Underwent Stress Cardiovascular Magnetic Resonance Research Articles

Artificial intelligence-based fully automated stress left ventricular ejection fraction as a prognostic marker in patients undergoing stress cardiovascular magnetic resonance.

This study aimed to determine in patients undergoing stress cardiovascular magnetic resonance (CMR) whether fully automated stress artificial intelligence (AI)-based left ventricular ejection fraction (LVEFAI) can provide incremental prognostic value to predict death above traditional prognosticators. Between 2016 and 2018, we conducted a longitudinal study that included all consecutive patients referred for vasodilator stress CMR. LVEFAI was assessed using AI algorithm combines multiple deep learning networks for LV segmentation. The primary outcome was all-cause death assessed using the French National Registry of Death. Cox regression was used to evaluate the association of stress LVEFAI with death after adjustment for traditional risk factors and CMR findings. In 9712 patients (66 ± 15 years, 67% men), there was an excellent correlation between stress LVEFAI and LVEF measured by expert (LVEFexpert) (r = 0.94, P < 0.001). Stress LVEFAI was associated with death [median (interquartile range) follow-up 4.5 (3.7-5.2) years] before and after adjustment for risk factors [adjusted hazard ratio, 0.84 (95% confidence interval, 0.82-0.87) per 5% increment, P < 0.001]. Stress LVEFAI had similar significant association with death occurrence compared with LVEFexpert. After adjustment, stress LVEFAI value showed the greatest improvement in model discrimination and reclassification over and above traditional risk factors and stress CMR findings (C-statistic improvement: 0.11; net reclassification improvement = 0.250; integrative discrimination index = 0.049, all P < 0.001; likelihood-ratio test P < 0.001), with an incremental prognostic value over LVEFAI determined at rest. AI-based fully automated LVEF measured at stress is independently associated with the occurrence of death in patients undergoing stress CMR, with an additional prognostic value above traditional risk factors, inducible ischaemia and late gadolinium enhancement.

Incremental prognostic value of fully-automatic machine-learning based global circumferential strain during a stress CMR exam

Abstract Background Left ventricular global circumferential strain using cardiovascular magnetic resonance (CMR) is an accurate indicator to predict cardiovascular events. Although several studies have shown the excellent prognostic value of stress CMR, the prognostic value of stress global circumferential strain (sGCS) remains unknown. Purpose To investigate the prognostic value of sGCS for predicting cardiovascular events using a fully automatic machine learning algorithm without human correction in consecutive patients referred for stress CMR. Methods Between 2016 and 2018, all consecutive patients referred for stress CMR were included and followed for the occurrence of major adverse cardiovascular events (MACE), defined by cardiovascular death or nonfatal myocardial infarction (MI). A fully automatic machine learning algorithm was trained and validated on unseen CMR studies (MAGNETOM Aera and Skyra, Siemens Healthcare, Erlangen, Germany) to assess the sGCS from short-axis cine images at stress. The algorithm combines multiple deep learning networks for detection and segmentation with an active contours approach. Cox regressions were performed to determine the prognostic value of sGCS. Results Among 2,906 consecutive patients who underwent stress CMR, the automatic sGCS was successfully computed in 2,859 (98.4%) patients (68% male, mean age 64±12 years). A total of 256 (8.8%) MACEs were observed during a median (IQR) follow-up period of 4.5 (3.7-5.3) years. Using Kaplan-Meier analysis, sGCS and the presence of inducible ischemia were significantly associated with the occurrence of MACE (hazard ratio, HR: 1.12 [95% CI, 1.08-1.17]; and HR: 8.48 [95% CI, 6.05-11.91], both p&amp;lt;0.001; respectively). After adjustment for traditional risk factors, inducible ischemia and late gadolinium enhancement (LGE), sGCS was an independent predictor of a higher incidence of MACE (adjusted HR: 1.12 [95% CI, 1.05-1.20]). Finally, sGCS showed an incremental prognostic value to predict MACE compared to a multivariable model including traditional risk factors, the presence of inducible ischemia and LGE (C-statistic improvement: 0.05, p=0.007; NRI= 0.169; IDI= 0.097; both p&amp;lt;0.001). Conclusions Automatic sGCS has an incremental prognostic value to predict MACE above traditional risk factors, and other stress CMR parameters.Survival curvesIncremental prognostic value

