Heart Association 13
Outcomes in the ASIAN-HF Registry MacDonald et al
ORIGINAL RESEARCH
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Razali Omar, Azmee Mohd Ghazi, Surinder Kaur Khelae, David
S.P. Chew, Lok Bin Yap, Azlan Hussin, Zulkeee Muhammad,
Mohd. Ghazi Azmee. University Malaya Medical Centre: Imran
Zainal Abidin, Ahmad Syadi Bin Mahmood Zhudi, Nor Ashikin
Md Sari, Ganiga Srinivasaiah Sridhar, Ahmad Syadi Mahmood
Zuhdi. Muhammad Dzar Ismail. Sarawak General Hospital
Heart Centre: Tiong Kiam Ong, Yee Ling Cham, Ning Zan
Khiew, Asri Bin Said, Alan Yean Yip Fong, Nor Hanim Mohd
Amin, Keong Chua Seng, Sian Kong Tan, Kuan Leong Yew.
Philippines
Manila Doctors Hospital: Eugenio Reyes (Country PI), Jones
Santos, Allan Lim. Makati Medical Center: Raul Lapitan, Ryan
Andal, Philippine Heart Center: Eleanor Lopez.
Singapore
National Heart Centre Singapore: Carolyn S.P. Lam (Country
PI), Kheng Leng David Sim, Boon Yew Tan, Choon Pin Lim,
Louis L.Y. Teo, Laura L. H. Chan. National University Heart
Centre: Lieng Hsi Ling, Ping Chai, Ching Chiew Raymond
Wong, Kian Keong Poh, Tan Tock Seng Hospital: Poh Shuan
Daniel Yeo, Evelyn M. Lee, Seet Yong Loh, Min Er Ching,
Deanna Z.L. Khoo, Min Sen Yew, Wenjie Huang. Changi
General Hospital-Parent: Kui Toh Gerard Leong, Jia Hao Jason
See, Yaozong Benji Lim, Svenszeat Tan, Colin Yeo, Siang Chew
Chai. Singapore General Hospital-Parent: Fazlur Rehman
Jaufeerally, Haresh Tulsidas, Than Aung. Khoo Teck Puat
Hospital: Hean Yee Ong, Lee Fong Ling, Dinna Kar Nee Soon.
Taiwan
Mackay Memorial Hospital, Taipei, Taiwan: Chung-Lieh Hung
(Country PI), Hung-I Yeh,Jen-Yuan Kuo, Chih-Hsuan Yen.
National Taiwan University Hospital: Juey-Jen Hwang, Kuo-
Liong Chien, Ta-Chen Su, Lian-Yu Lin, Jyh-Ming Juang, Yen-
Hung Lin, Fu-Tien Chiang, Jiunn-Lee Lin, Yi-Lwun Ho, Chii-Ming
Lee, Po-Chih Lin, Chi-Sheng Hung, Sheng-Nan Chang, Jou-Wei
Lin, Chih-Neng Hsu. Taipei Veterans General Hospital: Wen-
Chung Yu, Tze-Fan Chao, Shih-Hsien Sung, Kang-Ling Wang,
Hsin-Bang Leu, Yenn-Jiang Lin, Shih-Lin Chang, Po-Hsun
Huang, Li-Wei Lo, Cheng-Hsueh Wu. China Medical University
Hospital: Hsin-Yueh Liang, Shih-Sheng Chang, Lien-Cheng
Hsiao, Yu-Chen Wang, Chiung-Ray Lu, Hung-Pin Wu, Yen-Nien
Lin, Ke-Wei Chen, Ping-Han Lo, Chung-Ho Hsu, Li-Chuan
Hsieh.
Thailand
Ramathibodi Hospital: Tachapong Ngarmukos (Country PI),
Mann Chandavimol, Teerapat Yingchoncharoen, Prasart
Laothavorn. Phramongkutklao Hospital:Waraporn Tiyanon.
Maharaj Nakorn Chiang Mai Hospital: Wanwarang Wongchar-
oen, Arintaya Phrommintikul.
Acknowledgments
The contributions of all site investigators and clinical coordi-
nators are duly acknowledged. The lead author afrms that
this article is an honest, accurate, and transparent account of
the study being reported; that no important aspects of the
study have been omitted; and that any discrepancies from the
study as planned (and, if relevant, registered) have been
explained.
