Correlation between the extracellular volume fraction and hemodynamic parameters in light-chain cardiac amyloidosis patients

Introduction

Light-chain cardiac amyloidosis (AL-CA) is a rare cardiac disorder. Light-chain amyloidosis is primarily caused by the misfolding and reaggregation of amyloidogenic proteins. These proteins accumulate in cardiac tissue, which promotes the development of the disease (1). Amyloid protein accumulation can lead to severe health problems, including congestive heart failure, malignant arrhythmias, and sudden death (2). Studies have indicated that AL-CA is more common in elderly people, generally appearing around the age of 76 years. AL-CA is more prevalent in men. In America, the annual incidence rate for AL is consistently 1.2 cases per 100,000 (3). Although precise epidemiological data are lacking in vast portions of Asia, a study reported that the incidence rate in Japan is 14.30 cases per million population (4). Various diagnostic tools, such as cardiac magnetic resonance (CMR) (5), positron emission tomography/computed tomography (6), and cardiac computed tomography (7), can be used to quantitatively or qualitatively assess the severity of amyloid deposition. The extracellular volume fraction (ECV) assessed via CMR has been validated in multiple clinical studies as a prognostic marker and a treatment response metric in AL-CA patients (8-11). The ECV reflects not only amyloid deposition in the cardiac tissue, but also the myocardial edema and fibrosis caused by amyloid fibers (5,12). Hemodynamic parameters offer a detailed insight into cardiac function and are clinically significant for prognostic evaluation. In this study, for the first time, we explored the specific correlation between CMR measurements and invasive hemodynamic measurements. We present this article in accordance with the STROBE reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-24-2243/rc).

Methods

Study population

Our cross-sectional study consecutively enrolled patients diagnosed with AL-CA at Peking Union Medical College Hospital from May 2020 to March 2023. All patients underwent comprehensive evaluations by experienced hematologists and cardiologists, who determined the necessity of a myocardial biopsy to confirm a diagnosis. All patients in this study were diagnosed with AL-CA through myocardial biopsy, and hemodynamic measurements were also conducted during the myocardial biopsy procedure. The exclusion criteria were as follows: (I) the pathological results failed to confirm a diagnosis of AL-CA; (II) hemodynamic measurements were not completed using right heart catheterization; and (III) CMR was not performed before the initiation of treatment. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This study was approved by the Institutional Review Board for Clinical Research of Peking Union Medical College Hospital (No. I-24PJ0065). All patients were well informed about the study and potential risks and signed an informed consent before participation.

Clinical and CMR characteristics

Patient demographics, existing comorbid conditions, comprehensive medical history, laboratory results, administered medical treatments, and echocardiography data were collected. The cardiac stage was assessed using both the Mayo 2004 (13,14) and Mayo 2012 staging systems (15). The criteria for determining the involvement of organs other than the heart were as follows: (I) renal involvement indicated by a 24-hour total urinary protein greater than 0.5 g, primarily consisting of albumin; (II) peripheral nerves characterized by symmetrical sensory and/or motor neuropathies in the lower limbs; and (III) autonomic nerves characterized by gastric emptying disorders, pseudointestinal obstruction, and dysregulation of excretion, unrelated to organ infiltration. Echocardiographic measurements were conducted according to recent guidelines (16), and interventricular septal thickness, left ventricular posterior wall thickness, left ventricular end-diastolic diameter (LVEDD), left ventricular ejection fraction (LVEF), tricuspid regurgitation velocity, and tissue Doppler imaging of the mitral annulus were included. Additional calculations for pulmonary artery systolic pressure (PASP) and relative wall thickness were conducted using the formulas detailed in the “Supplemental methods” section of Appendix 1. Baseline CMR was performed on all patients to measure native T1, ECV, maximal left ventricular wall thickness, and left ventricular mass index (LVMI) (17). Detailed examination and calculation methods are provided in the “Supplemental methods” section of Appendix 1.