Fully automated assessment of global longitudinal strain by machine learning predicts death in patients undergoing stress CMR

Abstract Background Several studies described the independent prognostic value of left ventricular global longitudinal strain (GLS) using cardiovascular magnetic resonance (CMR) to predict death and cardiovascular events. While routine GLS evaluation is a time consuming and partially operator-dependent process, the potential interest of GLS using a fully automated method has not yet been established. Purpose To assess the feasibility and prognostic value of GLS to predict all-cause death using a fully automated machine-learning algorithm without human correction in consecutive patients referred for stress CMR. Methods Between 2016 and 2018, all consecutive patients referred for stress CMR were included and followed for the occurrence of all-cause death. A fully automated machine learning algorithm was trained and validated on unseen CMR studies (MAGNETOM Aera and Skyra) to assess the GLS from long-axis cine images acquired at rest. The algorithm combines multiple deep learning networks for detection and segmentation with an active contours approach. Cox regressions were performed to determine the prognostic value of GLS. Results In a retrospective study of 9,883 consecutive patients who underwent stress CMR between 2016 and 2018, automated GLS was successfully computed in 9,638 (97.5%) patients (67% male, mean age 66±12 years). A total of 510 (5.3%) deaths were observed during a median follow-up period of 4.5 years (interquartile range: 3.7-5.2). GLS, the presence of inducible ischemia and late gadolinium enhancement (LGE) were significantly associated with the occurrence of death (hazard ratio, HR: 1.22 [95% CI, 1.17-1.26]; HR: 2.23 [95% CI, 1.61-3.10]; and HR: 2.04 [95% CI, 1.41-2.95], respectively, all p&amp;lt;0.001). After adjustment for traditional risk factors, inducible ischemia and LGE, GLS was an independent predictor of a higher incidence of death (adjusted HR: 1.14 [95% CI, 1.08-1.20]). Automated GLS showed an incremental prognostic value to predict death compared to traditional risk factors, inducible ischemia and LGE (C-statistic improvement: 0.05; NRI=0.146; IDI=0.244; all p&amp;lt;0.001). Conclusions Fully automated GLS measured at rest has an incremental prognostic value to predict all-cause death above traditional risk factors, and other stress CMR parameters. This finding suggests that unsupervised GLS measurements are accurate and allow improved risk stratification.

Incremental prognostic value of fully-automatic machine-learning based global circumferential strain during a stress CMR exam

Abstract Funding Acknowledgements Type of funding sources: None. Background Left ventricular global circumferential strain using cardiovascular magnetic resonance (CMR) is an accurate indicator to predict cardiovascular events. Although several studies have shown the excellent prognostic value of stress CMR, the prognostic value of stress global circumferential strain (sGCS) remains unknown. Aim To investigate the prognostic value of sGCS for predicting cardiovascular events using a fully automatic machine learning algorithm without human correction in consecutive patients referred for stress CMR. Methods Between 2016 and 2018, all consecutive patients referred for stress CMR were included and followed for the occurrence of major adverse cardiovascular events (MACE), defined by cardiovascular death or nonfatal myocardial infarction (MI). A fully automatic machine learning algorithm was trained and validated on unseen CMR studies (MAGNETOM Aera and Skyra, Siemens Healthcare, Erlangen, Germany) to assess the sGCS from short-axis cine images at stress. The algorithm combines multiple deep learning networks for detection and segmentation with an active contours approach. Cox regressions were performed to determine the prognostic value of sGCS. Results Among 2,906 consecutive patients who underwent stress CMR, the automatic sGCS was successfully computed in 2,859 (98.4%) patients (68% male, mean age 64±12 years). A total of 256 (8.8%) MACEs were observed during a median (IQR) follow-up period of 4.5 (3.7–5.3) years. Using Kaplan-Meier analysis, sGCS and the presence of inducible ischemia were significantly associated with the occurrence of MACE (hazard ratio, HR: 1.12 [95% CI, 1.08–1.17]; and HR: 8.48 [95% CI, 6.05–11.91], both p&amp;lt;0.001; respectively). After adjustment for traditional risk factors, inducible ischemia and late gadolinium enhancement (LGE), sGCS was an independent predictor of a higher incidence of MACE (adjusted HR: 1.12 [95% CI, 1.05–1.20]). Finally, sGCS showed an incremental prognostic value to predict MACE compared to a multivariable model including traditional risk factors, the presence of inducible ischemia and LGE (C-statistic improvement: 0.05, p = 0.007; NRI= 0.169; IDI= 0.097; both p&amp;lt;0.001). Conclusions Automatic sGCS has an incremental prognostic value to predict MACE above traditional risk factors, and other stress CMR parameters.