AuthorsContributions
The corresponding author attests that all listed authors meet
authorship criteria and that no others meeting the criteria
have been omitted. All authors critically reviewed and
contributed to the intellectual content of the article. Lam,
MacDonald, and Anand were involved with the conception of
the study. Initial data preparation was done by Tay, who
performed the statistical analyses. MacDonald drafted the
article. Teng contributed to part of the draft article and
undertook revisions of the nal article. Lam, MacDonald,
Richards, Ling, and Anand provided the clinical expertise.
Lam, MacDonald, and Yap adjudicated all mortality and
causes of death. All authors have read and approved the nal
version of the article.
Sources of Funding
The ASIAN-HF (Asian Sudden Cardiac Death in Heart Failure)
study is supported by grants from the National Medical
Research Council of Singapore; Agency for Science, Tech-
nology, and Research; Biomedical Research Council; Asian
Network for Translational Research and Cardiovascular Trials
program; Boston Scientic Investigator-Sponsored Research
Program; and Bayer. The funders had no role in the design
and conduct of the study; collection, management, analysis,
and interpretation of the data; preparation, review, or
approval of the article; and decision to submit the article
for publication.
Disclosures
None relevant to the present work. Lam is supported by a
Clinician Scientist Award from the National Medical Research
Council Singapore. Lam has received research support from
Boston Scientic, Medtronic, and Vifor Pharma and has
consulted for Bayer, Novartis, Takeda, Merck, Astra Zeneca,
DOI: 10.1161/JAHA.119.012199 Journal of the American Heart Association 14
Outcomes in the ASIAN-HF Registry MacDonald et al
ORIGINAL RESEARCH
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Janssen Research & Development, and Menarini. She has
served on the clinical end point committee for DC Devices.
Richards is supported by a Senior Translational Research
(STaR) award from the National Medical Research Council of
Singapore; holds the New Zealand Heart Foundation Chair of
Cardiovascular Studies; has received research support from
Boston Scientic, Bayer, AstraZeneca, Medtronic, Roche
Diagnostics, Abbott Laboratories, Thermo Fisher, Critical
Diagnostics; and has consulted for Bayer, Novartis, Merck,
AstraZeneca, and Roche Diagnostics.
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... 9) The ASIAN-HF registry showed similar regional differences within Asia. 40) Here, mortality was highest in Southeast and South Asia and lower in Northeast Asia, which includes Japan and South Korea. 40) In the PARADIGM-HF trial, mortality was highest in Latin America and the Asia Pacific region and lowest in North America and Western Europe. ...
... 40) Here, mortality was highest in Southeast and South Asia and lower in Northeast Asia, which includes Japan and South Korea. 40) In the PARADIGM-HF trial, mortality was highest in Latin America and the Asia Pacific region and lowest in North America and Western Europe. 35) Results from REPORT-HF might explain some of these regional differences. ...
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Beta-blockers are first-line drugs in the treatment of chronic heart failure (CHF). However, there is no consensus on the specific effects of the beta-blockers of the I-III generation on energy metabolism in CHF. The aim of this study is to conduct a study of beta-blockers of different generations on myocardial energy metabolism in experimental CHF. CHF was modeled in white outbred rats by administering doxorubicin. The study drugs were administered intragastrically—new drug Hypertril (1-(β-phenylethyl)-4-amino-1,2,4-triazolium bromide)-3.5 mg/kg, Metoprolol—15 mg/kg, Nebivolol −10 mg/kg, Carvedilol 50 mg/kg, and Bisoprolol, 10 mg/kg. In the myocardium, the main indices of energy metabolism were determined—ATP, ADP, AMP, malate, lactate, pyruvate, succinate dehydrogenase (SDH) activity, and NAD-dependent malate dehydrogenase (NAD-MDH) activity. Traditional second-generation beta-blockers (Metoprolol and Bisoprolol) did not affect the studied indices of energy metabolism, and third-generation beta-blockers with additional properties—Carvedilol and, especially, Nebivalol and Hypertril—improved myocardial energy metabolism. The obtained results will help to expand our understanding of the effect of beta-blockers of various generations used to treat cardiovascular diseases on energy metabolism, and are also an experimental justification for the practical choice of these drugs in the complex therapy of CHF.
