Prognostic Value of Vasodilator Stress Perfusion Cardiovascular Magnetic Resonance in Patients With Prior Myocardial Infarction

Prognostic Value of Stress Perfusion Cardiac MRI in Cardiovascular Disease: A Systematic Review and Meta-Analysis of the Effects of the Scanner, Stress Agent, and Analysis Technique

Purpose To perform a systematic review and meta-analysis to assess the prognostic value of stress perfusion cardiac MRI in predicting cardiovascular outcomes. Materials and Methods A systematic literature search from the inception of PubMed, Embase, Web of Science, and China National Knowledge Infrastructure until January 2023 was performed for articles that reported the prognosis of stress perfusion cardiac MRI in predicting cardiovascular outcomes. The quality of included studies was assessed using the Quality in Prognosis Studies tool. Reported hazard ratios (HRs) of univariable regression analyses with 95% CIs were pooled. Comparisons were performed across different analysis techniques (qualitative, semiquantitative, and fully quantitative), magnetic field strengths (1.5 T vs 3 T), and stress agents (dobutamine, adenosine, and dipyridamole). Results Thirty-eight studies with 58 774 patients with a mean follow-up time of 53 months were included. There were 1.9 all-cause deaths and 3.5 major adverse cardiovascular events (MACE) per 100 patient-years. Stress-inducible ischemia was associated with a higher risk of all-cause mortality (HR: 2.55 [95% CI: 1.89, 3.43]) and MACE (HR: 3.90 [95% CI: 2.69, 5.66]). For MACE, pooled HRs of qualitative, semiquantitative, and fully quantitative methods were 4.56 (95% CI: 2.88, 7.22), 3.22 (95% CI: 1.60, 6.48), and 1.78 (95% CI: 1.39, 2.28), respectively. For all-cause mortality, there was no evidence of a difference between qualitative and fully quantitative methods (P = .79). Abnormal stress perfusion cardiac MRI findings remained prognostic when subgrouped based on underlying disease, stress agent, and field strength, with HRs of 3.54, 2.20, and 3.38, respectively, for all-cause mortality and 3.98, 3.56, and 4.21, respectively, for MACE. There was no evidence of subgroup differences in prognosis between field strengths or stress agents. There was significant heterogeneity in effect size for MACE outcomes in the subgroups assessing qualitative versus quantitative stress perfusion analysis, underlying disease, and field strength. Conclusion Stress perfusion cardiac MRI is valuable for predicting cardiovascular outcomes, regardless of the analysis method, stress agent, or magnetic field strength used. Keywords: MR-Perfusion, MRI, Cardiac, Meta-Analysis, Stress Perfusion, Cardiac MR, Cardiovascular Disease, Prognosis, Quantitative © RSNA, 2024 Supplemental material is available for this article.

Perfusion Imaging for the Heart

The use of myocardial perfusion imaging during a stress cardiac magnetic resonance (CMR) examination for the evaluation of coronary artery disease is now recommended by both US and European guidelines. Several studies have demonstrated high diagnostic accuracy for the detection of hemodynamically significant coronary artery disease. Stress perfusion CMR has been shown to be a noninvasive and cost-effective alternative to guide coronary revascularization.

AI-Based Fully Automated Left Atrioventricular Coupling Index as a Prognostic Marker in Patients Undergoing Stress CMR

BACKGROUND

The left atrioventricular coupling index (LACI) is a strong and independent predictor of heart failure (HF) in individuals without clinical cardiovascular disease. Its prognostic value is not established in patients with cardiovascular disease.

OBJECTIVES

This study sought to determine in patients undergoing stress cardiac magnetic resonance (CMR) whether fully automated artificial intelligence-based LACI can provide incremental prognostic value to predict HF.

METHODS

Between 2016 and 2018, the authors conducted a longitudinal study including all consecutive patients with abnormal (inducible ischemia or late gadolinium enhancement) vasodilator stress CMR. Control subjects with normal stress CMR were selected using propensity score matching. LACI was defined as the ratio of left atrial to left ventricular end-diastolic volumes. The primary outcome included hospitalization for acute HF or cardiovascular death. Cox regression was used to evaluate the association of LACI with the primary outcome after adjustment for traditional risk factors.

RESULTS

In 2,134 patients (65 ± 12 years, 77% men, 1:1 matched patients [1,067 with normal and 1,067 with abnormal CMR]), LACI was positively associated with the primary outcome (median follow-up: 5.2 years [IQR: 4.8-5.5 years]) before and after adjustment for risk factors in the overall propensity-matched population (adjusted HR: 1.18 [95% CI: 1.13-1.24]), in patients with abnormal CMR (adjusted HR per 0.1% increment: 1.22 [95% CI: 1.14-1.30]), and in patients with normal CMR (adjusted HR per 0.1% increment: 1.12 [95% CI: 1.05-1.20]) (all P < 0.001). After adjustment, a higher LACI of ≥25% showed the greatest improvement in model discrimination and reclassification over and above traditional risk factors and stress CMR findings (C-index improvement: 0.16; net reclassification improvement = 0.388; integrative discrimination index = 0.153, all P < 0.001; likelihood ratio test P < 0.001).

CONCLUSIONS

LACI is independently associated with hospitalization for HF and cardiovascular death in patients undergoing stress CMR, with an incremental prognostic value over traditional risk factors including inducible ischemia and late gadolinium enhancement.

The Role of Non-Invasive Multimodality Imaging in Chronic Coronary Syndrome: Anatomical and Functional Pathways

Coronary artery disease (CAD) is one of the major causes of mortality and morbidity worldwide, with a high socioeconomic impact. Currently, various guidelines and recommendations have been published about chronic coronary syndromes (CCS). According to the recent European Society of Cardiology guidelines on chronic coronary syndrome, a multimodal imaging approach is strongly recommended in the evaluation of patients with suspected CAD. Today, in the current practice, non-invasive imaging methods can assess coronary anatomy through coronary computed tomography angiography (CCTA) and/or inducible myocardial ischemia through functional stress testing (stress echocardiography, cardiac magnetic resonance imaging, single photon emission computed tomography-SPECT, or positron emission tomography-PET). However, recent trials (ISCHEMIA and REVIVED) have cast doubt on the previous conception of the management of patients with CCS, and nowadays it is essential to understand the limitations and strengths of each imaging method and, specifically, when to choose a functional approach focused on the ischemia versus a coronary anatomy-based one. Finally, the concept of a pathophysiology-driven treatment of these patients emerged as an important goal of multimodal imaging, integrating 'anatomical' and 'functional' information. The present review aims to provide an overview of non-invasive imaging modalities for the comprehensive management of CCS patients.

Safety, feasibility, and prognostic value of stress perfusion CMR in patients with MR-conditional pacemaker

AIMS

To assess the safety, feasibility, and prognostic value of stress cardiovascular magnetic resonance (CMR) in patients with pacemaker (PM).

METHODS AND RESULTS

Between 2008 and 2021, we conducted a bi-centre longitudinal study with all consecutive patients with MR-conditional PM referred for vasodilator stress CMR at 1.5 T in the Institut Cardiovasculaire Paris Sud and Lariboisiere University Hospital. They were followed for the occurrence of major adverse cardiovascular events (MACE) defined as cardiac death or non-fatal myocardial infarction. Cox regression analyses were performed to determine the prognostic value of CMR parameters. The quality of CMR was rated by two observers blinded to clinical details. Of 304 patients who completed the CMR protocol, 273 patients (70% male, mean age 71 ± 9 years) completed the follow-up (median [interquartile range], 7.1 [5.4-7.5] years). Among those, 32 experienced a MACE (11.7%). Stress CMR was well tolerated with no significant change in lead thresholds or pacing parameters. Overall, the image quality was rated good or excellent in 84.9% of segments. Ischaemia and late gadolinium enhancement (LGE) were significantly associated with the occurrence of MACE (hazard ratio, HR: 11.71 [95% CI: 4.60-28.2]; and HR: 5.62 [95% CI: 2.02-16.21], both P < 0.001). After adjustment for traditional risk factors, ischaemia and LGE were independent predictors of MACE (HR: 5.08 [95% CI: 2.58-14.0]; and HR: 2.28 [95% CI: 2.05-3.76]; both P < 0.001).

CONCLUSION

Stress CMR is safe, feasible and has a good discriminative prognostic value in consecutive patients with PM.

