Introduction

Accurate assessment of left ventricular (LV) function remains a cornerstone for risk stratification and optimal management of heart failure (HF).1, 2 The LV ejection fraction (LVEF) is a well-known and frequently used parameter expressed as a percentage of blood volume pumped out by the LV during contraction. LVEF has been indivisibly linked to the clinical diagnosis of HF and is considered a landmark to categorize HF into HFrEF (HF with reduced LVEF), HFpEF (HF with preserved LVEF), and even HFmrEF (HF with mid-range EF).1 The diagnostic types of HF are now regarded as separate disease entities, with a growing body of evidence considering the different types as unrelated syndromes.3, 4 However, HF shows a heterogeneous clinical course with complex structural and functional derangements, unsuitable to be classified using arbitrary LVEF cut-offs.2 Direct measurement of the LV pressure–volume status has derived pressure–volume loop analysis, visualizing dynamic LV movement in response to loading conditions or myocardial contractility.5 However, pressure–volume determination generally requires invasive measures, hindering its wide application for haemodynamic assessment in patients with HF. Several non-invasive modalities have been proposed based on a single-beat pressure–volume loop analysis,6, 7 estimating end-systolic or end-diastolic myocardial stiffness, arterial elastance, or vascular–ventricular coupling.7-11 However, myocardial contraction and relaxation are coupled processes that simultaneously changes in response to LV dysfunction.12 Thus, integrating the systolic and diastolic pressure–volume relationship would be an optimal approach for comprehensive haemodynamic assessment in HF. The present study aimed to evaluate the feasibility of a new haemodynamic index, the contraction–relaxation coupling index (CRC), as a novel predictor of clinical outcomes in patients with acute HF.

Methods Study population

Patient data were derived from the STrain for Risk Assessment and Therapeutic Strategies in patients with Acute Heart Failure (STRATS-AHF) registry (NCT03513653).13 The STRATS-AHF registry enrolled 4312 patients hospitalized for acute HF in three tertiary medical hospitals in Korea between 2009 and 2016. Patients presenting with symptoms or signs of HF and concurrent pulmonary congestion or objective findings of structural or functional LV abnormality were eligible for registration. Patients with acute coronary syndrome at the initial presentation were excluded. Transthoracic echocardiography was performed in 98% of the registered patients. The ethics committee at each participating centre approved the study protocol, which was conducted in accordance with the principles of the Declaration of Helsinki. The committee waived the requirement for informed consent due to the retrospective study design.

Echocardiography and calculation of the contraction–relaxation coupling index

A standard ultrasound machine with a 2.5 MHz probe was used to obtain echocardiographic images. Standard protocols were applied to acquire two-dimensional, M-mode, and Doppler parameters, following the guideline recommendations.14 The median time interval between the admission and the echocardiograms was 1 day [interquartile range (IQR) of 0 to 2 days]. To calculate the CRC, both LV end-systolic elastance (Ees) and LV end-diastolic elastance (Eed) were estimated (Figure 1). The Ees was derived using the pressure and volume data obtained at the end-systolic period using the formula: Ees = LV end-systolic pressure/LV end-systolic volume.15 The estimation for Ees assumed a linear end-systolic pressure–volume relationship (ESPVR) and a constant volume axis intercept (V0) of zero.15, 16 LV end-systolic pressure was approximated with a systolic blood pressure multiplied by 0.9, as previously validated.7, 17 For Eed estimation, we assumed the intraventricular pressure at the end of the isovolumic relaxation period to be zero. Based on the pressure–volume data obtained at the end-diastolic period, Eed was calculated using the formula: Eed = LV end-diastolic pressure/stroke volume. The LV end-diastolic pressure was approximated using the mitral Doppler flow parameters using the formula: LV end-diastolic pressure = 11.96 + 0.596 × E/e′, where E and e′ represent early diastolic mitral inflow velocity and mitral annular velocity, respectively.9, 18 We applied the modified Simpson's method to calculate the LV end-systolic and end-diastolic volumes.14 Finally, CRC was defined as the ratio of Eed to Ees (CRC = Eed/Ees). LVEF was calculated as a percentage of stroke volume to LV end-diastolic volume.14 We included patients whose blood pressure was measured at the time of echocardiography. Patients with missing values in either the end-systolic or the end-diastolic pressure–volume status were excluded, leaving a total of 3266 patients for further analysis.

