Assessment Of Left Ventricular Ejection Fraction Research Articles

Multicenter validation study for automated left ventricular ejection fraction assessment using a handheld ultrasound with artificial intelligence

We sought to validate the ability of a novel handheld ultrasound device with an artificial intelligence program (AI-POCUS) that automatically assesses left ventricular ejection fraction (LVEF). AI-POCUS was used to prospectively scan 200 patients in two Japanese hospitals. Automatic LVEF by AI-POCUS was compared to the standard biplane disk method using high-end ultrasound machines. After excluding 18 patients due to infeasible images for AI-POCUS, 182 patients (63 ± 15 years old, 21% female) were analyzed. The intraclass correlation coefficient (ICC) between the LVEF by AI-POCUS and the standard methods was good (0.81, p < 0.001) without clinically meaningful systematic bias (mean bias -1.5%, p = 0.008, limits of agreement ± 15.0%). Reduced LVEF < 50% was detected with a sensitivity of 85% (95% confidence interval 76%–91%) and specificity of 81% (71%–89%). Although the correlations between LV volumes by standard-echo and those by AI-POCUS were good (ICC > 0.80), AI-POCUS tended to underestimate LV volumes for larger LV (overall bias 42.1 mL for end-diastolic volume). These trends were mitigated with a newer version of the software tuned using increased data involving larger LVs, showing similar correlations (ICC > 0.85). In this real-world multicenter study, AI-POCUS showed accurate LVEF assessment, but careful attention might be necessary for volume assessment. The newer version, trained with larger and more heterogeneous data, demonstrated improved performance, underscoring the importance of big data accumulation in the field.

Shorter Door-to-ECG Time Is Associated with Improved Mortality in STEMI Patients.

Background: Delayed intervention for ST-segment elevation myocardial infarction (STEMI) is associated with higher mortality. The association of door-to-ECG (D2E) with clinical outcomes has not been directly explored in a contemporary US-based population. Methods: This was a three-year, 10-center, retrospective cohort study of ED-diagnosed patients with STEMI comparing mortality between those who received timely (<10 min) vs. untimely (>10 min) diagnostic ECG. Among survivors, we explored left ventricular ejection fraction (LVEF) dysfunction during the STEMI encounter and recovery upon post-discharge follow-up. Results: Mortality was lower among those who received a timely ECG where one-week mortality was 5% (21/420) vs. 10.2% (26/256) among those with untimely ECGs (p = 0.016), and in-hospital mortality was 6.0% (25/420) vs. 10.9% (28/256) (p = 0.028). Data to compare change in LVEF metrics were available in only 24% of patients during the STEMI encounter and 46.5% on discharge follow-up. Conclusions: D2E within 10 min may be associated with a 50% reduction in mortality among ED STEMI patients. LVEF dysfunction is the primary resultant morbidity among STEMI survivors but was infrequently assessed despite low LVEF being an indication for survival-improving therapy. It will be difficult to assess the impact of STEMI care interventions without more consistent LVEF assessment.

Harnessing the Power of AI: A Comprehensive Review of Left Ventricular Ejection Fraction Assessment With Echocardiography.

The quantification of left ventricular ejection fraction (LVEF) has important clinical utility in the assessment of cardiac function and is vital for the diagnosis of cardiovascular diseases. A transthoracic echocardiogram serves as the most commonly used tool for LVEF assessment for several reasons, including, its noninvasive nature, great safety profile, real-time image processing ability, portability, and cost-effectiveness. However, transthoracic echocardiogram is highly dependent on the clinical skill of the sonographer and interpreting physician. Moreover, even amongst well-trained clinicians, significant interobserver variability exists in the quantification of LVEF. In search of possible solutions, the usage of artificial intelligence (AI) has been increasingly tested in the clinical setting. While AI-derived ejection fraction is in the preliminary stages of development, it has shown promise in its ability to rapidly quantify LVEF, decrease variability, increase accuracy, and utilize higher-order processing capabilities. This review will delineate the latest advancements of AI in evaluating LVEF through echocardiography and explore the challenges and future trajectory of this emerging domain.

The Survival Benefit of Pegylated Liposomal Doxorubicin-Based Neoadjuvant Chemotherapy in the Management of Breast Cancer.

