Skip to main content
Download PDF
Download ePub

Left atrial expansion index measured with cardiovascular magnetic resonance estimates pulmonary capillary wedge pressure in dilated cardiomyopathy

Journal of Cardiovascular Magnetic Resonance volume 25, Article number: 71 (2023) Cite this article

Abstract

Background

Pulmonary capillary wedge pressure (PCWP) assessment is fundamental for managing dilated cardiomyopathy (DCM) patients. Although cardiovascular magnetic resonance (CMR) has become the gold-standard imaging technique for evaluating cardiac chamber volume and function, PCWP is not routinely assessed with CMR. Therefore, this study aimed to validate the left atrial expansion index (LAEI), a LA reservoir function parameter able to estimate filling pressure with echocardiography, as a novel CMR-measured parameter for non-invasive PCWP estimation in DCM patients.

Methods

We performed a retrospective, single-center, cross-sectional study. We included electively admitted DCM patients referred to our tertiary center for further diagnostic evaluation that underwent a clinically indicated right heart catheterization (RHC) and CMR within 24 h. PCWP invasively measured during RHC was used as the reference. LAEI was calculated from CMR-measured LA maximal and minimal volumes as LAEI =  ( (LAVmax-LAVmin)/LAVmin) × 100.

Results

We enrolled 126 patients (47 ± 14 years; 68% male; PCWP = 17 ± 9.3 mmHg) randomly divided into derivation (n = 92) and validation (n = 34) cohorts with comparable characteristics. In the derivation cohort, the log-transformed (ln) LAEI showed a strong linear correlation with PCWP (r = 0.81, p < 0.001) and remained a strong independent PCWP determinant over clinical and conventional CMR parameters. Moreover, lnLAEI accurately identified PCWP ≥ 15 mmHg (AUC = 0.939, p < 0.001), and the optimal cut-off identified (lnLAEI ≤ 3.85) in the derivation cohort discriminated PCWP ≥ 15 mmHg with 82.4% sensitivity, 88.2% specificity, and 85.3% accuracy in the validation cohort. Finally, the equation PCWP = 52.33- (9.17xlnLAEI) obtained from the derivation cohort predicted PCWP (-0.1 ± 5.7 mmHg) in the validation cohort.

Conclusions

In this cohort of DCM patients, CMR-measured LAEI resulted in a novel and useful parameter for non-invasive PCWP evaluation.

Background

Pulmonary capillary wedge pressure (PCWP) evaluation is fundamental for managing cardiac diseases since PCWP increase is the hemodynamic hallmark of left heart failure syndromes [1]. In clinical practice, PCWP is directly measured during invasive right heart catheterization (RHC). Cardiovascular magnetic resonance (CMR) has become the reference gold-standard imaging technique for evaluating cardiac chambers’ volume and function [2,3,4]. Small studies demonstrated the feasibility of assessing echocardiographic equivalent diastolic dysfunction parameters with CMR [5,6,7,8,9]. However, despite being promising [10], CMR-based diastolic dysfunction evaluation did not enter widespread clinical practice because it is perceived as cumbersome and impractical; therefore, PCWP evaluation is not performed during routine CMR exams. More recently, Garg. et al. were the first to estimate PCWP using a simple equation that included CMR-measured LA maximal volume (LAVmax) and left ventricular (LV) mass [11]. However, diagnostic performance for identifying elevated PCWP and the agreement with the invasive PCWP measurements were far from optimal [12].

The left atrial expansion index (LAEI) is a simple derived parameter describing left atrial compliance through the relative LA volume increase during the LA reservoir phase. Echo-measured LAEI estimated filling pressures in patients with chronic [13] and acute ischemic heart disease [14], with mitral regurgitation (MR)[15], and in a large cohort of patients with various chronic cardiac diseases [16]. However, whether CMR-measured LAEI could be used for PCWP evaluation has never been previously studied. Therefore, this study aimed to validate, in a cohort of dilated cardiomyopathy (DCM) patients, LAEI as a novel CMR-measured parameter for non-invasive PCWP estimation.

Methods

Study population

We performed a retrospective, single-center, cross-sectional study. We screened DCM patients referred for further diagnostic evaluation to our tertiary Center (Department of Cardiac, Thoracic, Vascular Sciences, and Public Health, the University of Padua Hospital) from February 2019 to February 2022. We included only the subject who underwent, within 24 h, clinically indicated RHC and CMR exams. All patients were elective hospitalization, hemodynamically stable, and underwent no therapeutic change between the two exams. We excluded patients with atrial fibrillation (n = 5), patients with MV prosthesis (n = 2), and patients with insufficient CMR image quality related to frequent ventricular ectopic beats such as bigeminy (n = 3).

