Delayed Treatment of Acute Myocardial Infarction With Ventricular Septal Rupture Due to Patient Fear During the COVID-19 Pandemic.

During the coronavirus disease 2019 (COVID-19) pandmic, more patients are presenting with complications late after acute myocardial infarction. We report the case of a 71-year-old man who delayed seeking medical care for 2 weeks, despite progressive shortness of breath, cough, and tactile fever, for fear of contracting COVID-19 in the hospital. Clinical and echocardiographic evaluation revealed a ventricular septal rupture secondary to acute myocardial infarction. The patient underwent urgent cardiac catheterization, followed by successful saphenous vein grafting to the left anterior descending coronary artery and open surgical repair of the ventricular septal rupture with a bovine pericardial patch. This case highlights a potential long-lasting negative effect that the COVID-19 pandemic will have on the care-seeking behavior and health of patients with acute cardiovascular disease.

Left ventricular geometry predicts optimal response to percutaneous mitral repair via MitraClip: Integrated assessment by two- and three-dimensional echocardiography.

OBJECTIVES: To assess impact of left ventricular (LV) chamber remodeling on MitraClip (MClp) response. BACKGROUND: MitraClip is the sole percutaneous therapy approved for mitral regurgitation (MR) but response varies. LV dilation affects mitral coaptation; determinants of MClp response are uncertain. METHODS: LV and mitral geometry were quantified on pre- and post-procedure two-dimensional (2D) transthoracic echocardiography (TTE) and intra-procedural three-dimensional (3D) transesophageal echocardiography (TEE). Optimal MClp response was defined as ≤mild MR at early (1-6 month) follow-up. RESULTS: Sixty-seven degenerative MR patients underwent MClp: Whereas MR decreased ≥1 grade in 94%, 39% of patients had optimal response (≤mild MR). Responders had smaller pre-procedural LV end-diastolic volume (94 ± 24 vs. 109 ± 25 mL/m2 , p = 0.02), paralleling smaller annular diameter (3.1 ± 0.4 vs. 3.5 ± 0.5 cm, p = 0.002), and inter-papillary distance (2.2 ± 0.7 vs. 2.5 ± 0.6 cm, p = 0.04). 3D TEE-derived annular area correlated with 2D TTE (r = 0.59, p < 0.001) and was smaller among optimal responders (12.8 ± 2.1 cm2 vs. 16.8 ± 4.4 cm2 , p = 0.001). Both 2D and 3D mitral annular size yielded good diagnostic performance for optimal MClp response (AUC 0.73-0.84, p < 0.01). In multivariate analysis, sub-optimal MClp response was associated with LV end-diastolic diameter (OR 3.10 per-cm [1.26-7.62], p = 0.01) independent of LA size (1.10 per-cm2 [1.02-1.19], p = 0.01); substitution of mitral annular diameter for LV size yielded an independent association with MClp response (4.06 per-cm2 [1.03-15.96], p = 0.045). CONCLUSIONS: Among degenerative MR patients undergoing MClp, LV and mitral annular dilation augment risk for residual or recurrent MR, supporting the concept that MClp therapeutic response is linked to sub-valvular remodeling.

Impact of Mitral Regurgitation Severity and Cause on Effort Tolerance-Integrated Stress Myocardial Perfusion Imaging and Echocardiographic Assessment of Patients With Known or Suspected Coronary Artery Disease Undergoing Exercise Treadmill Testing.

Background Mitral regurgitation ( MR ) has the potential to impede exercise capacity; it is uncertain whether this is because of regurgitation itself or the underlying cause of valvular insufficiency. Methods and Results The population comprised 3267 patients who underwent exercise treadmill myocardial perfusion imaging and transthoracic echocardiography within 6±8 days. MR was present in 28%, including 176 patients (5%) with moderate or greater MR . Left ventricular systolic function significantly decreased and chamber size increased in relation to MR , paralleling increments in stress and rest myocardial perfusion deficits (all P<0.001). Exercise tolerance (metabolic equivalents of task) decreased stepwise in relation to graded MR severity ( P<0.05). Workload was significantly lower with mild versus no MR (mean±SD, 9.8±3.0 versus 10.1±3.0; P=0.02); magnitude of workload reduction significantly increased among patients with advanced versus those with mild MR (mean±SD, 8.6±3.0 versus 9.8±3.0; P<0.001). MR -associated exercise impairment was accompanied by lower heart rate and blood pressure augmentation and greater dyspnea (all P<0.05). Both functional and nonfunctional MR subgroups demonstrated significantly decreased effort tolerance in relation to MR severity ( P≤0.01); impairment was greater with functional MR ( P=0.04) corresponding to more advanced left ventricular dysfunction and dilation (both P<0.001). Functional MR predicted reduced metabolic equivalent of task-based effort (B=-0.39 [95% CI, -0.62 to -0.17]; P=0.001) independent of MR severity. Among the overall cohort, advanced (moderate or greater) MR was associated with reduced effort tolerance (B=-1.36 [95% CI, -1.80 to -0.93]; P<0.001) and remained significant ( P=0.01) after controlling for age, clinical indexes, stress perfusion defects, and left ventricular dysfunction. Conclusions MR impairs exercise tolerance independent of left ventricular ischemia, dysfunction, and clinical indexes. Magnitude of exercise impairment parallels severity of MR .