Additional prognostic value of fully-automatic Global Longitudinal Strain using machine learning

Abstract Funding Acknowledgements Type of funding sources: None. Background Several studies described the independent prognostic value of left ventricular global longitudinal strain (GLS) using cardiovascular magnetic resonance (CMR) to predict cardiovascular events. However, the potential interest of GLS using a fully automatic method assessed at rest during a stress CMR exam was not well established. Aim To assess the prognostic value of GLS to predict all-cause death using a fully automatic machine learning algorithm without human correction in consecutive patients referred for stress CMR. Methods Between 2016 and 2018, all consecutive patients referred for stress CMR were included and followed for the occurrence of all-cause death. A fully automatic machine learning algorithm was trained and validated unseen CMR studies (MAGNETOM Aera and Skyra, Siemens Healthcare, Erlangen, Germany) to assess the GLS from long-axis cine images acquired at rest. The algorithm combines multiple deep learning networks for detection and segmentation with an active contours approach. Cox regressions were performed to determine the prognostic value of GLS. Results Among 9,883 consecutive patients who underwent stress CMR between 2016 and 2018 retrospectively included, the automatic GLS was successfully computed in 9,638 (97.5%) patients (67% male, mean age 66±12 years). A total of 510 (5.3%) deaths were observed during a median (IQR) follow-up period of 4.5 (3.7–5.2) years. GLS, the presence of inducible ischemia and late gadolinium enhancement (LGE) were significantly associated with the occurrence of death (hazard ratio, HR: 1.22 [95% CI, 1.17–1.26]; HR: 2.23 [95% CI, 1.61–3.10]; and HR: 2.04 [95% CI, 1.41–2.95], respectively, all p&amp;lt;0.001). After adjustment for traditional risk factors, inducible ischemia and LGE, GLS was an independent predictor of a higher incidence of death (adjusted HR: 1.14 [95% CI, 1.08–1.20]). Automatic GLS showed an incremental prognostic value to predict death compared to traditional risk factors, inducible ischemia and LGE (C-statistic improvement: 0.05; NRI=0.146; IDI=0.244; all p&amp;lt;0.001). Conclusions Automatic GLS measured at rest has an incremental prognostic value to predict all-cause death above traditional risk factors, and other stress CMR parameters.

Prognostic impact of artificial intelligence-based fully automated global circumferential strain in patients undergoing stress CMR

