Prognosis in patients with coronary heart disease and breath-holding limitations: a free-breathing cardiac magnetic resonance protocol at 3.0 T

Coronary heart disease (CHD) is primarily caused by coronary atherosclerosis, which causes coronary artery stenosis and myocardial ischemia. These changes then lead to myocardial infarction (MI) or sudden death. Cardiac magnetic resonance (CMR) imaging has been used to diagnose MI for a long time [1‐3]. Previous studies have demonstrated that the CMR markers of myocardial and microvascular damage add incremental prognostic information to clinical diagnoses [4‐7]; however, most of these studies lack prognostic information in patients with CHD and BH impairments. An absence of accepted CMR marker cutoff values to enable optimized risk stratifications in clinical practice is also lacking. Conventional CMR (CCMR) protocols used in our center primarily include routine cine and late gadolinium enhancement (LGE) imaging. Data acquisitions are typically performed with breath-holding (BH). In clinical practice, a few of patients with CHD quit CCMR examinations because it was difficult to perform BH in the time needed.

Accelerated single-shot methods for cine imaging using compressed sensing (CS) acceleration [8] and LGE imaging using motion correction (MOCO) techniques are accepted for free-breathing imaging [9]. CS cine imaging has shown good consistency for assessing left ventricular function with standard cine protocols, and MOCO LGE has shown good consistency for MI detection with conventional LGE [10‐13]. Although these two techniques had high consistency in cardiac functional evaluations and myocardial infarction detection compared with traditional techniques, no research has been performed on the application of these two techniques to prognosticate patients with CHD and BH limitations. This study aimed to develop a comprehensive free-breathing cardiac magnetic resonance (fCMR) protocol, including CS cine and MOCO LGE sequences, for patients with CHD and BH limitations. We also sought to explore the value of this protocol in determining prognoses in patients with CHD and BH limitations.

Clinical outcomes were collected during follow-up. The primary clinical outcome of the study was the composite endpoint of major adverse cardiac events (MACE), defined as a composite of all-cause mortality, and new heart failure diagnosis. All-cause death was defined as any death during the follow-up period. Heart failure (HF) was defined as clinical syndromes with symptoms and/or signs caused by a structural and/or functional cardiac abnormality and corroborated by elevated natriuretic peptide levels and/or objective evidence of pulmonary or systemic congestion [21]. Outcome data were collected from scheduled study follow-up visits, monthly telephone calls, and reviews of the electronic case records.

Statistical analyses were performed using the dedicated software, SPSS 20.0 (SPSS Inc., Chicago, USA) and and MedCalc10.0 (MedCalc Software, Ostend, Belgium) software. Continuous data were checked for normality using the Shapiro–Wilk test and presented as the mean ± standard or median (interquartile range [IQR]). Qualitative variables were expressed as percentages. The Kappa statistic was used to evaluate the consistency between readers 1 and 2 for the IQ assessments. Kaplan–Meier survival analysis was used to analyze qualitative variables associated with MACE, reporting X². Cox proportional hazards regression analysis was used to analyze continuous variables associated with MACE, reporting hazard ratios (HRs) and 95% confdence intervals (CIs). In our study, because the sample was small, variables showing values of p < 0.01 on univariate analysis were entered into multivariate Cox proportional hazard regression analysis. Cumulative survival rate and rate of MACE were estimated using a Kaplan–Meier curve evaluated by the log-rank test. Receiver operating characteristics (ROC) curves, used to derive the optimal cutoff points of LVEF, 3D-GPLS, and IS, forecasted the MACE from the non-MACE group. Areas under the curve (AUC), sensitivities, and specificities were respectively calculated. Probability values were 2-sided, and values of p < 0.05 were considered significant.

After reviewing fCMR findings in the study, MOCO LGE detected 62 cases with positive ischemic LGE, 15 cases had non-ischemic LGE. Representative cases are shown in Fig. 3. The mean fCMR parameters value of 67 subjects were as follow: infarction size 22.9% (9 to 40%), LVEF 43.5% ± 19.0%, LVEDV 167.7% (124 to 223.9%), LVESV 87.5% (53.3 to 152.9%), LVSV 69.9% ± 27.8%, LVEDM 117.7 g ± 35.3 g, 3D-GPLS − 8.5% (− 5.4 to − 11.2%). 67 patients were divided into 3 groups according 2021 ESC Guidelines for the diagnosis of heart failure [21]: 20 patients were diagnosed as heart failure with preserved EF (HFpEF; defined as EF ≥ 50%), 22 patients were diagnosed as heart failure with mid-range EF (HFmrEF; an EF of 40–49%), 25 patients were diagnosed as heart failure with reduced EF (HFrEF; defined as EF < 40%).

All the enrolled patients completed follow-up over a median of 31 months (3.8 to 38.2 months), 25 composite events (37.3%, 10 deaths, and 15 hospitalizations due to recurrence of heart failure) were observed.

Cox proportional hazards regression analysis was performed to investigate associations between baseline characteristics, fCMR parameters and MACE (Table 3). In the univariate analysis, IS (hazard ratio [HR] 1.05; 95% confidence interval [CI] 1.03–1.07; p < 0.001), LVEF (HR 0.94; 95% CI 0.92–0.94; p < 0.001), 3D-GPLS (HR 1.15; 95% CI 1.09–1.29; p < 0.001), LVEDM, heart failure, age, diabetes mellitus, hypertension, ACE inhibitor use were significantly associated with MACE. LVEDV, LVESV, and LVSV were used to calculate LVEF. Therefore, LVEDV, LVESV, and LVSV were not incorporated into the univariate and multivariate analysis. According to the rule of thumb for multivariate analyses, 25 incidents were recorded during the follow-up, maximum 4 variables should be included in the multivariate analysis. The purpose of the study was to evaluate the value of fCMR parameters in the prognosis of patients with CHD and BH limitation, three fCMR parameters including IS, LVEF and 3D-GPLS, and HF classification were chose in the multivariate analysis. Multivariate analysis showed IS (HR 1.02; 95% CI 1.00–1.05; p = 0.048) and HFpEF (HR 0.20; 95% CI 0.04–0.98; p = 0.048) correlated positively with MACE.

Kaplan–Meier analyses showed a significant and progressive decrease in survival rates in the patients with HFpEF group, followed by the HFmrEF group, and then by the HFrEF group (p < 0.001) (Fig. 4A). The Kaplan–Meier analysis showed a significantly lower survival rate in the IS \(>\) 20% group than in the IS \(\le\) 20% group (p = 0.010)(Fig. 4B), and a significantly lower survival rate in the 3D-GPLS \(<\) − 6% group than in the 3D-GPLS \(\ge\) − 6% group (p < 0.001) (Fig. 4C).

The data in this study was derived from patients with CHD and BH limitation who could not perform CCMR examinations and used fCMR as an alternative examination. The fCMR protocol obtained robust images and could be used to perform prognostic assessments of patients with CHD and BH limitation. This protocol can be used as an alternative imaging technique for patients with breath-holding restrictions.

In the study, CCMR imaging was not performed on patients who could not hold their breath for an extended amount of time since the CCMR acquisitions required multiple breath-holds to avoid respiratory image artifacts [22, 23]. Also, to achieve sufficiently high spatial and/or temporal resolutions during CCMR imaging, segmented k-space data are acquired over multiple heartbeats resulting in segmented data acquisitions that are prone to motion artifacts, leading to suboptimal image quality. Motion artifacts on CCMR images of patients with BH limitations are widely recognized, meaning that CCMR imaging in these patients has mostly been abandoned. Single-shot readout protocols effectively eliminate breathing motion artifacts in both CS cine [24‐26] and MOCO LGE images [27]. Moreover, MOCO LGE is characterized as a protocol that provides motion correction and has allowed the fCMR protocol to obtain robust images under free-breathing, which has been confirmed by our study.

Generally, the validation of novel techniques is often performed through comparative studies with reference techniques considered as the gold standard. For LVF quantification, standard segmented BH cine imaging is the most accurate and reproducible imaging technique [10, 12]. However, this method cannot be performed in our patients with BH limitations. Therefore, in this study, we showed that almost all CS cine imaging yielded robust SAX images; the high IQ scores of the CS cine images translated into high reliability for measuring left ventricular function and strain. A few LAX CS cine images had poor IQ scores, similar to what has been previously published [28], which could have been due to flow-related artifacts occurring in the phase-encoding direction during systole since the temporal domain sparsity might be limited in the anatomic regions where there is very high flow [29]. LGE have been widely used to detect subendocardial infarcts in patients with CHD, determine the extent of MI, and provide diagnostic information for the treatment and prognosis of patients with CHD [4, 30]. Previous studies have reported no differences in MI detection between MOCO LGE and conventional LGE [13, 28]. Although we did not obtain conventional LGE imaging, all MOCO LGE imaging yielded diagnostic images. In our study, fifteen patients with CHD confirmed by DSA were diagnosed as non-ischemic LGE on MOCO LGE imaging. This negative finding after MOCO LGE imaging could be explained by incomplete cardiac coverage, missed small subendocardial LGE due to poor contrast with the blood pool [31], or good compensation and no MI occurrence in the patients with CHD.

In this study, 37.3% of patients experienced MACE during a median follow-up of 31 months. This incidence of MACE is higher than in previous studies [4‐7] and is likely because all patients in the study had BH impairments, and most were vulnerable. Our study was first to show that HFpEF, and IS derived from MOCO LGE, had independent values in predicting MACE in patients with CHD and BH limitation. LVEF remains the primary parameter for HF characterization and the primary inclusion criterion for clinical trials of HF [32]. The LVEF ≤ 35% derived from standard segmented BH cine imaging has been recognized as a strong predictor of adverse outcomes [5, 7]. In our study, the optimal cutoff value for LVEF, derived from CS cine, in predicting MACE was 34.2%, with a high sensitivity but low specificity. Considering the multiple mechanisms involved in MACE, it seems unlikely that LVEF will provide adequate prognosis information for all patients [5]. Comprehensive CMR stratification tools, including 3D-GPLS and IS, could improve risk stratification significantly. 3D-GPLS is, perhaps, a more sensitive measure of myocardial contractile function than LVEF [4]. In our study, the optimal cutoff value for 3D-GPLS and IS in predicting MACE was − 5.65% and 26.1%, respectively. 3D-GPLS had high specificity but low sensitivity, whereas IS had both high sensitivity and specificity. LVEF, 3D-GPLS, and IS have individual advantages in providing prognoses and can complement one another. The benefit of the fCMR protocol is not limited to a patient’s BH ability, broadening CMR imaging applications and allowing the realization of imaging goals in patients with BH limitations. The fCMR protocol has been successfully applied to the prognostic evaluations of patients with CHD, making up for the deficiency of CCMR imaging while allowing patients the opportunity to obtain CMR images. Therefore, cardiologists can understand the occurrence, development, and prognosis of patients with CHD and BH limitation, and further to give accurate and individualized treatments.

There were some limitations to this study. First, it was a single-center study and, therefore, reflects the use of clinical protocols at our institution. Another major limitation is the lack of CCMR imaging, and due to practical reasons, fCMR imaging was systematically performed when CCMR methods could not be implemented, which could have selected for a group that benefited most from fCMR imaging. The sample size was also small, and the follow-up time was short. Future studies should be performed with larger sample sizes and longer follow-up times. Moreover, patients with CHD should have received optimal medical treatments according to the hospital guidelines, such as receiving the combination medical regimens, including sacubitril/valsartan. In our study, the absence of assessing the use of these medications is a limitation for risk assessments. Finally, the fCMR protocol did not include advanced MR sequences, such as mapping, perfusion, and blood flow. We did not evaluate the incremental benefit of T2-weighted STIR imaging for myocardial edema because an optimized sequence in currently not available.

For patients who were unable to undergo CCMR imaging, the fCMR protocols obtained robust images that could be used to evaluate cardiac function and detect myocardial infarction, and could be used to perform prognostic assessments. This protocol is promising as an alternative technique to CCMR and could replace CCMR in the future.