All enrolled patients were included in the “Padua Cardiac Magnetic Resonance Imaging Registry,” this specific cohort had never been published previously; the local ethics committee approved the study, and all patients provided informed consent. The datasets for the current study are available from the corresponding author upon reasonable request.

Right heart catheterization

RHC was performed with a Swan-Ganz catheter (SGC) through femoral transvenous access. PCWP values were measured from the pressure–time recordings at the end of a normal expiration by averaging at least three cardiac cycles with the SGC-inflated balloon in the pulmonary capillary wedge position (confirmed by fluoroscopy, pressure-waveform, and oxygen saturation > 95% from a blood sample obtained from the catheter tip). PCWP ≥ 15 mmHg was defined as elevated. [17]

Cardiovascular magnetic resonance

All patients were imaged using a 1.5T CMR scanner (Magnetom Avanto, Siemens Healthineers, Erlangen, Germany) with an ECG‐triggering and phased array coil system, following the standard protocol [18]. Cine images were acquired during expiratory breath-holds using a balanced, steady-state, free precession (SSFP) and included multiple short-axis (slice thickness 6.0 mm, gap 2.0 mm; repetition time 2.5–3.8 ms; echo time 1.1–1.6 ms, average in-plane resolution 1.5 × 2.4 mm, flip angle 45° to 60°, temporal resolution 40–45 ms) and 4-chambers (ch), 2-ch and 3-ch long axis.

CMR measurements were performed by an operator blinded to RHC and clinical data using CVi42® software (Circle Cardiovascular Imaging Inc, Calgary, Canada). LV and right ventricular (RV) volumes were measured, excluding papillary muscles, from the endocardial border tracings on short-axis images at end-diastole (ED) and end-systole (ES). LV ejection fraction (EF) and RVEF were calculated from the corresponding volumes with the conventional formula. LV mass was calculated by subtracting endocardial from epicardial LV ED volume tracings and multiplying it by 1.05 g/cm3. Left atrial maximum volume (LAVmax) and minimum volume (LAVmin) were calculated applying the biplane area-length (BAL) method from the LA areas contoured respectively at ES and ED in both long-axis 4Ch and 2Ch views [2, 4]. Pulmonary veins were excluded from LA tracings. Moreover, also LA appendage was excluded from LA tracings due to its inconsistent visualization in the 2-Ch view. LAEI was calculated using the following formula:

$${\text{LAEI}}\, = \,\left( {\left( {{\text{LAVmax}} - {\text{LAVmin}}} \right)/{\text{LAVmi}}n} \right)\, \times \,100.$$

Furthermore, LV mass and LAVmax were also used for calculating PCWP with the equation proposed by Garg et al. PCWP = 6.1352 +  (0.07204xLAVmax) +  (0.02256xLVmass) [11]. In an independent cohort of 25 patients, the LA volumes and LAEI were measured, in addition to the BAL method, with the short-axis (SAX) volumetry obtained from the LA endocardial border tracings at ED and ES on SSFP short-axis images acquired encompassing the whole left atrium [2, 4].

Reproducibility analysis

Inter- and intra-reader variability analyses were performed in 20 randomly selected cases with repeated measurements on the same images by the same reader at least four weeks later and by a second independent reader, blinded to all prior measurements.

Statistical analysis

Continuous variables were summarized as mean ± standard deviation and categorical variables as absolute number with percentage (%). Independent samples T-test and Chi-Square analysis were used for subgroups comparison. Linear correlation was assessed with the Pearson correlation coefficient. lnLAEI was derived by log-transformed LAEI. Multivariate linear regression analysis models tested with the F-test the independent and additive predictive role of lnLAEI for PCWP prediction over clinical and other CMR parameters. Receiver operating characteristic (ROC) curves tested lnLAEI diagnostic accuracy for PCWP ≥ 15 mmHg identification, and the Youden index analysis derived the optimal lnLAEI cut-off. The performance of lnLAEI for elevated PCWP identification was tested and compared against the Garg Eq. with ROC curves analysis in the validation cohort using the De Long method for the area under the curve (AUC) comparisons. The agreement of PCWP = 55.33– (9.17xlnLAEI) with the invasively measured PCWP was analyzed in the validation cohort using Bland–Altman analysis and compared with the performance of Garg Eq.. Inter- and intra-reader variability was tested with the coefficient of variation (CoV) and the intraclass correlation coefficient (ICC). LA volumes and LAEI measured with SAX and BAL methods were compared in an independent cohort of 25 patients using paired T-test and Pearson correlation coefficient analysis. P-value < 0.05 was considered statistically significant. Statistical analysis was performed using SPSS 26.0 (SPSS, Chicago, Illinois, US) and MedCalc 19.6.1 (MedCalc, Ostend, Belgium).