To determine whether fully automated artificial intelligence-based global circumferential strain (GCS) assessed during vasodilator stress cardiovascular magnetic resonance (CMR) can provide incremental prognostic value. Between 2016 and 2018, a longitudinal study included all consecutive patients with abnormal stress CMR defined by the presence of inducible ischaemia and/or late gadolinium enhancement (LGE). Control subjects with normal stress CMR were selected using a propensity score-matching. Stress-GCS was assessed using a fully automatic machine learning algorithm based on featured-tracking imaging from short-axis cine images. The primary outcome was the occurrence of major adverse clinical events (MACE) defined as cardiovascular mortality or nonfatal myocardial infarction. Cox regressions evaluated the association between stress-GCS and the primary outcome after adjustment for traditional prognosticators. In 2670 patients [65 ± 12 years, 68% men, 1:1 matched patients (1335 with normal and 1335 with abnormal CMR)], stress-GCS was associated with MACE [median follow-up 5.2 (4.8–5.5) years] after adjustment for risk factors in the propensity-matched population (adjusted hazard ratio [HR]: 1.12 [95% CI: 1.06–1.18]) and patients with normal CMR (HR: 1.43 [95% CI: 1.30–1.57], both P < 0.001), but not in patients with abnormal CMR (P = 0.33). In patients with normal CMR, an increased stress-GCS > −10% showed the best improvement in model discrimination and reclassification above traditional and stress CMR findings (C-statistic improvement: 0.27; NRI = 0.538; IDI = 0.108, all P < 0.001; LR-test P < 0.001). Stress-GCS is independently associated with MACE in patients undergoing stress CMR, with an incremental prognostic value over traditional risk factors and stress CMR findings in the group of patients with normal CMR. Prognostic value of AI-based Stress-GCS (Fig. 1).

Prognostic impact of artificial intelligence-based fully automated global circumferential strain in patients undergoing stress CMR.

To determine whether fully automated artificial intelligence-based global circumferential strain (GCS) assessed during vasodilator stress cardiovascular (CV) magnetic resonance (CMR) can provide incremental prognostic value. Between 2016 and 2018, a longitudinal study included all consecutive patients with abnormal stress CMR defined by the presence of inducible ischaemia and/or late gadolinium enhancement. Control subjects with normal stress CMR were selected using a propensity score-matching. Stress-GCS was assessed using a fully automatic machine-learning algorithm based on featured-tracking imaging from short-axis cine images. The primary outcome was the occurrence of major adverse clinical events (MACE) defined as CV mortality or nonfatal myocardial infarction. Cox regressions evaluated the association between stress-GCS and the primary outcome after adjustment for traditional prognosticators. In 2152 patients [66 ± 12 years, 77% men, 1:1 matched patients (1076 with normal and 1076 with abnormal CMR)], stress-GCS was associated with MACE [median follow-up 5.2 (4.8-5.5) years] after adjustment for risk factors in the propensity-matched population [adjusted hazard ratio (HR), 1.12 (95% CI, 1.06-1.18)], and patients with normal CMR [adjusted HR, 1.35 (95% CI, 1.19-1.53), both P < 0.001], but not in patients with abnormal CMR (P = 0.058). In patients with normal CMR, an increased stress-GCS showed the best improvement in model discrimination and reclassification above traditional and stress CMR findings (C-statistic improvement: 0.14; NRI = 0.430; IDI = 0.089, all P < 0.001; LR-test P < 0.001). Stress-GCS is not a predictor of MACE in patients with ischaemia, but has an incremental prognostic value in those with a normal CMR although the absolute event rate remains low.

Impairment in quantitative microvascular function in non-ischemic cardiomyopathy as demonstrated using cardiovascular magnetic resonance

Microvascular dysfunction (MVD) is present in various cardiovascular diseases and portends worse outcomes. We assessed the prevalence of MVD in patients with non-ischemic cardiomyopathy (NICM) as compared to subjects with preserved ejection fraction (EF) using stress cardiovascular magnetic resonance (CMR). We retrospectively studied consecutive patients with NICM and 58 subjects with preserved left ventricular (LV) EF who underwent stress CMR between 2011-2016. MVD was defined visually as presence of a subendocardial perfusion defect and semiquantitatively by myocardial perfusion reserve index (MPRI<1.51). MPRI was compared between groups using univariate analysis and multivariable linear regression. In total, 41 patients with NICM (mean age 51 ± 14, 59% male) and 58 subjects with preserved LVEF (mean age 51 ± 13, 31% male) were identified. In the NICM group, MVD was present in 23 (56%) and 11 (27%) by semiquantitative and visual evaluation respectively. Compared to those with preserved LVEF, NICM patients had lower rest slope (3.9 vs 4.9, p = 0.05) and stress perfusion slope (8.8 vs 11.7, p<0.001), and MPRI (1.41 vs 1.74, p = 0.02). MPRI remained associated with NICM after controlling for age, gender, hypertension, ethnicity, diabetes, and late gadolinium enhancement (log MPR, β coefficient = -0.19, p = 0.007). MVD-as assessed using CMR-is highly prevalent in NICM as compared to subjects with preserved LVEF even after controlling for covariates. Semiquantitative is able to detect a greater number of incidences of MVD compared to visual methods alone. Further studies are needed to determine whether treatment of MVD is beneficial in NICM.