Hemodynamic measurements and definitions

Baseline hemodynamic data were obtained through right heart catheterization at Peking Union Medical College Hospital, following the institution’s standard protocols. All measurements, such as right atrial pressure, right ventricular systolic and end-diastolic pressures, systolic, diastolic, and mean pulmonary arterial pressures [systolic pulmonary artery pressure (sPAP), diastolic pulmonary artery pressure (dPAP), mean pulmonary artery pressure (mPAP)], and pulmonary artery wedge pressure (PAWP), were recorded with patients resting in a supine position. All pressure parameters were measured at the end of expiration without holding the breath. Using a continuous cardiac output (CO) monitoring module, CO was determined by averaging three consecutive measurements for precision. Blood pressure and heart rate were carefully recorded during the procedure. Further calculations incorporated the cardiac index (CI), stroke volume, and pulmonary vascular resistance, with detailed formulas available in the “Supplemental methods” section of Appendix 1.

Statistical analysis

Statistical analyses were conducted using R software version 4.3.1 (R Foundation for Statistical Computing, Vienna, Austria). Continuous variables with a normal distribution were expressed as mean ± standard deviation, and an independent samples t-test was used for group comparisons. For non-normally distributed continuous variables, data were presented as median (interquartile range), and group comparisons were performed using the rank-sum test. Categorical variables were presented as frequency (percentage), and comparisons between groups were made using the chi-square test. Pearson’s correlation coefficients were calculated to assess the relationships during the analysis. Next, quantile regression was employed to ascertain the impact of ECV on various hemodynamic parameters. In hierarchical clustering analysis, we used three hemodynamic parameters: CI, PAWP, and mPAP. We calculated inter-cluster distances and performed hierarchical clustering. Finally, we divided all patients into two groups. The variables that deviated from a normal distribution were ln-transformed before analysis to improve their fit in the linear regression model. Then, the logistic regression model was utilized to investigate the association between ECV and various clinical parameters across different hemodynamic statuses. The odds ratio (OR) and the 95% confidence interval (95% CI) were calculated. Subsequently, the receiver operating characteristic (ROC) curve was used to identify the optimal ECV cut-off value. The best threshold values were determined by utilizing the Youden index. Statistical significance was determined by P values below 0.05.

Results

Demographic and clinical baseline characteristics

Figure 1 shows the enrollment process. According to the inclusion and exclusion criteria, a total of 40 patients were ultimately enrolled. The included patients were predominantly male (n=28), with an average age of 58 years. The average body mass index was 23.23 kg/m², and the mean body surface area was 1.81 m². Some 55% of participants exhibited heart failure symptoms classified as New York Heart Association (NYHA) functional class III–IV. The median N-terminal pro-B-type natriuretic peptide (NT-proBNP) was 4,251 ng/L, and the median cardiac troponin I concentration was 0.108 µg/L. According to the Mayo 2004 staging system, the majority of cases were classified as stage III, predominantly stage IIIb. Based on the Mayo 2012 staging system, most patients were classified as stage III or IV. Arrhythmias were present in 20% of cases, mainly atrial or ventricular tachycardia. The comorbid conditions observed were hypertension (n=3), diabetes (n=4), coronary artery disease (n=6), and stroke (n=3). Renal function was typically normal, with an average estimated glomerular filtration rate (eGFR) of 89.93 mL/min/1.73 m². Most patients had λ-type AL-CA (80%), and 52.5% of patients had multiple organ involvement. The median difference between involved and uninvolved serum free light-chains (dFLC) was 390.5 mg/L. The median diagnostic delay from symptom onset was 261 days. Echocardiography showed that 77.5% had normal LVEF, and 10% had LVEF below 40%. The average E/e' ratio was 19, and the median PASP was 32 mmHg. The average LVEDD was 43 mm. However, increased relative wall thickness (0.63±0.13) suggested that ventricular wall thickening was a primary characteristic of AL-CA.

Figure 1 The flowchart of patient enrollment in this study. AL-CA, light-chain cardiac amyloidosis; PUMCH, Peking Union Medical College Hospital.

CMR and hemodynamic parameters

The mean left ventricular global ECV was 0.52, the average of native T1 was 1,455 ms, and the median of LVMI was 74 g/m² (Table 1). Figure 2A shows the delayed enhancement phase of CMR, revealing transmural late gadolinium enhancement (LGE). Figure 2B illustrates the measurement of the native T1, which was 1,429 ms for that patient example. Figure 2C displays the ECV measurement for the patient, with a result of 0.50.