Purpose: This study aims to evaluate the short-term outcomes and prognosis and the cardiac safety of pegylated liposomal doxorubicin (PLD)-based neoadjuvant chemotherapy (NAC) compared with epirubicin-based therapy in breast cancer treatment. Methods: In total, 304 patients diagnosed with stages II and III breast cancer were enrolled that included 97 cases treated with PLD and 207 controls treated with epirubicin in NAC. The effectiveness of the antibreast cancer treatment was evaluated using overall survival (OS) and disease-free survival (DFS) metrics, whereas cardiac toxicity was measured through the left ventricular ejection fraction (LVEF) and electrocardiogram (ECG) assessments. Results: The 5-year DFS and OS rates in the PLD group were 84.5% and 88.7% (with 15 recurrences and 11 deaths), respectively, whereas in the control group, these rates were 72.9% and 79.2% (with 56 recurrences and 43 deaths). Regarding cardiac toxicity, there was no significant difference in ECG abnormalities or LVEF decline between the two groups. Conclusions: The study suggests that PLD-based NAC may provide substantial benefits in terms of DFS and OS, along with a safe cardiac toxicity profile, in patients with stage II-III breast cancer.

Influence of chest wall conformation on reproducibility of main echocardiographic indices of left ventricular systolic function.

The possible influence of chest wall conformation, as noninvasively assessed by Modified Haller Index (MHI, the ratio of chest transverse diameter over the distance between sternum and spine), on reproducibility of both left ventricular ejection fraction (LVEF) and global longitudinal strain (GLS) has never been previously investigated. Two equal groups of healthy individuals, matched by age, sex, and cardiovascular risk factors and categorized according to MHI in those with concave-shaped chest wall (MHI>2.5) and those with normal chest shape (MHI≤2.5), who underwent transthoracic echocardiography implemented with echocardiographic deformation imaging between June 2018 and May 2019, were retrospectively analyzed. LVEF and GLS were measured twice by the two echocardiographers in a double blinded manner. Intra-class correlation coefficients (ICCs), bias and limits of agreement determined with Bland-Altman analysis were calculated for repeated measurements of both LVEF and GLS. Thirty-four healthy individuals with MHI>2.5 (54.9±6.4 years, 58.8% females) and 34 matched controls with MHI≤2.5 (52.5±8.1 years, 50% females) were separately analyzed. In comparison to MHI≤2.5 group, the MHI>2.5 group was found with significantly smaller cardiac chambers and significantly lower GLS magnitude (-15.8±2.5 vs. -22.2±1.3%, P<0.001), despite similar LVEF (61.3±6.4 vs. 61.1±3.6%, P=0.87). In the MHI>2.5 group, intra-rater and inter-rater ICCs were ≤0.5 for both LVEF and LV-GLS, whereas in the MHI≤2.5 group intra-rater and inter-rater ICCs values indicated good reliability for LVEF and excellent reliability for GLS. The greatest bias and largest limits of agreement were detected for LVEF assessment (bias ranging from -1.09 to 2.94%, with the 95% limits of agreement ranging from -13.9 to 21.3%) in individuals with MHI>2.5. On the other hand, the smallest bias and narrowest limits of agreement were obtained for GLS measurement (bias ranging from -0.26 to 0.09%, with the 95% limits of agreement ranging from -1.4 to 1.4%) in participants with normal chest wall conformation (MHI≤2.5). The test reliability of LVEF and GLS is strongly influenced by the chest wall conformation. MHI might represent an innovative approach for selecting the best echocardiographic method for LV systolic function estimation in the individual case.

Cardiovascular sequelae of trastuzumab and anthracycline in long-term survivors of breast cancer

ObjectivesLong-term follow-up of patients treated with trastuzumab largely focuses on those with reduced left ventricular ejection fraction (LVEF) on treatment completion. This study sought to evaluate the prevalence of cardiovascular...

Artificial Intelligence-Powered Left Ventricular Ejection Fraction Analysis Using the LVivoEF Tool for COVID-19 Patients.

