Improving the assessment of left ventricular diastolic dysfunction by including left atrial strain in the algorithm: LA Strain and LV diastolic dysfunction assessment.

BACKGROUND: The latest guidelines on echocardiographic assessment of left ventricular diastolic dysfunction (LVDD) leave a significant proportion of patients with LVDD status undetermined. We aimed to examine the implication of an alternative algorithm incorporating left atrial (LA) strain as a tiebreaker on the indeterminate LVDD category. METHODS AND RESULTS: We included 823 patients who underwent echocardiography and cardiac MRI within 7 days. LVDD was assessed by echocardiography following contemporary guidelines and an alternative algorithm including LA reservoir strain as a tie-breaker. LVDD was examined for its association with LV myocardial scar burden by cardiac MRI, and a composite outcome. 275 (33%) patients had LVDD, of whom 119 had advanced grades of LVDD (grade II-III), and 117 (14%) had indeterminate LVDD grade. When LA strain was applied at cut points of 18%, 24% and 35%, subjects were reclassified as normal or LVDD dependent accordingly. Reclassification allowed a similar outcome risk-stratification as the current guidelines. CONCLUSIONS: LA reservoir strain improved LVDD assessment by eliminating indeterminate status/grade while maintaining the same effective outcome stratification as current guidelines.

Diagnostic Performance and Clinical Impact of Photon-Counting Detector Computed Tomography in Coronary Artery Disease.

BACKGROUND: Photon-counting detector-computed tomography (PCD-CT) has emerged as a promising technology, offering improved spatial resolution. OBJECTIVES: This study aimed to evaluate the clinical impact and diagnostic performance of PCD-CT vs conventional energy-integrating detector computed tomography (EID-CT) for obstructive coronary artery disease (CAD). METHODS: From 2022 to 2023, we retrospectively identified 7,833 consecutive patients who underwent clinically indicated coronary computed tomography angiography (CCTA) at a single center, with either PCD-CT (n = 3,876; NAEOTOM Alpha [Siemens Healthineers]) or EID-CT (n = 3,957; Revolution Apex 256 [GE HealthCare] or Aquilion ONE ViSION 320 [Canon Medical Systems]) scanners. Subsequent invasive coronary angiography (ICA) and percutaneous or surgical revascularization were performed as part of routine clinical care. Among those referred for ICA after coronary CTA, the presence of obstructive CAD in each vessel was determined by coronary CTA (severe stenosis on visual assessment per the Coronary Artery Disease Reporting and Data System) and ICA (≥50% diameter stenosis on quantitative coronary angiography) in a blinded fashion. The diagnostic performance of EID-CT and PCD-CT was compared by using quantitative coronary angiography as the reference standard. RESULTS: Patients who underwent PCD-CT were less frequently referred to subsequent ICA than those undergoing EID-CT (9.9% vs 13.1%; P < 0.001). Among those who underwent ICA, revascularization was more frequently performed in the PCD-CT group than in the EID-CT group (43.4% vs 35.5%; P = 0.02). In the vessel-level analysis (n = 1,686), specificity (98.0% vs 93.0%; P < 0.001), positive predictive value (83.3% vs 63.0%; P = 0.002), and diagnostic accuracy (97.2% vs 92.8%; P < 0.001) were improved by PCD-CT. Sensitivity (90.9% vs 90.7%; P = 0.95) and negative predictive value (98.9% vs 98.7%; P = 0.83) for obstructive CAD were similar between the PCD-CT and EID-CT groups, respectively. CONCLUSIONS: PCD-CT exhibited excellent diagnostic performance for detecting obstructive CAD. Compared with patients undergoing conventional EID-CT, fewer patients were referred to ICA after PCD-CT, but those referred were more likely to undergo revascularization.

Patient-Specific Myocardial Infarction Risk Thresholds From AI-Enabled Coronary Plaque Analysis.