... This might be of clinical relevance as not only large clinical trials but also some HF registries have included a significantly smaller proportion of women than men. [13][14][15][16][17][18][19][20] This may limit the generalizability of their results and thus represents an important source of bias. The reasons for this male predominance remain unresolved. ...
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on behalf of the REAL-HF investigators, Association of sex with in-hospital management and outcomes of patients with heart failure: data from the REAL-HF registry, American Heart Journal (2024), doi: https://doi. Short title: The impact of sex in patients hospitalized for heart failure Total word count: 3381 (text including abstract) Grants and financial support: Prof. Gian Luca Erre declares the following financial support (University of Sassari-finanziamento di Ateneo una tantum per la ricerca 2020-2021) Conflict of Interest and Relationship with industry: None The authors take responsibility for all aspects of the reliability and freedom from bias of the data presented and their discussed interpretation. Abstract Background. There are sex differences in HF patients. It is not clear whether such differences mainly reflect cultural behaviours and clinical inertia, and the role of sex on clinical outcomes is still controversial. We aimed to investigate the association of sex with in-hospital management and outcomes in patients with HF. Methods. We analyzed data of 4016 adult patients hospitalized for HF in 2020-2021 and enrolled in a multicentre national registry. Results. Women (n=1818[45%]) were older than men (83vs77 years, p<0.0001), with a higher prevalence of arterial hypertension (73%vs69%, p=0.011) and atrial fibrillation. Women presented more frequently with HF and preserved ejection fraction-HFpEF (55%vs32%, p<0.001). They were more often hospitalized in internal medicine departments (71%vs51%), and men in highly specialized cardiology units (49%vs29%). When considering HF pharmacological treatments at discharge in the subgroup with reduced ejection fraction-HFrEF (n=1525), there were no significant differences (49% of women treated with GDMT [guideline-directed medical therapy] vs 52% of men, p=0.197). Sex was not associated either with hospital readmissions (30-days OR[95%CI]=0.89[0.71-1.11], p=0.304; 1-year OR[95%CI]=1.02[0.88-1.19], p=0.777) or with mortality (in-hospital OR[95%CI]=1.14[0.73-1.78], p=0.558; 1-year OR[95%CI]=1.08[0.87-1.33], p=0.478). Similar results were obtained when considering different HF categories based on left ventricular ejection fraction. Conclusions. Women and men exhibited distinct clinical profiles. Although this may have had an impact on hospital pathways (non-cardiology/cardiology units) and pharmacological prescriptions, sex per se did not appear as an independent determinant of clinical choices. Moreover, when considering homogeneous groups, women were not undertreated. Finally, female sex was not associated with worse clinical outcomes. 3
... The absolute death rate from HF within 5 years of diagnosis remains approximately 50%, despite increasing cure rates. In 2012, the global expense of treating HF surpassed $100 billion [8]. Another study showed that the individual annual cost of HF management in the United States ranged from $10,832 to $17,744, with nationwide expenses of $20.9 billion in 2012 and a projected $53.1 billion by 2030. ...
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Objectives In Indonesia, the poor prognosis and high hospital readmission rates of patients with heart failure (HF) have yet to receive focused attention. However, machine learning (ML) approaches can help to mitigate these problems. We aimed to determine which ML models best predicted HF severity and hospital readmissions and could be used in a patient self-monitoring mobile application. Methods In a retrospective cohort study, we collected the data of patients admitted with HF to the Siloam Diagram Heart Center in 2020, 2021, and 2022. Data was analyzed using the Orange data mining classification method. ML support algorithms, including artificial neural network (ANN), random forest, gradient boosting, Naïve Bayes, tree-based models, and logistic regression were used to predict HF severity and hospital readmissions. The performance of these models was evaluated using the area under the curve (AUC), accuracy, and F1-scores. Results Of the 543 patients with HF, 3 (0.56%) were excluded due to death on admission. Hospital readmission occurred in 138 patients (25.6%). Of the six algorithms tested, ANN showed the best performance in predicting both HF severity (AUC = 1.000, accuracy = 0.998, F1-score = 0.998) and readmission for HF (AUC = 0.998, accuracy = 0.975, F1-score = 0.972). Other studies have shown variable results for the best algorithm to predict hospital readmission in patients with HF. Conclusions The ANN algorithm performed best in predicting HF severity and hospital readmissions and will be integrated into a mobile application for patient self-monitoring to prevent readmissions.