We sought to prospectively investigate the accuracy of an artificial intelligence (AI)-based tool for left ventricular ejection fraction (LVEF) assessment using a hand-held ultrasound device (HUD) in COVID-19 patients and to examine whether reduced LVEF predicts the composite endpoint of in-hospital death, advanced ventilatory support, shock, myocardial injury, and acute decompensated heart failure. COVID-19 patients were evaluated with a real-time LVEF assessment using an HUD equipped with an AI-based tool vs. assessment by a blinded fellowship-trained echocardiographer. Among 42 patients, those with LVEF < 50% were older with more comorbidities and unfavorable exam characteristics. An excellent correlation was demonstrated between the AI and the echocardiographer LVEF assessment (0.774, p < 0.001). Substantial agreement was demonstrated between the two assessments (kappa = 0.797, p < 0.001). The sensitivity, specificity, PPV, and NPV of the HUD for this threshold were 72.7% 100%, 100%, and 91.2%, respectively. AI-based LVEF < 50% was associated with worse composite endpoints; unadjusted OR = 11.11 (95% CI 2.25-54.94), p = 0.003; adjusted OR = 6.40 (95% CI 1.07-38.09, p = 0.041). An AI-based algorithm incorporated into an HUD can be utilized reliably as a decision support tool for automatic real-time LVEF assessment among COVID-19 patients and may identify patients at risk for unfavorable outcomes. Future larger cohorts should verify the association with outcomes.

Empowering Medical Students: Harnessing Artificial Intelligence for Precision Point-of-Care Echocardiography Assessment of Left Ventricular Ejection Fraction

Introduction. Point-of-care ultrasound (POCUS) use is now universal among nonexperts. Artificial intelligence (AI) is currently employed by nonexperts in various imaging modalities to assist in diagnosis and decision making. Aim. To evaluate the diagnostic accuracy of POCUS, operated by medical students with the assistance of an AI-based tool for assessing the left ventricular ejection fraction (LVEF) of patients admitted to a cardiology department. Methods. Eight students underwent a 6-hour didactic and hands-on training session. Participants used a hand-held ultrasound device (HUD) equipped with an AI-based tool for the automatic evaluation of LVEF. The clips were assessed for LVEF by three methods: visually by the students, by students + the AI-based tool, and by the cardiologists. All LVEF measurements were compared to formal echocardiography completed within 24 hours and were evaluated for LVEF using the Simpson method and eyeballing assessment by expert echocardiographers. Results. The study included 88 patients (aged 58.3 ± 16.3 years). The AI-based tool measurement was unsuccessful in 6 cases. Comparing LVEF reported by students’ visual evaluation and students + AI vs. cardiologists revealed a correlation of 0.51 and 0.83, respectively. Comparing these three evaluation methods with the echocardiographers revealed a moderate/substantial agreement for the students + AI and cardiologists but only a fair agreement for the students’ visual evaluation. Conclusion. Medical students’ utilization of an AI-based tool with a HUD for LVEF assessment achieved a level of accuracy similar to that of cardiologists. Furthermore, the use of AI by the students achieved moderate to substantial inter-rater reliability with expert echocardiographers’ evaluation.

Abstract 17216: Limited Concordance of Core Lab Left Ventricular Ejection Fraction With Automated Assessments: A Sub-Study From VALOR-HCM

Introduction: The performance of automated left ventricular ejection fraction (LVEF) assessment in patients with altered cardiac chamber geometry, such as those with obstructive hypertrophic cardiomyopathy (HCM), is not well described. Aims: Accurate assessment of LVEF is required for dose titration and continuation of myosin inhibitors in obstructive HCM. From the VALOR-HCM trial (NCT04349072), the agreement of two commercially available software packages was compared with blinded core lab assessment of LVEF. Methods: From baseline and week 16, among 112 patients randomized to either mavacamten or placebo, 178 echocardiograms were analyzed. Representative clips were manually selected, and automated LVEFs were calculated. Comparisons of LVEFs were made using Pearson’s correlation coefficients and Bland-Altman analysis. Results: With core lab assessment, the overall median LVEF was 68% (Q1 66%, Q3 70%). The correlation with vendor 1 was moderate (r = 0.47, 95% CI 0.35-0.58, p &lt;0.001) and weak with vendor 2 (r =0.21, 95% CI 0.07-0.35, p =0.004). Correlation was also weak between vendor 1 and 2 (r = 0.14, 95% CI 0.00-0.28, p = 0.06). Vendors 1 and 2 had an underestimation bias of LVEF of 8% (95% CI 6-9%) and 8% (95% CI 7-9%), respectively, compared to core lab LVEF (Figure 1). Conclusions: In VALOR-HCM, the concordance of LVEF between different commercially available software packages with each other and with a core lab assessment was limited. Further study is needed to evaluate and improve the performance of automated LVEF tools in obstructive HCM patients who have altered cardiac chamber geometry.