Definition of the contraction–relaxation coupling index. The diagram represents the left ventricular (LV) single-beat pressure–volume loop. The contraction–relaxation coupling index (CRC) was defined as the ratio of LV Eed to LV Ees. The detailed calculation of CRC is described in the Methods section. EDPVR, end-diastolic pressure–volume relationship; ESPVR, end-systolic pressure–volume relationship; Ped, end-diastolic pressure; Pes, end-systolic pressure; SV, stroke volume; SW, stroke work; Ved, end-diastolic volume; Ves, end-systolic volume. Study outcomes

The primary outcome was a composite endpoint of 1 year all-cause mortality or hospitalization for HF. The vitality status of the study population was obtained from the National Death Records. Patients were followed up and censored at the date of composite endpoint or at the last date of the 1 year follow-up period from the index hospitalization. Each component of the composite endpoint was defined as the secondary outcome. The complete follow-up rate was 97%.

Statistical analysis

For further analysis, we stratified the study population into three groups using CRC tertiles as cut-offs: Tertile 1 (CRC ≤ 0.17), Tertile 2 (0.17 < CRC ≤ 0.40), and Tertile 3 (0.40 < CRC). Baseline characteristics were presented as medians with IQR for continuous variables and as numbers and frequencies for categorical variables. Intergroup differences were compared using the Kruskal–Wallis test or the χ2 test. We used the Cox proportional hazards regression model to estimate the hazard ratio (HR) of the primary outcome according to the CRC included as a continuous variable and as tertiles. HR was estimated with adjustment for demographics, comorbidities, initial laboratory tests, concomitant mediations, LVEF, and LV strain. For multivariable adjustment, correlation matrices across the covariates were checked and confirmed no significant interactions. In the secondary outcome, the risk of HF hospitalization was estimated, with death treated as a competing risk. Missing values in the covariates were replaced using multiple imputation methods. The incremental predictive value of the CRC was evaluated by constructing models with sequential addition of age and sex (Model 1), clinical variables (Model 2), LVEF (Model 3) or pressure–volume indices (Ees, Eed, and CRC) (Model 4), and both LVEF and pressure–volume indices (Model 5). The discriminatory performance of the models was assessed and compared using Harrell's concordance statistic (c-statistic). We additionally compared the discriminatory performance of Model 4 with CRC against the model with LV strain. We employed random permutations for a robust calculation of confidence intervals (CIs) for HRs, selecting random subsamples 1,000 times repeatedly. We applied Kaplan–Meier curves to plot the distribution of time-to-first event for any components of the primary outcome according to the CRC or LVEF tertiles, with differences in the event-free rate assessed using the log-rank test. The restricted cubic spline Cox regression analysis was applied with adjustment of covariates to discover a potential nonlinear association between CRC and the primary outcome. The spline analysis was further stratified by medical treatment at baseline including renin–angiotensin system (RAS) inhibitors, beta-blockers, and diuretics. We hypothesized that the prognostic effect of LVEF improvement would differ between patients with high and low CRC. Among the study population, 742 (22.7%) patients underwent follow-up echocardiography within 1 year after discharge (median interval: 8 months) and the difference in LVEF (ΔLVEF) between the initial examination and the follow-up was calculated. Additional spline analysis was employed to identify the difference in the association of ΔLVEF with the primary outcome between patients in Tertile 1 (high Ees and low Eed) and those in Tertile 3 (low Ees and high Eed). All statistical analyses were performed using R software, version 4.0.2 (R Development Core Team, Vienna, Austria). Statistical significance was set at P < 0.05.