Prognostic yield of stress CMR to evaluate chronic coronary syndromes: a systematic review and meta-analysis

Abstract Background Assessment of ischemia with stress cardiovascular magnetic resonance (CMR) imaging is recommended in patients with stable chest pain and intermediate or high pre-test probability of coronary artery disease (CAD). Purpose To provide an updated synthesis on prognostic significance of stress CMR imaging in patients with stable chest pain and suspected or known CAD. Methods After prospective registration and approval of the study protocol, we performed a systematic review and meta-analysis of studies published between 2000 through 2021, enrolling ≥100 patients, and reporting outcome data of CAD patients undergoing stress CMR. Odds ratios (ORs) and 95% confidence intervals (CIs) for all-cause death, cardiovascular (CV) death and major adverse cardiac events (MACE: CV death and myocardial infarction), were pooled through inverse variance random-effects meta-analysis to compute summary effect size. Annualized event rates (AERs) were extracted from each study and compared by χ2-statistic. A warranty period was defined as the time interval with an AER &amp;lt;1%. Results We identified a total of 33 studies pooling an overall tested population of 68920 patients (mean age 62 years; 56% males; known CAD 32%; 386117 person-years). Ischemia was found in 13617 (20%). Mean follow-up was 3.5±2.1 years. Presence of ischemia was associated with increased risk of all-cause death (OR 2.0 95% CI: 1.7–2.3), CV death (OR 6.4 95% CI: 4.5–9.1), and MACE (OR 5.0 95% CI: 3.6–6.8). Cumulative AERs for all-cause death, CV death and MACE were 2.97%, 2.51%, and 3.99% in patients with ischemia, and 1.40%, 0.59%, and 0.98% in patients without ischemia, respectively (p&amp;lt;0.0001 for all comparisons). Conclusion Stress CMR imaging yields robust prognostic information in patients with suspected or known CAD. Presence of ischemia is associated with increased risk of all-cause death, CV death and MACE. Patients with negative stress CMR have a very low risk (&amp;lt;1%) of CV death and MACE with a warranty period of at least 3.5 years. Funding Acknowledgement Type of funding sources: None.

Hemodynamic response and safety of vasodilator stress cardiovascular magnetic resonance in patients with permanent pacemakers or implantable cardioverter-defibrillators.

Vasodilator stress cardiovascular magnetic resonance (CMR) is a powerful diagnostic modality, but data toward its use in patients with permanent pacemakers (PPMs) or implantable cardioverter-defibrillators (ICDs) is limited. Patients with ICDs (>1% pacing) or PPMs who underwent regadenoson single photon emission computed tomography (SPECT) and all patients with ICDs or PPMs who underwent stress CMR were retrospectively identified. SPECT tests were analyzed for hemodynamic responses and new pacing requirements; CMR studies were examined for safety, device characteristics and programming, hemodynamic responses, and image quality. Changes from baseline were evaluated with the Related-Samples Wilcoxon Signed Rank Test. Of 67 patients (median age 65 [IQR 58-72] years, 31 [46%] female, 31 [46%] Black), 47 underwent SPECT and 20 CMR. With regadenoson SPECT, 89% of patients experienced tachycardic responses above resting heart rates (+19 [13-32] beats per minute, p < .01). During stress CMR, 10 (50%) devices were asynchronously paced approximately 10 beats per minute above resting rates, and the remaining were temporarily deactivated. Those with asynchronous pacing had no changes in heart rates, whereas patients with deactivated devices had near uniform heart rate accelerations. Image quality was diagnostic in the majority of stress CMR sequences, with nonconditional ICDs contributing 40 of 57 (70%) of nondiagnostic segments. This data supports the safety of vasodilator stress CMR with promising diagnostic quality images in patients with CMR conditional ICDs and PPMs. Despite a near uniform tachycardic response to regadenoson in the SPECT environment, high rates of asynchronous pacing during vasodilator stress CMR did not result in competitive pacing or adverse arrhythmic events. Further studies are needed to validate these findings and confirm the diagnostic and prognostic performance of stress CMR in these individuals.