... The reason for the importance of the problem of CHF is that this disease has an extremely unfavorable prognosis. Thus, the annual mortality rate among patients with functional class III-IV CHF reaches 60% and only half of less severe patients survive for 5 years from the date of diagnosis [6][7][8][9]. To date, recommendations have been developed for the treatment of CHF, which include the 2 prescription of ACE inhibitors, diuretics, cardiac glycosides and beta blockers [10,11]. ...
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Background: beta blockers of three generations are first-line drugs in the treatment of chronic heart failure (CHF). However, to date there is no clear understanding of the effect of beta blockers on myocardial energy metabolism disorders in CHF. The aim of the study: to conduct a study of beta-blockers of different generations on myocardial energy metabolism in the experimental CHF. Methods: CHF was modeled in white outbred rats by administering doxorubicin. The study drugs were administered intragastrically – new drug Hypertril (1-(β-phenylethyl)-4-amino-1,2,4-triazolium bromide)-3.5 mg/kg, metoprolol - 15 mg/kg, Nebivolol -10 mg/kg, Carvedilol 50 mg/kg, Bisoprolol, 10 mg/kg. In the myocardium, the main indices of energy metabolism were determined - ATP, ADP, AMP, malate, lactate, pyruvate, succinate dehydrogenase (SDH) activity, NAD-dependent malate dehydrogenase (NAD-MDH) activity. Results: traditional second-generation beta-blockers (Metoprolol and Bisoprolol) did not affect the studied indices of energy metabolism, and third-generation beta-blockers with additional properties - Carvedilol and, especially, Nebivalol and Hypertril improved myocardial energy metabolism. Conclusions: obtained results will help to expand our understanding of the effect of beta-blockers of various generations used to treat cardiovascular diseases on energy metabolism, and are also an experimental justification for the practical choice of these drugs in the complex therapy of CHF. Keywords: chronic heart failure; beta blockers; energy metabolism; mitochondria; Hypertril
Article
Importance Heart failure (HF) is a leading cause of death in the US. The current evidence on the burdens of HF in Asian American populations, especially Asian American subgroups, is limited and inconsistent. Objective To assess and compare the incidence and prevalence of HF in Asian American subgroups. Design, Setting, and Participants This retrospective cohort study used electronic health record data from patients 40 years or older with health care encounters from January 1, 2015, to December 31, 2019, recorded in the Oracle Electronic Health Record Real-World Data database, which has more than 100 health care systems across the US contributing to the database as of February 2024. For prevalence analysis, the study samples were those who had at least 1 encounter in the study calendar year. For incidence analysis, participants were additionally limited to those without HF before the study year who also had encounter(s) the year before the study year. Data analysis was performed from August 1, 2023, to July 31, 2024. Exposure Race and ethnicity were determined using patient self-reported data, which were categorized as Black, East Asian, South Asian, Southeast Asian, other Asian (without specified ethnicity), and White. Main Outcomes and Measures Outcomes were incidence and prevalence of HF, identified using recorded International Classification of Diseases, Ninth Revision, Clinical Modification and International Statistical Classification of Diseases, Tenth Revision, Clinical Modification codes. Age- and sex-standardized incidence and prevalence were used to calculate the risk ratio of each racial and ethnic group compared with White patients. Results Incidence and prevalence analyses were performed for 6 845 791 patients (mean [SD] age, 62.1 [12.5] years; 59.9% female; 2.8% Asian, 6.7% Black, and 90.5% White) and for 13 440 234 patients (mean [SD] age, 61.7 [12.7] years; 57.0% female; 2.9% Asian, 7.1% Black, and 90.0% White), respectively. Using the 2015 population as the standard, age- and sex-standardized HF incidence was 2.26% (95% CI, 2.07%-2.45%) for Southeast Asian patients, 1.56% (95% CI, 1.31%-1.82%) for South Asian patients, and 1.22% (95% CI, 1.06%-1.38%) for East Asian patients compared with 1.58% (95% CI, 1.57%-1.59%) for White patients and 2.39% (95% CI, 2.36%-2.42%) for Black patients. Similarly, heterogeneous rates in Asian American subgroups were also observed in the prevalence analysis. Conclusions and Relevance In this study of HF outcomes, the disparities between Southeast and East Asian patients were larger than those between Black and White patients, with the estimates in Southeast Asian patients being similar to those of Black patients. These findings reinforce that individual Asian ethnicities and cardiovascular risk factors should be considered in the assessment of HF risks.