Abstract 16984: A Wearable Device Reproducibly Identifies Premature Ventricular Contractions and Tachyarrhythmias in Heart Failure Patients

Introduction: occurrence of ventricular arrhythmias (VAs) has a dramatic impact on prognosis of patients with heart failure (HF). Hypothesis: wearable devices, including smartwatch (SW), might provide real-time informations in patients at risk of VAs. Aims: we therefore evaluated if SW detection of QRS interval width and voltage could reproducibly detect VAs and/or premature ventricular contractions (PVCs) in HF patients. Methods: HF patients (N=100) underwent continuous electrocardiographic (ECG) recordings through telemetry and SW during hospital admission. Left ventricular ejection fraction (EF) and laboratory pro-BNP levels assessments were obtained as well. Correlation of the ECG results was addressed using Pearson’s analysis for normally distributed data and Spearman’s analysis for non-parametric data. Bland-Altman was used for reliability between telemetry and SW ECG measurements. Results: mean EF was 45.92±12.12%; mean pro-BNP was 2826±5823 pg/mL. Using the SW and the ECG data, QRS width and voltage correlation coefficients were 0.990 (p&lt;0.001) and 0.974 (p&lt;0.001), respectively. Concordance was found between the two assessments for QRS width (bias, 0.20; SD 3.0; lower limit, –5.68; upper limit, 6.08), and voltage (bias, -0.029; SD, 0.611; lower limit, –1.227; upper limit, 1.169). Moreover, we found correlation coefficients of 0.964 (p&lt;0.001) and 0.970 (p&lt;0.001), respectively, for PVCs width and PVCs voltage using SW and telemetry or other ECG report. Similarly, both techniques showed positive concordance for PVCs width (bias, 0.368; SD, 2.65; lower limit, -4.83; upper limit, 5.56) and PVCs voltage (bias, 0.54; SD, 1.25; lower limit, -1.91; and upper limit, 2.996). Remarkably, VAs identification by the SW was reproducible and standardized. Conclusions: continuous monitoring through SW might be considered in HF patients upon discharge.

Abstract 18812: Premortem Risk Assessment Among Countywide Sudden Deaths

Introduction: Heart failure (HF) and left ventricular ejection fraction (EF) assessment have been hallmarks of sudden cardiac death (SCD) risk stratification for over 20 years. However, their sensitivity, specificity, and positive predictive value (PPV) for true arrhythmic death in the general population are unknown because nearly all SCD studies presume arhythmic causes. Aims: To define sensitivity, specificity, and PPV of current SCD risk stratification criteria for the autopsy-confirmed arrhythmic cause among countywide sudden deaths. Methods: POST SCD (POstmortem SysTematic Investigation of Sudden Cardiac Death) is a prospective cohort study using autopsy, clinical records, and toxicology to adjudicate arrhythmic (SAD; potentially rescuable with implantable defibrillator) or non-arrhythmic (non-SAD; e.g., tamponade, overdose) causes among presumed SCDs (pSCDs) in San Francisco County. We analyzed sensitivity, specificity, and PPV for syncope, EF≤35%, diagnosed HF, or the presence of any of 3 factors to discriminate SADs among pSCDs. Results: Between February 2011 and September 2022, 845 of 904 (93%) pSCDs 18-90 years had complete medical records available, of which 491 (58%) were SADs. Seventy-three pSCDs had history of syncope, with similar prevalence in SADs and non-SADs (p=0.4), while prevalence of HF was higher in SADs vs. non-SADs (88/491[18%] vs. 39/354 [11%], p=0.003). EF assessment was performed in 206 pSCDs (24%, 133 SADs, 73 non-SADs); prevalence of EF ≤ 35% was similar between SADs and non-SADs (p=0.112). A greater proportion of SADs had ≥1 factor than non-SADs (122/491[25%] vs. 65/354 [18%], p=0.015). Sensitivity of syncope, EF≤35%, HF, ≥1 factor for SAD was 9, 26, 17, 25%, specificity was 92, 82, 90, 82%, and PPV was 60, 73, 69, 65%, respectively. Conversely, 369 of 491 (75%) SADs had no risk factors before sudden death. Defibrillators were implanted in 4/491 (0.8%) SADs. Conclusions: In this 11-year countywide postmortem study, conventional risk factors had low sensitivity and PPV but high specificity for autopsy-confirmed arrhythmic cause of sudden death. Three-fourths of SADs had no risk factors before sudden death, highlighting the need for improved risk prediction in the general population.