Figure 2 Cardiac magnetic resonance images of one light-chain cardiac amyloidosis patient in the cohort. (A) Late gadolinium enhancement image; (B) native T1 mapping image; (C) extracellular volume fraction pseudo-color image.

The heart rates and blood pressures were normal. The mean CI (2.2±0.6) L/min/m² and mean stroke volume (48±18) mL/min in this cohort were slightly lower. However, the PAWP (17±8) mmHg and right atrial pressure (10±6) mmHg were elevated. An increase in the mPAP (27±11) mmHg and pulmonary vascular resistance (2.9±2.7) Wood units was considered to result from left ventricular dysfunction.

Correlations between CMR and hemodynamic parameters

Figure 3A-3C demonstrates that the correlation between ECV and a single hemodynamic parameter was not strong. We further manipulated these hemodynamic parameters through simple calculations to form three new parameters: mPAP/CI ratio, PAWP/CI ratio, and (PAWP + mPAP)/CI ratio. Among all patients in this cohort, the mean ± standard deviation of the mPAP/CI ratio, PAWP/CI ratio, and (PAWP + mPAP)/CI ratio was 14.0±9.1, 8.8±5.5, and 22.9±14.3 mmHg/L/min/m², respectively (Table S1). Figure 3D-3F displays the correlation analysis between the ECV and the three new parameters. The mPAP/CI ratio demonstrated a significantly stronger correlation with the ECV (R =0.609, P<0.001) compared to single parameters. The correlations of the PAWP/CI ratio (R =0.621, P<0.001) and the (PAWP + mPAP)/CI ratio (R =0.626, P<0.001) showed sequential improvements over the mPAP/CI ratio. Additionally, we also conducted correlation analysis of native T1 and LVMI with hemodynamic parameters; however, neither reached statistical significance (Figures S1,S2).

Figure 3 Correlations between ECV and different hemodynamic parameters were conducted using the Pearson test. (A) CI, (B) PAWP, and (C) mPAP, the three traditional hemodynamic parameters, showed moderate correlation with ECV fraction. However, (D) mPAP/CI ratio, (E) PAWP/CI ratio, and (F) (PAWP + mPAP)/CI ratio demonstrated a stronger correlation. CI, cardiac index; ECV, extracellular volume fraction; mPAP, mean pulmonary artery pressure; PAWP, pulmonary artery wedge pressure.

We further utilized quantile regression analysis to examine the relationship between ECV and hemodynamics. Figure 4A illustrates that when the CI was notably reduced, its correlation with the ECV decreased. Conversely, the correlation between ECV and PAWP was unaffected by changes in PAWP (Figure 4B). Figure 4C,4D demonstrates a similar pattern: as the mPAP and the mPAP/CI ratio increased to the 75th percentile, the correlation with ECV significantly increased. However, at the 25th and 50th percentiles, the correlations remained the same. The PAWP/CI ratio exhibited a unique pattern, in which the correlation between the ECV and this parameter progressively strengthened as the PAWP/CI ratio increased (Figure 4E). When the (PAWP + mPAP)/CI ratio increased to the 75th percentile, its correlation with ECV significantly strengthened. In contrast, at the 25th and 50th percentiles, the correlations remained unchanged (Figure 4F). This pattern was similar to that observed with mPAP and the mPAP/CI ratio.

Figure 4 Quantile regression analysis between ECV and various hemodynamic parameters. (A) Under conditions of extremely CI, the correlation with ECV tends to decrease slightly. (B) The correlation between different levels of PAWP and ECV was generally consistent. (C) mPAP, (D) mPAP/CI ratio, and (F) (PAWP + mPAP)/CI ratio all demonstrated a notably stronger correlation with ECV at higher values. In contrast, the (E) PAWP/CI ratio showed a gradually increasing correlation with ECV as the values increased. CI, cardiac index; ECV, extracellular volume fraction; mPAP, mean pulmonary artery pressure; PAWP, pulmonary artery wedge pressure.