Article
Aims We analysed baseline characteristics and guideline‐directed medical therapy (GDMT) use and decisions in the European Society of Cardiology (ESC) Heart Failure (HF) III Registry. Methods and results Between 1 November 2018 and 31 December 2020, 10 162 patients with acute HF (AHF, 39%, age 70 [62–79], 36% women) or outpatient visit for HF (61%, age 66 [58–75], 33% women), with HF with reduced (HFrEF, 57%), mildly reduced (HFmrEF, 17%) or preserved (HFpEF, 26%) ejection fraction were enrolled from 220 centres in 41 European or ESC‐affiliated countries. With AHF, 97% were hospitalized, 2.2% received intravenous treatment in the emergency department, and 0.9% received intravenous treatment in an outpatient clinic. AHF was seen by most by a general cardiologist (51%) and outpatient HF most by a HF specialist (48%). A majority had been hospitalized for HF before, but 26% of AHF and 6.1% of outpatient HF had de novo HF. Baseline use, initiation and discontinuation of GDMT varied according to AHF versus outpatient HF, de novo versus pre‐existing HF, and by ejection fraction. After the AHF event or outpatient HF visit, use of any renin–angiotensin system inhibitor, angiotensin receptor–neprilysin inhibitor, beta‐blocker, mineralocorticoid receptor antagonist and loop diuretics was 89%, 29%, 92%, 78%, and 85% in HFrEF; 89%, 9.7%, 90%, 64%, and 81% in HFmrEF; and 77%, 3.1%, 80%, 48%, and 80% in HFpEF. Conclusion Use and initiation of GDMT was high in cardiology centres in Europe, compared to previous reports from cohorts and registries including more primary care and general medicine and regions more local or outside of Europe and ESC‐affiliated countries.
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Background: Large-scale and contemporary population-based studies of heart failure incidence are needed to inform resource planning and research prioritisation but current evidence is scarce. We aimed to assess temporal trends in incidence and prevalence of heart failure in a large general population cohort from the UK, between 2002 and 2014. Methods: For this population-based study, we used linked primary and secondary electronic health records of 4 million individuals from the Clinical Practice Research Datalink (CPRD), a cohort that is representative of the UK population in terms of age and sex. Eligible patients were aged 16 years and older, had contributed data between Jan 1, 2002, and Dec 31, 2014, had an acceptable record according to CPRD quality control, were approved for CPRD and Hospital Episodes Statistics linkage, and were registered with their general practice for at least 12 months. For patients with incident heart failure, we extracted the most recent measurement of baseline characteristics (within 2 years of diagnosis) from electronic health records, as well as information about comorbidities, socioeconomic status, ethnicity, and region. We calculated standardised rates by applying direct age and sex standardisation to the 2013 European Standard Population, and we inferred crude rates by applying year-specific, age-specific, and sex-specific incidence to UK census mid-year population estimates. We assumed no heart failure for patients aged 15 years or younger and report total incidence and prevalence for all ages (>0 years). Findings: From 2002 to 2014, heart failure incidence (standardised by age and sex) decreased, similarly for men and women, by 7% (from 358 to 332 per 100 000 person-years; adjusted incidence ratio 0·93, 95% CI 0·91-0·94). However, the estimated absolute number of individuals with newly diagnosed heart failure in the UK increased by 12% (from 170 727 in 2002 to 190 798 in 2014), largely due to an increase in population size and age. The estimated absolute number of prevalent heart failure cases in the UK increased even more, by 23% (from 750 127 to 920 616). Over the study period, patient age and multi-morbidity at first presentation of heart failure increased (mean age 76·5 years [SD 12·0] to 77·0 years [12·9], adjusted difference 0·79 years, 95% CI 0·37-1·20; mean number of comorbidities 3·4 [SD 1·9] vs 5·4 [2·5]; adjusted difference 2·0, 95% CI 1·9-2·1). Socioeconomically deprived individuals were more likely to develop heart failure than were affluent individuals (incidence rate ratio 1·61, 95% CI 1·58-1·64), and did so earlier in life than those from the most affluent group (adjusted difference -3·51 years, 95% CI -3·77 to -3·25). From 2002 to 2014, the socioeconomic gradient in age at first presentation with heart failure widened. Socioeconomically deprived individuals also had more comorbidities, despite their younger age. Interpretation: Despite a moderate decline in standardised incidence of heart failure, the burden of heart failure in the UK is increasing, and is now similar to the four most common causes of cancer combined. The observed socioeconomic disparities in disease incidence and age at onset within the same nation point to a potentially preventable nature of heart failure that still needs to be tackled. Funding: British Heart Foundation and National Institute for Health Research.