BACKGROUND: Plaque quantification from coronary computed tomography angiography has emerged as a valuable predictor of cardiovascular risk. Deep learning can provide automated quantification of coronary plaque from computed tomography angiography. We determined per-patient age- and sex-specific distributions of deep learning-based plaque measurements and further evaluated their risk prediction for myocardial infarction in external samples. METHODS: In this international, multicenter study of 2803 patients, a previously validated deep learning system was used to quantify coronary plaque from computed tomography angiography. Age- and sex-specific distributions of coronary plaque volume were determined from 956 patients undergoing computed tomography angiography for stable coronary artery disease from 5 cohorts. Multicenter external samples were used to evaluate associations between coronary plaque percentiles and myocardial infarction. RESULTS: Quantitative deep learning plaque volumes increased with age and were higher in male patients. In the combined external sample (n=1847), patients in the ≥75th percentile of total plaque volume (unadjusted hazard ratio, 2.65 [95% CI, 1.47-4.78]; P=0.001) were at increased risk of myocardial infarction compared with patients below the 50th percentile. Similar relationships were seen for most plaque volumes and persisted in multivariable analyses adjusting for clinical characteristics, coronary artery calcium, stenosis, and plaque volume, with adjusted hazard ratios ranging from 2.38 to 2.50 for patients in the ≥75th percentile of total plaque volume. CONCLUSIONS: Per-patient age- and sex-specific distributions for deep learning-based coronary plaque volumes are strongly predictive of myocardial infarction, with the highest risk seen in patients with coronary plaque volumes in the ≥75th percentile.

Epicardial fat modifies the relationship between coronary calcium score and all-cause mortality: The St. Francis Heart Study.

Objective: Epicardial fat is associated with cardiovascular risk factors and adverse outcomes. However, it is not clear if epicardial fat remains to be a mortality risk when coronary calcium score (CAC) is taken into account. Methods: We studied the 1005 participants from the St. Francis Heart Study who were apparently healthy with CAC scores at 80th percentile or higher for age and gender, randomly assigned to placebo or statin therapy. At baseline, lipid profiles and non-contrast CT images were obtained where the epicardial fat volume was analyzed. Likelihood ratio testing was used to assess the additional prognostic value of epicardial fat to CAC for the risk of all-cause mortality. Results: Increased epicardial fat volume was associated with higher CAC. For each unit increase in lnCAC, the average epicardial fat volume increased by 3.34 mL/m2. After a mean follow-up period of 17 years, 179 (18%) participants died. Increased epicardial fat volume was associated with an adjusted hazard ratio of 1.11 (95% CI: 1.02 to 1.20) predicting all-cause mortality. In the stratified analysis testing strata of epicardial fat and CAC, those with increased epicardial fat and increased CAC had the highest risk of death. Compared with a model containing lnCAC and traditional risk factors, a model additionally containing epicardial fat volume yielded a better model fit (likelihood ratio test p < 0.001). Conclusion: Increased epicardial fat volume is associated with increased all-cause mortality risk. In addition, it portends incremental prognostic value to CAC score in mortality prediction.

A machine learning-derived risk score to predict left ventricular diastolic dysfunction from clinical cardiovascular magnetic resonance imaging.

Introduction: The evaluation of left ventricular diastolic dysfunction (LVDD) by clinical cardiac magnetic resonance (CMR) remains a challenge. We aimed to train and evaluate a machine-learning (ML) algorithm for the assessment of LVDD by clinical CMR variables and to investigate its prognostic value for predicting hospitalized heart failure and all-cause mortality. Methods: LVDD was characterized by echocardiography following the ASE guidelines. Eight demographic and nineteen common clinical CMR variables including delayed enhancement were used to train Random Forest models with a Bayesian optimizer. The model was evaluated using bootstrap and five-fold cross-validation. Area under the ROC curve (AUC) was utilized to evaluate the model performance. An ML risk score was used to stratify the risk of heart failure hospitalization and all-cause mortality. Results: A total of 606 consecutive patients underwent CMR and echocardiography within 7 days for cardiovascular disease evaluation. LVDD was present in 303 subjects by echocardiography. The performance of the ML algorithm was good using the CMR variables alone with an AUC of 0.868 (95% CI: 0.811-0.917), which was improved by combining with demographic data yielding an AUC 0.895 (95% CI: 0.845-0.939). The algorithm performed well in an independent validation cohort with AUC 0.810 (0.731-0.874). Subjects with higher ML scores (>0.4121) were associated with increased adjusted hazard ratio for a composite outcome than subjects with lower ML scores (1.72, 95% confidence interval 1.09-2.71). Discussion: An ML algorithm using variables derived from clinical CMR is effective in identifying patients with LVDD and providing prognostication for adverse clinical outcomes.

SwinUNet: a multiscale feature learning approach to cardiovascular magnetic resonance parametric mapping for myocardial tissue characterization.

Objective.Cardiovascular magnetic resonance (CMR) can measure T1 and T2 relaxation times for myocardial tissue characterization. However, the CMR procedure for T1/T2 parametric mapping is time-consuming, making it challenging to scan heart patients routinely in clinical practice. This study aims to accelerate CMR parametric mapping with deep learning.Approach. A deep-learning model, SwinUNet, was developed to accelerate T1/T2 mapping. SwinUNet used a convolutional UNet and a Swin transformer to form a hierarchical 3D computation structure, allowing for analyzing CMR images spatially and temporally with multiscale feature learning. A comparative study was conducted between SwinUNet and an existing deep-learning model, MyoMapNet, which only used temporal analysis for parametric mapping. The T1/T2 mapping performance was evaluated globally using mean absolute error (MAE) and structural similarity index measure (SSIM). The clinical T1/T2 indices for characterizing the left-ventricle myocardial walls were also calculated and evaluated using correlation and Bland-Altman analysis.Main results. We performed accelerated T1 mapping with ≤4 heartbeats and T2 mapping with 2 heartbeats in reference to the clinical standard, which required 11 heartbeats for T1 mapping and 3 heartbeats for T2 mapping. SwinUNet performed well in all the experiments (MAE < 50 ms, SSIM > 0.8, correlation > 0.75, and Bland-Altman agreement limits < 100 ms for T1 mapping; MAE < 1 ms, SSIM > 0.9, correlation > 0.95, and Bland-Altman agreement limits < 1.5 ms for T2 mapping). When the maximal acceleration was used (2 heartbeats), SwinUNet outperformed MyoMapNet and gave measurement accuracy similar to the clinical standard.Significance. SwinUNet offers an optimal solution to CMR parametric mapping for assessing myocardial diseases quantitatively in clinical cardiology.

Association of pulmonary transit time by cardiac magnetic resonance with heart failure hospitalization in a large prospective cohort with diverse cardiac conditions.

BACKGROUND: Longer pulmonary transit time (PTT) is closely associated with hemodynamic abnormalities. However, the implications on heart failure (HF) risk have not been investigated broadly in patients with diverse cardiac conditions. In this study we examined the long-term risk of HF hospitalization associated with longer PTT in a large prospective cohort with a broad spectrum of cardiac conditions. METHODS: All subjects were prospectively recruited to undergo cardiac magnetic resonance (CMR). The dynamic images of first-pass perfusion were acquired to assess peak-to-peak pulmonary transit time (PTT) which was subsequently normalized to RR interval duration. The risk of HF was examined using Cox proportional hazards models adjusted for baseline confounding risk factors. RESULTS: Among 506 consecutively consented patients undergoing clinical cardiac MR with diverse cardiac conditions, the mean age was 63 ± 14 years and 373 (73%) were male. After a mean follow up duration of 4.5 ± 3.0 years, 70 (14%) patients developed hospitalized HF and of these 6 died. A normalized PTT ≥ 8.2 was associated with a significantly increased adjusted HF hazard ratio of 3.69 (95% CI 2.02, 6.73). The HF hazard ratio was 1.26 (95% CI 1.18, 1.33) for each 1 unit increase in PTT which was higher among those preserved (1.70, 95% CI 1.20, 2.41) compared to those with reduced left ventricular ejection fraction (< 50%) (1.18, 95% CI 1.09, 1.27). PTT remained a significant risk factor of hospitalized HF after additional adjustment for N-terminal pro-hormone brain natriuretic peptide (NT-proBNP) or left ventricular global longitudinal strain with additionally demonstrated incremental model improvement through likelihood ratio testing. CONCLUSIONS: Our findings support the role of PTT in assessing HF risk among patients with broad spectrum of cardiac conditions with reduced as well as preserved ejection fraction. Longer PTT duration is an incremental risk factor for HF when baseline global longitudinal strain and NT-proBNP are taken into consideration.

StrainNet: Improved Myocardial Strain Analysis of Cine MRI by Deep Learning from DENSE.