The impact of ECV on different hemodynamic statuses

Given the strong correlation between ECV and the composite parameters of CI, PAWP, and mPAP, we employed hierarchical cluster analysis incorporating these three hemodynamic parameters to classify all patients into two groups. Figure 5A shows the dendrogram from the hierarchical clustering analysis, with group 1 containing 18 patients and group 2 containing 22 patients. Table 2 presents the hemodynamic differences between these two groups. Patients in group 2 exhibited significantly lower CI (P=0.024) and higher PAWP and mPAP (both P<0.001). Table 2 also shows other variables between the two groups. Only ECV and dFLC were significantly different—both were greater in group 2 than in group 1 (P<0.05). Table 3 presents the logistic regression model, using group 1 and group 2 as a binary outcome. This model incorporated CMR parameters and other clinical variables. According to the univariate logistic regression analysis, ECV was significantly associated with different outcomes [OR, 1.204 (95% CI, 1.065–1.428); P=0.011]. Figure 5B shows the ROC curve, indicating that ECV had good discriminatory power for different hemodynamic statuses [the area under the curve (AUC) was 0.758], with an optimal ECV cut-off value of 0.52.

Figure 5 The cluster dendrogram and ROC curve. (A) The cluster dendrogram of hierarchical clustering analysis, including cardiac index, PAWP, and mPAP, which divided the overall patients into two groups. (B) The ROC curve illustrated that the optimal cutoff value of ECV for predicting various hemodynamic statuses was 0.52, with an AUC of 0.758. AUC, area under the curve; CI, confidence interval; ECV, extracellular volume fraction; FPR, false positive rate; mPAP, mean pulmonary artery pressure; PAWP, pulmonary artery wedge pressure; ROC, receiver operating characteristic; TPR, true positive rate.

Discussion

This study of AL-CA patients investigated the relationships between CMR measurement and hemodynamic parameters, leading to the following conclusions: (I) the ECV is weakly correlated with single hemodynamic parameters, yet demonstrates a stronger correlation with composite parameters that represent both systolic and diastolic functions; (II) the correlation between ECV and other single and composite hemodynamic parameters varies across different percentile ranges except PAWP; (III) ECV is associated with worse hemodynamic status.

ECV refers to the proportion of the extracellular matrix volume within the entire myocardium. In AL-CA, ECV appears to reflect the amount of amyloid deposition in the extracellular space (18). Our understanding suggests that amyloid deposition in the cardiac tissue and its direct toxicity result in cardiac dysfunction, leading to a poor prognosis. Bravo et al. explored the relationship between ECV, left ventricular mass, and myocardial strain, with their results showing that amyloid deposition varied in different myocardial segments, which led to varied strain patterns (18). However, studies exploring the relationship between the ECV and invasive hemodynamic parameters are still lacking. This study primarily focused on commonly used hemodynamic parameters measured by right heart catheterization and that are meaningful in clinical practice. The CI is the ratio of CO to body surface area, reflecting left ventricular systolic function. PAWP and mPAP are widely used clinical indicators for assessing left ventricular diastolic function, especially in restrictive cardiomyopathy. Our study revealed a weak correlation between ECV and any single hemodynamic parameter, including left ventricular systolic function and diastolic function. However, when composite hemodynamic parameters were used, the correlation strengthened. These findings indicate that the most significant correlations with ECV were associated with parameters reflecting both systolic and diastolic function. An increasing number of studies have focused on the clinical practice of composite hemodynamic parameters. A meta-analysis revealed that the PAWP/CO was significantly elevated in patients with heart failure with preserved ejection fraction (HFpEF) compared to controls, aiding in the diagnosis of HFpEF (19). Another meta-analysis demonstrated that both PAWP/CO and mPAP/CO served as prognostic indicators in various cardiopulmonary diseases, including pulmonary hypertension and HFpEF (20). Moreover, according to a previously published study, the PAWP/CI ratio has prognostic value in Mayo IIIb stage AL-CA patients (21). This further underscores the important clinical significance of composite hemodynamic parameters.

We further used quantile regression to analyze the correlation between ECV and varying degrees of cardiac dysfunction. The results showed that, except for the PAWP, the other hemodynamic parameters displayed varying degrees of correlation with the ECV as they changed. The worse the left ventricular function was, the greater the impact of ECV on cardiac function. This finding also suggested that the damage caused by amyloid deposition to cardiac function is not necessarily linear and that its impact increases with worsening cardiac function.