Reproducibility of Left Ventricular Function Derived From Cardiac Magnetic Resonance and Gated 13N-Ammonia Positron Emission Tomography Myocardial Perfusion Imaging: A Head-to-Head Comparison Using Hybrid Positron Emission Tomography/Magnetic Resonance

Cardiac magnetic resonance (CMR) and gated 13N-ammonia positron emission tomography myocardial perfusion imaging (PET-MPI) offer accurate and highly comparable global left ventricular ejection fraction (LVEF) measurements. In addition to accuracy, however, reproducibility is crucial to avoid variations in LVEF assessment potentially negatively impacting treatment decisions. We performed a head-to-head comparison of the reproducibility of LVEF measurements derived from simultaneously acquired CMR and PET-MPI using different state-of-the-art commercially available software. 93 patients undergoing hybrid PET/MR were retrospectively included. LVEF was derived from CMR and PET-MPI at two separate core labs, using two state-of-the-art software packages for CMR (cvi42 and Medis Suite MR) and PET (QPET and CardIQ Physio). Intra- and inter-reader agreement was assessed using correlation and Bland-Altman (BA) analyses. While intra- and inter-reader reproducibility of LVEF was high among both modalities and all software packages (r≥0.87 and ICC≥0.91, all significant at p<0.0001), LVEF derived from PET-MPI and analyzed with QPET outperformed all other analyses (intra-reader reproducibility: r=0.99, ICC=0.99; inter-reader reproducibility: r=0.98, ICC=1.00; Pearson correlations significantly higher than all others at p≤0.0001). BA analyses showed smaller biases for LVEF derived from PET-MPI (-0.1% and +0.9% for intra-reader, -0.4% and -0.8% for inter-reader agreement) than those derived from CMR (+0.7% and +2.8% for intra-reader, -0.9% and -2.2% for inter-reader agreement) with similar results for BA limits of agreement. Gated 13N-ammonia PET-MPI provides equivalent reproducibility of LVEF compared to CMR. It may offer a valid alternative to CMR for patients requiring LV functional assessment.

Diagnostic accuracy of point-of-care ultrasound with artificial intelligence-assisted assessment of left ventricular ejection fraction

Focused cardiac ultrasound (FoCUS) is becoming standard practice in a wide spectrum of clinical settings. There is limited data evaluating the real-world use of FoCUS with artificial intelligence (AI). Our objective was to determine the accuracy of FoCUS AI-assisted left ventricular ejection fraction (LVEF) assessment and compare its accuracy between novice and experienced users. In this prospective, multicentre study, participants requiring a transthoracic echocardiogram (TTE) were recruited to have a FoCUS done by a novice or experienced user. The AI-assisted device calculated LVEF at the bedside, which was subsequently compared to TTE. 449 participants were enrolled with 424 studies included in the final analysis. The overall intraclass coefficient was 0.904, and 0.921 in the novice (n = 208) and 0.845 in the experienced (n = 216) cohorts. There was a significant bias of 0.73% towards TTE (p = 0.005) with a level of agreement of 11.2%. Categorical grading of LVEF severity had excellent agreement to TTE (weighted kappa = 0.83). The area under the curve (AUC) was 0.98 for identifying an abnormal LVEF (<50%) with a sensitivity of 92.8%, specificity of 92.3%, negative predictive value (NPV) of 0.97 and a positive predictive value (PPV) of 0.83. In identifying severe dysfunction (<30%) the AUC was 0.99 with a sensitivity of 78.1%, specificity of 98.0%, NPV of 0.98 and PPV of 0.76. Here we report that FoCUS AI-assisted LVEF assessments provide highly reproducible LVEF estimations in comparison to formal TTE. This finding was consistent among senior and novice echocardiographers suggesting applicability in a variety of clinical settings.

Clinical comparison of a handheld cardiac ultrasound device for the assessment of left ventricular function.

