COVID-19 is associated with cardiac dysfunction. This study tested the relative prognostic role of left (LV), right and bi- (BiV) ventricular dysfunction on mortality in a large multicenter cohort of ...patients during and after acute COVID-19 hospitalization.
All hospitalized COVID-19 patients who underwent clinically indicated transthoracic echocardiography within 30 days of admission at four NYC hospitals between March 2020 and January 2021 were studied. Images were re-analyzed by a central core lab blinded to clinical data. Nine hundred patients were studied (28% Hispanic, 16% African-American), and LV, RV and BiV dysfunction were observed in 50%, 38% and 17%, respectively. Within the overall cohort, 194 patients had TTEs prior to COVID-19 diagnosis, among whom LV, RV, BiV dysfunction prevalence increased following acute infection (p<0.001). Cardiac dysfunction was linked to biomarker-evidenced myocardial injury, with higher prevalence of troponin elevation in patients with LV (14%), RV (16%) and BiV (21%) dysfunction compared to those with normal BiV function (8%, all p<0.05). During in- and out-patient follow-up, 290 patients died (32%), among whom 230 died in the hospital and 60 post-discharge. Unadjusted mortality risk was greatest among patients with BiV (41%), followed by RV (39%) and LV dysfunction (37%), compared to patients without dysfunction (27%, all p<0.01). In multivariable analysis, any RV dysfunction, but not LV dysfunction, was independently associated with increased mortality risk (p<0.01).
LV, RV and BiV function declines during acute COVID-19 infection with each contributing to increased in- and out-patient mortality risk. RV dysfunction independently increases mortality risk.
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.
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.
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).
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.
An
in silico
study is performed to investigate fluid dynamic effects of central venous catheter (CVC) placement within patient-specific cavo-atrial junctions. Prior studies show the CVC infusing a ...liquid, but this study focuses on the placement without any liquid emerging from the CVC. A 7 or 15-French double-lumen CVC is placed virtually in two patient-specific models; the CVC tip location is altered to understand its effect on the venous flow field. Results show that the CVC impact is trivial on flow in the superior vena cava when the catheter-to-vein ratio ranges from 0.15 to 0.33. Results further demonstrate that when the CVC tip is directly in the right atrium, flow vortices in the right atrium result in elevated wall shear stress near the tip hole. A recirculation region characterizes a spatially variable flow field inside the CVC side hole. Furthermore, flow stagnation is present near the internal side hole corners but an elevated wall shear stress near the curvature of the side hole’s exit. These results suggest that optimal CVC tip location is within the superior vena cava, so as to lower the potential for platelet activation due to elevated shear stresses and that CVC geometry and location depth in the central vein significantly influences the local CVC fluid dynamics. A thrombosis model also shows thrombus formation at the side hole and tip hole. After modifying the catheter design, the hemodynamics change, which alter thrombus formation. Future studies are warranted to study CVC design and placement location in an effort to minimize CVC-induced thrombosis incidence.
T1 mapping is a promising quantitative tool for assessing diffuse cardiomyopathies. The purpose of this study is to quantify in vivo accuracy of the Modified Look-Locker Inversion Recovery (MOLLI) ...cardiac T1 mapping sequence against the spin echo gold standard, which has not been done previously. T1 accuracy of MOLLI was determined by comparing with the gold standard inversion recovery spin echo sequence in the calf muscle, and with a rapid inversion recovery fast spin echo sequence in the heart. T1 values were obtained with both conventional MOLLI fitting and MOLLI fitting with inversion efficiency correction. In the calf (n = 6), conventional MOLLI fitting produced inconsistent T1 values with error ranging from 8.0% at 90° to 17.3% at 30°. Modified MOLLI fitting with inversion efficiency correction improved error to under 7.4% at all flip angles. In the heart (n = 5), modified MOLLI fitting with inversion correction reduced T1 error to 5.5% from 14.0% by conventional MOLLI fitting. This study shows that conventional MOLLI fitting can lead to significant in vivo T1 errors when not accounting for the lower adiabatic inversion efficiency often experienced in vivo.