Purpose: To develop a three-dimensional (two dimensions + time) convolutional neural network trained with displacement encoding with stimulated echoes (DENSE) data for displacement and strain analysis of cine MRI. Materials and Methods: In this retrospective multicenter study, a deep learning model (StrainNet) was developed to predict intramyocardial displacement from contour motion. Patients with various heart diseases and healthy controls underwent cardiac MRI examinations with DENSE between August 2008 and January 2022. Network training inputs were a time series of myocardial contours from DENSE magnitude images, and ground truth data were DENSE displacement measurements. Model performance was evaluated using pixelwise end-point error (EPE). For testing, StrainNet was applied to contour motion from cine MRI. Global and segmental circumferential strain (Ecc) derived from commercial feature tracking (FT), StrainNet, and DENSE (reference) were compared using intraclass correlation coefficients (ICCs), Pearson correlations, Bland-Altman analyses, paired t tests, and linear mixed-effects models. Results: The study included 161 patients (110 men; mean age, 61 years ± 14 [SD]), 99 healthy adults (44 men; mean age, 35 years ± 15), and 45 healthy children and adolescents (21 males; mean age, 12 years ± 3). StrainNet showed good agreement with DENSE for intramyocardial displacement, with an average EPE of 0.75 mm ± 0.35. The ICCs between StrainNet and DENSE and FT and DENSE were 0.87 and 0.72, respectively, for global Ecc and 0.75 and 0.48, respectively, for segmental Ecc. Bland-Altman analysis showed that StrainNet had better agreement than FT with DENSE for global and segmental Ecc. Conclusion: StrainNet outperformed FT for global and segmental Ecc analysis of cine MRI.Keywords: Image Postprocessing, MR Imaging, Cardiac, Heart, Pediatrics, Technical Aspects, Technology Assessment, Strain, Deep Learning, DENSE Supplemental material is available for this article. © RSNA, 2023.

Accuracy and limitation of plaque detection by coronary CTA: a section-to-section comparison with optical coherence tomography.

Plaques identified by Coronary CT angiography (CCTA) are important in clinical diagnosis and primary prevention. High-risk plaque features by CCTA have been extensively validated using optical coherence tomography (OCT). However, since their general diagnostic performance and limitations have not been fully investigated, we sought to compare CCTA with OCT among consecutive vessel sections. We retrospectively compared 188 consecutive plaques and 84 normal sections in 41 vessels from 40 consecutive patients referred for chest pain evaluation who had both CCTA and OCT with a median time lapse of 1 day. The distance to reference points were used to co-register between the modalities and the diagnostic performance of CCTA was evaluated against OCT. Plaque categories evaluated by CT were calcified, non-calcified and mixed. The diagnostic performance of CCTA was excellent for detecting any plaque identified by OCT with the sensitivity, specificity, negative and positive predictive values and accuracy of 92%, 98%, 99%, 84% and 93%, respectively. The lower than expected negative predictive value was due to failure of detecting sub-millimeter calcified (≤ 0.25 mm2) (N = 12) and non-calcified plaques (N = 4). Misclassification of plaque type accounted for majority of false negative findings (25/41, 61%) which was most prevalent among the mixed plaque (19/41, 46%). There was calcification within mixed plaques (N = 5) seen by CCTA but missed by OCT. Our findings suggest that CCTA is excellent at identifying coronary plaques except those sub-millimeter in size which likely represent very early atherosclerosis, although the clinical implication of very mild atherosclerosis is yet to be determined.

Myocardial Injury on CMR in Patients With COVID-19 and Suspected Cardiac Involvement.

BACKGROUND: Myocardial injury in patients with COVID-19 and suspected cardiac involvement is not well understood. OBJECTIVES: The purpose of this study was to characterize myocardial injury in a multicenter cohort of patients with COVID-19 and suspected cardiac involvement referred for cardiac magnetic resonance (CMR). METHODS: This retrospective study consisted of 1,047 patients from 18 international sites with polymerase chain reaction-confirmed COVID-19 infection who underwent CMR. Myocardial injury was characterized as acute myocarditis, nonacute/nonischemic, acute ischemic, and nonacute/ischemic patterns on CMR. RESULTS: In this cohort, 20.9% of patients had nonischemic injury patterns (acute myocarditis: 7.9%; nonacute/nonischemic: 13.0%), and 6.7% of patients had ischemic injury patterns (acute ischemic: 1.9%; nonacute/ischemic: 4.8%). In a univariate analysis, variables associated with acute myocarditis patterns included chest discomfort (OR: 2.00; 95% CI: 1.17-3.40, P = 0.01), abnormal electrocardiogram (ECG) (OR: 1.90; 95% CI: 1.12-3.23; P = 0.02), natriuretic peptide elevation (OR: 2.99; 95% CI: 1.60-5.58; P = 0.0006), and troponin elevation (OR: 4.21; 95% CI: 2.41-7.36; P < 0.0001). Variables associated with acute ischemic patterns included chest discomfort (OR: 3.14; 95% CI: 1.04-9.49; P = 0.04), abnormal ECG (OR: 4.06; 95% CI: 1.10-14.92; P = 0.04), known coronary disease (OR: 33.30; 95% CI: 4.04-274.53; P = 0.001), hospitalization (OR: 4.98; 95% CI: 1.55-16.05; P = 0.007), natriuretic peptide elevation (OR: 4.19; 95% CI: 1.30-13.51; P = 0.02), and troponin elevation (OR: 25.27; 95% CI: 5.55-115.03; P < 0.0001). In a multivariate analysis, troponin elevation was strongly associated with acute myocarditis patterns (OR: 4.98; 95% CI: 1.76-14.05; P = 0.003). CONCLUSIONS: In this multicenter study of patients with COVID-19 with clinical suspicion for cardiac involvement referred for CMR, nonischemic and ischemic patterns were frequent when cardiac symptoms, ECG abnormalities, and cardiac biomarker elevations were present.

Prevalence and Impact of Neoatherosclerosis on Clinical Outcomes After Percutaneous Treatment of Second-Generation Drug-Eluting Stent Restenosis.

BACKGROUND: Clinical and morphological factors associated with lipidic versus calcified neoatherosclerosis within second-generation drug-eluting stents and the impact of lipidic versus calcified neoatherosclerosis on long-term outcomes after repeat intervention have not been well studied. METHODS: A total of 512 patients undergoing optical coherence tomography before percutaneous coronary intervention for second-generation drug-eluting stents in-stent restenosis were included. Neoatherosclerosis was defined as lipidic or calcified neointimal hyperplasia in ≥3 consecutive frames or ruptured lipidic neointimal hyperplasia. The primary outcome was target lesion failure (cardiac death, target vessel myocardial infarction, definite stent thrombosis, or clinically driven target lesion revascularization). RESULTS: The overall prevalence of neoatherosclerosis was 28.5% (146/512): 56.8% lipidic, 30.8% calcified, and 12.3% both lipidic and calcific. The prevalence increased as a function of time from stent implantation: 20% at 1 to 3 years, 30% at 3 to 7 years, and 75% >7 years. Renal insufficiency, poor lipid profile, and time from stent implantation were associated with lipidic neoatherosclerosis, whereas severe renal insufficiency, female sex, and time from stent implantation were associated with calcified neoatherosclerosis. Multivariable Cox regression revealed that female sex and lipidic neoatherosclerosis were associated with more target lesion failure, whereas stent age and final minimum lumen diameter after reintervention were related to lower target lesion failure. Calcified neoatherosclerosis was not related to adverse events after reintervention for in-stent restenosis given a large enough minimum lumen diameter was achieved. CONCLUSIONS: Lipidic but not calcified neoatherosclerosis was associated with poor subsequent outcomes after repeat revascularization if optimal stent expansion was achieved in lesions with calcified neoatherosclerosis.

Temporospatial characterization of ventricular wall motion with real-time cardiac magnetic resonance imaging in health and disease.

Cardiac magnetic resonance imaging (MRI) has been largely dependent on retrospective cine for data acquisition. Real-time imaging, although inferior in image quality to retrospective cine, is more informative about motion dynamics. We herein developed a real-time cardiac MRI approach to temporospatial characterization of left ventricle (LV) and right ventricle (RV) wall motion. This approach provided two temporospatial indices, temporal periodicity and spatial coherence, for quantitative assessment of ventricular function. In a cardiac MRI study, we prospectively investigated temporospatial characterization in reference to standard volumetric measurements with retrospective cine. The temporospatial indices were found to be effective for evaluating the difference of ventricular performance between the healthy volunteers and the heart failure (HF) patients (LV temporal periodicity 0.24 ± 0.037 vs. 0.14 ± 0.021; RV temporal periodicity 0.18 ± 0.030 vs. 0.10 ± 0.014; LV spatial coherence 0.52 ± 0.039 vs. 0.38 ± 0.040; RV spatial coherence 0.50 ± 0.036 vs. 0.35 ± 0.035; all in arbitrary unit). The HF patients and healthy volunteers were well differentiated in the scatter plots of spatial coherence and temporal periodicity while they were mixed in those of end-systolic volume (ESV) and ejection fraction (EF) from volumetric measurements. This study demonstrated the potential of real-time cardiac MRI for intricate analysis of ventricular function beyond retrospective cine.

Optical Flow Analysis of Left Ventricle Wall Motion with Real-Time Cardiac Magnetic Resonance Imaging in Healthy Subjects and Heart Failure Patients.

In cardiology, magnetic resonance imaging (MRI) provides a clinical standard for measuring ventricular volumes. Owing to their reliability, volumetric measurements with cardiac MRI have become an essential tool for quantitative assessment of ventricular function. However, as volumetric indices are indirectly related to myocardial motion that drives ventricular filling and ejection, cardiac MRI cannot provide comprehensive evaluation of ventricular performance. To overcome this limitation, the presented work sought to measure ventricular wall motion directly with optical flow analysis of real-time cardiac MRI. By modeling left ventricle (LV) walls in real-time images based on myocardial architecture, we developed an optical flow approach to analyzing LV radial and circumferential wall motion for improved quantitative assessment of ventricular function. For proof-of-concept, a cardiac MRI study was conducted with healthy volunteers and heart failure (HF) patients. It was found that, as real-time images provided sufficient temporal information for correlation analysis between different LV wall motion velocity components, optical flow assessment detected the difference of ventricular performance between the HF patients and the healthy volunteers more effectively than volumetric measurements. We expect that this model-based optical flow assessment with real-time cardiac MRI would offer intricate analysis of ventricular function beyond conventional volumetric measurements.

Clinical implications of compromised 82Rb PET data acquisition.

BACKGROUND: We wished to document the prevalence and quantitative effects of compromised 82Rb PET data acquisitions on myocardial flow reserve (MFR). METHODS AND RESULTS: Data were analyzed retrospectively for 246 rest and regadenoson-stress studies of 123 patients evaluated for known or suspected CAD. An automated injector delivered pre-determined activities of 82Rb. Automated quality assurance algorithms identified technical problems for 7% (9/123) of patients. Stress data exhibited 2 instances of scanner saturation, 1 blood peak detection, 1 blood peak width, 1 gradual patient motion, and 2 abrupt patient motion problems. Rest data showed 1 instance of blood peak width and 2 abrupt patient motion problems. MFR was lower for patients with technical problems flagged by the quality assurance algorithms than those without technical problems (1.5 ± 0.5 versus 2.1 ± 0.7, P = 0.01), even though rest and stress ejection fraction, asynchrony and relative myocardial perfusion measures were similar for these two groups (P > 0.05), suggesting that MFR accuracy was adversely affected by technical errors. CONCLUSION: It is important to verify integrity of 82Rb data to ensure MFR computation quality.

Comparing left atrial indices by CMR in association with left ventricular diastolic dysfunction and adverse clinical outcomes.

Left atrial (LA) features are altered when diastolic dysfunction (DD) is present. The relations of LA features to the DD severity and to adverse outcomes remain unclear using CMR images. We sought to compare LA features including volumes, emptying fraction, and strains as predictors of left ventricular (LV) DD and adverse outcomes. We compared four groups including normal controls (n = 32), grade I DD (n = 69), grade II DD (n = 42), and grade III DD (n = 21). DD was graded by echocardiography following the current ASE guidelines. Maximum LA volume (LAVmax), minimum LA volume (LAVmin), and LA emptying fraction (LAEF) were assessed using CMR cine images. Phasic LA strains including reservoir, conduit, and booster pump strain were assessed by feature tracking. The outcome was a composite of hospital admissions for heart failure and all-cause mortality analyzed using Cox proportional hazard models. LAVmax and LAVmin were progressively larger while LAEF and LA strain measures were lower with worsening degree of DD (all p < 0.001). Among 132 patients with DD, 61 reached the composite outcome after on average 36-months of follow-up. Each of the LA parameters except for LA conduit strain was an independent predictor of the outcome in the adjusted Cox proportional hazard models (all p < 0.001). They remained significant outcome predictors after the model additionally adjusted for LV longitudinal strain. The AUC of outcome prediction was highest by LAEF (0.760) followed by LA reservoir strain (0.733) and LAVmin (0.725). Among all the LA features, increased LA volumes, reduced LAEF, reduced LA reservoir and booster pump strains were all associated with DD and DD severity. While LA strains are valuable, conventional parameters such as LAEF and LAVmin remain to be highly effective in outcome prediction with comparable performance.

