Edited by: Maria-Angela Losi, University of Naples Federico II, Italy
Reviewed by: Giovanna Gallo, Sapienza University of Rome, Italy; Dan Octavian Nistor, Târgu Mureş Emergency Institute for Cardiovascular Diseases and Transplantation (IUBCVT), Romania
This article was submitted to Heart Failure and Transplantation, a section of the journal Frontiers in Cardiovascular Medicine
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
We sought to explore the significance of resting cardiac power/mass in predicting adverse outcome in patients with heart failure with preserved ejection fraction (HFpEF).
This prospective cohort study included patients with HFpEF and without significant valve disease or right ventricular dysfunction. Cardiac power was normalized to left ventricular (LV) mass and expressed in W/100 g of LV myocardium. Multivariate Cox regression analysis was used to evaluate the association between resting cardiac power/mass and composite endpoint, which included all-cause mortality and heart failure (HF) hospitalization.
A total of 2,089 patients were included in this study. After an average follow-up of 4.4 years, 612 (29.30%) patients had composite endpoint, in which 331 (15.84%) died and 391 (18.72%) experienced HF hospitalization. In multivariate Cox regression analysis, resting power/mass < 0.7 W/m2 was independently associated with composite endpoint, all-cause mortality, cardiovascular mortality and HF hospitalization, with hazard ratios (HR) of 1.309 [95% confidence interval (CI): 1.108–1.546,
Resting cardiac power determined by non-invasive echocardiography is independently associated with the risk of adverse outcomes in HFpEF patients and provides incremental prognostic information.
Chronic heart failure (HF), characterized by decreased cardiac systolic and/or diastolic function, is a primary cause of morbidity and mortality worldwide (
Cardiac power refers to the rate at which the heart pumps blood out and delivers it to the periphery (
Whereas cardiac power has been well studied in HF patients with reduced EF (HFrEF) and in those with normal EF but no HF (
This study was approved by the Ethics Board of the No. 988 Hospital of Joint Logistic Support Force of the Chinese PLA and was conducted in line with the ethical guidelines of the 1975 Declaration of Helsinki. Written informed consent was obtained from each patient. The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
This prospective cohort study recruited patients who were hospitalized in the Cardiology Department of the No. 988 Hospital of Joint Logistic Support Force of the Chinese PLA (Zhengzhou, China) from April 2012 to December 2021. We used the following criteria to select HFpEF patients: those with (i) history of HF hospitalization; (ii) HF syndromes and/or signs, (iii) EF ≥ 50%, and (iv) N-terminal pro-brain natriuretic peptide (NT-proBNP) ≥ 125 pg/mL. Additionally, eligible HFpEF patients were required to be stable and well-compensated without medication changes for at least 6 weeks prior to enrollment. Patients who had one of the following conditions were excluded: EF < 50% at rest, enlarged right ventricle, significant valve disease (≥ moderate stenosis or regurgitation, prosthetic valve replacement, surgical or percutaneous valve repair, rheumatic valve disease), hospitalization for uncompensated HF or unstable coronary heart disease in the prior 6 weeks, heart transplant, metastatic malignant tumor, severe liver disease or receiving palliative care. Via electronic medical records, we collected patient detailed medical history, baseline clinical characteristics, laboratory indexes and echocardiographic parameters.
Cardiac power normalized by LV mass at rest was calculated by the following formulas, in which 0.222 is the conversion constant to W/100 g of LV myocardium: resting cardiac power/mass = 0.222 × cardiac output × mean blood pressure (BP)/LV mass; cardiac output = stroke volume × heart rate; mean BP = diastolic BP + 1/3 × systolic BP (
To evaluate the severity of patient comorbidities, we defined comorbidity score as the number of patient comorbidities referring to the Charlson Comorbidity Index (CCI) (
Until December 31, 2021, all patients were followed up via telephone or medical record every 6 months for the composite endpoint which consisted of all-cause mortality or HF hospitalization, and the causes of death was also recorded. Furthermore, we contacted the attending physician of each patient who had an event to reconfirm their outcome. For patients who did not have an event, survival time was defined as the period from the day of physical examination to the last date of follow-up.