Clustering of patients with inconclusive non-invasive stress testing referred for vasodilator stress cardiovascular magnetic resonance

BACKGROUND

Inconclusive non-invasive stress testing is associated with impaired outcome. This population is very heterogeneous, and its characteristics are not well depicted by conventional methods.

AIMS

To identify patient subgroups by phenotypic unsupervised clustering, integrating clinical and cardiovascular magnetic resonance data to unveil pathophysiological differences between subgroups of patients with inconclusive stress tests.

METHODS

Between 2008 and 2020, consecutive patients with a first inconclusive non-invasive stress test referred for stress cardiovascular magnetic resonance were followed for the occurrence of major adverse cardiovascular events (defined as cardiovascular death or myocardial infarction). A cluster analysis was performed on clinical and cardiovascular magnetic resonance variables.

RESULTS

Of 1402 patients (67% male; mean age 70±11years) who completed the follow-up (median 6.5years, interquartile range 5.6-7.5years), 197 experienced major adverse cardiovascular events (14.1%). Three distinct phenogroups were identified based upon unsupervised hierarchical clustering of principal components: phenogroup 1=history of percutaneous coronary intervention with viable myocardial infarction and preserved left ventricular ejection fraction; phenogroup 2=atrial fibrillation with preserved left ventricular ejection fraction; and phenogroup 3=coronary artery bypass graft with non-viable myocardial scar and reduced left ventricular ejection fraction. Using survival analysis, the occurrence of major adverse cardiovascular events (P=0.007), cardiovascular mortality (P=0.002) and all-cause mortality (P<0.001) differed among the three phenogroups. Phenogroup 3 presented the worse prognosis. In each phenogroup, ischaemia was associated with major adverse cardiovascular events (phenogroup 1: hazard ratio 2.79, 95% confidence interval 1.61-4.84; phenogroup 2: hazard ratio 2.59, 95% confidence interval 1.69-3.97; phenogroup 3: hazard ratio 3.16, 95% confidence interval 1.82-5.49; all P<0.001).

CONCLUSIONS

Cluster analysis of clinical and cardiovascular magnetic resonance variables identified three phenogroups of patients with inconclusive stress testing, with distinct prognostic profiles.

Machine-Learning Score Using Stress CMR for Death Prediction in Patients With Suspected or Known CAD

BACKGROUND

In patients with suspected or known coronary artery disease, traditional prognostic risk assessment is based on a limited selection of clinical and imaging findings. Machine learning (ML) methods can take into account a greater number and complexity of variables.

OBJECTIVES

This study sought to investigate the feasibility and accuracy of ML using stress cardiac magnetic resonance (CMR) and clinical data to predict 10-year all-cause mortality in patients with suspected or known coronary artery disease, and compared its performance with existing clinical or CMR scores.

METHODS

Between 2008 and 2018, a retrospective cohort study with a median follow-up of 6.0 (IQR: 5.0-8.0) years included all consecutive patients referred for stress CMR. Twenty-three clinical and 11 stress CMR parameters were evaluated. ML involved automated feature selection by random survival forest, model building with a multiple fractional polynomial algorithm, and 5 repetitions of 10-fold stratified cross-validation. The primary outcome was all-cause death based on the electronic National Death Registry. The external validation cohort of the ML score was performed in another center.

RESULTS

Of 31,752 consecutive patients (mean age: 63.7 ± 12.1 years, and 65.7% male), 2,679 (8.4%) died with 206,453 patient-years of follow-up. The ML score (ranging from 0 to 10 points) exhibited a higher area under the curve compared with Clinical and Stress Cardiac Magnetic Resonance score, European Systematic Coronary Risk Estimation score, QRISK3 score, Framingham Risk Score, and stress CMR data alone for prediction of 10-year all-cause mortality (ML score: 0.76 vs Clinical and Stress Cardiac Magnetic Resonance score: 0.68, European Systematic Coronary Risk Estimation score: 0.66, QRISK3 score: 0.64, Framingham Risk Score: 0.63, extent of inducible ischemia: 0.66, extent of late gadolinium enhancement: 0.65; all P < 0.001). The ML score also exhibited a good area under the curve in the external cohort (0.75).

CONCLUSIONS

The ML score including clinical and stress CMR data exhibited a higher prognostic value to predict 10-year death compared with all traditional clinical or CMR scores.