A few clinical studies have investigated the efficacy of using hemodynamic parameters for assessing patient prognosis. Russo et al. reported that patients with right atrial pressure exceeding 10 mmHg tend to have poorer prognoses (22). Martens et al. reported that patients with a CI less than 2.2 L/min/m² had a poorer prognosis, whereas PAWP did not affect prognosis (23). Currently, there are no universally accepted cut-off values of hemodynamic data in assessing prognosis. Therefore, we employed hierarchical clustering analysis to categorize patients into two groups based on their hemodynamic status. The logistic regression analysis demonstrated that ECV was associated with worse hemodynamic status in AL-CA patients. This finding underscored that ECV, indicative of amyloid deposition and fibrosis, more accurately reflected cardiac dysfunction compared to other variables.

With the advancement in technology, more patients have the opportunity to undergo CMR to screen the etiology of left ventricular hypertrophy. However, the use of invasive hemodynamic measurements remains highly limited. Although ECV cannot substitute for invasive hemodynamic evaluation, the optimal cut-off value identified in this study can assist clinicians in assessing cardiac function prior to obtaining invasive hemodynamic data. Moreover, several previously published studies have demonstrated the value of ECV from multiple perspectives. For example, Duca et al. conducted serial CMR scans on patients with cardiac amyloidosis to measure ECV before and after treatment, finding that therapy could reduce ECV, and the degree of reduction was predictive of prognosis (9). Pucci et al. reported a correlation between ECV and the extent of amyloid deposits and fibrosis in myocardial biopsy samples from patients with AL-CA (12). Building on these findings, this study further explored the relationship between ECV and hemodynamic parameters, which directly reflect cardiac function. Our results showed that ECV not only indicated the structural damage caused by amyloid infiltration but could also serve as a predictor of functional impairment. This study fills an important gap by establishing the clinical value of ECV in assessing cardiac function.

Our study also has some limitations. Despite rigorous statistical methods aimed at ensuring robustness and validity, the retrospective nature of the analysis may have introduced biases from historical data collection and patient selection. The small sample size further limits the generalization of our findings, as they may not adequately represent the broader population. Additionally, the small sample size also limited our ability to use multivariate logistic regression to investigate whether ECV is independently associated with deteriorated hemodynamic status, unaffected by other variables. Furthermore, we must acknowledge that the relationship between CMR signal intensity and contrast agent concentration is linear primarily at lower concentrations and within a limited range of imaging acquisition parameters (24,25). This phenomenon has affected the correlation analysis results in our study, thereby hindering an accurate reflection of the relationship between ECV and hemodynamic parameters. Considering that the linear relationship between Hounsfield units and iodine concentration is well established in computed tomography (26), we sincerely hope to explore the correlation between ECV measured by computed tomography and hemodynamics in future studies. This study only examined the relationship between the global left ventricular ECV and hemodynamic parameters. However, ECV in different segments may have varying impacts on cardiac function. Further research is needed to investigate whether such differences exist.

Conclusions

This study confirmed that ECV correlates more effectively with composite hemodynamic parameters than it does with any single parameter. As the hemodynamic status deteriorated, the correlation between ECV and hemodynamic parameters strengthened. Moreover, ECV can predict a worse hemodynamic status. These findings underscore the potential of ECV to improve clinical assessments, indicating its usefulness in customizing treatment strategies.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-24-2243/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-24-2243/dss

Funding: This work was supported by the Science, Technology & Innovation Project of Xiongan New Area (grant No. 2023XAGG0069), National Key Research and Development Program of China (grant No. 2022YFC2703100), and National High Level Hospital Clinical Research Funding (grant No. 2022-PUMCH-D-002).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-24-2243/coif). All authors report funding from the Science, Technology & Innovation Project of Xiongan New Area (grant No. 2023XAGG0069), National Key Research and Development Program of China (grant No. 2022YFC2703100), and National High Level Hospital Clinical Research Funding (grant No. 2022-PUMCH-D-002). The authors have no other conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This study was approved by the Institutional Review Board for Clinical Research of Peking Union Medical College Hospital (No. I-24PJ0065). All patients were well informed about the study and potential risk and signed an informed consent before participation.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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