Categorical variables are presented as frequencies (%), and continuous variables are presented as the mean ± standard deviation or median (interquartile range). Differences between groups were evaluated by the chi-squared test for categorical variables and Student’s
We log-transformed (log10) NT-proBNP, and used the median values of resting cardiac power/mass and log NT-proBNP as cutoffs. The log-rank test was used to compare survival times on Kaplan–Meier curves across different groups. The prognostic value of resting cardiac power/mass was evaluated by using a Cox proportional hazards model adjusted for the following covariates: age, gender, body mass index (BMI, calculated by weight divided by the square of height), New York Heart Association (NYHA) class, LVEF, comorbidity score, estimated glomerular filtration rate (eGFR, calculated by a modified Modification of Diet in Renal Disease equation), angiotensin-converting enzyme inhibitors (ACEI)/angiotensin receptor antagonists (ARB), beta blockers and aldosterone antagonists. The prognostic discrimination of resting cardiac power/mass was assessed by comparing the incremental improvement of the Harrell’s C statistic, as well as the integrated discrimination improvement (IDI) and the continuous net reclassification improvement (NRI) at the event rate. Sensitivity analysis was further performed to explore the association between resting cardiac power/mass (as a continuous variable) and all-cause mortality and HF hospitalization among the following subgroups: age (< 75 or ≥ 75 years), BMI (< 18.5 kg/m2, 18.5–23.9 kg/m2, 24–27.9 kg/m2 or ≥ 28 kg/m2), NYHA class (class I, II, III or IV), comorbidity score (0–3 or ≥ 4), eGFR (< 60 mL/min/1.73 m2 or ≥ 60 mL/min/1.73 m2) and log NT-proBNP (< 2.5 or ≥ 2.5). R software version 4.0.3 (Institute for Statistics and Mathematics, Vienna, Austria
Exclusion of ineligible patients produced a final cohort of 2089 HFpEF patients. Baseline measurements of resting cardiac power/mass were available for the 2089 patients. Detailed baseline characteristics are shown in
Baseline characteristics of patients with HFpEF.
Resting cardiac power/mass < 0.7 W/m2 ( |
Resting cardiac power/mass ≥ 0.7 W/m2 ( |
||
Age (years) | 77.90 ± 11.37 | 74.78 ± 11.39 | <0.001 |
Male (%) | 983 (96.28%) | 1,018 (95.32%) | 0.163 |
Smoking (%) | 376 (37.12%) | 386 (36.28%) | 0.363 |
Alcohol (%) | 326 (32.06%) | 339 (31.83%) | 0.475 |
BMI (kg/m2) | 24.48 ± 2.93 | 24.52 ± 3.21 | 0.788 |
Systolic BP (mmHg) | 128.67 ± 16.72 | 137.40 ± 16.60 | <0.001 |
Diastolic BP (mmHg) | 66.66 ± 8.80 | 75.41 ± 9.50 | <0.001 |
Heart rate (bpm) | 66.56 ± 8.50 | 77.10 ± 10.49 | <0.001 |
I | 149 (14.59%) | 151 (14.14%) | 0.767 |
II | 445 (43.58%) | 505 (47.28%) | 0.090 |
III | 317 (31.05%) | 325 (30.43%) | 0.760 |
IV | 110 (10.77%) | 87 (8.15%) | 0.040 |
eGFR (mL/min/1.73 m2) | 87.79 ± 24.94 | 90.36 ± 23.52 | 0.016 |
FBG (mmol/L) | 5.78 ± 1.61 | 5.81 ± 1.72 | 0.654 |
NT-proBNP (pg/mL) | 416.9 (257.43–828.55) | 345.6 (228.05–617.60) | 0.001 |
LVEF (%) | 61 (58–64) | 63 (61–66) | <0.001 |