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Background: Most data on mortality and prognostic factors in patients with heart failure come from North America and Europe, with little information from other regions. Here, in the International Congestive Heart Failure (INTER-CHF) study, we aimed to measure mortality at 1 year in patients with heart failure in Africa, China, India, the Middle East, southeast Asia and South America; we also explored demographic, clinical, and socioeconomic variables associated with mortality. Methods: We enrolled consecutive patients with heart failure (3695 [66%] clinic outpatients, 2105 [34%] hospital in patients) from 108 centres in six geographical regions. We recorded baseline demographic and clinical characteristics and followed up patients at 6 months and 1 year from enrolment to record symptoms, medications, and outcomes. Time to death was studied with Cox proportional hazards models adjusted for demographic and clinical variables, medications, socioeconomic variables, and region. We used the explained risk statistic to calculate the relative contribution of each level of adjustment to the risk of death. Findings: We enrolled 5823 patients within 1 year (with 98% follow-up). Overall mortality was 16·5%: highest in Africa (34%) and India (23%), intermediate in southeast Asia (15%), and lowest in China (7%), South America (9%), and the Middle East (9%). Regional differences persisted after multivariable adjustment. Independent predictors of mortality included cardiac variables (New York Heart Association Functional Class III or IV, previous admission for heart failure, and valve disease) and non-cardiac variables (body-mass index, chronic kidney disease, and chronic obstructive pulmonary disease). 46% of mortality risk was explained by multivariable modelling with these variables; however, the remainder was unexplained. Interpretation: Marked regional differences in mortality in patients with heart failure persisted after multivariable adjustment for cardiac and non-cardiac factors. Therefore, variations in mortality between regions could be the result of health-care infrastructure, quality and access, or environmental and genetic factors. Further studies in large, global cohorts are needed. Funding: The study was supported by Novartis.
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Aims To characterize regional and ethnic differences in heart failure (HF) across Asia. Methods and results We prospectively studied 5276 patients with stable HF and reduced ejection fraction (≤40%) from 11 Asian regions (China, Hong Kong, India, Indonesia, Japan, Korea, Malaysia, Philippines, Singapore, Taiwan, and Thailand). Mean age was 59.6 ± 13.1 years, 78.2% were men, and mean body mass index was 24.9 ± 5.1 kg/m2. Majority (64%) of patients had two or more comorbid conditions such as hypertension (51.9%), coronary artery disease (CAD, 50.2%), or diabetes (40.4%). The prevalence of CAD was highest in Southeast Asians (58.8 vs. 38.2% in Northeast Asians). Compared with Chinese ethnicity, Malays (adjusted odds ratio [OR] 1.97, 95% CI 1.63–2.38) and Indians (OR 1.44, 95% CI 1.24–1.68) had higher odds of CAD, whereas Koreans (OR 0.38, 95% CI 0.29–0.50) and Japanese (OR 0.44, 95% CI 0.36–0.55) had lower odds. The prevalence of hypertension and diabetes was highest in Southeast Asians (64.2 and 49.3%, respectively) and high-income regions (59.7 and 46.2%, respectively). There was significant interaction between ethnicity and region, where the adjusted odds were 3.95 (95% CI 2.51–6.21) for hypertension and 4.91 (95% CI 3.07–7.87) for diabetes among Indians from high- vs. low-income regions; and 2.60 (95% CI 1.66–4.06) for hypertension and 2.62 (95% CI 1.73–3.97) for diabetes among Malays from high- vs. low-income regions. Conclusions These first prospective multi-national data from Asia highlight the significant heterogeneity among Asian patients with stable HF, and the important influence of both ethnicity and regional income level on patient characteristics. ClinicalTrials.gov identifier NCT01633398.