Machine learning derived segmentation of phase velocity encoded cardiovascular magnetic resonance for fully automated aortic flow quantification.

BACKGROUND: Phase contrast (PC) cardiovascular magnetic resonance (CMR) is widely employed for flow quantification, but analysis typically requires time consuming manual segmentation which can require human correction. Advances in machine learning have markedly improved automated processing, but have yet to be applied to PC-CMR. This study tested a novel machine learning model for fully automated analysis of PC-CMR aortic flow. METHODS: A machine learning model was designed to track aortic valve borders based on neural network approaches. The model was trained in a derivation cohort encompassing 150 patients who underwent clinical PC-CMR then compared to manual and commercially-available automated segmentation in a prospective validation cohort. Further validation testing was performed in an external cohort acquired from a different site/CMR vendor. RESULTS: Among 190 coronary artery disease patients prospectively undergoing CMR on commercial scanners (84% 1.5T, 16% 3T), machine learning segmentation was uniformly successful, requiring no human intervention: Segmentation time was < 0.01 min/case (1.2 min for entire dataset); manual segmentation required 3.96 ± 0.36 min/case (12.5 h for entire dataset). Correlations between machine learning and manual segmentation-derived flow approached unity (r = 0.99, p < 0.001). Machine learning yielded smaller absolute differences with manual segmentation than did commercial automation (1.85 ± 1.80 vs. 3.33 ± 3.18 mL, p < 0.01): Nearly all (98%) of cases differed by ≤5 mL between machine learning and manual methods. Among patients without advanced mitral regurgitation, machine learning correlated well (r = 0.63, p < 0.001) and yielded small differences with cine-CMR stroke volume (∆ 1.3 ± 17.7 mL, p = 0.36). Among advanced mitral regurgitation patients, machine learning yielded lower stroke volume than did volumetric cine-CMR (∆ 12.6 ± 20.9 mL, p = 0.005), further supporting validity of this method. Among the external validation cohort (n = 80) acquired using a different CMR vendor, the algorithm yielded equivalently small differences (∆ 1.39 ± 1.77 mL, p = 0.4) and high correlations (r = 0.99, p < 0.001) with manual segmentation, including similar results in 20 patients with bicuspid or stenotic aortic valve pathology (∆ 1.71 ± 2.25 mL, p = 0.25). CONCLUSION: Fully automated machine learning PC-CMR segmentation performs robustly for aortic flow quantification - yielding rapid segmentation, small differences with manual segmentation, and identification of differential forward/left ventricular volumetric stroke volume in context of concomitant mitral regurgitation. Findings support use of machine learning for analysis of large scale CMR datasets.

Tissue-based markers of right ventricular dysfunction in ischemic mitral regurgitation assessed via stress cardiac magnetic resonance and three-dimensional echocardiography.

Ischemic mitral regurgitation (iMR) augments risk for right ventricular dysfunction (RVDYS). Right and left ventricular (LV) function are linked via common coronary perfusion, but data is lacking regarding impact of LV ischemia and infarct transmurality-as well as altered preload and afterload-on RV performance. In this prospective multimodality imaging study, stress CMR and 3-dimensional echo (3D-echo) were performed concomitantly in patients with iMR. CMR provided a reference for RVDYS (RVEF < 50%), as well as LV function/remodeling, ischemia and infarction. Echo was used to test multiple RV performance indices, including linear (TAPSE, S'), strain (GLS), and volumetric (3D-echo) approaches. 90 iMR patients were studied; 32% had RVDYS. RVDYS patients had greater iMR, lower LVEF, larger global ischemic burden and inferior infarct size (all p < 0.05). Regarding injury pattern, RVDYS was associated with LV inferior ischemia and infarction (both p < 0.05); 80% of affected patients had substantial viable myocardium (< 50% infarct thickness) in ischemic inferior segments. Regarding RV function, CMR RVEF similarly correlated with 3D-echo and GLS (r = 0.81-0.87): GLS yielded high overall performance for CMR-evidenced RVDYS (AUC: 0.94), nearly equivalent to that of 3D-echo (AUC: 0.95). In multivariable regression, GLS was independently associated with RV volumetric dilation on CMR (OR - 0.90 [CI - 1.19 to - 0.61], p < 0.001) and 3D echo (OR - 0.43 [CI - 0.84 to - 0.02], p = 0.04). Among patients with iMR, RVDYS is associated with potentially reversible processes, including LV inferior ischemic but predominantly viable myocardium and strongly impacted by volumetric loading conditions.