Aims Despite its high incidence and mortality risk, there is no evidence‐based treatment for non‐ischaemic cardiogenic shock (CS). The aim of this study was to evaluate the use of mechanical circulatory support (MCS) for non‐ischaemic CS treatment. Methods and results In this multicentre, international, retrospective study, data from 890 patients with non‐ischaemic CS, defined as CS due to severe de‐novo or acute‐on‐chronic heart failure with no need for urgent revascularization, treated with or without active MCS, were collected. The association between active MCS use and the primary endpoint of 30‐day mortality was assessed in a 1:1 propensity‐matched cohort. MCS was used in 386 (43%) patients. Patients treated with MCS presented with more severe CS (37% vs. 23% deteriorating CS, 30% vs. 25% in extremis CS) and had a lower left ventricular ejection fraction at baseline (21% vs. 25%). After matching, 267 patients treated with MCS were compared with 267 patients treated without MCS. In the matched cohort, MCS use was associated with a lower 30‐day mortality (hazard ratio 0.76, 95% confidence interval 0.59–0.97). This finding was consistent through all tested subgroups except when CS severity was considered, indicating risk reduction especially in patients with deteriorating CS. However, complications occurred more frequently in patients with MCS; e.g. severe bleeding (16.5% vs. 6.4%) and access‐site related ischaemia (6.7% vs. 0%). Conclusion In patients with non‐ischaemic CS, MCS use was associated with lower 30‐day mortality as compared to medical therapy only, but also with more complications. Randomized trials are needed to validate these findings.

Left ventricular ejection fraction (LVEF) is a key parameter in evaluating left ventricular (LV) function using echocardiography (Echo), but its manual measurement by the modified biplane Simpson (MBS) method is time consuming and operator dependent. We investigated the feasibility of a server-based, commercially available and ready-to use-artificial intelligence (AI) application based on convolutional neural network methods that integrate fully automatic view selection and measurement of LVEF from an entire Echo exam into a single workflow. We prospectively enrolled 1083 consecutive patients who had been referred to Echo for diagnostic or therapeutic purposes. LVEF was measured independently using MBS and AI. Test–retest variability was assessed in 40 patients. The reliability, repeatability, and time efficiency of LVEF measurements were compared between the two methods. Overall, 889 Echos were analyzed by cardiologists with the MBS method and by the AI. Over the study period of 10 weeks, the feasibility of both automatic view classification and seamlessly measured LVEF rose to 81% without user involvement. LVEF, LV end-diastolic and end-systolic volumes correlated strongly between MBS and AI (R = 0.87, 0.89 and 0.93, p < 0.001 for all) with a mean bias of +4.5% EF, −12 mL and −11 mL, respectively, due to impaired image quality and the extent of LV function. Repeatability and reliability of LVEF measurement (n = 40, test–retest) by AI was excellent compared to MBS (coefficient of variation: 3.2% vs. 5.9%), although the median analysis time of the AI was longer than that of the operator-dependent MBS method (258 s vs. 171 s). This AI has succeeded in identifying apical LV views and measuring EF in one workflow with comparable results to the MBS method and shows excellent reproducibility. It offers realistic perspectives for fully automated AI-based measurement of LVEF in routine clinical settings.

Background/Introduction Two-dimensional echocardiography (Echo) is a feasible method for assessing left ventricular (LV) ejection fraction (EF) in daily practice. However, the interpretation of Echo exams depends on the user's expertise and may vary between different operators. A novel, vendor-neutral artificial intelligence (AI) performs both, automated evaluation of Echo exams and calculations of biplane LV EF in one workflow. Purpose We sought to assess the ability of the AI to automatically identify appropriate LV 4- and 2-chamber views (4CV) (2CV) from routine Echo examinations and compare the resulting biplane EF with conventional hand-tracing biplane Simpson method (Human). Methods We prospectively enrolled 311 patients who underwent clinically indicated Echo exams. Biplane LV EF was manually traced online on 4CV and 2CV by cardiologists (Human). After completion of the exam, the AI-based solution recognized the optimal LV 4CV and 2CV according to quality and depth criteria and automatically performed the calculation of biplane EF by endocardial borderline detection without Human's interaction. Spearman's correlation (R) and Bland-Altman analysis with limits of agreement (LOA) were assessed for bias between the two methods. In a subgroup of 20 patients, Echo exams were automatically reanalyzed by the AI, and conventional biplane Simpson of LV EF was performed by two cardiologists blinded to the previous results to determine intraclass correlation (ICC). Significance was defined as a 2-tailed p value &lt;0.05. Results 311 patients (median age 72 years [19–97]; 40% female) received an Echo for valvular heart disease, ischemic and non-ischemic heart failure or other indications (39, 31, 19 and 11%). 16 cases (5%) did not pass AI's criteria due to poor Echo imaging or impaired acoustic window of patients. In 53 patients (17%) either 4CV or 2CV were recognized, but the AI system successfully identified both 4CV and 2CV in 242 patients (overall feasibility 78%). For these 242 patients, correlation between AI and Human biplane LV EF was r=0.83 (p&lt;0.001) (Figure 1 left). The absolute mean bias between methods was 5.2% (p&lt;0.001) and absolute LOA ranged from −9.0 to +19.4% (Figure 1 right). ICC of LV EF by Human was 0.77 (p&lt;0.001). The AI's ability to correctly re-/classify 4CV and 2CV was 100% with an ICC of 1 for fully automated LV EF measurements. Conclusion The results provided by the AI-based software showed very good capability to identify 4CV and 2CV and good LV EF result compared to Human manual tracings, especially since patients were not pre-selected. However, differences between AI and Human measurements are not negligible and warrant further investigations. Funding Acknowledgement Type of funding sources: None.