Recently developed handheld ultrasound devices (HHUD) represent a promising method to evaluate the cardiovascular abnormalities at the point of care. However, this technology has not been rigorously evaluated. The aim of this study was to explore the correlation and the agreement between the LVEF (Left Ventricular Ejection Fraction) visually assessed by a moderately experienced sonographer using an HHUD compared to the routine LVEF assessment performed at the Echocardiography Laboratory. This was a prospective single center study which enrolled 120 adult inpatients and outpatients referred for a comprehensive Echocardiography (EC). The mean age of the patients was 69.9 ± 12.5 years. There were 47 females (39.2%). The R-squared was r 0.94 (p < 0.0001) and the ICC was 0.93 (IC 95% 0.91-0.95, p ≤ 0.0001). The Bland-Altman plot showed limits of agreement (LOA): Upper LOA 10.61 and Lower LOA - 8.95. The overall agreement on the LVEF assessment when it was stratified as "normal" or "reduced" was 89.1%, with a kappa of 0.77 (p < 0.0001). When the LVEF was classified as "normal", "mildly reduced", "moderately reduced", or "severely reduced," the kappa was 0.77 (p < 0.0001). The kappa between the HHUD EC and the comprehensive EC for the detection of RWMAs in the territories supplied by the LAD, LCX and RCA was 0.85, 0.73 and 0.85, respectively. With current HHUD, an averagely experienced operator can accurately bedside visual estimate the LVEF. This may facilitate the incorporation of this technology in daily clinical practice improving the management of patients.

Deep Learning to Estimate Left Ventricular Ejection Fraction From Routine Coronary Angiographic Images

BackgroundCine images during coronary angiography contain a wealth of information besides the assessment of coronary stenosis. We hypothesized that deep learning (DL) can discern moderate-severe left ventricular dysfunction among patients undergoing coronary angiography. ObjectivesThe purpose of this study was to assess the ability of machine learning models in estimating left ventricular ejection fraction (LVEF) from routine coronary angiographic images. MethodsWe developed a combined 3D-convolutional neural network (CNN) and transformer to estimate LVEF for diagnostic coronary angiograms of the left coronary artery (LCA). Two angiograms, left anterior oblique (LAO)-caudal and right anterior oblique (RAO)-cranial projections, were fed into the model simultaneously. The model classified LVEF as significantly reduced (LVEF ≤40%) vs normal or mildly reduced (LVEF>40%). Echocardiogram performed within 30 days served as the gold standard for LVEF. ResultsA collection of 18,809 angiograms from 17,346 patients from Mayo Clinic were included (mean age 67.29; 35% women). Each patient appeared only in the training (70%), validation (10%), or testing set (20%). The model exhibited excellent performance (area under the receiver operator curve [AUC] 0.87; sensitivity 0.77; specificity 0.80) in the training set. The model’s performance exceeded human expert assessment (AUC, sensitivity, and specificity of 0.86, 0.76, and 0.77, respectively) vs (AUC, sensitivity, and specificity of 0.76-0.77, 0.50-0.44, and 0.90-0.93, respectively). In additional sensitivity analyses, combining the LAO and RAO views yielded a higher AUC, sensitivity, and specificity than utilizing either LAO or RAO individually. The original model combining CNN and transformer was superior to DL models using either 3D-CNN or transformers. ConclusionsA novel DL algorithm demonstrated rapid and accurate assessment of LVEF from routine coronary angiography. The algorithm can be used to support clinical decision-making and form the foundation for future models that could extract meaningful data from routine angiography studies.

Late-onset cardiotoxicity in patients with HER2-positive metastatic breast cancer receiving trastuzumab-based therapy.

Patients with human epidermal growth factor receptor 2 (HER2)-positive metastatic breast cancer (MBC) typically receive long-term trastuzumab treatment for several years. The aim of our study is to identify the incidence and characterize late-onset cardiotoxicity in patients with HER2-positive MBC receiving trastuzumab-based therapy. We retrospectively reviewed charts of HER2-positive MBC patients who received >1 year of trastuzumab-based therapy at the Massachusetts General Hospital Cancer Center over three-year period. The primary endpoint was development of trastuzumab-induced cardiotoxicity (TIC). Secondary endpoints included time to TIC development, incidence/duration of trastuzumab interruption due to TIC, incidence of permanent discontinuation of trastuzumab due to TIC, clinic visit, or hospitalization due to TIC. Thirty-seven patients were included. Mean age was 56 years (range: 33-78 years, SD 9.5). Seven patients received prior doxorubicin and 14 patients received previous or concurrent breast irradiation. Mean duration of trastuzumab-based therapy was 57 months (range: 14-140 months, SD 39.3). Seven patients (18.9%) experienced TIC resulting in treatment interruption for two patients (28 and 78 days). The median time from starting trastuzumab therapy to TIC was 14 months (interquartile range: 11-29.5 months). The mean number of left ventricular ejection fraction (LVEF) assessment completed per year was 2.7 (range: 1.2-6.6, SD 1.1). Cardiotoxicity occurred in a minority of patients with HER2-positive MBC receiving trastuzumab-based therapy for more than one year. LVEF reductions to below the institutional lower limit of normal and therapy modifications were uncommon.