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A gap in the knowledge on the status of heart failure (HF) in Asia versus other regions led to the creation of a working group of Asian experts from 9 countries or regions (Hong Kong, Indonesia, Malaysia, Philippines, Singapore, South Korea, Taiwan, Thailand, and Vietnam). Each expert sought the best available data from local publications, registries, or clinical practice. The prevalence of HF in Asia was generally similar to global values (1% to 3%), but with some outliers. There were substantial variations in healthcare spending, and the average cost of HF hospitalization varied from 813 US$ in Indonesia to nearly 9000 US$ in South Korea. Comorbidities were frequent, particularly hypertension, diabetes mellitus, and dyslipidemia. Modifiable risk factors such as smoking were alarmingly common in some countries. Asian HF patients spent between 5 and 12.5 days in hospital, and 3% to 15% were readmitted for HF by 30 days. The pharmacological treatment of Asian patients generally followed international guidelines, including renin–angiotensin–aldosterone system inhibitors (61% to 90%), diuretics (76% to 99%), beta-blockers (32% to 78%), and digoxin (19% to 53%), with some room for improvement in terms of life-saving therapies. Our review supports implementation of a more comprehensive and organized approach to HF care in Asia.
Article
Background Heart failure with preserved ejection fraction (HFpEF) is a global public health problem. Unfortunately, little is known about HFpEF across Asia. Methods and results We prospectively studied clinical characteristics, echocardiographic parameters and outcomes in 1204 patients with HFpEF (left ventricular ejection fraction ≥50%) from 11 Asian regions, grouped as Northeast Asia (Hong Kong, Taiwan, China, Japan, Korea, n = 543), South Asia (India, n = 252), and Southeast Asia (Malaysia, Thailand, Singapore, Indonesia, Philippines, n = 409). Mean age was 68 ±12 years (37% were < 65 years) and 50% were women. Seventy per cent of patients had ≥2 co‐morbidities, most commonly hypertension (71%), followed by anaemia (57%), chronic kidney disease (50%), diabetes (45%), coronary artery disease (29%), atrial fibrillation (29%) and obesity (26%). Southeast Asian patients had the highest prevalence of all co‐morbidities except atrial fibrillation, South Asians had the lowest prevalence of all co‐morbidities except anaemia and obesity, and Northeast Asians had more atrial fibrillation. Left ventricular hypertrophy and concentric remodelling were most prominent among Southeast and South Asians, respectively (P < 0.001). Overall, 12.1% of patients died or were hospitalized for heart failure within 1 year. Southeast Asians were at higher risk for adverse outcomes, independent of co‐morbidity burden and cardiac geometry. Conclusion These first prospective multinational data from Asia show that HFpEF affects relatively young patients with a high burden of co‐morbidities. Regional differences in types of co‐morbidities, cardiac remodelling and outcomes of HFpEF across Asia have important implications for public health measures and global HFpEF trial design.
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This publication describes uniform definitions for cardiovascular and stroke outcomes developed by the Standardized Data Collection for Cardiovascular Trials Initiative and the US Food and Drug Administration (FDA). The FDA established the Standardized Data Collection for Cardiovascular Trials Initiative in 2009 to simplify the design and conduct of clinical trials intended to support marketing applications. The writing committee recognizes that these definitions may be used in other types of clinical trials and clinical care processes where appropriate. Use of these definitions at the FDA has enhanced the ability to aggregate data within and across medical product development programs, conduct meta-analyses to evaluate cardiovascular safety, integrate data from multiple trials, and compare effectiveness of drugs and devices. Further study is needed to determine whether prospective data collection using these common definitions improves the design, conduct, and interpretability of the results of clinical trials.