Ischemic mitral regurgitation (iMR) predisposes to right ventricular (RV) pressure and volume overload, providing a nidus for RV dysfunction (RVDYS) and non-ischemic fibrosis (NIF). Echocardiography ...(echo) is widely used to assess iMR, but performance of different indices as markers of RVDYS and NIF is unknown.
iMR patients prospectively underwent echo and cardiac magnetic resonance (CMR) within 72 hours. Echo quantified iMR, assessed conventional RV indices (TAPSE, RV-S', fractional area change FAC), and strain via speckle tracking in apical 4-chamber (global longitudinal strain RV-GLS) and parasternal long axis orientation (transverse strain). CMR volumetrically quantified RVEF, and assessed ischemic pattern myocardial infarction (MI) and septal NIF.
73 iMR patients were studied; 36% had RVDYS (EF<50%) on CMR among whom LVEF was lower, PA systolic pressure higher, and MI size larger (all p<0.05). CMR RVEF was paralleled by echo results; correlations were highest for RV-GLS (r = 0.73) and lowest for RV-S' (r = 0.43; all p<0.001). RVDYS patients more often had CMR-evidenced NIF (54% vs. 7%; p<0.001). Whereas all RV indices were lower among NIF-affected patients (all p≤0.006), percent change was largest for transverse strain (48.3%). CMR RVEF was independently associated with RV-GLS (partial r = 0.57, p<0.001) and transverse strain (r = 0.38, p = 0.002) (R = 0.78, p<0.001). Overall diagnostic performance of RV-GLS and transverse strain were similar (AUC = 0.930.87-0.99|0.910.84-0.99, both p<0.001), and yielded near equivalent sensitivity and specificity (85%|83% and 80%|79% respectively).
Compared to conventional echo indices, RV strain parameters yield stronger correlation with CMR-defined RVEF and potentially constitute better markers of CMR-evidenced NIF in iMR.
The goal of this study was to determine the prevalence of post-myocardial infarction (MI) left ventricular (LV) thrombus in the current era and to develop an effective algorithm (predicated on ...echocardiography echo) to discern patients warranting further testing for thrombus via delayed enhancement (DE) cardiac magnetic resonance (CMR).
LV thrombus affects post-MI management. DE-CMR provides thrombus tissue characterization and is a well-validated but an impractical screening modality for all patients after an MI.
A same-day echo and CMR were performed according to a tailored protocol, which entailed uniform echo contrast (irrespective of image quality) and dedicated DE-CMR for thrombus tissue characterization.
A total of 201 patients were studied; 8% had thrombus according to DE-CMR. All thrombi were apically located; 94% of thrombi occurred in the context of a left anterior descending (LAD) infarct-related artery. Although patients with thrombus had more prolonged chest pain and larger MI (p ≤ 0.01), only 18% had aneurysm on echo (cine-CMR 24%). Noncontrast (35%) and contrast (64%) echo yielded limited sensitivity for thrombus on DE-CMR. Thrombus was associated with stepwise increments in basal → apical contractile dysfunction on echo and quantitative cine-CMR; the echo-measured apical wall motion score was higher among patients with thrombus (p < 0.001) and paralleled cine-CMR decrements in apical ejection fraction and peak ejection rates (both p < 0.005). Thrombus-associated decrements in apical contractile dysfunction were significant even among patients with LAD infarction (p < 0.05). The echo-based apical wall motion score improved overall performance (area under the curve 0.89 ± 0.44) for thrombus compared with ejection fraction (area under the curve 0.80 ± 0.61; p = 0.01). Apical wall motion partitions would have enabled all patients with LV thrombus to be appropriately referred for DE-CMR testing (100% sensitivity and negative predictive value), while avoiding further testing in more than one-half (56% to 63%) of patients.
LV thrombus remains common, especially after LAD MI, and can occur even in the absence of aneurysm. Although DE-CMR yielded improved overall thrombus detection, apical wall motion on a noncontrast echocardiogram can be an effective stratification tool to identify patients in whom DE-CMR thrombus assessment is most warranted. (Diagnostic Utility of Contrast Echocardiography for Detection of LV Thrombi Post ST Elevation Myocardial Infarction; NCT00539045).
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