Results

Study population

The study population comprised 126 DCM patients (47 ± 14.2 years; 68% male, PCWP = 16.6 ± 9.3 mmHg). Clinical and CMR parameters are summarized in Table 1. We randomly divided our subjects into a derivation (n = 92) and a validation (n = 34) cohort. The clinical and CMR parameters were highly comparable between the two groups (Table 1).

Table 1 Study population clinical and CMR parameters and comparison between the derivation and validation cohorts
Full size table

Derivation cohort

LAEI and PCWP correlation

LAEI showed a strong linear correlation with PCWP (r = 0.76; p < 0.001). However, a logarithmic curve better fitted the association between LAEI and PCWP, with a further improvement in lnLAEI linear correlation with PCWP (r = 0.81; p < 0.001) (Fig. 1). PCWP increased, and LAEI decreased with MR worsening (Additional file 1: Table S1), but the logarithmic correlation between PCWP and LAEI was maintained in patients with MR ≥ moderate and with MR < moderate (Additional file 1: Fig. S1).

Fig. 1
figure 1

PCWP correlation with LAEI (blue) and lnLAEI (orange) in the derivation cohort

Full size image

Comparison between elevated and normal PCWP

In the derivation cohort, the subgroup with PCWP ≥ 15 mmHg (n = 52) had a higher heart rate (HR), larger LAVmax, lower systolic blood pressure, LVEF, RVEF, and lnLAEI than the subgroup with PCWP < 15 mmHg (n = 40) (Table 2).

Table 2 Clinical and CMR parameters comparison between PCWP ≥ 15 and < 15 mmHg subgroups of the derivation cohort (n = 92)
Full size table

Univariate and multivariate analysis for PCWP prediction

At the univariate analysis, HR, LVEF, RVEF, LAVmax, MR grade, and lnLAEI resulted in PCWP determinants. The multivariate analysis for PCWP prediction included Model 1 comprising MR grade, LAVmax, RVEF, LVEF, and HR, in which only HR and RVEF remained independent determinants of PCWP. Notably, adding lnLAEI in Model 2 to the variables already included in Model 1 significantly improved the predictive power (Model 1: Adj-R2 = 0.422, F = 6.562 vs. Model 2: Adj-R2 = 0.682; F = 17.135; p < 0.001 from Model 1). Moreover, lnLAEI remained the only PCWP independent predictor along with HR in Model 2 (Table 3).

Table 3 Univariate and multivariate linear analysis for PCWP prediction in the derivation cohort
Full size table

ROC curve analysis

lnLAEI identified accurately PCWP ≥ 15 mmHg with an AUC = 0.939 (p < 0.001). The derived optimal cut-off lnLAEI ≤ 3.85 had 80.8% sensitivity and 97.5% specificity for discriminating PCWP ≥ 15 mmHg in the derivation cohort (Fig. 2).

Fig. 2
figure 2

lnLAEI ROC curve for PCWP ≥ 15 mmHg discrimination in the derivation cohort. lnLAEI ≤ 3.85 was the optimal cut-off

Full size image

Validation cohort

ROC curve analysis

lnLAEI confirmed an excellent accuracy for PCWP ≥ 15 mmHg identification in the validation cohort (AUC = 0.927, P < 0.001). Furthermore, when lnLAEI AUC was compared with the performance of the Garg Eq. [11] for PCWP ≥ 15 mmHg identification, lnLAEI performed significantly better (ΔAUC = 0.238, p = 0.002) (Fig. 3). The validation cohort optimal cut-off for PCWP ≥ 15 mmHg identification (lnLAEI ≤ 3.89) was superimposable to the lnLAEI cut-off previously identified in the derivation cohort (lnLAEI ≤ 3.85). Moreover, in the validation cohort, lnLAEI ≤ 3.85 had a comparable sensitivity (lnLAEI = 82.4%, Garg Eq. = 88.2%; p = 0.529) but higher specificity (lnLAEI = 88.2%, Garg Eq. = 35.3%; p < 0.001), accuracy (lnLAEI = 85.3%, Garg Eq. = 61.8%; p = 0.041), and positive predictive value (lnLAEI = 87.5%, Garg Eq. = 57.7%; p = 0.010) than Garg Eq. for PCWP ≥ 15 mmHg identification (Table 4).