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Background: Implantable cardioverter defibrillators (ICDs) are lifesaving devices for patients with heart failure (HF) and reduced ejection fraction. However, utilization and determinants of ICD insertion in Asia are poorly defined. We determined the utilization, associations of ICD uptake, patient-perceived barriers to device therapy and, impact of ICDs on mortality in Asian patients with HF. Methods and results: Using the prospective ASIAN-HF (Asian Sudden Cardiac Death in Heart Failure) registry, 5276 patients with symptomatic HF and reduced ejection fraction (HFrEF) from 11 Asian regions and across 3 income regions (high: Hong Kong, Japan, Korea, Singapore, and Taiwan; middle: China, Malaysia, and Thailand; and low: India, Indonesia, and Philippines) were studied. ICD utilization, clinical characteristics, as well as device perception and knowledge, were assessed at baseline among ICD-eligible patients (EF ≤35% and New York Heart Association Class II-III). Patients were followed for the primary outcome of all-cause mortality. Among 3240 ICD-eligible patients (mean age 58.9±12.9 years, 79.1% men), 389 (12%) were ICD recipients. Utilization varied across Asia (from 1.5% in Indonesia to 52.5% in Japan) with a trend toward greater uptake in regions with government reimbursement for ICDs and lower out-of-pocket healthcare expenditure. ICD (versus non-ICD) recipients were more likely to be older (63±11 versus 58±13 year; P<0.001), have tertiary (versus ≤primary) education (34.9% versus 18.1%; P<0.001) and be residing in a high (versus low) income region (64.5% versus 36.5%; P<0.001). Among 2000 ICD nonrecipients surveyed, 55% were either unaware of the benefits of, or needed more information on, device therapy. ICD implantation reduced risks of all-cause mortality (hazard ratio, 0.71; 95% confidence interval, 0.52-0.97) and sudden cardiac deaths (hazard ratio, 0.33; 95% confidence interval, 0.14-0.79) over a median follow-up of 417 days. Conclusions: ICDs reduce mortality risk, yet utilization in Asia is low; with disparity across geographic regions and socioeconomic status. Better patient education and targeted healthcare reforms in extending ICD reimbursement may improve access. Clinical trial registration: URL: https://clinicaltrials.gov/ct2/show/NCT01633398. Unique identifier: NCT01633398.
Article
Aims: Clinical features and outcomes in the novel phenotype heart failure with mid-range ejection fraction [HFmrEF, ejection fraction (EF) 40-49%] were compared with heart failure with reduced EF (HFrEF, EF <40%) and preserved EF (HFpEF, EF ≥50%). Methods and results: In the Swedish Heart Failure Registry, we assessed the association between baseline characteristics and EF group using multivariable logistic regressions, and the association between EF group and all-cause mortality using multivariable Cox regressions. Of 42 061 patients, 56% had HFrEF, 21% had HFmrEF, and 23% had HFpEF. Characteristics were continuous for age (72 ± 12 vs. 74 ± 12 vs. 77 ± 11 years), proportion of women (29% vs. 39% vs. 55%), and 13 other characteristics. Coronary artery disease (CAD) was distinctly more common in HFrEF (54%) and HFmrEF (53%) vs. HFpEF (42%); adjusted odds ratio for CAD in HFmrEF vs. HFpEF was 1.52 [95% confidence interval (CI) 1.41-1.63]. For six additional characteristics HFmrEF resembled HFrEF, for seven characteristics HFmrEF resembled HFpEF, and for 10 characteristics there was no pattern. The adjusted hazard ratio (HR) for mortality in HFrEF vs. HFpEF was 1.35 (95% CI 1.14-1.60) at 30 days, 1.26 (95% CI 1.17-1.35) at 1 year, and 1.20 (95% CI 1.14-1.26) at 3 years. In contrast, HFmrEF and HFpEF had a similar prognosis (HR 1.06, 95% CI 0.86-1.30 at 30 days; HR 1.08, 95% CI 1.00-1.18 at 1 year; and HR 1.06, 95% CI 1.00-1.12 at 3 years). Three-year mortality was higher in HFmrEF than in HFpEF in the presence of CAD (HR 1.11, 95% CI 1.02-1.21), but not in the absence of CAD (HR 1.02, 95% CI 0.94-1.12; P for interaction <0.001). Conclusions: HFmrEF was an intermediate phenotype, except that CAD was more common in HFmrEF and HFrEF vs. HFpEF, crude all-cause mortality was lower in HFmrEF and HFrEF, adjusted all-cause mortality was lower in HFmrEF and HFpEF, and CAD portended a higher adjusted risk of death in HFmrEF and HFrEF.