Left-ventricular volumes and ejection fraction from cardiac ECG-gated 15O-water positron emission tomography compared to cardiac magnetic resonance imaging using simultaneous hybrid PET/MR

Background15O-water PET is the gold standard for noninvasive quantification of myocardial blood flow. In addition to evaluation of ischemia, the assessment of cardiac function and remodeling is important in all cardiac diseases. However, since 15O-water is freely diffusible and standard uptake images show little contrast between the myocardium and blood pool, the assessment of left-ventricular (LV) volumes and ejection fraction (EF) is challenging. Therefore, the aim of the present study was to investigate the feasibility of calculating LV volumes and EF from first-pass analysis of 15O-water PET, by comparison with cardiac magnetic resonance imaging (CMR) using a hybrid PET/MR scanner. MethodsTwenty-four patients with known or suspected CAD underwent a simultaneous ECG-gated cardiac PET/MR scan. The 15O-water first-pass images (0-50 seconds) were analyzed using the CarPET software and the CMR images were analyzed using the software Segment, for LV volumes and EF calculations. The LV volumes and EF were compared using correlation and Bland–Altman analysis. In addition, inter- and intra-observer variability of LV volumes and EF were assessed for both modalities. ResultsThe correlation between PET and CMR was strong for volumes (r > 0.84) and moderate for EF (r = 0.52), where the moderate correlation for EF was partly due to the small range of EF values. Agreement was high for all parameters, with a slight overestimation of PET values for end-diastolic volume but with no significant mean bias for other parameters. Inter- and intra-observer agreement of volumes was high and comparable between PET and CMR. For EF, inter-observer agreement was higher for PET and intra-observer agreement was higher for CMR. ConclusionLV volumes and EF can be calculated by first-pass analysis of a 15O-water PET scan with high accuracy and comparable precision as with CMR.

Incidence and surveillance of acute cardiovascular toxicities in paediatric acute lymphoblastic leukaemia: A retrospective population-based single-centre cohort study

AimHere, we studied the incidence of acute cardiovascular toxicities in children treated for acute lymphoblastic leukaemia (ALL). MethodsWe performed a population-based single-centre longitudinal retrospective cohort study in 70 children diagnosed and treated with anthracycline-containing therapy against ALL at Karolinska University Hospital during 2015–2019 with a follow-up period of at least three months. Cardiovascular surveillance for these patients included echocardiography with measurements of left ventricular ejection fraction (LVEF) and shortening fraction (LVSF), electrocardiography and non-invasive blood pressure monitoring. ResultsNo patient experienced a significant decrease in LVEF or LVSF during or early after primary cancer treatment including anthracyclines. Surveillance with LVEF and LVSF was unable to predict the trajectory to severe clinical heart failure in one patient following treatment. Pericardial effusion prior to therapy initiation occurred in 13.6% of the patients. The incidence of intracardiac thrombosis and arterial hypertension was 8.5%, and 20%, respectively. ConclusionEarly cardiovascular toxicities were common in this paediatric ALL cohort. We confirm that early routine LVEF and LVSF assessments were insufficient to identify patients at risk of subsequent treatment-related heart failure. This underlines the unmet need of more sensitive methods for cardiovascular surveillance in children treated for cancer to reduce the burden of cardiovascular morbidity and mortality.

Fully Automated 3D echocardiographic algorithms: accurate and time saving - the answer for 3D in routine clinical practice?