Fig. 3
figure 3

ROC curves comparison between lnLAEI and Garg Eq. for PCWP ≥ 15 mmHg identification in the validation cohort

Full size image
Table 4 Diagnostic accuracy comparison between lnLAEI ≤ 3.85 and Garg Eq. in the validation cohort
Full size table

lnLAEI equation for PCWP prediction

The equation PWCP = 52.33- (9.17xlnLAEI) obtained from the derivation cohort was able to predict in the validation cohort invasively measured PCWP without systematic bias and with a better agreement (bias = 0.1 ± 5.7 mmHg) than Garg Eq. (Bias = -1.1 ± 9.8 mmHg). (Fig. 4).

Fig. 4
figure 4

Bland–Altman analysis comparing the invasively measured PCWP with PCWP calculated with lnLAEI Eq. (top) and Garg Eq. (bottom) in the validation cohort

Full size image

Reproducibility analysis

LAEI showed good inter- and intra-reader reproducibility (Table 5).

Table 5 Intra-inter reader variability analysis in 20 random cases
Full size table

BAL and SAX methods comparison for LA assessment

In an independent cohort of 25 patients, LAVmax, LAVmin and LAEI measured both with BAL and SAX methods were compared. SAX volumetry provided slightly larger LAVmax ( BAL-SAX = − 6.6 ml/m2, p < 0.001), LAVmin (BAL-SAX = − 5.4 ml/m2, p = 0.002), and slightly lower LAEI values (BAL-SAX = 6.3%, p < 0.002) than the BAL method. Of note, the correlation between the two techniques was excellent for all three parameters (LAVmax = 0.98, LAVmin = 0.99, LAEI = 0.99, all p < 0.001) (Table 6).

Table 6 Biplane area-length and short-axis volumetry comparison for LA assessment in an independent cohort of 25 patients
Full size table

Discussion

We demonstrated that CMR-measured LAEI provided valuable insight for non-invasive PCWP evaluation in this cohort of DCM patients. The main findings of the study were: (i) LAEI had a strong logarithmic correlation with PCWP; (ii) lnLAEI was an independent determinant of PCWP and provided added predictive value after accounting for other clinical and CMR PCWP determinants; (iii) lnLAEI ≤ 3.85 cut-off obtained from the derivation cohort had 85.3% accuracy in identifying PCWP ≥ 15 mmHg in the validation cohort; (iv) PWCP = 52.33- (9.17xlnLAEI) obtained from the derivation cohort was able to predict PCWP (− 0.1 ± 5.7 mmHg) in the validation cohort; (v) LAEI was more accurate than Garg Eq. in discriminating normal vs. elevated PCWP and for PCWP quantitative estimation.

CMR is the gold standard imaging technique for quantifying the size, mass, and global and regional LV and RV function and accurately assessing myocardial scar and fibrosis. Therefore nowadays, CMR is the reference imaging technique for cardiomyopathies evaluation [19]. However, despite the importance of PCWP as the leading hemodynamic hallmark responsible for heart failure decompensation [20] and its association with outcomes [21, 22], CMR does not currently provide routine non-invasive PCWP evaluation.

In our study, we found that CMR-measured LAEI had a strong logarithmic association with PCWP (r = 0.81; p < 0.001), as previously found with Echo-measured LAEI [13, 16]. Interestingly, CMR-measured lnLAEI identified elevated PCWP in the validation cohort with an accuracy (85.3%) similar to what had been previously found with Echo-measured LAEI (88% in the whole population and 82% in the subgroup with LVEF < 50%)[16] but with an improved intra- inter-operator variability, that resulted comparable to what had been reported in previous CMR studies [23, 24]. In this study, we assessed LA volumes and LAEI with the BAL method, although both BAL and SAX volumetry are currently accepted for LA assessment [2, 4]. We tested the interchangeability of the two approaches in an independent cohort of 25 patients, and we found that both methods provided highly concordant measurements despite slightly larger LA volumes and smaller LAEI values with the SAX volumetry compared to the BAL method. We might speculate that, although we proved the role of LAEI for PCWP assessment solely with the BAL method, SAX volumetry might also be adopted for LAEI calculation and PCWP estimation, although it remains to be formally demonstrated.

Notably, lnLAEI alone explained 65% of the PCWP variance in the derivation cohort in the univariate analysis. In Model 2, R2 was only marginally higher (R2 = 0.68) than lnLAEI alone (R2 = 0.65), underlying the trivial influence of the other parameters on PCWP estimation when lnLAEI was included in the Model. In Model 2, after accounting for clinical and CMR PCWP determinants (HR, LVEF, RVEF, LAVmax and MR), lnLAEI remained the strongest independent determinant of PCWP. Interestingly, despite worse MR determined as expected higher PCWP and lower LAEI, the logarithmic correlation between the two parameters remained stable independently of MR severity, similarly to Echo-measured LAEI [15, 16].