Index of microcirculatory resistance in the assessment of coronary microvascular dysfunction in hypertrophic cardiomyopathy

Introduction and objectivesCoronary microvascular dysfunction (CMD) is one of the most important pathophysiological features in hypertrophic cardiomyopathy (HCM). The index of microcirculatory resistance (IMR) is an invasive method to assess the coronary microcirculation. The aim was to assess CMD in patients with HCM by IMR. MethodsAdult patients with HCM without epicardial coronary artery disease underwent cardiac catheterization for the assessment of CMD by IMR (normal cut-off value ≤22.0) and coronary flow reserve (CFR) (normal cut-off value ≥2). Cardiovascular magnetic resonance (CMR) was performed to assess the ischemic burden by perfusion imaging during regadenoson-induced hyperemia, and the extent of myocardial fibrosis was assessed by late gadolinium enhancement (LGE), native T1 mapping and extracellular volume (ECV). ResultsFourteen patients were enrolled with a mean age of 62.8±6.2years, 8 (57.1%) males, of whom 9 (64.3%) had obstructive HCM. Using IMR, CMD was detected in 4 (29%) patients. Among four patients with an IMR>22.0, all had non-obstructive HCM and two had angina. CFR<2 was reported in eight patients (57%). Concordance between IMR and CFR (both normal or both abnormal) was verified in 6 patients (43%). Among four patients with IMR>22.0, perfusion defects were found in two of the three patients who underwent stress CMR. Increased ECV (>28%) was documented in two of the patients with IMR>22 and in three of the patients with IMR≤22.0. LGE was >15% in 2 of the patients with IMR>22 and in 4 with IMR≤22.0. ConclusionsIMR assessment in HCM is feasible and safe. Patients with abnormal IMR seemed to have more significant tissue abnormalities on CMR.

Machine learning score using stress CMR for prediction of all-cause death in patients with suspected or known coronary artery disease a large registry with &gt; 200,000 patient-years of follow-up

Traditional prognostic risk assessment in patients undergoing noninvasive imaging is based upon a limited selection of clinical and imaging findings. Machine learning (ML) can consider a greater number and complexity of variables. Therefore, we investigated the feasibility and accuracy of ML to predict 10-year all-cause mortality in patients undergoing stress cardiovascular magnetic resonance (CMR), and compared the performance to existing clinical or CMR metrics. Between 2008 and 2018, a retrospective cohort study with a median follow-up of 6.0 years (IQR: 5.0–8.0) included all consecutive patients referred for stress CMR. The primary outcome was all-cause death based on the National Death Registry. ML involved automated feature selection by random survival forest, model building with a boosted ensemble algorithm, and 10-fold stratified cross-validation. Among the 31,752 consecutive patients (mean age 63.7 ± 12.1 years and 65.7% males), 2679 (8.4%) died at 206,453 patient-years of follow-up. Five clinical and 4 CMR parameters were selected by ML including: age, gender, BMI, history of PAD, renal failure, extent of ischemia and LGE, LVEF and LV end-diastolic volume indexed. Regarding the calibration, C-index on the training sets was 0.730 and 0.735 on the test sets, which attests to the very good robustness of ML models. Machine learning exhibited a higher area-under-curve compared with the FRS, ESC-SCORE and QRISK3 score alone for predicting all-cause mortality (ML score: 0.76 vs. FRS: 0.60, ESC-SCORE: 0.63, QRISK3: 0.62; all P < 0.001) ( Fig. 1 ). Machine learning combining clinical and stress CMR data was found to predict 10-year all-cause mortality significantly better than existing clinical scores alone.