Background/Introduction Cardiac magnetic resonance imaging (CMR) is regarded as the reference method in assessing left ventricular (LV) ejection fraction (EF). However, 2-dimensional echocardiography (2D-Echo) is the most frequently used technique due to availability and practicability. The interpretation of 2D-Echo examinations depends on the user's expertise and may vary between different operators. A novel vendor-independent software based on artificial intelligence (AI) performs both, automated evaluation of 2D-Echo exams and calculations of LV EF in one workflow. Purpose We sought to assess the ability of the AI to automatically identify appropriate LV 4- and 2-chamber views (4CV) (2CV) from 2D-Echo exams and validate the resulting EF with CMR. Methods We consecutively enrolled 128 patients who underwent clinically indicated CMR examinations and performed a standard 2D-Echo at the same day. The server-based AI solution recognized the optimal LV 4CV and 2CV from 2D-Echo according to quality and depth criteria and automatically performed calculation of biplane EF by endocardial borderline detection. LV EF from CMR and AI were supervised by independent cardiologists blinded to the mutual results. Pearson's correlation (R) and Bland-Altman analysis with limits of agreement (LOA) were performed in order to assess bias between the two methods. Significance was defined as a 2-tailed P value &lt;0.05. Results CMR was performed and LV EF was measured in all 128 patients. The median age was 60 years [20–86], 65% were males and CMR was performed due to coronary artery diseases (33%), suspected/florid myocarditis (20%) or further diagnosis of non-ischemic heart failure (47%). Eleven cases (9%) did not pass AI's criteria due to impaired acoustic window or poor 2D-Echo images. The AI system detected either 4CV or 2CV (ratio 1.2) in 13 patients (10%), and both 4CV and 2CV in 104 patients (81% overall feasibility) with a correct classification of 100%. For these 104 patients, excellent correlation was found for AI's biplane LV EF and LV EF from CMR with r=0.91 (p&lt;0.001) (Figure 1, left). However, the absolute mean bias between AI and CMR was 3.5% (p&lt;0.001) and LOAs were −10.6 and +17.5% (Figure 1, right). Conclusion The results provided by the AI-based software showed good capabilities and a perfect classification rate to identify 4CV and 2CV. In addition, the LV EF results were excellent compared to CMR, especially since our study did not include “echocardiographically” pre-selected patients. However, differences between AI and CMR measurements are not negligible and warrant further investigation. Funding Acknowledgement Type of funding sources: None.

Funding Acknowledgements Type of funding sources: None. Background Evaluation of right ventricle (RV) function has clinical value when investigating patients with symptomatic heart failure. However, the assessment of RV function using two-dimensional echocardiography (Echo) is operator-dependent and is time-consuming. Technological advances have enabled the use of artificial intelligence (AI) based algorithms for functional analysis with high reproducibility, facilitated by manufacturer-neutral platform. Purpose We sought to determine the feasibility of using an AI-based application to measure RV function in patients referred for diagnostic evaluation of heart failure, and to compare the results with operator-generated (Human) measurements in order to assess the accuracy and efficiency of the AI. Methods We conducted a study on 190 consecutive patients, comparing the analysis times and accuracies of tricuspid annular plane systolic excursion (TAPSE) and fractional area change (FAC) measurements using both Human and AI-based methods on three consecutive heart beats of the same Echo clip. The participants manually measured TAPSE in M-mode and FAC by tracking the RV endocardium in diastole and systole, while the AI automatically assessed these parameters in a single apical RV-centered 4-chamber view. The results of these measurements were then compared to determine the accuracy and efficiency of the AI. Results The measurement feasibility by the AI was 92% (n=175) on the RV clips. The main reasons for exclusion were poor image quality and insufficient representation of the RV free wall. The analysis time for TAPSE and FAC was significantly shorter for the AI compared to the Human (14±4 seconds vs. 101±21 seconds, p&lt;0.001). Figure 1 shows that there was a significant correlation between TAPSE and FAC measurements made by AI and Human (R=0.66 and 0.64, p&lt;0.001), but there was a mean bias of 3.3 mm (±23.7 mm) for TAPSE and 3.9% (±16.7%) for FAC between the two methods. Conclusions The AI-based application demonstrated good feasibility and significantly reduced analysis time for measuring TAPSE and FAC in patients referred for diagnostic evaluation of heart failure. However, the measurement bias between AI and Human was not negligible, warranting further investigation to determine the cause of this discrepancy and whether this AI-based application is an alternative to the manual method for assessing RV function.