Electrocardiographic manifestations of COVID-19: Effect on cardiac activation and repolarization.

BACKGROUND: Prolonged QT intervals are reported in patients with COVID-19. Additionally, virus particles in heart tissue and abnormal troponin levels have been reported. Consequently, we hypothesize that cardiac electrophysiologic abnormalities may be associated with COVID-19. METHODS: This is a retrospective study between March 15th, 2020 and May 30th, 2020 of 828 patients with COVID-19 and baseline ECG. Corrected QT (QTc) and QRS intervals were measured from ECGs performed prior to intervention or administration of QT prolonging drugs. QTc and QRS intervals were evaluated as a function of disease severity (patients admitted versus discharged; inpatients admitted to medical unit vs ICU) and cardiac involvement (troponin elevation >0.03 ng/ml, elevated B-natriuretic peptide (BNP) or NT pro-BNP >500 pg/ml). Multivariable analysis was used to test for significance. Odds ratios for predictors of disease severity and mortality were generated. FINDINGS: Baseline QTc of inpatients was prolonged compared to patients discharged (450.1±30.2 versus 423.4±21.7  msec, p<0.0001) and relative to a control group of patients with influenza (p=0.006). Inpatients with abnormal cardiac biomarkers had prolonged QTc and QRS compared to those with normal levels (troponin - QTc: 460.9±34.6 versus 445.3±26.6  msec, p<0.0001, QRS: 98.7±24.6 vs 90.5±16.9  msec, p<0.0001; BNP - QTc: 465.9±33.0 versus 446.0±26.2  msec, p<0.0001, QRS: 103.6±25.3 versus 90.6±17.6 msec, p<0.0001). Findings were confirmed with multivariable analysis (all p<0.05). QTc prolongation independently predicted mortality (8.3% increase in mortality for every 10  msec increase in QTc; OR 1.083, CI [1.002, 1.171], p=0.04). INTERPRETATION: QRS and QTc intervals are early markers for COVID-19 disease progression and mortality. ECG, a readily accessible tool, identifies cardiac involvement and may be used to predict disease course. FUNDING: St. Francis Foundation.

The association of reduced left ventricular strains with increased extracellular volume and their collective impact on clinical outcomes.

BACKGROUND: Myocardial fibrosis and left ventricular (LV) longitudinal strain are independently associated with adverse clinical outcomes. However, the relationship between tissue properties and strain indices as well as their collective impact on outcomes are yet to be fully elucidated. We aim to investigate the relationship between LV global longitudinal strain (GLS), global circumferential strain (GCS) and global radial strain (GRS) with extracellular volume (ECV) and their collective impact. METHODS: Consecutive patients referred for clinical cardiovascular magnetic resonance (CMR) due to cardiomyopathy were prospectively enrolled. All patients underwent CMR with T1 mapping. ECV was calculated incorporating native and post-contrast T1 as well as hematocrit. LV GLS, GCS, and GRS were assessed by feature tracking. Hazard ratios and Kaplan-Meier curves were produced to assess the association between strains and T1 mapping indices with a composite outcome of all-cause mortality and hospitalized heart failure. RESULTS: The study consisted of 259 patients with mixed referring diagnoses of non-ischemic/ischemic cardiomyopathy and 21 normal controls. Decreased GLS, GCS and GRS were associated with increased ECV, increased native T1, and reduced post-contrast T1 in a dose dependent manner when T1 or ECV was in the abnormal range. After a mean follow-up of 31 ± 23 months, 41 events occurred including 37 heart failure admissions and 4 deaths. Kaplan-Meier plots demonstrated that reduced strains were associated with reduced event-free survival predominantly in patients with increased ECV (≥ 28.3%). The worst outcome was among those with both reduced strains and increased ECV. In the multivariable models, increased ECV, reduced post-contrast T1 and reduced strains in all 3 directions remained predictors of outcome risk, respectively. CONCLUSION: Our findings highlight the intrinsic link between altered CMR tissue properties and impaired myocardial mechanical performance and additionally demonstrate improved risk stratification by characterizing tissue property among patients with reduced strain.