Comorbidity score | 3.52 ± 1.90 | 3.15 ± 1.81 | <0.001 |
Ischemic heart disease (%) | 522 (51.13%) | 412 (38.58%) | <0.001 |
Hypertension (%) | 671 (65.72%) | 706 (66.10%) | 0.853 |
Atrial fibrillation (%) | 163 (15.96%) | 113 (10.58%) | <0.001 |
Diabetes mellitus (%) | 464 (45.45%) | 496 (46.44%) | 0.648 |
Anti-platelet | 732 (71.69%) | 677 (63.39%) | <0.001 |
Statins | 701 (68.66%) | 626 (58.61%) | <0.001 |
Calcium channel blocker | 621 (60.82%) | 608 (56.93%) | 0.071 |
ACEI/ARB | 563 (55.14%) | 539 (50.47%) | 0.032 |
Beta blocker | 506 (49.56%) | 415 (38.86%) | <0.001 |
Aldosterone antagonist | 276 (27.03%) | 227 (21.25%) | 0.002 |
Diuretic | 549 (53.77%) | 472 (44.19%) | <0.001 |
The 2089 HFpEF patients were followed up for 4.4 years on average, with no patients lost to follow-up. Among them, 612 (29.30%) patients experienced composite endpoint, in which 331 (15.84%) experienced death from any cause, 147 (7.04%) died from cardiovascular causes, and 391 (18.72%) experienced HF hospitalization. Of these, 357 (34.97%) patients who had composite endpoint, 216 (65.26%) patients who died and 225 (57.54%) patients hospitalized with HF had resting cardiac power/mass values below 0.7 W/m2.
Kaplan–Meier curves of the incidences of adverse outcomes are presented in
Kaplan–Meier survival curves for prediction of composite endpoint
Kaplan–Meier survival curves for prediction of composite endpoint
After adjustment for commonly recognized risk factors (age, gender, BMI, NYHA class, LVEF, comorbidity score, eGFR, use of ACEI/ARB, beta blocker and aldosterone antagonist, log NT-proBNP), in multivariate analysis, resting cardiac power/mass < 0.7 W/m2 was independently associated with the incidence of composite endpoint, all-cause mortality, cardiovascular mortality and HF hospitalization, with hazard ratios (HR) of 1.309 [95% confidence interval (CI): 1.108–1.546,
Outcomes of HFpEF patients by resting cardiac power/mass categories.
Outcomes | Resting cardiac power/mass ≥ 0.7 W/m2 ( |
Resting cardiac power/mass < 0.7 W/m2 ( |
|
HR (95%CI) | HR (95%CI) | ||
255 (23.88%) | 357 (34.97%) | ||
Unadjusted | 1.00 (Ref) | 1.654 (1.408–1.943) | <0.001 |
Model | 1.00 (Ref) | 1.349 (1.142–1.593) | <0.001 |
Model + log NT-proBNP | 1.00 (Ref) | 1.309 (1.108–1.546) | 0.002 |
115 (10.77%) | 216 (21.16%) | ||
Unadjusted | 1.00 (Ref) | 2.213 (1.764–2.775) | <0.001 |
Model | 1.00 (Ref) | 1.726 (1.367–2.180) | <0.001 |
Model + log NT-proBNP | 1.00 (Ref) | 1.697 (1.344–2.143) | <0.001 |
37 (3.46%) | 110 (10.77%) | ||
Unadjusted | 1.00 (Ref) | 3.549 (2.445–5.151) | <0.001 |
Model | 1.00 (Ref) | 2.541 (1.727–3.737) | <0.001 |
Model + log NT-proBNP | 1.00 (Ref) | 2.513 (1.711–3.689) | <0.001 |
166 (15.54%) | 225 (22.04%) | ||
Unadjusted | 1.00 (Ref) | 1.582 (1.294–1.933) | <0.001 |
Model | 1.00 (Ref) | 1.331 (1.065–1.613) | 0.011 |
Model + log NT-proBNP | 1.00 (Ref) | 1.294 (1.052–1.592) | 0.015 |
We further explored the predictive value of resting cardiac power/mass by C-index (
Reclassification and discrimination statistics for outcomes by resting cardiac power/mass.