We found lnLAEI to be an accurate parameter for dichotomizing elevated vs. normal PCWP as lnLAEI ≤ 3.85 was able to identify in the validation cohort PCWP ≥ 15 mmHg with an accuracy of 85.3% (sensitivity 82.4%, specificity 88.5%). However, despite the dichotomized approach (normal vs. elevated PCWP) being the current fundament for non-invasive PCWP evaluation as in echocardiography [25], a quantitative estimation of PCWP would be theoretically preferable since PCWP is a continuous parameter. We derived and validated an equation for PCWP estimation from lnLAEI (PWCP = 52.33- (9.17xlnLAEI)) instead of a more complex multivariate regression equation because lnLAEI alone had a predictive power comparable to Model 2 (Model 2: R2 = 0.68 vs. lnLAEI: R2 = 0.65) with the advantage of providing a practical and user-friendly equation. However, from our findings, the agreement between the lnLAEI equation and invasive PCWP was still modest in some patients, suggesting that the lnLAEI equation should be currently adopted solely as an integrative parameter for further PCWP quantitative insight. Therefore, the dichotomized evaluation of elevated vs. normal PCWP with lnLAEI should currently remain the cornerstone for a reliable non-invasive PCWP assessment with CMR.

Importantly, we found that in our cohort of DCM patients, the CMR-measured LAEI was more accurate than the Garg Eq. for discriminating normal vs. elevated PCWP (Accuracy 85.3% vs. 61.8% for PCWP ≥ 15 mmHg identification) and that PWCP = 52.33- (9.17xlnLAEI) was more accurate than Garg Eq. for quantitative estimation of PCWP. We might speculate that the superiority of lnLAEI over Garg Eq. might be explained by the fact that Garg Eq. was based on sole anatomical parameters (LAVmax and LV mass) and derived from a population that included only 6.2% of patients with reduced LVEF.

Other LA reservoir function parameters might play a role in PCWP assessment, and a recent study showed that LA longitudinal strain assessed during rest and stress CMR discriminated patients with elevated PCWP and heart failure with preserved ejection fraction [26]. These results suggest that CMR-measured LA functional parameters describing the reservoir phase might improve cardiac filling pressure evaluation over static CMR parameters [27], retracing the similar research path recently performed by echocardiography on filling pressure assessment [16, 17, 28,29,30].

CMR-measured LAEI might become a widespread and practical volume-based LA reservoir parameter for non-invasive PCWP evaluation because it could be easily obtained without additional CMR acquisitions other than conventional cine 4-ch and 2-ch long-axis. Moreover, LAEI calculation is not time-consuming since it only requires the additional measurement of LAVmin to LAVmax, which is already measured in most laboratories. Finally, calculating LAEI does not require any dedicated software package and, therefore, could be promptly integrated into the routine clinical activity of every CMR laboratory.

Limitations

This study was a single-center and retrospective study. A selection bias is possible since patients were referred to our tertiary center for further diagnostic assessment. However, we focused our research on a selected cohort of patients, and our findings were strengthened by assessing LAEI over a wide range of PCWP values and by providing internal validation of our results in an independent validation cohort. CMR and RHC exams were not simultaneous. However, the time-lapse was minimal, and the patients were hemodynamically stable and did not undergo therapeutic changes between the exams. All patients included were in sinus rhythm; therefore, the performance of LAEI in patients with atrial fibrillation has not been assessed. In this study, LAEI was calculated with the BAL method. Although the SAX volumetry provided highly concordant LAEI values compared to the BAL method in an independent cohort of 25 patients, the two approaches still provided slightly different LA volumes and LAEI values. Therefore, future studies are needed to assess the interchangeability between the approaches for LAEI calculation and PCWP evaluation and other potential differences due to changes in acquisition protocols (i.e., slice thickness and gap).

We did not assess LA strain; therefore, a direct comparison of LAEI with LA strain cannot be performed. Finally, future multicentric and prospective studies are needed to confirm our findings for external validation, to compare different methods for LAEI calculation formally, to explore the performance of LAEI measured with CMR in different cardiac diseases (i.e., heart failure with preserved ejection fraction) and ethnic groups (i.e., different BMI, age).

Conclusions

In this cohort of DCM patients, CMR-measured LAEI resulted in an accurate parameter for non-invasive dichotomization of normal versus elevated PCWP. Moreover, additional quantitative PCWP insight might be obtained using a simple LAEI-derived equation.