Long-Term Prognostic Value of Stress CMR in Patients With Heart Failure and Preserved Ejection Fraction

ObjectivesThe objectives of this study were to investigate the long-term prognostic value of inducible myocardial ischemia assessed by vasodilator stress cardiovascular magnetic resonance (CMR) in patients with HFpEF. BackgroundSome studies suggest that ischemia could play a key role in HF in patients with preserved ejection fraction (HFpEF). MethodsBetween 2008 and 2019, consecutive patients prospectively referred for stress CMR with HFpEF as defined by current guidelines, without known coronary artery disease (CAD), were followed for the occurrence of major adverse cardiovascular events (MACE), as defined by cardiovascular mortality or nonfatal myocardial infarction (MI). Secondary composite outcomes included cardiovascular mortality or hospitalization for acute HF. Cox regression analysis was performed to determine the prognostic value of inducible ischemia or late gadolinium enhancement (LGE) by CMR. ResultsAmong the 1,203 patients with HFpEF (73 ± 13 years of age; 29% males) who underwent stress CMR and completed follow-up (6.9 years interquartile range [IQR]: 6.7 to 7.7 years]), 108 experienced a MACE (9%). Kaplan-Meier analysis showed inducible ischemia and LGE were significantly associated with MACE (HR: 6.63; 95% confidence interval [CI]: 4.54 to 9.69; and HR: 2.56; 95% CI: 1.60 to 4.09, respectively; both p < 0.001) and secondary outcomes (HR: 8.40; 95% CI: 6.31 to 11.20; p < 0.001; and HR: 1.87; 95% CI: 1.27 to 2.76, respectively; p = 0.002). In multivariate analysis, inducible ischemia and LGE were independent predictors of MACE (HR: 6.10; 95% CI: 4.14 to 9.00; p < 0.001 and HR: 1.62; 95% CI: 1.06 to 2.49; p = 0.039; respectively). ConclusionsStress CMR-inducible myocardial ischemia and LGE have accurate discriminative long-term prognostic value in HFpEF patients without known CAD to predict the occurrence of MACE.

Incremental prognostic value of coronary flow reserve determined by phase-contrast cine cardiovascular magnetic resonance of the coronary sinus in patients with diabetes mellitus

BackgroundAlthough non-invasive assessment of coronary flow reserve (CFR) by cardiovascular magnetic resonance (CMR) provides prognostic information for patients with diabetes mellitus (DM), the incremental prognostic value of CMR-derived CFR remains unclear.PurposeTo evaluate the incremental prognostic value of CMR-derived CFR for patients with DM who underwent stress CMR imaging.Materials and methodsA total of 309 patients with type 2 DM [69 ± 9 years; 244 (78%) male] assessed between 2009 and 2019 were retrospectively reviewed. Coronary sinus blood flow (CSBF) was measured using phase contrast (PC) cine CMR. CFR was calculated as the CSBF during adenosine triphosphate infusion divided by that at rest. Major adverse cardiac events (MACE) were defined as death, acute coronary syndrome, hospitalization due to heart failure exacerbation, or sustained ventricular tachycardia. The incremental prognostic value of CFR over clinical and CMR variables was assessed by calculating the C-index and net reclassification improvement (NRI).ResultsDuring a median follow-up of 3.8 years, 42 patients (14%) experienced MACE. The annualized event rate was significantly higher among patients with CFR < 2.0, regardless of the presence of late gadolinium enhancement (LGE) (1.4% vs. 9.8%, p = 0.011 in the LGE (−) group; 1.8% vs. 16.9%, p < 0.001 in the LGE (+) group). In addition, this trend was maintained in the subgroups stratified by presence or absence of ischemia (0.3% vs. 6.7%, p = 0.007 in the ischemia (−) group; 3.9% vs. 17.1%, p = 0.001 in the ischemia (+) group). Adding CFR to the risk model (age + gender + left ventricular ejection fraction + %LGE + %ischemia) resulted in a significant increase of the C-index from 0.838 to 0.870 (p = 0.038) and an NRI of 0.201 (0.004–0.368, p = 0.012).ConclusionPC cine CMR-derived CFR of the coronary sinus may be useful as a prognostic marker for DM patients, incremental to common clinical and CMR parameters. Due to the high prevalence of coronary microvascular dysfunction, the addition of CFR to conventional vasodilator stress CMR imaging may improve risk stratification for patients with DM.

Haemodynamic effects of pharmacologic stress with adenosine in patients with left ventricular systolic dysfunction

BackgroundIn patients with heart failure, downregulation of adenosine receptor gene expression and impaired adenosine-related signal transduction may result in a diminished response to adenosine. This may have implications for cardiac stress testing. We evaluated the haemodynamic response to intravenous adenosine in patients with left ventricular systolic dysfunction (LVSD) undergoing stress cardiovascular magnetic resonance imaging (CMR). Methods and resultsWe retrospectively examined 497 consecutive patients referred for clinical stress CMR. Blood pressure and heart rate responses with intravenous adenosine were compared in patients with normal, mild-moderately impaired and severely impaired LV systolic function (ejection fraction [EF] > 55%, 36–55% and < 35%, respectively).Following 2 min of adenosine infusion, there was a significant difference between the groups in the heart rate change from baseline, with a diminished heart rate response in patients with LVSD (p < 0.001). An increase in the dose of adenosine (up to 210 μg/kg/min) was required to achieve a sufficient haemodynamic response in more patients with severe LVSD (41%) than those with mild-moderately impaired and normal LV systolic function (24% and 19%, respectively, p < 0.001). Even with increased doses of adenosine in subjects with severe LVSD, peak haemodynamic response remained blunted. With multivariate analysis age (p < 0.001) and LVEF (p = 0.031) were independent predictors of heart rate response to adenosine. ConclusionPatients with reduced LVEF referred for stress CMR may have a blunted heart rate response to adenosine. Further study is warranted to determine whether this may be associated with reduced diagnostic accuracy and also the potential utility of further dose increases or alternative stressors.

Ventricular response to stress predicts outcome in adult patients with a systemic right ventricle

Previous studies demonstrated that ventricular response to stress cardiovascular magnetic resonance (CMR) is frequently abnormal in patients with a systemic right ventricle (RV). However, the clinical implications of these findings remained unknown. We sought to evaluate whether abnormal response to stress CMR predicts adverse outcome in patients with a systemic RV. Thirty-nine adult patients (54% male; mean age 26, range 18-65 years) with a systemic RV underwent stress CMR to determine the response of RV volumes and ejection fraction (EF). During follow-up, cardiac events, defined as hospitalization for heart failure, cardiac surgery, aborted cardiac arrest, or death, were recorded. The prognostic value of an abnormal response to stress, defined as lack of a decrease in RV end-systolic volume (ESV) or lack of an increase in RV EF, was assessed. We frequently observed an abnormal response to stress, as RV ESV did not decrease in 17 patients (44%), and RV EF did not increase in 15 patients (38%). After a mean follow-up period of 8.1 years, 8 (21%) patients had reached the composite end point. The inability to decrease RV ESV during stress was predictive for cardiac events with a hazard ratio of 2.3 (95% CI 1.19-88.72, P = .034), as was the inability to increase RV EF with a hazard ratio of 2.3 (95% CI 1.31-81.59, P = .027). Stress CMR potentially has important prognostic value in patients with a systemic RV. Patients with a systemic RV who show abnormal cardiac response to stress have a substantially higher risk of adverse outcome.