Results Baseline characteristics and echocardiographic parameters

The median age was 74 years, and 52.5% of the patients were men (Table 1). The median LVEF was 42% and HFrEF was present in 50.1% of the study population. The median CRC was 0.3, with a median Ees of 1.9 mmHg/mL and median Eed of 0.5 mmHg/mL. When compared with patients in Tertile 1, those in Tertile 3 were younger and had a higher proportion of male patients. HFpEF was the major type of HF in Tertile 1 (86.0%), while HFrEF was the major type (96.9%) in Tertile 3. However, a considerable overlap of the three HF types was found in Tertile 2 (Figure 2). LV end-systolic and end-diastolic volumes and left atrial diameter were the highest in Tertile 3. The prescription rates for medical treatment were also the highest in Tertile 3.

Table 1. Baseline characteristics of the study population Variable All patients (N = 3266) Tertile 1 (CRC ≤ 0.17) (N = 1075) Tertile 2 (0.17 < CRC ≤ 0.40) (N = 1079) Tertile 3 (0.40 < CRC) (N = 1112) P for difference Age, years 74 (64–81) 76 (68–82) 74 (64–81) 71 (60–78) <0.001 Male, % 1714 (52.5) 455 (42.3) 566 (52.5) 693 (62.3) <0.001 BMI, kg/m2 23.1 (20.8–25.7) 23.8 (21.6–26.4) 23.1 (20.6–25.4) 22.5 (20.6–25.1) <0.001 Medical history Hypertension 1906 (58.4) 697 (64.8) 642 (59.5) 567 (51.0) <0.001 Diabetes mellitus 1117 (34.2) 346 (32.2) 363 (33.6) 408 (36.7) 0.076 Ischaemic heart disease 1097 (33.6) 320 (29.8) 377 (34.9) 400 (36.0) 0.005 Atrial fibrillation 886 (27.1) 310 (28.8) 320 (29.7) 256 (23.0) 0.001 NYHA functional Class IV 1279 (39.2) 404 (37.6) 436 (40.4) 439 (39.5) 0.392 Heart failure phenotype HFpEF 1108 (33.9) 924 (86.0) 178 (16.5) 6 (0.5) <0.001 HFmrEF 514 (15.7) 109 (10.1) 378 (35.0) 27 (2.4) HFrEF 1637 (50.1) 38 (3.5) 522 (48.4) 1077 (96.9) Physical examination Systolic BP, mmHg 127 (110–146) 135 (118–135) 130 (113–149) 118 (104–134) <0.001 Diastolic BP, mmHg 72 (63–83) 74 (64–84) 74 (63–84) 70 (60–80) <0.001 Heart rate, beats/min 83 (70–99) 78 (65–92) 85 (70–101) 88 (75–102) <0.001 Laboratory findings BUN, mg/dL 21 (16–30) 20 (15–28) 21 (16–31) 21 (16–32) 0.001 Creatinine, mg/dL 1.07 (0.82–1.52) 1.02 (0.79–1.45) 1.07 (0.82–1.53) 1.11 (0.86–1.60) 0.002 NT-proBNP, pg/mL 4403.5 (1636.3–11013.3) 2600.2 (970.4–6822.3) 4690.0 (1716.0–11535.0) 5971.4 (2661.4–14706.0) <0.001 Echocardiographic parameters LV end-diastolic volume, mm3 111 (78–155) 82 (60–108) 108 (80–143) 154 (118–199) <0.001 LV end-systolic volume, mm3 62 (35–104) 31 (22–44) 62 (44–87) 114 (85–152) <0.001 LVEF, % 42 (29–57) 61 (56–66) 42 (36–47) 25 (21–30) <0.001 LA diameter, mm 44 (39–50) 43 (37–49) 44 (39–50) 45 (41–51) <0.001 E wave, m/s 0.8 (0.6–1.1) 0.8 (0.6–1.0) 0.8 (0.6–1.1) 0.9 (0.7–1.1) <0.001 E/e′ ratio 16.5 (11.7–23.1) 13.1 (9.8–17.7) 16.3 (11.6–22.0) 21.1 (15.6–28.4) <0.001 Ees, mmHg/mL 1.9 (1.1–3.3) 3.9 (2.8–5.5) 1.9 (1.4–2.6) 0.9 (0.7–1.3) <0.001 Eed, mmHg/mL 0.5 (0.4–0.7) 0.4 (0.3–0.6) 0.5 (0.4–0.7) 0.7 (0.5–0.9) <0.001 CRC (Eed/Ees) 0.3 (0.1–0.5) 0.1 (0.1–0.1) 0.3 (0.2–0.3) 0.7 (0.5–0.9) <0.001 Medication at discharge RAS inhibitors 2286 (70.0) 668 (62.1) 767 (71.1) 851 (76.5) <0.001 beta-blockers 1998 (61.2) 598 (55.6) 699 (64.8) 701 (63.0) 0.010 Diuretics 2399 (73.5) 758 (70.5) 758 (70.3) 883 (79.4) <0.001 MRA 1475 (45.2) 405 (37.7) 42 (42.8) 608 (54.7) <0.001 Follow-up echocardiographya 742 (22.7) 195 (18.1) 235 (21.8) 312 (28.1) <0.001 Interval duration, months 8 (6–10) BMI, body mass index; BP, blood pressure; BUN, blood urea nitrogen; CRC, contraction–relaxation coupling index; Eed, LV end-diastolic elastance; Ees, LV end-systolic elastance; HFmrEF, heart failure with mid-range ejection fraction; HFpEF, heart failure with preserved ejection fraction; HFrEF, heart failure with reduced ejection fraction; LA, left atrium; LV, left ventricle; LVEF, left ventricular ejection fraction; MRA, mineralocorticoid receptor antagonist; RAS, renin–angiotensin system.

Scatterplot for distribution of patients by Ees and Eed. Patients were labelled according to their heart failure phenotypes. The solid lines represent the cut-off values of the contraction–relaxation coupling index (CRC) tertiles. Considerable overlap was observed among the three heart failure types across the cut-offs, especially in Tertile 2. HF, heart failure.

Independent predictive value of contraction–relaxation coupling index

During a median follow-up of 12 months (IQR 8–12 months), 914 (28%) patients experienced the primary outcome. After adjusting for clinical variables; LVEF, Ees, Eed, and CRC were independently associated with a higher risk of the primary outcome (Table 2). However, with further adjustment for LVEF, a significant association was observed for CRC (HR: 1.73, 95% CI: 1.40–2.14), but not for Ees and Eed. An independent association was also observed for CRC with model adjustment for HF phenotypes (HR: 1.62, 95% CI: 1.35–1.95). A significant difference was observed in the event-free rate for the primary outcome among the tertile groups (log-rank P < 0.001) (Figure 3). With Tertile 1 as a reference, an increasing trend in the primary outcome was observed with the highest risk in Tertile 3 (HR: 1.74, 95% CI: 1.47–2.07). For LVEF, however, substantial overlap was found in survival curves between the mid-tertile and high-tertile groups, showing no significant difference in the primary outcome risk (HR: 1.13, 95% CI: 0.95–1.33) (Figure S1). The independent association of CRC with the primary outcome was maintained after the adjustment for LV strain (Table S1). For the secondary outcome, higher CRC was an independent predictor for higher mortality risk at 1 year. The significant association of the CRC with HF hospitalization was attenuated after the adjustment for LVEF (Table S2).

Table 2. Predictors for 1 year mortality and hospitalization for heart failure Variable Univariable HR (95% CI) P Clinical variables adjustment HR (95% CI) P LV EF adjustment HR (95% CI) P LV EF adjustment (category) HR (95% CI) P Age, per 10 years increase 1.41 (1.33–1.49) <0.001 Male 1.10 (0.97–1.26) 0.145 BMI, kg/m2 0.93 (0.91–0.94) <0.001 Hypertension 0.98 (0.86–1.12) 0.766 Diabetes mellitus 1.22 (1.07–1.39) 0.004 Ischaemic heart disease 1.10 (0.96–1.26) 0.159 Atrial fibrillation 1.14 (0.99–1.31) 0.074 BUN, mg/dL 1.01 (1.01–1.02) <0.001 Creatinine, mg/dL 1.05 (1.02–1.07) <0.001 NT-proBNP, per 1000 pg/mL increase 1.01 (1.01–1.02) <0.001 RAS inhibitors 0.53 (0.46–0.60) <0.001 β-blockers 0.55 (0.48–0.62) <0.001 Diuretics 0.75 (0.65–0.87) <0.001 MRA 0.68 (0.60–0.78) <0.001 LVEF, per 10% decrease 1.07 (1.02–1.11) 0.002 1.13 (1.08–1.18) <0.001 HF phenotype: HFpEF 1 (reference) 1 (reference) HF phenotype: HFmrEF 1.03 (0.84–1.25) 0.790 1.06 (0.86–1.29) 0.605 HF phenotype: HFrEF 1.30 (1.12–1.50) <0.001 1.53 (1.31–1.80) <0.001 Ees, mmHg/mL 0.96 (0.92–0.99) 0.008 0.91 (0.87–0.95) <0.001 0.96 (0.91–1.01) 0.079 0.95 (0.90–0.99) 0.030 Eed, mmHg/mL 1.41 (1.18–1.67) <0.001 1.29 (1.07–1.55) 0.008 1.04 (0.84–1.29) 0.696 1.09 (0.89–1.34) 0.391 CRC (Eed/Ees) 1.54 (1.34–1.78) <0.001 1.80 (1.56–2.09) <0.001 1.73 (1.40–2.14) <0.001 1.62 (1.35–1.95) <0.001 BMI, body mass index; BUN, blood urea nitrogen; CI, confidence interval; CRC, contraction–relaxation coupling index; Eed, LV end-diastolic elastance; Ees, LV end-systolic elastance; HFmrEF, heart failure with mid-range ejection fraction; HFpEF, heart failure with preserved ejection fraction; HFrEF, heart failure with reduced ejection fraction; HR, hazard ratio; LVEF, left ventricular ejection fraction; MRA, mineralocorticoid receptor antagonist; RAS, renin–angiotensin system.

Kaplan–Meier curve for 1 year composite endpoint according to the contraction–relaxation coupling index (CRC) tertiles. A significant difference was observed in the event-free rate among the CRC tertiles during the follow-up period. After adjustment for covariates, Tertile 3 showed the highest risk of the primary outcome. CI, confidence interval.

Incremental predictive value of contraction–relaxation coupling index

The addition of clinical variables to Model 1 resulted in a significant improvement in the discrimination of outcome events (Model 2) (Table S3). When compared with Model 2, moderate but significant gains in model performance were observed with the addition of LVEF (Model 3) or pressure–volume indices (Model 4). The model with CRC showed the highest performance (c-statistic: 0.701, 95% CI: 0.696–0.705). However, the addition of LVEF to Model 4 with CRC did not result in a significant improvement in model performance (Model 5). The calibration plot of Model 4 with CRC demonstrated a linear relationship between the predicted outcome risk and the observed event rate (Figure S2). No significant difference in outcome discrimination was found for Model 4 with CRC against the model with LV strain (c-statistic: 0.703, 95% CI: 0.699–0.707) (Table S4).

Comparison of nonlinear association of contraction–relaxation coupling index with the primary outcome according to medical treatment

We observed a continuous increase in the risk of primary outcome with an increase in CRC (HR: 1.23, 95% CI: 1.14–1.33 per one-standard deviation increment) (Figure 4A). The estimated HRs in patients on RAS inhibitors demonstrated a relatively gentle curve across the range of CRC compared with those in patients not taking RAS inhibitors (Figure 4B). Patients with higher CRC (