Abstract Funding Acknowledgements Type of funding sources: None. Introduction 3D left ventricular ejection fraction (LVEF) quantification methods are more accurate and reproducible than 2D echocardiography, however, conventional 3D is time consuming and requires extensive user expertise, thus hindering its routine implementation in busy echocardiography laboratories and its use by inexperienced physicians. HeartModel A.I. (HM) is a simple, fast, recently validated 3D automated analysis software that detects LV endocardial surfaces and calculates LVEF. The aim of this work is to evaluate the performance of HM with experienced and inexperienced physicians, its time saving potential and to assess whether this software can be a better alternative to 2D measurements in routine echocardiography. Methods Prospective analysis of 30 nonconsecutive patients referred for transthoracic echocardiogram in a university hospital echocardiography lab, from 1st February 2021 to 31st March 2021. 2D biplane LVEF was measured by an experienced and inexperienced physician (less than 250 echocardiograms performed), then the same physicians used the automated analysis software to assess LVEF (blinded for each other results). The time to make the measurements was registered. Comparisons of agreement between LVEF measurements (experienced versus inexperienced physicians) included linear regression with Pearson correlation coefficients and Bland-Altman analyses to assess the bias and limits of agreement (defined as 2SD around the mean). Results A total of 30 patients were included, mean age of 68.6 ± 20.1 years and 60% male. HM showed significantly lower acquisition times in both inexperienced (72±17s versus 173± 44s, P&amp;lt;0.01) and experienced (56±12s versus 126±29s, P&amp;lt;0.01) physicians. The difference in time of acquisition between 2D and HM was approximately 101s for inexperienced users and around 70s for experienced users. Regarding LVEF assessment, HM acquisitions compared to 2D measurements showed stronger correlations between experienced and inexperienced physicians (r= 0,98, P&amp;lt;0,01 versus r= 0,92, P&amp;lt;0,01) with minimal bias (−0,5 versus −0,6) and stronger agreement (HM limits of agreement: ± 5,8% versus 2D limits of agreement: ± 12,5%) Conclusion 3D LVEF assessment by HM significantly reduced acquisition times and exhibited higher interobserver agreement than 2D Simpson’s biplane method. These results suggest that automated 3D algorithms, such as HM, may play a key role in implementing 3D measurements in routine practice in busy echocardiography laboratories and allow the use of 3D echocardiography at early stages of physicians training.

Prognostic value of myocardial scar burden using cardiac magnetic resonance in ischemic and non-ischemic cardiomyopathy

Abstract Funding Acknowledgements Type of funding sources: None. Myocardium is a unique muscle type designed to perform a specific function; conducting electrical activity to both ventricle simultaneously to contract and relax in a synchronized manner. Therefore, evaluation of the myocardial muscle function only using left ventricular ejection fraction (LVEF) would be inaccurate. However, assessment of the myocardium on the tissue level, is expected to provide more precise data regarding the ability to conduct, contract and relax properly. Purpose is to evaluate the prognostic value of myocardial scar using CMR on the clinical outcome in patients with ischemic cardiomyopathy (ICM) and non-ischemic cardiomyopathy (NICM). Methods 154 patients with either ICM or NICM underwent CMR (using 1.5T) for assessment of LVEF, detection and quantification of any myocardial scar by calculating the percentage (%) of scar mass to the whole myocardial mass (using manual quantification of scarred tissue in each myocardial segment, summed and divided by the total myocardial mass). Patients were followed up for at least 6 months for any cardiac events. These events were scaled according to severity from 1 (asymptomatic) to 7 (most severe), including; no symptoms, mild non-acute chest pain, mild dyspnoea (NYHA I-II), hospital admission due to decompensated heart failure, syncope, documented ventricular arrhythmia and sudden cardiac arrest/death, respectively. Statistical correlation between the amount of scarred myocardium in both ICM (group I) and NICM (group II) patients and the severity of the clinical events during the period of the follow up was performed. The study included 154 patients, 56% were males, mean age of 61 years with minimal follow up duration of 6 months. Patients were divided into: group I with ICM (58%) and group II with NICM (42%). Clinical presentation was ranging from eventless (10%), chest pain (18%), heart failure (15%), hospitalization (35%), syncope (1%), ventricular tachycardia (&amp;lt;1%) and up to cardiac arrest (&amp;lt;1%). Direct relationship was observed between scar size and event severity in the two groups (P value &amp;lt; 0.001). However, inverted relationship between LVEF and event severity in group I (P value of &amp;lt; 0.001) was detected but not in group II (P value 0.128), as shown in figure 1. Low EF was mainly linked with hospitalization in both ICM and NICM. It was observed that serious cardiac events were less detected in patient with mean scar mass &amp;lt; 5.4–8.4% . However, patients who have experienced sudden cardiac arrest have a mean scar mass of 15.9 % and patients with ventricular tachycardia with mean of 9.8%. Conclusion The amount of scarred myocardium is found to be directly linked to the severity of the clinical event in both ICM and NICM. Therefore, quantification of myocardial scar could be used as a predictor for cardiac events, hospitalization and sudden cardiac death.