Availability of data and materials

The datasets for the current study are available from the corresponding author upon reasonable request.

Abbreviations

4Ch:

Four-chambers

2Ch:

Two-chambers

AUC:

Area under the curve

BAL:

Biplane area-length

CoV:

Coefficient of variation

CMR:

Cardiovascular magnetic resonance

DCM:

Dilated cardiomyopathy

Eq:

Equation

ED:

End-diastole

ES:

End-systole

EF:

Ejection fraction

HR:

Heart rate

ICC:

Intraclass correlation coefficient

LAEI:

Left atrial expansion index

lnLAEI:

Log-transformed LAEI

LAVmax:

Left atrial maximal volume

LAVmin:

Left atrial minimal volume

LV:

Left ventricular

MR:

Mitral regurgitation

MV:

Mitral valve

PCWP:

Pulmonary capillary wedge pressure

RHC:

Right heart catheterization

ROC:

Receiver operating characteristic

RV:

Right ventricular

SGC:

Swan-Ganz catheter

SAX:

Short-Axis

References

  1. McDonagh TA, Metra M, Adamo M, Gardner RS, Baumbach A, Böhm M, Burri H, Butler J, Čelutkienė J, Chioncel O, et al. 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: developed by the Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC) with the special contribution of the Heart Failure Association (HFA) of the ESC. Eur Heart J. 2021;42:3599–726.

    Article  CAS  PubMed  Google Scholar 

  2. Petersen SE, Khanji MY, Plein S, Lancellotti P, Bucciarelli-Ducci C. European Association of Cardiovascular Imaging expert consensus paper: a comprehensive review of cardiovascular magnetic resonance normal values of cardiac chamber size and aortic root in adults and recommendations for grading severity. Eur Heart J Cardiovasc Imaging. 2019;20:1321–31.

    Article  PubMed  Google Scholar 

  3. Kawel-Boehm N, Maceira A, Valsangiacomo-Buechel ER, Vogel-Claussen J, Turkbey EB, Williams R, Plein S, Tee M, Eng J, Bluemke DA. Normal values for cardiovascular magnetic resonance in adults and children. J Cardiovasc Magn Reason. 2015;17:1–29.

    Google Scholar 

  4. Kawel-Boehm N, Hetzel SJ, Ambale-Venkatesh B, Captur G, Francois CJ, Jerosch-Herold M, Salerno M, Teague SD, Valsangiacomo-Buechel E, van der Geest RJ, Bluemke DA. Reference ranges (“normal values”) for cardiovascular magnetic resonance (CMR) in adults and children: 2020 update. J Cardiovasc Magn Reson. 2020;22:87.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Paelinck BP, de Roos A, Bax JJ, Bosmans JM, van Der Geest RJ, Dhondt D, Parizel PM, Vrints CJ, Lamb HJ. Feasibility of tissue magnetic resonance imaging: a pilot study in comparison with tissue Doppler imaging and invasive measurement. J Am Coll Cardiol. 2005;45:1109–16.

    Article  PubMed  Google Scholar 

  6. Rathi VK, Doyle M, Yamrozik J, Williams RB, Caruppannan K, Truman C, Vido D, Biederman RWW. Routine evaluation of left ventricular diastolic function by cardiovascular magnetic resonance: a practical approach. J Cardiovasc Magn Reson. 2008;10:36.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Kawaji K, Codella NCF, Prince MR, Chu CW, Shakoor A, LaBounty TM, Min JK, Swaminathan RV, Devereux RB, Wang Y, Weinsaft JW. Automated segmentation of routine clinical cardiac magnetic resonance imaging for assessment of left ventricular diastolic dysfunction. Circ Cardiovasc Imaging. 2009;2:476–84.

    Article  PubMed  Google Scholar 

  8. Cao JJ, Wang Y, McLaughlin J, Haag E, Rhee P, Passick M, Toole R, Cheng J, Berke AD, Lachman J, Reichek N. Left ventricular filling pressure assessment using left atrial transit time by cardiac magnetic resonance imaging. Circ Cardiovasc Imaging. 2011;4:130–8.

    Article  PubMed  Google Scholar 

  9. Posina K, McLaughlin J, Rhee P, Li L, Cheng J, Schapiro W, Gulotta RJ, Berke AD, Petrossian GA, Reichek N, Cao JJ. Relationship of phasic left atrial volume and emptying function to left ventricular filling pressure: a cardiovascular magnetic resonance study. J Cardiovasc Magn Reson. 2013;15:99.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Chamsi-Pasha MA, Zhan Y, Debs D, Shah DJ. CMR in the evaluation of diastolic dysfunction and phenotyping of HFpEF: current role and future perspectives. JACC Cardiovasc Imaging. 2020;13:283–96.

    Article  PubMed  Google Scholar 

  11. Garg P, Gosling R, Swoboda P, Jones R, Rothman A, Wild JM, Kiely DG, Condliffe R, Alabed S, Swift AJ. Cardiac magnetic resonance identifies raised left ventricular filling pressure: prognostic implications. Eur Heart J. 2022;43:2511–22.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Genovese D, Badano LP, Marra MP. Left atrial volume and left ventricular mass for pulmonary capillary wedge pressure assessment with cardiovascular magnetic resonance: accurate enough for clinical use? Eur Heart J. 2023;44(5):425–6.

    Article  PubMed  Google Scholar 

  13. Hsiao S-H, Chiou K-R, Lin K-L, Lin S-K, Huang W-C, Kuo F-Y, Cheng C-C, Liu C-P. Left atrial distensibility and E/e’ for estimating left ventricular filling pressure in patients with stable angina. A comparative echocardiography and catheterization study. Circ J. 2011;75:1942–50.

    Article  PubMed  Google Scholar 

  14. Hsiao S-H, Chiou K-R, Porter TR, Huang W-C, Lin S-K, Kuo F-Y, Cheng C-C, Lin K-L, Lin S-L. Left atrial parameters in the estimation of left ventricular filling pressure and prognosis in patients with acute coronary syndrome. Am J Cardiol. 2011;107:1117–24.

    Article  PubMed  Google Scholar 

  15. Hsiao S-H, Huang W-C, Lin K-L, Chiou K-R, Kuo F-Y, Lin S-K, Cheng C-C. Left atrial distensibility and left ventricular filling pressure in acute versus chronic severe mitral regurgitation. Am J Cardiol. 2010;105:709–15.

    Article  PubMed  Google Scholar 

  16. Genovese D, Muraru D, Marra MP, Carrer A, Previtero M, Palermo C, Tarantini G, Parati G, Iliceto S, Badano LP. Left atrial expansion index for noninvasive estimation of pulmonary capillary wedge pressure: a cardiac catheterization validation study. J Am Soc Echocardiogr. 2021;34:1242–52.

    Article  PubMed  Google Scholar 

  17. Smiseth Chair OA, Morris DA, Cardim N, Cikes M, Delgado V, Donal E, Flachskampf FA, Galderisi M, Gerber BL, Gimelli A, et al. Multimodality imaging in patients with heart failure and preserved ejection fraction: an expert consensus document of the European Association of Cardiovascular Imaging. Eur Heart J Cardiovasc Imaging. 2022;23(2):e34–61.

    Article  Google Scholar 

  18. Kramer CM, Barkhausen J, Bucciarelli-Ducci C, Flamm SD, Kim RJ, Nagel E. Standardized cardiovascular magnetic resonance imaging (CMR) protocols: 2020 update. J Cardiovasc Magn Reson. 2020;22:17.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Patel AR, Kramer CM. Role of cardiac magnetic resonance in the diagnosis and prognosis of nonischemic cardiomyopathy. JACC Cardiovasc Imaging. 2017;10:1180–93.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Zile MR, Bennett TD, St John Sutton M, Cho YK, Adamson PB, Aaron MF, Aranda JM Jr, Abraham WT, Smart FW, Stevenson LW, et al. Transition from chronic compensated to acute decompensated heart failure: pathophysiological insights obtained from continuous monitoring of intracardiac pressures. Circulation. 2008;118:1433–41.

    Article  PubMed  Google Scholar 

  21. Zile MR, Bennett TD, El Hajj S, Kueffer FJ, Baicu CF, Abraham WT, Bourge RC, Warner Stevenson L. Intracardiac pressures measured using an implantable hemodynamic monitor: relationship to mortality in patients with chronic heart failure. Circ Heart Fail. 2017;10(1):e003594.

    Article  PubMed  Google Scholar 

  22. Mascherbauer J, Zotter-Tufaro C, Duca F, Binder C, Koschutnik M, Kammerlander AA, Aschauer S, Bonderman D. Wedge pressure rather than left ventricular end-diastolic pressure predicts outcome in heart failure with preserved ejection fraction. JACC Heart Fail. 2017;5:795–801.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Chirinos JA, Sardana M, Ansari B, Satija V, Kuriakose D, Edelstein I, Oldland G, Miller R, Gaddam S, Lee J, et al. Left atrial phasic function by cardiac magnetic resonance feature tracking is a strong predictor of incident cardiovascular events. Circul Cardiovasc Imaging. 2018;11: e007512.

