Feasibility of the Contraction–Relaxation Coupling Index in Outcome Prediction for Patients with Acute Heart Failure

Figures

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  Figure 1. 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.

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  Figure 2. 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.

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  Figure 3. 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.

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  Figure 4. Spline curves for estimated risk of the primary outcome according to contraction–relaxation coupling index (CRC). The spline curves represent the estimated hazard ratios (HRs) (blue solid line) and 95% confidence band (blue shade) for the primary outcome across the CRC range in the overall study population (A) and after stratification by medical treatment including RAS inhibitors (B), beta‐blockers (C), and diuretics (D). The spline curves with confidence bands in patients without medical treatment are presented with grey dotted lines. HRs were estimated using the median CRC as a reference with adjustment for covariates.