Outcomes | C-index |
Continuous NRI |
IDI |
|||
Model | 0.721 (0.699–0.743) | 1.0 (Ref) | 1.0 (Ref) | |||
Model + log NT-proBNP | 0.739 (0.722–0.756) | 0.018 | 8.4 (3.0–18.0) | 0.133 | 1.4 (0.1–2.9) | 0.033 |
Model + resting cardiac power/mass | 0.726 (0.714–0.738) | 0.005 | 13.4 (1.8–23.2) | 0.013 | 0.6 (0.1–1.1) | 0.020 |
Model + log NT-proBNP + resting cardiac power/mass | 0.742 (0.729–0.755) | 0.021 | 13.1 (2.9–21.6) | 0.007 | 1.9 (0.8–3.2) | <0.001 |
Model | 0.741 (0.712–0.770) | 1.0 (Ref) | 1.0 (Ref) | |||
Model + log NT-proBNP | 0.753 (0.724–0.781) | 0.011 | 8.0 (3.0–25.7) | 0.013 | 1.6 (0.1–3.0) | 0.040 |
Model + resting cardiac power/mass | 0.748 (0.719–0.777) | 0.060 | 13.8 (2.4–24.0) | 0.020 | 0.9 (0.1–1.8) | 0.004 |
Model + log NT-proBNP + resting cardiac power/mass | 0.759 (0.731–0.788) | 0.001 | 17.0 (11.4–28.3) | 0.040 | 2.3 (0.7–8.7) | 0.020 |
Model | 0.862 (0.832–0.892) | 1.0 (Ref) | 1.0 (Ref) | |||
Model + log NT-proBNP | 0.891 (0.872–0.910) | 0.029 | 22.3 (3.7–47.4) | 0.140 | 4.5 (1.0–9.5) | <0.001 |
Model + resting cardiac power/mass | 0.877 (0.858–0.896) | 0.014 | 25.2 (2.9–49.4) | 0.040 | 2.5 (0.1–6.0) | 0.047 |
Model + log NT-proBNP + resting cardiac power/mass | 0.902 (0.873–0.931) | 0.040 | 33.1 (4.9–55.3) | 0.007 | 7.5 (2.2–14.4) | <0.001 |
Model | 0.717 (0.689–0.745) | 1.0 (Ref) | 1.0 (Ref) | |||
Model + log NT-proBNP | 0.747 (0.721–0.773) | <0.001 | 4.0 (1.7–15.0) | 0.027 | 1.3 (1.2–3.9) | 0.077 |
Model + resting cardiac power/mass | 0.723 (0.696–0.750) | 0.037 | 4.6 (2.3–14.9) | 0.006 | 0.5 (0.1–1.5) | 0.058 |
Model + log NT-proBNP + resting cardiac power/mass | 0.749 (0.723–0.775) | <0.001 | 6.0 (4.7–15.2) | 0.026 | 1.7 (1.2–4.3) | 0.007 |
For composite endpoint, the continuous NRI after adding resting cardiac power/mass in the original model with N-terminal pro-brain natriuretic peptide was 13.1% (95%CI: 2.9–21.6%,
Resting cardiac power/mass was independently associated with all-cause mortality across most patient subgroups (
Resting cardiac power/mass for the prediction of all-cause mortality: subgroup analysis. The prognostic value of resting cardiac power/mass is considered in several patient subgroups, after adjustment for age, gender, BMI, NYHA class, LVEF, comorbidity score, eGFR, log NT-proBNP, ACEI/ARB, beta blocker, and aldosterone antagonist. Other abbreviations as in
Resting cardiac power/mass for the prediction of heart failure hospitalization: subgroup analysis. The prognostic value of resting cardiac power/mass is considered in several patient subgroups, after adjustment for age, gender, BMI, NYHA class, LVEF, comorbidity score, eGFR, log NT-proBNP, ACEI/ARB, beta blocker, and aldosterone antagonist. Other abbreviations as in
In the present study, we comprehensively investigated the potential prognostic role of cardiac power estimated by non-invasive echocardiography in patients with stable HFpEF. In this study, we found that (i) cardiac power normalized to LV mass at rest was independently associated with adverse outcomes in patients with HFpEF, and that (ii) incorporating resting cardiac power/mass (reflective of comprehensive cardiac function) and NT-proBNP (indicative of myocardial stretch) into a model with established risk factors enhanced the prognostic value for those endpoints.