    Article  Google Scholar 

  24. Chirinos JA, Phan TS, Syed AA, Hashmath Z, Oldland HG, Koppula MR, Tariq A, Javaid K, Miller R, Varakantam S, et al. Late systolic myocardial loading is associated with left atrial dysfunction in hypertension. Circ Cardiovasc Imaging. 2017;10: e006023.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Nagueh SF, Smiseth OA, Appleton CP, Byrd BF 3rd, Dokainish H, Edvardsen T, Flachskampf FA, Gillebert TC, Klein AL, Lancellotti P, et al. Recommendations for the evaluation of left ventricular diastolic function by echocardiography: an update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging. J Am Soc Echocardiogr. 2016;29:277–314.

    Article  PubMed  Google Scholar 

  26. Backhaus SJ, Lange T, George EF, Hellenkamp K, Gertz RJ, Billing M, Wachter R, Steinmetz M, Kutty S, Raaz U, et al. Exercise stress real-time cardiac magnetic resonance imaging for noninvasive characterization of heart failure with preserved ejection fraction. Circulation. 2021;143:1484–98.

    Article  CAS  PubMed  Google Scholar 

  27. Backhaus SJ, Schuster A. Atrial functional assessment at rest and during exercise stress in left ventricular diastolic dysfunction. Eur Heart J. 2022;43(36):3493–4.

    Article  PubMed  Google Scholar 

  28. Inoue K, Khan FH, Remme EW, Ohte N, García-Izquierdo E, Chetrit M, Moñivas-Palomero V, Mingo-Santos S, Andersen ØS, Gude E, et al. Determinants of left atrial reservoir and pump strain and use of atrial strain for evaluation of left ventricular filling pressure. Eur Heart J Cardiovasc Imaging. 2021;23(1):61–70.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Singh A, Addetia K, Maffessanti F, Mor-Avi V, Lang RM. LA strain for categorization of lv diastolic dysfunction. JACC Cardiovasc Imaging. 2017;10:735–43.

    Article  PubMed  Google Scholar 

  30. Singh A, Medvedofsky D, Mediratta A, Balaney B, Kruse E, Ciszek B, Shah AP, Blair JE, Maffessanti F, Addetia K, et al. Peak left atrial strain as a single measure for the non-invasive assessment of left ventricular filling pressures. Int J Cardiovasc Imaging. 2019;35:23–32.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

None.

Funding

None.

Author information

Authors and Affiliations

  1. Cardiology Unit, Department of Cardiac-Thoracic-Vascular Sciences and Public Health, University of Padova, Padova, Italy

    Davide Genovese, Laura De Michieli, Giacomo Prete, Manuel De Lazzari, Marco Previtero, Donato Mele, Giuseppe Tarantini, Sabino Iliceto & Martina Perazzolo Marra

  2. Cardiology Unit, Cardio-Neuro-Vascular Department, Ca’ Foncello Hospital, Treviso, Italy

    Davide Genovese & Carlo Cernetti

Authors
  1. Davide Genovese
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Laura De Michieli
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Giacomo Prete
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Manuel De Lazzari
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Marco Previtero
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Donato Mele
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Carlo Cernetti
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Giuseppe Tarantini
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Sabino Iliceto
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Martina Perazzolo Marra
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Study conception and design: DG, MPM. Data collection: DG, LDM, GP, MDL, MP, GT, MPM. Analysis and interpretation of the results: DG, LDM, MPM. Draft manuscript preparation: DG, LDM, MPM. Critical and constructive revision of the draft manuscript with a substantial intellectual contribution to the final manuscript version: GP, MDL, MP, DM, CC, GT, SI. Read and approved the final version of the submitted manuscript: all authors.

Corresponding author

Correspondence to Davide Genovese.

Ethics declarations

Ethics approval and consent to participate

All enrolled patients were included in the “Padua Cardiac Magnetic Resonance Imaging Registry”; the local ethics committee approved the study, and patients provided informed consent.

Consent for publication

Not applicable.

Competing interests

The authors declare that they do not have any competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1: Fig. S1.

PCWP and LAEI logarithmic correlation for MR ≥ moderate and MR < moderate. Table S1. PCWP and LAEI values by MR degrees.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Genovese, D., De Michieli, L., Prete, G. et al. Left atrial expansion index measured with cardiovascular magnetic resonance estimates pulmonary capillary wedge pressure in dilated cardiomyopathy. J Cardiovasc Magn Reson 25, 71 (2023). https://doi.org/10.1186/s12968-023-00977-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12968-023-00977-2

Keywords

Journal of Cardiovascular Magnetic Resonance

ISSN: 1532-429X

Contact us