As is well known, LV diastolic dysfunction plays a fundamental and predominant role in the pathophysiology of HFpEF (
Despite having a “preserved” EF, patients with HFpEF nonetheless experience abnormalities in LV systolic performance (
Cardiac power is a comprehensive quantitative indicator that can be used to evaluate cardiac function via non-invasive echocardiography (
In the present study, we found that the HFpEF patients with lower resting cardiac power/mass were more likely to be older, used more cardiovascular medications, have higher NYHA class and NT-proBNP level, as well as more comorbidities, indicating that the patients in this subgroup might have poorer health at baseline and more risk factors for adverse outcomes. After adjustment for multiple covariates, such as age and comorbidities, our study brought new evidence that resting cardiac power/mass is independently associated with composite endpoint, all-cause mortality, cardiovascular mortality, and HF hospitalization in patients with HFpEF. Meanwhile, resting cardiac power/mass significantly promoted the prediction efficiency of both traditional risk factors and NT-proBNP, supporting a pathophysiological link between reduced cardiac performance and the mortality and HF progression later in life as aging and comorbidities advance. Furthermore, the results of our sensitivity analysis showed that although not all subgroups showed a statistically significant association, the risk of adverse outcomes within these subgroups was higher in patients with lower resting cardiac power/mass than in those with higher resting cardiac power/mass, indicating the stable and independent prediction efficacy of resting cardiac power/mass among HFpEF patients. Clinically, resting cardiac power/mass is easily obtained by measuring blood pressure and stroke volume, the latter of which can be measured by Doppler echocardiography. Intriguingly, the technical setup used to determine cardiac power is very similar to that used in a standard diastolic stress test, so determining whether integrated application of the two tests may provide incremental diagnostic or prognostic significance in HFpEF patients deserves further exploration.
Our study has some limitations: first, it should be noted that this was a single-center study and 95.79% of our patients were male because we included patients from the veteran population, thus we failed to observe the association between rest cardiac power and adverse outcomes in gender subgroup, and our conclusion might be more suitable for male patients. We further conducted Cox proportional hazard model and adjusted gender, age and other covariates, and the results were still significant, suggesting that rest cardiac power/mass predicts the adverse outcomes in HFpEF patients independent of gender, therefore the results of this study were still representative to some extent, and studies conducted with more female patients are needed to validate our findings. Secondly, compared with invasive measurements, the non-invasive measurements of stroke volume may be more inclined to error. Thirdly, this study failed to perform speckle tracking echocardiography and thus lacked the information of global longitudinal strain, which helps determine the impaired systolic function in patients with normal ejection fraction and should be fully considered in the further studies. Last, the prognostic value of peak or reserved cardiac power indexed to LV mass has not been evaluated in patients with HFpEF.
This study explored the association between resting cardiac power/mass and the risk of adverse outcomes in patients with HFpEF, finding that lower resting cardiac power/mass is an independent predictor of these adverse outcomes and also has incremental prognostic value over established risk factors and NT-proBNP. Cardiac power as an integrated indicator of cardiac performance may be considered for risk stratification of long-term adverse outcomes in patients with HFpEF. This measurement provides more comprehensive and accurate guidance for treatment and prognostic evaluation of patients with HFpEF, and further promotes the integration and optimization of cardiac function monitoring indicators.
The original contributions presented in this study are included in the article/
This study was approved by the Ethics Board of the No. 988 Hospital of Joint Logistic Support Force of the Chinese PLA. The patients/participants provided their written informed consent to participate in this study.
SW designed the protocol, provided methodological expertise, drafted the manuscript and performed statistical analyses. AC and XD supervised patient recruitment and study procedures and conducted study procedures. All authors read and approved the final manuscript.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
We thank John Daniel from Liwen Bianji (Edanz) (
The Supplementary Material for this article can be found online at: