Measurement of cardiac structure and function from images  ejection fraction [LVEF]) guides care for millions. This is best assessed using cardiovascular magnetic resonance (CMR), but image analysis is currently performed by individual clinicians, which introduces error. We sought to develop a machine learning algorithm for volumetric analysis of CMR images with demonstrably better precision than human analysis.A fully automated machine learning algorithm was trained on 1923 scans  and used to segment the LV blood volume and myocardium. Performance was quantified by measuring precision on an independent multi-site validation dataset with multiple pathologies with n = 109 patients, scanned twice. This dataset was augmented with a further 1277 patients scanned as part of routine clinical care to allow qualitative assessment of generalization ability by identifying mis-segmentations. Machine learning algorithm (‘machine’) performance was compared to three clinicians (‘human’) and a commercial tool .P < 0.05), translating to a 46% reduction in required trial sample size using an LVEF endpoint.Machine analysis was quicker (20 s per patient) than human (13 min). Overall machine mis-segmentation rate was 1 in 479 images for the combined dataset, occurring mostly in rare pathologies not encountered in training. Without correcting these mis-segmentations, machine analysis had superior precision to three clinicians (e.g. scan-rescan coefficients of variation of human vs machine: LVEF 6.0% vs 4.2%, LV mass 4.8% vs. 3.6%; both We present a fully automated algorithm for measuring LV structure and global systolic function that betters human performance for speed and precision.The online version contains supplementary material available at 10.1186/s12968-022-00846-4. Measures of cardiac size, mass, and function derived from imaging are some of the most fundamental biomarkers in medicine. For example, left ventricular (LV) ejection fraction (LVEF) determines selection for drug therapy in heart failure –3, detecLVEF was initially proposed as a simple way of measuring heart function using cardiac catheterization . The intRecent developments in deep learning using convolutional neural networks (CNN)—computational models inspired by the architecture of the human brain—have revolutionized automated image analysis . The potWe hypothesized that a carefully trained, fully automated machine learning analysis could be developed and proven to exceed human performance on any CMR scanner and any  disease. This approach requires a means of evaluating and comparing CMR measures of myocardial structure and function, but this is hampered by the lack of a truth standard. Most existing approaches report measurement accuracy, treating expert analysis (or a consensus thereof) as a truth standard, but this is fundamentally flawed because of the inherent variability and subjectivity of humans. We therefore concentrate on measurement precision (reproducibility) and develop an evaluation framework using an independent dataset to quantify measurement precision (reproducibility), which determines clinical smallest detectable interval change and research study sample size.An overview of the study design is illustrated in Fig. We use deep fully CNNs to annotate (“segment”) the LV blood pool and myocardium from CMR image datasets , 21. HerAll image acquisition reflected standard international recommendations  and convFour CNNs were used in total: a SAx model (containing the blood pool only), a 2-chamber (2Ch) model and a 4-chamber (4Ch) model. Diastole was identified as the cardiac phase with largest blood volume and a further CNN, trained only in diastole, was used to segment the myocardium.–4 with no decay and image augmentation  was used to artificially increase the variability in the training set—the technical details of each model is listed in Additional file A U-net architecture  was adop2—see Fig. All images were normalized to a standard reference frame by: centering the SAx images to the point of intersection with the long axis (Lax) images (2Ch and 4Ch), rotating so the SAx and 2Ch intersection aligned to the y-axis , and scaling to an in-plane pixel size of 1 mmSince the basal extent of the myocardium is difficult to determine from SAx images alone, we used the 2Ch and 4Ch images to define the mitral annulus, below which any volume was discarded . A planeWe developed a generalizable scanner-agnostic and disease-independent model using anonymized data from imaging datasets of 1,923 patients previously recruited via written informed consent to clinical studies, all with approval from the local research ethics committees and complying to the principles of the Helsinki declaration. Subjects were scanned with two field strengths 1.5 and 3 T), three CMR manufacturers , 10 scanner models, 13 institutions from three countries  and reviewed and adjusted by a single expert with > 15 years’ clinical experience (JCM). Clinician segmentation was performed on cvi42 software  using the semi-automated threshold tool with the ‘smoothed contour’ option enabled and post-hoc freehand correction as needed—the choice of tool was informed by previous work showing this to be most precise. SegmentaFollowing model parameter optimization, an iterative approach was adopted for model refinement. At each iteration of the model, expert readers  used custom-built tools to inspect model segmentation predictions. We found that anatomically implausible segmentations could be traced back to the original training data and amending the training segmentation could remove such errors in subsequent models. The basal slice was particularly difficult and multiple approaches were trialed. For the final segmentation approach for training, we annotated the entire basal slice and one additional atrial slice, allowing the mitral annular plane correction algorithm to precisely define slice inclusion/exclusion with respect to the ventricle—a departure from conventional approaches .The LV was segmented in each SAx image using volumes calculated from all SAx images in that cardiac phase. End-systole  and at end-diastole (largest volume) were identified and their volumes used to derive LV end diastolic volume (LVEDV), end systolic volume (LVESV), LVEF, and myocardial mass (LVM).Machine learning model performance is often reported in terms of measurement accuracy  by measNo images used in model evaluation had been included in the training data.www.thevolumesresource.com) obtained on 109 subjects who were scanned, then removed from the scanner before being scanned again. The dataset contains multiple pathologies , scanned at two field strengths at five institutions, as previously described . Errors were seen in rare scenarios not encountered in training and in cases where contextual information assimilated by the human expert, was missed by the machine—examples include ventricular thrombus, artifact, effusions, poor image piloting, and unusual extracardiac findings  images on the Precision dataset, mostly because of missing contours, particularly at the base.The LV metrics  derived from human and machine segmentations were highly correlated on the test (precision) dataset with a CCC range of 0.94 to 0.95. Absolute measurement differences between human and machine methods on the precision dataset were —see Additional file P < 0.05). The increased machine precision translates to a better minimal detectable change  and a potential reduction in clinical trial sample size by 46% if LVEF was used as an endpoint . The mean values of machine-derived LV metrics were: LVEF 65% , LVEDV 145 ml, LVESV 51 ml, LVM 94 g. See Additional file We present a fully automated method of CMR LV volumetric analysis and demonstrate that it has superior precision to a human expert. Widespread adoption has the potential to standardize global care, reduce the need for clinical expert time, and significantly reduce sample sizes for clinical trials.Automated CMR analysis has already demonstrated faster performance with non-inferiority to humans . Here, wMachine errors were seen in circumstances not encountered in the training data, such as a laminar thrombus mimicking the LV wall, or a pericardial effusion resembling LV blood  so that we can focus on true physiological  variability, which is crucial for serial assessment in clinical cardiology (e.g. monitoring for signs of cardiotoxicity during chemotherapy) and in clinical trials. Here, we have shown how image analysis variability can be minimized, but in future work we will extend this to automate the image acquisition process using deep learning to prescribe consistent image planes.The difference between scan-rescan coefficients of variation Fig.  and the Clinical adoption of machine learning methods is slow due to challenges with data access, integrating computer science and clinical domains, as well as validation, transparency, ethical and regulatory concerns . Here, wThere are significant differences between normative reference ranges for cardiac structure and function reported in the literature,  andTechnical limitations of the algorithm include the method of co-registering the data from SAx, 2Ch and 4Ch images in 3D, which assumes consistent breath hold at the time of each acquisition, which may not always be the case. We will investigate ways of compensating for breath hold inconsistencies and integration into true 3D volumes in future work.Other limitations include the limited number of human observers from a single centre used to benchmark the Precision dataset. This took 210 h of manual segmentation, but we acknowledge that validation with more clinicians from more centres is required before we can generalize the finding that the algorithm outperforms humans.We present an automated, generalized (any scanner and disease) method of measuring cardiac structure and systolic function that—without any intervention or correction—outperforms human analysis for precision.Additional file 1: Table S1. Technical details of each neural network model (U-net) used. Table S2. Demographics and descriptive analysis of the generalizability cohort with a total of 1,277 patients. BMI = body mass index; LVEDV = left ventricle end diastolic volume; LVEF = left ventricle ejection fraction; LVESV = left ventricle end systolic volume; LGE = late gadolinium enhancement; LVM = left ventricle mass. Table S3. Comparison of Mean values (standard deviation in brackets) for LV metrics computed using three different methods in the Precision dataset. LVEDV: left ventricular end-diastolic volume; LVESV: left ventricular end-systolic volume; LVEF: left ventricular ejection fraction; LVM: LVM: left ventricular mass; LVSV: left ventricular stroke volume. Table S4. Comparison of scan-rescan precision metrics between human, machine and cvi42. LVEDV: left ventricular end diastolic volume; LVESV: left ventricular end systolic volume; LVEF: left ventricular ejection fraction; LVM: left ventricular mass; LVSV: left ventricular troke Volume Table S5. Sample size calculation based on Precision dataset. sd = standardized difference. Table S6. Pilot Study for Normal reference range showing mean  for each LV metric for machine-derived CMR volumes from a set of 98 healthy subjects. The combined reference range is presented as well as sex- and age-stratified ranges. Table S7. Breakdown of segmentation error by type and location on the validation (precision) dataset, which contains a total of 5058 images.


This review focuses on the obese-diabetic phenotype of heart failure with preserved ejection fraction highlighting the interaction between obesity, diabetes, and coronary microvascular dysfunction in the development and progression of left ventricular remodeling. Recent therapeutic advances are reviewed.Heart failure with preserved ejection fraction is a growing epidemic and accounts for half of all patients with heart failure. Increasing prevalence, morbidity, and clinical inertia have spurred a rethinking of the pathophysiology of heart failure with preserved ejection fraction. Unlike heart failure with reduced ejection fraction, heart failure with preserved ejection fraction has distinct clinical phenotypes. The obese-diabetic phenotype is the most often encountered phenotype in clinical practice and shares the greatest burden of morbidity and mortality. Left ventricular remodeling plays a major role in its pathophysiology. Understanding the interplay of obesity, diabetes mellitus, and inflammation in the pathophysiology of left ventricular remodeling may help in the discovery of new therapeutic targets to improve clinical outcomes in heart failure with preserved ejection fraction. Anti-diabetic agents like glucagon-like-peptide 1 analogs and sodium-glucose co-transporter 2 are promising therapeutic modalities for the obese-diabetic phenotype of heart failure with preserved ejection fraction and aggressive weight loss  Heart failure with preserved ejection fraction (HFpEF) is a growing epidemic . Unlike The cornerstone of HF is LV remodeling. In HFrEF, systolic dysfunction leads to eccentric hypertrophy with LV wall thinning and replacement fibrosis. In HFpEF, the LV wall thickens leading to concentric hypertrophy (LVH)  with imp2+ handling  trial had a BMI > 35 kg/m2  accumulates in multiple depots. The subcutaneous depot accounts for around 80% of the total AT . VisceraWeight gain leads to AT accumulation through adipocyte hypertrophy or hyperplasia. While expanding VAT becomes dysfunctional and inflamed thereby promoting low-grade systemic inflammation . IncreasAfter undergoing hypertrophy, VAT shifts from an anti-inflammatory state that facilitates AT angiogenesis and lipid storage to a pro-inflammatory state with production of monocyte chemoattractant protein- (MCP1), C-X-C motif chemokine 12 leukotriene B4, and colony-stimulating factor 1 that promote proliferation of classically activated macrophages and macrophages AT infiltration .Adipogenesis modulates the AT remodeling process and hypoxia is the trigger behind angiogenesis, extracellular matrix remodeling, and inflammation . Inflamm2O2). High oxidative stress uncouples NO synthase (eNOS), reduces NO availability, and decreases soluble guanylate cyclase (sGC) stimulation that lowers the activity of cyclic guanosine monophosphate (cGMP) and protein kinase G (PKG). Low PKG activity leads to cardiomyocytes hypertrophy and decreases titin phosphorylation that increases LV stiffness  dependent defect in the endothelial cell metabolic programing and angiogenesis may affect the progression of perivascular and myocardial fibrosis in HFpEF  Figure .The role of adipocyte dysfunction in the development of obese-HFpEF is still evolving. Adipocyte homeostasis is maintained by the modulation of pro-inflammatory and anti-inflammatory cytokines. Obesity leads to an excess of pro-inflammatory cytokines and adipokine dysregulation. Adipose tissue exerts an endocrine effect via adipokines. Adipocyte dysfunction caused by obesity leads to alteration in adipokine levels that promotes LV remodeling and ultimately, HfpEF . ElevateIn vitro adiponectin has multiple beneficial effects such as stimulation of AMP-activated protein kinase (AMPK)-dependent and extracellular-signal-regulated kinase (Low adiponectin levels in obesity contribute to an increase in the risk for cardiovascular (CV) disease . Adiponed kinase  signalind kinase . These bd kinase .Resistin is an adipocytokine secreted in macrophages by pro-inflammatory cytokines . IncreasAccumulation of VAT when obesity worsens plays a major role in the development and progression of cardiometabolic conditions. In T2D, VAT is a strong predictor of insulin resistance  and incrIn the Multi-Ethnic Study of Atherosclerosis (MESA) , patientIn patients with obese-HfpEF, VAT accumulation is associated with LVDD and positively correlates with increased LV mass , sphericIncreased pericardial/EAT is independently associated with both obesity and T2D . EAT is 2 consumption and peripheral extraction in patients with HfpEF, indicating a worse hemodynamic profile in these patients  have increased LV mass index (LVMI), large left atrial size (LA), and high E/e’ velocity by echocardiography . The asspatients .3 for women and > 120 cm3 for men correlated with a twofold increased incidence of HF in women and 53% higher risk in men. Increased EAT predominantly enhanced the risk of HFpEF (p < 0.001) and not HFrEF (p.31)  .Nearly 50% of patients with HFpEF have chronic kidney disease (CKD) . The etiObesity itself leads to a glomerulopathy, i.e., obesity-related glomerulopathy (ORG) that is characterized by maladaptive glomerular hypertrophy and focal segmental glomerulosclerosis . Other pManagement of CKD in patients with obesity and HFpEF is challenging. Targeting the RAAS showed promise in retrospective studies with a greater reduction of proteinuria seen in obese than non-obese individuals . HoweverWeight loss improves proteinuria and has a favorable effect on the estimated glomerular filtration rate . BariatrBesides T2D macrovascular complications, the direct effects of T2D on the myocardium have received increased attention over the last decade .2+-ATPase activity indicating a different pathophysiological process when compared to non-T2D HFpEF   . Patientctively) .2 vs. 30.7 kg/m2 in patients with and without T2D. Unsurprisingly, patients with T2D-HFpEF had more severe initial presentations and more frequent hospitalizations. They also had more LVH and higher filling pressures (E/e’) by echocardiography. Cardiac Magnetic Resonance Imaging (CMR) reveals a trend toward higher LV Mass and higher levels of fibrosis in T2D than patients with non-T2D HFpEF  and had an increased rate of mortality and hospitalization for HF .In HFpEF, patients with T2D commonly have higher BMIs than their non-diabetic counterparts. In an ancillary study of the Phosphodiesterase-5 inhibition to improve clinical status and Exercise capacity in Diastolic HF (RELAX) trial , BMI wer2D HFpEF . Patient2  . The cau2  and qual2 . In HFpEfunction . The eff2, respectively). Moreover, in the T2D cohort, 52% of the patients had a BMI > 30 kg/m2 vs. 38% of the patients in the non-T2D cohort  were analyzed into 3 subgroups, patients with T2D treated with insulin , patients with T2D not on insulin , and patients withoutT2D (n = 969 patients)  trial , patientatients) . The ITDatients) . Howeveratients) . Thus, tTherapy in HfpEF, specifically in the obese-T2D-HfpEF phenotype is searching for novel therapeutic approaches. Targeting CMD is an innovative approach but so far results have not been promising . Increas2 in all groups) hints at a low prevalence of VAT and EAT in this population.Given the neutral findings of the above trials, increasing NO may not be the most effective way to remedy endothelial dysfunction. Vericiguat, an sGC stimulator bypasses NO production and can stimulate the production of cGMP that as previously mentioned prevents further LV remodeling. Vericiguat did not improve the primary endpoints of NT-ProBNP levels and left atrial volumes but did improve quality of life in a clinical trial . A lowerRegardless, despite the high prevalence of CMD in HFpEF , targetiSodium retention secondary to increased aldosterone production plays a major role in obese-HFpEF. It accounts for the responsiveness to diuretics but excess natriuresis can accelerate renal dysfunction . Experim2  as the first step, then pharmacotherapy and bariatric surgery as the next step. Newer advances in anti-diabetic medications have led to a successful strategy of targeting obesity and HF in patients with T2D changing the management paradigm for these patients.Glucagon-like peptide-1 (GLP-1) analogs are coming back in T2D and recent trials demonstrate efficacy in CV disease  and weigGLP-1 agonists have also been shown to be effective in reducing EAT which is a target in HFpEF . In 95 pReduction of adiposity is an essential therapeutic aim in obese-T2D-HFpEF. Before recent semaglutide trials, pharmaceutical agents approved for weight loss by the Food and Drug Administration (FDA) at best resulted in 7% weight loss . GLP-I aSodium-glucose co-transporter 2 inhibitors (SGLT-2i) are extremely beneficial in HFrEF . The act2 (HR 0.85 C.I 0.7–1.03) did not derive as much benefit as those with BMI < 30 kg/m2 (HR.7 C.I 0.62–0.88). The findings of the awaited dapagliflozin trials  trial , empaglin trials  may stren trials .Metabolic surgery, specifically GB, is the most effective intervention for weight loss. It prevents the occurrence of HFpEF in patients with severe obesity. However metabolic surgery has complications and requires careful and long-term monitoring. Compared with LBM, metabolic surgery results in a greater weight loss and a 23% risk reduction in HF . Exercisp < 0.0001) measured by cardiac MRI  fractional area change at 2 years (2)] and the effects of caloric restriction and exercise were additive. However, there was no difference in the quality of life (as reported on the Minnesota Living with Heart Failure Questionnaire) or LVMI in either group. High intensity and moderate continuous exercise regimens do not significantly improve in peak VO2 compared with guideline-directed exercise regimens  in MESA with every 5% weight loss being associated with a 1.3% decrease in LVMI and LV mass-to volume ratio (diac MRI . The Utadiac MRI . Mean ba 2 years . Smaller 2 years . The effregimens  (Table 3Adherence to LBM is strongly recommended for patients with obesity, T2D, and HFpEF and physicians need to be pro-active to effectively help patients lose weight. Metabolic surgery though beneficial is marred by strict indications  and mult2.Heart failure with preserved ejection fraction remains a therapeutic conundrum. The obese-T2D phenotype has distinct pathophysiology encompassing inflammation, CMD, and LV remodeling. Obesity is at the crux of the pathophysiology and weight reduction must be prioritized in these patients. Quantification of VAT and EAT may better help risk-stratify patients at greatest risk of HFpEF and further studies are needed to assess their impact on management. Mineralocorticoid receptor antagonists and anti-diabetic agents like semaglutide and SGLT-2 inhibitors hold promise as useful adjunct agents for obese-T2D-HFpEF and should be studied in randomized clinical trials. Lifestyle and behavioral modifications should be offered to all patients and metabolic surgery may be considered in patients with BMI > 35 kg/mAD-P and TL contributed to conception and design of the article, wrote the manuscript, and drafted the figures. TT and RS edited certain sections of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The involvement of natriuretic peptides was studied during the hypertrophic remodeling transition mediated by sequential exposure to chronic hemodynamic overload. We induced hypertension in rats by pressure (renovascular) or volume overload  during 6 and 12 weeks of treatment. We also studied the consecutive combination of both models in inverse sequences: RV 6 weeks/DS 6 weeks and DS 6 weeks/RV 6 weeks. All treated groups developed hypertension. Cardiac hypertrophy and left ventricular ANP gene expression were more pronounced in single DS than in single RV groups. BNP gene expression was positively correlated with left ventricular hypertrophy only in RV groups, while ANP gene expression was positively correlated with left ventricular hypertrophy only in DS groups. Combined models exhibited intermediate values between those of single groups at 6 and 12 weeks. The latter stimulus associated to the second applied overload is less effective than the former to trigger cardiac hypertrophy and to increase ANP and BNP gene expression. In addition, we suggest a correlation of ANP synthesis with volume overload and of BNP synthesis with pressure overload-induced hypertrophy after a prolonged treatment. Volume and pressure overload may be two mechanisms, among others, involved in the differential regulation of ANP and BNP gene expression in hypertrophied left ventricles. Plasma ANP levels reflect a response to plasma volume increase and volume overload, while circulating BNP levels seem to be regulated by cardiac BNP synthesis and ventricular hypertrophy. Hemodynamic overload is a major determinant of the cardiac morphometric and functional response in cardiovascular diseases. Hypertension is a leading cause of congestive heart failure around the world. Elevation of the blood pressure results in left ventricular hypertrophy, an independent risk factor for cardiovascular mortality . RemodelThe natriuretic peptide (NP) family includes atrial (ANP) and B-type (BNP) natriuretic peptides that are secreted by the heart and are important regulators of cardiovascular homeostasis. They act on target organs through guanylyl cyclase- coupled receptors to induce diuresis, natriuresis and vasodilation, thus reducing cardiac preload and afterload in response to stress. They also modulate cardiac growth and display anti-inflammatory and anti-fibrotic properties . Gene exOur lab previously described the combination of treatments to induce pressure and volume overload in defined times and sequences, in order to resemble the natural evolution of ventricular function in hypertensive disease and to evaluate the differential behavior of ANP and BNP , 2010. WWe hypothesized that ANP and BNP could be used as biochemical markers for volume and pressure overload, respectively, and that a prolonged overload would be required to display differential profiles of ANP and BNP, and these would help to characterize the transition from one hypertrophic pattern to the other. Therefore, the aim of the present study was to evaluate the effect of a chronic hypertensive process in rats with RV and DS procedures along 6 to 12 weeks of treatment, as well as in rats subjected to combination of RV and DS in different sequences of induction. We measured the modifications of systolic blood pressure, cardiac hypertrophy indexes, cardiac function and the synthesis and secretion of ANP and BNP.ad libitum . The protocols were approved by the Institutional Review Board at the University of Buenos Aires.Male Sprague-Dawley rats weighing 180–220 g were obtained from the Animal Care Facility of the School of Pharmacy and Biochemistry at the University of Buenos Aires. The animals were housed in an environment with controlled temperature and humidity, 12-h light/dark cycle, and access to water and food The experimental design is shown in n) was 10–12 for experimental groups and 12–18 for sham groups.Appropriate sham animals for each experimental group were included (see detailed description of procedures below). Sham animals for RV6 and DS6 groups did not show any statistical difference among them for several parameters  and wereThe surgical procedures were performed as previously described . After bRats were anesthetized as described above and subjected to left nephrectomy . They reSix weeks after induction of RV treatment, some animals were randomly selected to undergo renal artery declipping. The clips were removed carefully by a surgery performed close to the initial incision. Upon recovery, rats received DOCA injections and 1% NaCl treatment during six additional weeks. Sham animals were subjected to simulated surgery, vehicle injections and drank tap water.After 6 weeks of DOCA-salt treatment, a subgroup of rats was given no further DOCA injections or NaCl and underwent surgery for right renal artery constriction with 0.28 mm clip for six additional weeks. This group was named DS6/RV6. Sham animals discontinued vehicle injections and were subjected to simulated surgery.Systolic blood pressure was measured in conscious animals using the standard tail-cuff method . All measurements were performed between 9:00 am and 1:00 pm, after 3–5 days of training.At the end of the experimental period, blood samples were obtained from the abdominal cava vein under anesthesia and immediately placed into ice-chilled plastic tubes containing 15% EDTA. Then, a solution of KCl 1 mol/L was injected to induce diastolic arrest. The hearts were immediately removed, washed in cold phosphate buffered saline, blotted and weighed. Heart samples were carefully dissected by the same operator and the four cardiac chambers were weighed individually. The interventricular septum was included with the left chamber. Samples were frozen in liquid nitrogen and kept at −80°C for analysis.The ratio between heart weight and body weight (HW/BW) was calculated to evaluate cardiac hypertrophy. The ratios for each individual chamber weight to body weight were also calculated. LVW/BW and RVW/BW indicated left and right ventricular hypertrophy, and LAW/BW and RAW/BW indicated left and right atrial hypertrophy, respectively.Atrial and BNP were extracted from plasma samples as previously described , 2010. RTotal RNA was isolated from left ventricular samples using Trizol  and subjected to Northern Blot analysis as previously described . ScanninEchocardiography was performed using an Acuson Sequoia C512 Ultrasound System with a 14-MHz linear transducer. Echocardiographic studies were performed under light anesthesia using ketamine (35 mg/kg) plus xylazine (5 mg/kg). The chest was shaved and the animal was positioned on a warm pad. Electrode needles were connected to each limb, and the electrocardiogram was simultaneously recorded. Rats were imaged in a shallow left lateral decubitus position. Two-dimensional parasternal short-axis imaging plane were used to obtain M-mode tracings at the level of the papillary muscles. LV internal dimensions and LV wall thickness were determined at systole and diastole. End-diastolic measurements were taken at the maximal LV diastolic dimension, and end systole was defined as the time of the most anterior systolic excursion of the posterior wall. Measurements were taken from three consecutive beats for each rat. Transmitral Doppler inflow waves were used to measure peak early diastolic filling velocity (E wave), peak filling velocity at atrial contraction (A wave), and the ratio between them (E/A) as well as isovolumetric relaxation time (ms), assessing diastolic function as previously described .Systolic function was evaluated from LV dimensions by the cubed method as percentage of LV ejection fraction (LVEF): LVEF (%) = [(LVEDD3–LVESD3)/LVEDD3] × 100, where LVEDD and LVESD are LV end-diastolic diameter and LV end-systolic diameter, respectively. Diastolic and systolic wall stress was calculated by using hemodynamic and echocardiographic measurements according to Laplace’s law, where Diastolic stress = LV End-diastolic pressure x LV-diameter in diastole/2 × diastolic posterior wall thickness, and Systolic stress = LV systolic pressure × LV-diameter in systole/2 × posterior wall thickness in systole.P-values less than 0.05 were considered statistically significant and are indicated in the figures and tables.All data are expressed as mean ± standard error of the mean (SEM). Statistical analysis was performed across all eight groups by one-way ANOVA, using GraphPad Prism software . For multiple comparisons, we performed Tukey-Kramer post-test comparing the mean of each group with the mean of every other group to identify significant differences. Z test was used to compare two correlation coefficients . Both combined treatments RV6/DS6 and DS6/RV6 had lower HW/BW compared to the single groups RV12 and DS12, respectively.We calculated hypertrophy indexes and assessed functional parameters by echocardiography . All hypP < 0.05). Finally, right atrial hypertrophy (RAW/BW) was observed in DS groups at 6 weeks and was even more pronounced after 12 weeks. In contrast, right atrial hypertrophy developed in RV only after 12 weeks of treatment. Taken together, these results suggest that pronounced cardiac hypertrophy elicited by DS treatment may be sustained by an earlier development of hypertrophy in the right atria. Finally, both combined treatments RV6/DS6 and DS6/RV6 had less increase in RAW/BW compared to the single groups RV12 and DS12, respectively.At the individual chamber level, RV as well as DS groups developed left ventricular hypertrophy when compared to sham animals, reaching similar LVW/BW ratios at 6 and 12-weeks of treatment. Single treated hypertensive groups developed right ventricular hypertrophy (RVW/BW) at 6 and 12 weeks. All hypertensive groups also developed left atrial hypertrophy, but only DS groups showed a time-dependent increase in LAW/BW  showed significantly blunted ANP levels compared to DS6 and DS12.Plasma BNP levels increased in both RV6 and RV12 groups , and onlP < 0.001), but only moderately at 12 weeks . Both combined treatments showed increased ANP mRNA levels compared to sham; RV6/DS6 showed significantly lower expression than RV12 or DS12.We evaluated ANP and BNP gene expression as an indicator of natriuretic peptide synthesis in cardiac tissues. Northern Blot analysis showed a time-dependent increase of ANP mRNA expression in the left ventricle for RV and DS, with higher values at 12 weeks in both treatments . ANP genMeanwhile, BNP mRNA did not significantly increase in RV6 and DS6, but increased 0.75-fold in RV12 and 1.35-fold in DS12 . Only DSz test between correlation coefficients for RV vs. DS: −4.7411, P: 0.0002). This result can be explained, at least in part, by the time dependent increase in SBP in response to RV treatment between 6 and 12 weeks, which is not observed in DS treatment  and cardiac hypertrophy (HW/BW ratio). There was a positive correlation between SBP and HW/BW when all the groups were taken together  and alsoment see .r: 0.7717, P: 0.1263) nor RV groups . In contrast, the concentration of circulating BNP correlated with BNP gene expression in RV groups , but not in DS groups .We analyzed the relationship between plasma levels of ANP and BNP peptides and their ventricular gene expression (graphs not shown). We did not find any correlation between circulating ANP and ANP gene expression neither in DS . Moreover, a stronger positive correlation was exhibited when the DS groups were evaluated separately . For the RV groups evaluated separately, the correlation was not statistically significant .In addition, ANP mRNA showed positive correlation with the degree of left ventricular hypertrophy (LVW/BW ratio) when all groups were analyzed together .Finally, left ventricular BNP mRNA expression showed positive correlation with left ventricular hypertrophy only in the RV groups , but notRemodeling of the heart in response to persistent hemodynamic overload can lead to a spectrum of geometric, functional and hormonal patterns. We aimed to identify differential profiles of cardiac natriuretic hormones ANP and BNP in rats undergoing sequential combination of 6 weeks of renovascular followed by 6 weeks of DOCA-salt treatment or vice versa, and compared to the single treatments applied for 6 or 12 weeks. Our results show that in the models combining 6 weeks of each treatment, regardless of the sequence of presentation the overload stimulus associated to the second treatment is less effective than the first stimulus to sustain cardiac hypertrophy and to increase ANP and BNP expression. Moreover, in a chronic setting ANP correlates with volume overload while BNP correlates with pressure overload- induced cardiac hypertrophy. Thus, the combination of overload stimuli in different sequences provide an experimental tool to gain understanding about the events involved in the transition of hemodynamic states that occur along the evolution of cardiac disease.The development of hypertension in response to RV was gradual and time-dependent, in agreement with previous studies in this model  while inRenovascular model developed cardiac hypertrophy at 6 and 12 weeks, consistent with previous studies . In DS tAll groups exhibited cardiovascular remodeling, with increased left ventricular wall thickness without dilation of the left ventricular chamber. The results of ejection fraction and shortening fraction show that left ventricular systolic function was preserved and none of the groups presented severe ventricular dysfunction. In addition, DS treatment leads to an earlier compromise in both diastolic and systolic function compared to RV. However, RV and DS did not show distinctive features of defined concentric or eccentric hypertrophy. Hypertensive disease per definition involves both pressure and volume overload components, therefore RV and DS are not 100% pure models for pressure and volume, respectively. Both models rather have a predominant component of pressure or volume, which leads to geometric patterns that are not completely concentric or eccentric, as it would happen in hypertrophic versus dilated cardiomyopathies of genetic origin.Both combined models (RV6/DS6 and DS6/RV6) had lower SBP levels at 12 weeks than the single overloaded models RV12 and DS12, respectively. Cardiac hypertrophy, as well as the hypertrophy of the four chambers at 12 weeks was less pronounced in the combined treated groups than in the single treated models. A similar behavior was observed in our previous study, where we evaluated combined models at 4 weeks . These rThe circulating ANP and BNP levels are the result of a balance between gene regulation, peptide release and clearance by NPR-C receptors and the metallopeptidase neprilysin . In addiThe main source of ANP in plasma and the main stimulus for the release of ANP in DS is volume overload that leads to stretching of the right atrium. In contrast, the RV model has predominantly pressure overload that mainly involves stretching of the left ventricular myocardium. These facts may explain why plasma ANP increases much more in response to DS than to RV treatment. Furthermore, these results are also in agreement with Yokota et al. who have reported a marked increase in cardiac ANP production and its secretion into the circulation in DS model .However, in the RV model when pressure overload is suppressed and replaced by DS treatment (group RV6/DS6) the ANP levels did not further increase, and showed similar levels to RV6 and RV12. A possible explanation to this observation is that after the interruption of RV model there is a partial regression of the hypertensive process . MoreoveWe previously showed that plasma BNP levels increased only after 4 weeks of RV treatment  and in tWe evaluated ANP and BNP gene expression as a more reliable index of natriuretic peptide synthesis than the tissue protein level, since the amount of tissue protein may be influenced by several processes like synthesis, storage, intragranular cleavage, clearance, and intracrine/autocrine release or endocrine secretion . More reThe results obtained in the present study allow us to suggest that hypertrophic growth and NPs gene induction are processes that develop in parallel in the single-overloaded models. Our results show that DS treatment elicits an earlier induction of ANP gene expression compared to RV, since ANP mRNA was increased at 6 weeks in DS treated animals but not in RV. ANP mRNA expression markedly increased in RV and DS treatment from 6 to 12 weeks, and at 12 weeks there was no difference between RV and DS treatment. In contrast, BNP mRNA did not increase at 6 weeks in RV and DS but increased in both treatments at 12 weeks. These data are in agreement with previous reports  and our In RV model, the increase in gene expression was not sufficient to increase plasma levels on ANP at 12 weeks. A recent study also showed that the accumulation and release of ANP from myocyte granules depends on the pathogenesis of hypertension, and described a higher number of ANP secretory granules in a salt loading model compared to two-kidney 1-clip hypertension, providing a possible explanation for the lack of increase in plasma ANP in our RV model . AnotherIn the combined models, left ventricular ANP mRNA expression showed intermediate levels between those observed in both single treated RV12 and DS12 and their corresponding sham groups, supporting the concept that combined models display an intermediate behavior between those observed in single models.BNP gene expression in the left ventricle was more positively correlated with left ventricular hypertrophy index only in RV groups, while ANP gene expression was positively correlated with left ventricular hypertrophy index only in DS groups. These results strongly suggest that throughout the evolution of left ventricular hypertrophy, ventricular re-expression of ANP is mainly induced in volume-overloaded DS groups and BNP in pressure-overloaded RV model. Both combined groups had a similar increase of left ventricular hypertrophy. However, left ventricular BNP expression was higher in DS6/RV6. Then, the induction of both ventricular hypertrophy and the natriuretic peptide expression in the combined treated groups would be two independent processes.Plasma ANP did not correlate with ANP gene expression in left ventricle neither in DS nor RV groups, while plasma BNP correlated with left ventricular BNP mRNA in RV groups, but not in DS groups. These results suggest that plasma ANP levels not only are influenced by cardiac ANP secretion, but also by other tissue sources. On the other hand, circulating BNP levels in RV model seems to be regulated by cardiac BNP synthesis, which depends, among other factors, of ventricular hypertrophy. In agreement, The switch RV-to-DS treatment as well as the opposite sequence DS-to-RV in chronic combined models of hypertension slowed down the progression of hypertension, the cardiac hypertrophy, and the increase of ANP and BNP expression, although the cardiac overload continued for 12 weeks. This behavior differs from the acute combined models, where we reported that the second stimulus of mechanical overload determines the evolution of hypertrophy and the synthesis and secretion of NPs , 2010. IAs mentioned above, RV and DS are not pure models and have a predominant component of pressure or volume overload, respectively. We chose these models because alternative surgical procedures for pure hemodynamic overload in rats such as aortic constriction (pressure) and aortocaval shunt (volume) involve complex surgical interventions that are difficult to revert; therefore they are not amenable to perform regression and combination studies . In addiThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The animal study was reviewed and approved by the Institutional Review Board at the University of Buenos Aires.CC, SC, and BF conceived and designed the study. CC, SC, and MRF performed the experiments and statistical analysis. MD, GG, and RG performed echocardiography analysis and wrote sections of the manuscript. CC and CH performed Northern Blot analysis. CC, SC, MRF, GG, NK, MC, and BF wrote the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Paravalvular leak incidence after mitral surgical replacement ranges from 7% to 17%. Between 1% and 5% of these are clinically significant. Large PVLs can cause important clinical manifestations such as heart failure or haemolysis. Current guidelines consider that surgical reparation is the gold-standard therapy in symptomatic patients with paravalvular leak. However, these recommendations are based in non-randomized observational registries. On the other hand, transcatheter paravalvular leak closure has shown excellent results with a low rate of complications, and nowadays it is considered the first option in selected patients in some experienced centres. In this review, we summarize the clinical manifestations, diagnosis, procedural details, and results of transcatheter mitral PVL closure. Paravalvular leak (PVL) is defined as the presence of any channel between the anatomical annulus and the prosthetic valve that causes a regurgitation jet between two chambers of the heart. As life expectancy continues to grow in developed countries, one of the consequences is that valvular heart disease is progressively more and more common. For example, in the United States or Europe, these pathologies affect up to 2.5% of the population, and prevalence is much higher in patients older than 75 years old. When severe valvular disease is established, surgical percutaneous valve replacement is usually needed. In Europe, mitral valve regurgitation and stenosis comprised 21% and 5% of all referrals for valve interventions. On the other hand, over recent years, in the United States more than 120,000 procedures, have been performed including at least 14,000 mitral surgical valve replacements . The vasEuropean Society of Cardiology (ESC) and American Heart Association (AHA) guidelines on management of valvular heart disease still consider redo surgery as the first option for symptomatic patients with a PVL ,5. NeverAs mentioned before, the majority of PVLs are trivial or mild, without causing any manifestation, so they remain underdiagnosed. However, some of them will be haemodynamically significant and will trigger different symptoms . The diaThe most common clinical manifestation is heart failure, especially in the largest ones. Mitral regurgitation will increase both intracavitary left chambers pressures, worsening the NYHA class function or developing pulmonary oedema. When it persists through the time, cardiac remodelling could appear and left atrium and ventricle volumes increase, pulmonary hypertension appears and even right-heart chambers may be affected.Physical examination is very important in these cases. A new cardiac murmur can help us to suspect this complication, but it is not a very specific tool. Furthermore, during examination, we can find signs of heart failure such as rales or peripheral oedema.Haemolysis is the second most frequent manifestation. In contrast with aortic leaks, haemolysis rate is higher in the mitral ones because they occur in systole, which implies a higher velocity of the regurgitation jet. Finally, anaemia may appear which increases the probability of new acute heart failure episodes.Blood shear stress due to the PVL could also provoke more anaemia because of acquired Von Willebrand (VW) syndrome . VW protBlood tests can be helpful to suspect this problem. Not only will there be high levels of bilirubin, but also other analytical parameters will be altered. Low haptoglobin and high LDH (lactated dehydrogenase) will be observed because of erythrocytes’ destruction. It is possible to observe iron deficiency too, and reticulocytes and schistocytes rate will be increased . FurtherIdentifying all these clinical and analytical manifestations and connecting them to PVL is vital in order to try to correct them and ameliorate patients´ symptoms.Diagnosis and characterization of PVLs are challenging. As we have discussed, it should be suspected when an onset of abnormal murmur appears at physical examination after a valvular replacement, especially in those patients who were admitted due to heart failure and/or haemolytic anaemia. In this situation, a transthoracic echocardiography (TTE) should be performed firstly. Rergardless, multimodal imaging is instrumental in guiding diagnostic and therapeutic strategies when managing PVL. These imaging techniques are summarized below and in the central illustration .TTE is the initial diagnostic test of choice for all patients with suspected PVL. Although TTE is an excellent method for the assessment of valvular gradients, it is often limited by acoustic shadowing from mechanical components of prosthetic valves, annular calcification or prosthetic valve sewing rings . AcoustiTransoesophageal echocardiography is the gold standard when performing an exhaustive analysis of the PVL that can further characterize the leak regurgitation location, size, and severity . Two-dimNowadays, the clockwise format from “surgical view” is used to improve communication between interventional cardiologist and imaging specialists. In this scheme, the 12 o’clock position is at the mitral–aortic continuity, the left atrial appendage corresponds to the 9 o’clock position, and the interatrial septum is adjacent to the 3 o’clock position  Figure .The approach for detecting and grading prosthesis regurgitation is described in Mitral PVL: qualitative parameters are used for mitral paravalvular regurgitation such as colour-flow regurgitant jet area, jet density, and systolic pulmonary venous flow reversal, a specific sign of severe mitral regurgitation. Due to the Coanda effect, the jet may be underestimated by jet area measurement. Quantitative parameters, such as vena contracta diameter and regurgitant volume and fraction, are also helpful. Although the proximal isovelocity surface area (PISA) approach has not been validated in the setting of paravalvular regurgitation, the presence of a large PISA could be consistent with more severe regurgitation.Intracardiac echography (ICE) is less useful due to presence of acoustic shadowing, but it may be useful in certain instances . It can be performed without general anaesthesia, making procedures shorter and safer and further enabling the treatment of patients that may have been turned down for intervention. Ruparelia N. et al. reached acceptable procedural success rates (77.8%) with similar functional improvement and no related complications .Cardiac MRI has a limited role within the diagnostic of PVL. Virtually all prosthetic valves  can be imaged by CMR . Phase-cThe most important contribution of cardiac CT is the capacity for anatomical characterization of PVL in patients with significantly limited echocardiographic images, which has an important role in pre-procedural planning . HelicalIn terms of fusion imaging, development of computed tomography (CT)–fluoroscopy fusion imaging has allowed CT imaging to provide a valuable tool for guidance during percutaneous PVL closure . With thIn selected complex cases, 3D printed models could be useful in the pre-procedure planning  Figure .Medical management has a limited role in patients with symptomatic PVLs, as it cannot resolve the underlying cause.Both ACC/AHA 2020 and ESC 2021 Guidelines consider that surgical reparation is the gold-standard therapy in symptomatic PVL ,5. HowevTranscatheter closure of PVL has shown excellent results in different cases with a low rate of complications. This approach is contraindicated in the presence of active endocarditis, prosthesis instability or large PVL affecting more than 30% of the circumference .For this purpose, different devices can be used. Most of them consist of a waist with two discs at each end . The OccPre-procedural planning is one of the most important steps of a successful procedure. Localization and characterization of the leak are crucial, and the closure strategy must be decided before initiating the procedure based on the previous information. In our opinion, transcatheter closure of mitral PVL should be guided by 3D transoesophageal echocardiography under general anaesthesiaIn the antegrade approach, once a successful transeptal puncture is performed, a diagnostic catheter  is advanced into the left atrium. In most cases, the use of a deflectable catheter  is recommended ,8. AfterIn the retrograde approach, a hydrophilic guidewire  over a catheter  is often used to cross the PVL from the left ventricle to the left atrium. After crossing, an arteriovenous wire loop is often created in the left atrium; therefore, a transeptal puncture is needed. Finally, the delivery sheath is advanced from the venous access and the device is deployed .Transapical access could be an alternative for mitral PVL closure   Figure . The maiBefore releasing the device, echocardiography reassessment should be made in order to discard complications, such as disc movement restriction or LVOT (left ventricle outflow tract) obstruction. In anterior and septal PVL, an extremely rare but severe complication could be LVOT obstruction. It should be suspected in patients with septal hypertrophy, and it must be assessed by echocardiography during the procedure. In case this complication occurs, the device should not be released and its orientation or size should be changed . In the same way, disc movement blockage should be always checked before device releasing; this is particularly important in monodisc prosthesis. This complication could be prevented using smaller devices, changing the orientation of the device, deploying the “ventricular” disc inside the PVL tunnel or using devices such as the AVPII (the disc size is the same as the body of the device). However, these complications are very rare, but disc blockage is usually the reason for urgent surgery after PVL closure .Furthermore, sometimes a residual leak is detected before releasing. In this situation, a simultaneous deployment of two devices can be performed, or a second device can be deployed sequentially . If the For patients under anticoagulation therapy, this should be continued after the procedure. Dual antiplatelet therapy for at least 3 months is recommended in non-anticoagulated patients . Post-procedural imaging with TEE to assess device position and residual regurgitation is recommended. The timing of a follow-up TEE varies between institutions, but we recommend an initial early TEE 3 months after the procedure. If persisting or new PVL are observed, percutaneous or surgical management can be chosen depending on the size of the remaining PVL.p < 0.001), as reflected in the UK and Irish registry [Redo surgery for PVL closure has been demonstrated to improve outcomes. However, it offers high morbidity and mortality rates. Redo surgery has elevated rate of complication and leak closure success decreases with any extra intervention . On the registry . Moreoveregistry . Furtherregistry . Table 2PVL is a frequent complication of surgical valve replacement, with significant morbidity and mortality. Emerging data indicate that percutaneous closure of PVL is a safe and effective alternative to surgical closure. This procedure has become the first-line treatment in clinical practice in experienced centres.


Atrial fibrillation (AF) is associated with various major adverse cardiac events such as ischemic stroke, heart failure, and increased overall mortality. However, its association with lethal ventricular arrhythmias such as ventricular tachycardia (VT), ventricular flutter (VFL), and ventricular fibrillation (VF) is controversial. We conducted this study to determine whether AF can increase the risk of VT, VFL, and VF. We utilized the Korean National Health Insurance Service database for this nationwide population-based study. This study enrolled people who underwent a nationwide health screen in 2009 for whom clinical follow-up data were available until December 2018. Primary outcome endpoint was the occurrence of VT, VFL, or VF in people who were and were not diagnosed with new-onset AF in 2009. We analyzed a total of 9,751,705 people. In 2009, 12,689 people were diagnosed with new-onset AF (AF group). The incidence (events per 1000 person-years of follow-up) of VT, VFL, and VF was 2.472 and 0.282 in the AF and non-AF groups, respectively. After adjustment for covariates, new-onset AF was associated with 4.6-fold increased risk (p < 0.001) of VT, VFL, and VF over 10 years of follow-up. The risk of VT, VFL, and VF was even higher if identification of AF was based on intensified criteria . In conclusion, the incidence of VT, VFL, and VF was significantly increased in people with new-onset AF. The potential risk of suffering lethal ventricular arrhythmia in people with AF should be considered in clinical practice. Overall mortality in AF patients is significantly higher compared with that of the general population7. The underlying causes of the increased mortality are malignancy (23.1%), infection (17.3%), heart failure (14.5%), and stroke (6.5%) in a study based on a Japanese community AF cohort8. Administrative data from Germany suggest that accompanying comorbidities play an important role in the increased mortality among AF patients9. Although non-cardiac causes are responsible for a large proportion of mortality among AF patients, right- and left-sided heart failure, syncope, side effects of antiarrhythmic drugs, myocardial infarction, and ischemic stroke are important cardiac causes of death. Unlike the aforementioned cardiac events, the risk of lethal ventricular arrhythmias such as ventricular tachycardia (VT) and ventricular fibrillation (VF) in AF patients is not fully understood. In real-world clinical practice, AF and ventricular arrhythmia often coexist; a prior case-controlled, cross-sectional study demonstrated that the prevalence of AF was threefold higher in patients who experienced out of hospital cardiac arrest due to VF10. The study was limited by cross-sectional design and could not demonstrate a chronological association between AF and VF.Atrial fibrillation (AF) is one of the most common supraventricular tachyarrhythmias, affecting 1–2% of the general population, and is associated with a significantly increased risk of adverse cardiac events11. It is not surprising that such genetic variations also affect the ventricle and cause lethal ventricular arrhythmias. Furthermore, a rapid ventricular rate can stimulate the ventricle, similar to rapid ventricular pacing which can induce VT or VF during electrophysiology studies. Decreased coronary perfusion due to rapid ventricular rate might provoke ventricular arrhythmias if the patient already has tachycardia-induced cardiomyopathy or significant coronary artery stenosis. We aimed to investigate whether AF is associated with increased risk of lethal ventricular arrhythmias using Korean National Health Insurance Service (K-NHIS) data. Utilizing a prospectively collected large-scale database, we examined the chronological association between AF and ventricular arrhythmia.Atrial fibrillation is often caused by genetic mutations in ion-channels or cardiomyopathy-related genetic variantshttps://nhiss.nhis.or.kr/) and the institutional review board.The K-NHIS database was used to conduct this study. The people in Republic of Korea are mandatory subscribers of the K-NHIS, which is an exclusive single medical insurance system managed by the government. Medical data stored in the K-NHIS database represents the entire population of Korea. Regular health screenings are offered to all subscribers of K-NHIS and include a health questionnaire including physical activity, alcohol, and smoking status; laboratory tests such as complete blood cell counts, lipid profile, creatinine level, liver function tests, and serum fasting blood sugar; chest X-ray; and measurement of blood pressure, body weight, height, and waist circumference. Identification of people diagnosed with AF, VT, ventricular flutter (VFL), VF, and other medical conditions was conducted using inpatient and outpatient service records of the K-NHIS. A cohort consisting of people who underwent a nationwide health screening at a certain time point is a valuable research source due to the large number of subjects, sufficient follow-up duration, and vast medical records stored in the K-NHIS database. Researchers are permitted to analyze healthcare information data stored in the K-NHIS database if the study protocols are approved by the official review committee of the government , stroke, or heart failure during the screening period were excluded from this study. The exact international classification of disease  codes used in this study are described in Supplementary Table Occurrence of ventricular arrhythmias, composed of VT, VFL, and VF, was the outcome endpoint of this study and was compared between people who were and were not diagnosed with new-onset AF in 2009. The incidence of VT, VFL, and VF was defined as the number of events calculated over 1000 person-years of follow-up.12. The diagnosis of VT, VFL, and VF was based on one inpatient or outpatient record. Previous studies have validated the robustness of these definitions14. Baseline demographics of the study cohort including age, sex, weight, height, smoking status, alcohol, and physical activity and medical comorbidities such as hypertension, diabetes mellitus, dyslipidemia, and chronic kidney disease  were retrieved from the K-NHIS database.Identification of newly diagnosed AF in 2009 was defined as ≥ one inpatient or outpatient record of ICD-10 code. People who were and were not diagnosed with new-onset AF in 2009 were defined as the AF group and non-AF group, respectively. New-onset AF was further classified into AF 1 and AF 2 by the following criteria: (i) AF 1: one outpatient record and (ii) AF 2: ≥ two outpatient records or ≥ one inpatient record based on ICD-10 codes in the K-NHIS database. One inpatient record was required for diagnosis of heart failure® software version 9.2 .Continuous variables were compared by Student’s t-test. Categorical variables were analyzed with the Chi-square test or Fisher’s exact test as appropriate. The cumulative incidence of outcome endpoint  was depicted by Kaplan–Meier curve analysis, and the log-rank t-test was used for comparisons between the groups. Cox-regression analysis was used to calculate non-adjusted and adjusted hazard ratios (HR) with 95% confidence interval (CI). For multivariate adjustment, three models were used: (i) adjusted for age and sex; (ii) adjusted for age, sex, body mass index (BMI), smoking status, alcohol consumption status, and regular physical activity; and (iii) adjusted for age, sex, BMI, smoking status, alcohol consumption status, regular physical activity, hypertension, diabetes mellitus, and dyslipidemia. All clinical follow-up data was used for Kaplan–Meier curve analysis and Cox-regression analysis until primary event occurred or censored . Censored data was no longer used for any analysis. The time-scale used in our Cox-regression models was time-on-study, adjusting for age as a covariate. The proportional hazards assumption was assessed by visual estimation of Kaplan–Meier curve. All Cox-regression analysis in this study showed continuous and steady divergence of cumulative incidence of lethal ventricular arrhythmia between groups suggesting assumption of proportional hazards was fulfilled. All tests were two-tailed, and p values ≤ 0.05 were considered statistically significant. All statistical analyses were performed with SASA total of 10,601,284 people underwent a nationwide health check-up in 2009. People were excluded from the study if they were younger than 20 years or had a prior history of AF, PVC, VT, VFL, VF, heart failure, or stroke. Finally, 9,751,705 people were included in this study. In 2009, a total of 12,689 new-onset AF cases was detected, with 5063 and 7626 cases of AF 1 (one outpatient code) and AF 2 (≥ two outpatient records or ≥ one inpatient record), respectively. Study flow is summarized in Fig. Baseline demographics of people who were and were not diagnosed with new-onset AF in 2009 are summarized in Table During the follow-up period , a total of 25,581 ventricular arrhythmia  events occurred. In the AF group, the incidence of ventricular arrhythmia was 2.472 , and it was 0.282  in the non-AF group. The non-adjusted HR was 8.762 , and 3.126 , respectively. After multivariate adjustment, AF 1 and AF 2 were associated with a 3.386- and 5.221-fold increased risk of ventricular arrhythmia compared with the non-AF group, respectively  in people younger than 65 years and by 4.637-fold (3.871–5.556) higher in people 65 or older  during screening period (2002 to 2008)  on ventricular arrhythmia was evaluated in our study. Based on large sample size, we performed subgroup analyses and revealed that age and dyslipidemia had significant interactions with new-onset AF regarding development of ventricular arrhythmia.16. Furthermore, obligatory atrioventricular delay prevents atrial signal conduction to the ventricles during the vulnerable period. Despite these protective roles of physiologic conduction delay by the atrioventricular node, it is possible that rapid ventricular activation by AF might create conditions that enable the occurrence of ventricular arrhythmias. Under high adrenergic activation situations such as physical exercise, atrioventricular delay and atrioventricular nodal effective refractive period can progressively shorten, allowing rapid activation of ventricles and possibly the R-on-T phenomenon15. Prior study actually reported a R-on-T phenomenon in AF patients which was recorded in implantable cardioverter defibrillator suggesting that AF can be an atypical trigger of lethal ventricular arrhythmias even in patients without pre-excitation17. Accompanied pre-excitation in AF patients is itself a significant risk factor for sudden death and inappropriate synchronization during direct current cardioversion can be an another potential cause of AF degenerating into VF18.Physiologic conduction delays in the atrioventricular node and the atrioventricular nodal effective refractory period prevent rapid conduction of atrial signals, especially during AF, to the ventricles21 and prior studies demonstrated that sympathetic nervous system is activated in AF patients and has critical role in AF development22. Furthermore, AF is associated with significant patient discomfort which might activate sympathetic tone23. Activation of sympathetic nervous system might be another possible underlying mechanism linking AF with lethal ventricular arrhythmias.Sympathetic denervation has shown to relieve electrical storm suggesting sympathetic activation may play an important role in occurrence of ventricular fibrillation24. The underlying cause of atrial myopathy is not clear, but aging, increased pressure of the left atrium, inflammation, and genetic predisposition are proposed mechanisms26. These pathologic processes might induce myopathy not only in the atrium, but also in the ventricles, predisposing patients to development of ventricular arrhythmias. A common underlying myopathy in both the atrium and the ventricle might explain the significantly increased risk of ventricular arrhythmia in people with AF a phenomenon observed in this study.Atrial myopathy is a potential underlying pathophysiology of AF characterized by fibrofatty infiltration and loss of sarcomeres27. Genetic variations in SCN5A and KCNE5 gene have been suggested as a potential underlying link between AF and Brugada syndrome30. It is possible that various genetic variations associated with channelopathy or cardiomyopathy can affect both atrium and ventricle causing both AF and lethal ventricular arrhythmias.Genetic variations can also play a role in co-existence of AF and lethal ventricular arrhythmias. Prior study has reported a phenomenon of new-onset AF as the first clinical manifestation of Brugada syndrome, a genetic disorder predisposing to significantly increased of lethal ventricular arrhythmia31. Since the atria and ventricles are electrically connected to each other by the atrioventricular node and His bundle, arrhythmias arising from the atrium can influence the ventricle, and vice versa. The atrium and ventricle are also mechanically correlated. For example, a decrease in left ventricular systolic function due to AF and subsequent development of heart failure can provoke ventricular arrhythmias. Frequent PVCs can also depress left ventricular ejection fraction, which can elevate left atrial pressure and create a favorable environment for AF as shown in our previous study31.We previously reported an increased risk of AF in people with PVC32. Our study is in accordance with the study in a more general AF population. Presence of AF in patients presenting with lethal ventricular arrhythmia was associated with a significantly increased mortality probably due to progressive worsening of heart function, increased shocks from implantable cardioverter-defibrillators, systematic inflammation, and heterogeneous atrioventricular conduction33. Such interactions between atrial and ventricular arrhythmias will be a valuable area of future research.In patients with implantable cardioverter-defibrillators for secondary prevention, presence of AF was associated with an increased risk of lethal ventricular arrhythmias in a prior study34. By improving ventricular function and ganglionic plexus modulation, RFCA might have the potential to reduce lethal ventricular arrhythmias in people with AF, a hypothesis that should be tested in future trials.Whether radiofrequency catheter ablation (RFCA) of AF can reduce the risk of ventricular arrhythmia is of potential interest. In patients with AF and advanced heart failure, RFCA improves left ventricular ejection fraction and clinical outcomes such as hospitalization for heart failure and overall mortality36. Second, our study was exclusively based on an East Asian population, and caution is needed when applying our results to other ethnic groups. Third, genetic evaluation such as whole exon sequencing, which might reveal common pathogenic variants responsible for both AF and ventricular arrhythmia, was not available in this study.This study has several limitations. First, there might be inaccuracies in coding, missing data, and selection bias since this was a retrospective study based on data stored in a nationwide health insurance organization. However, our strategy to identify various diseases such as AF, stroke, heart failure, hypertension, or diabetes mellitus has been validated in multiple prior studiesNew-onset AF was closely associated with a higher incidence of ventricular arrhythmia, a composite of VT, VFL, and VF. The potential risk of lethal ventricular arrhythmias in people with AF should be kept in mind in real-world clinical practice.Supplementary Information.


Despite progressive improvements over the decades, the rich temporally resolved data in an echocardiogram remain underutilized. Human assessments reduce the complex patterns of cardiac wall motion, to a small list of measurements of heart function. All modern echocardiography artificial intelligence (AI) systems are similarly limited by design – automating measurements of the same reductionist metrics rather than utilizing the embedded wealth of data. This underutilization is most evident where clinical decision making is guided by subjective assessments of disease acuity. Predicting the likelihood of developing post-operative right ventricular failure (RV failure) in the setting of mechanical circulatory support is one such example. Here we describe a video AI system trained to predict post-operative RV failure using the full spatiotemporal density of information in pre-operative echocardiography. We achieve an AUC of 0.729, and show that this ML system significantly outperforms a team of human experts at the same task on independent evaluation. The echocardiogram allows for a comprehensive assessment of the cardiac musculature and valves, but its rich temporally resolved data remain underutilized. Here, the authors develop a video AI system trained to predict post-operative right ventricular failure. A variety of clinical scoring systems have been developed with modest predictive power, with a maximum area under the receiver operating characteristic curve (AUC) of 0.60 in held-out datasets7. Without reliable methods to predict RV failure in the pre-operative setting, we have neither the means to decide in whom to aggressively intervene, nor an efficient method to randomize patients to trials that evaluate the efficacy of right ventricular treatment options. The gold standard method for determining which patients should receive advanced right ventricular support devices thus remains a ‘clinical gestalt’8, involving the patients’ clinical course, lab parameters, and a qualitative assessment of myocardial function using a trans-thoracic echocardiogram. In this study, we describe an echocardiography machine learning system that enables time resolved characterization of motion parameters from each scan. We use this ML system to predict post-operative RV failure in LVAD (Left ventricular assist device) patients, using pre-operative echocardiograms alone. We compare the predictions of our ML system to those of contemporary RV failure risk scores, and further show that our ML system outperforms heart failure echocardiography experts in independent clinical evaluation. Figure Predicting which patients will go on to develop RV failure after implantation of a left ventricular assist device has so far remained beyond the abilities of both human experts and existing automated algorithms13. These systems typically draw conclusions from static images. Our video AI system processes two parallel spatiotemporal streams of data from echocardiography videos. The greyscale video channel and optical flow streams are combined within the convolutional neural network architecture with concatenation of activations prior to the terminal fully connected layers. We tested a variety of approaches, including various pre-training strategies, optimizers, input streams, and model architectures . The corresponding Precision-Recall curves are shown in Supplementary Fig In recent years, artificial intelligence has enabled automated systems to meet or exceed the performance of clinical experts across a range of image analysis tasks, from detection and diagnosis of disease to prediction of disease progression14. A heart transplant remains the gold standard for treating patients with end-stage heart failure. Demand, however, far outpaces the supply of transplantable hearts16. LVADs offer a mechanical alternative to transplantation, and the number of patients supported by these battery powered mechanical pumps have steadily grown17. In the contemporary era, an estimated 3500 LVAD implants are performed each year, approximately equal to the number of annual heart transplants in the United States18. Unfortunately, a third of all patients implanted with LVADs, develop a clinically significant degree of right ventricular failure (RV failure) soon after the procedure19. Underlying RV dysfunction and physiological changes under LVAD flow are both thought to contribute to the development of severe post-operative RV failure, which remains the single largest contributor to short-term mortality in this patient population21.Heart failure affects more than 6.5 million people in the United States alone, with an estimated 960,000 new cases diagnosed each year22. Semi-quantitative echocardiographic measures and invasive hemodynamics are additional tools available to clinicians for the assessment of the right ventricle in the pre-operative setting. Echocardiographic measures such as TAPSE (tricuspid annular plane systolic excursion) and longitudinal strain are often used to characterize the severity of RV dysfunction. Patients with evidence of pre-operative RV dysfunction (via either echocardiography or invasive hemodynamic measurements) are typically hospitalized for aggressive pharmacological management and optimization22. Treatment regiments include diuretics to lower right ventricular preload, and inhaled drugs such as nitric oxide or phosphodiesterase inhibitors (PDE-5) to reduce right ventricular afterload23. Delaying LVAD implantation in a rapidly deteriorating patient is, however, not recommended, and often in such situations the risk of delaying surgery far outweighs the benefits of pre-operative cardiovascular optimization24. In the operating room, the LVAD implant proceeds via a median sternotomy, or thoracotomy with an additional smaller incision for aortic access. The LVAD is implanted into the left ventricular apex after coring out a segment of myocardium, and the synthetic aortic outflow graft is then sutured onto the ascending aorta. Intra-operative complications, although rare, may warrant reassessment of the right ventricle. Research has shown that outcomes are more favorable following planned right ventricular assist device (RVAD) implantation, compared to emergent RVAD implantations where treatment is initiated after rapid deterioration of right ventricular function after initial LVAD implant25. The challenging prediction problem is thus compounded by the narrow therapeutic window available in the pre-operative phase.An array of clinical biomarkers such as serum creatinine, alanine aminotransferase (ALT), blood urea nitrogen (BUN) are clinically used as surrogates of RV dysfunction27. Most pre-operative scans were acquired at a median of 9 days (IQR 13 days) prior to LVAD implant. Patients undergoing surgery for a LVAD implant typically recover in the setting of a cardiac intensive care unit, where hemodynamic parameters (via invasive catheters) and clinical course determines the diagnosis of post-operative RV failure. We used the latest MCS-ARC  consensus definitions to grade each patient for post-operative RV failure to provide accurate and standardized clinical ground truth labels . Multiple scans were available from each patient, but no patients overlapped between the training, validation, and test datasets. One hundred eighty two patients (25.13%) were adjudicated to have post-operative RV failure.A dataset containing pre-operative and post-operative clinical variables along with paired trans-thoracic echocardiography studies was collected from three hospitals in the United States. The dataset consisted of 941 consecutive patients who had LVAD implants. 44 records were discarded due to missing data on duration of post-operative inotropes that prevented the adjudication of RV failure status. Data from an additional 173 patients were discarded because of missing apical 4-chamber echocardiograms or insufficient number of frames per video  and the CRITT score  for our dataset are similar to previously published reports  allocation policies. Anecdotally 50% of all surviving LVAD patients at 5-years are bridged to a heart transplant in our current dataset33. Although not all LVAD patients are suitable candidates for a heart transplant, a more liberal threshold for the ML system may be used to inform listing strategy for heart transplantation owing to the poor short and long-term survival of LVAD patients who go on to develop post-operative RV failure.All current automated echocardiography systems—much like human echocardiography interpretations—are inherently reductionist in nature; a complex sequence and pattern of cardiac contraction is reduced to an outline of one or more chambers, from which a few global metrics of heart function are then calculatedrts Fig. 1,4. We e38. While decisions are rarely based on echocardiography alone, the combination of poor clinical presentation and ‘severely depressed’ pre-operative RV function may be predictive of post-operative disease22. A blinded clinical benchmark has to our knowledge, not been conducted for this clinical problem. The team of board-certified cardiologists and a sonographer with 12 years of clinical sonography experience were blinded patient outcomes, and graded scans independently from the remainder of the research team. A web-based annotation tool  was configured for measuring echocardiographic metrics of right ventricular function38. In addition to quantitative metrics, the clinical team also identified the patients they predicted would go on to develop RV failure after the operation. These measures were calculated for all 121 patients in the testing dataset  and an additional subset of 86  randomly selected patients from the validation set. The AUCs of the manually extracted metrics ranged between 0.525–0.571. The AI system  outperformed both clinical readers and all quantitative echocardiographic metrics. The best performing manual echo metric  had an AUC of 0.5623  . The clinical reader team had a specificity of 37.89% and a sensitivity of 76.09%; for the same sensitivity, the specificity of the AI system was 54.95%.We benchmarked the performance of our AI system against a clinical heart failure echocardiography team. In clinical practice, echocardiographic assessment is used as an important adjunct metric to gauge the likelihood of downstream RV failure. Numerous metrics such as TAPSE, right ventricular diameters, fractional area change, and right ventricular longitudinal strain have all been described as potential predictors for post-LVAD RV failure25) Fig. . The AUC40. In this paper we utilized gradient backpropagation to generate saliency maps41. These are computed based on the imputed gradient of the target output with respect to input, where only non-negative gradients are backpropagated; in non-technical terms, the goal of this technique is to find input data that would exemplify the features the network uses to predict RV Failure (or lack thereof). We show that for each patient, regions of activation were localized exclusively to the myocardium and valves. The cardiac chambers (ventricles and atria) themselves showed no activation. Furthermore, motion characteristics of specific regions of the heart contribute towards the prediction of RV failure at different phases of the cardiac cycle  may have biased the results with overestimations of predictive power, especially when using performance metrics sensitive to changes in class imbalance45. We have employed the latest standardized definitions of post-operative RV failure28. In the past, definitions of post-operative RV failure were largely based on the need to implant a right ventricular assist device—an intervention driven by surgeon preference and institution specific nuance. The current definitions allow for standardized and generalizable ground truth labels for post-operative RV failure across all participating institutions. By abstaining from defining ‘mild’ and ‘moderate’ RV failure (as only more ‘severe’ grades were found to impact long-term outcomes), the current MCS-ARC guidelines offer a more clinically relevant target for our ML system.The poor predictive performance of contemporary clinical risk scores is well documented. Many of the input variables are consequences rather than true predictors of RV failure. Most risk scores were developed without internal cross-validation or falter when evaluated on held-out datasets47. Most recently, Ouyang et al. described a weakly supervised video segmentation system to calculate ejection fraction using left ventricular tracings in conjunction with spatiotemporal convolutions47. Our methods offer a number of key improvements: First, instead of segmenting cardiac chambers, our AI system directly analyses spatiotemporal information from the cardiac musculature and valves by default—the principal regions of interest in all cardiac diseases. This enables the algorithm to characterize subtle, regional aberrations in myocardial motion, that traditional manually extracted echocardiographic measures fail to capture. Secondly, our end-to-end system tracks features of importance without human supervision in the form of segmentation masks. This method is not dependent on cardiac view plane or chamber, enabling rapid deployment of our methods to a diverse array of echocardiography problems. Finally, we use two streams of spatiotemporal information in the form of greyscale video channels and optical flow to directly predict downstream outcomes of interest. Combined two-stream networks achieve state-of-the-art performance on large video recognition datasets48. Our system makes inferences on a single study within 500 ms on a single Nvidia GeForce RTX 2080Ti GPU. An additional computational overhead of 10 s, however, is needed for calculating optical flow per input video. In the context of our clinical problem, the additional computational overhead of calculating optical flow is acceptable. Future work may focus on integrating faster deep learning methods of optical flow estimation within the AI pipeline for applications that require real time inference49.All contemporary echocardiography ML systems rely on supervised segmentation algorithms to outline cardiac chambers50. Our echocardiography ML system outperforms manually calculated metrics of myocardial function in predicting RV failure. Automating the calculations of these hand-measured metrics using image segmentation algorithms are therefore unlikely to be predictive of our outcome of interest, further supporting our rationale for transitioning to an end-to-end architecture. While our AI system was trained using the largest echocardiographic heart failure dataset of its kind, there remain several limitations of our work. The retrospective nature of our dataset and the lack of a standardized echocardiography acquisition protocol limits the quality and timing of the scans prior to the index operation. Most scans were taken 9 days prior to LVAD surgery, though this window was larger for some patients. Furthermore, a comprehensive assessment of right ventricular function remains challenging without additional echocardiographic views 51. Madani et al. describe an image-based neural network for automated echocardiography view classification that could be integrated into a multi-view RV failure risk prediction system, and it is plausible that superior performance can be attained using prospectively collected echocardiographic data from multiple view planes52. Key to such efforts are standardized and comprehensive protocols for acquisition of scans at pre-defined timepoints before index surgery. Prospective evaluation in the clinical setting will be essential to understand the limitations of our technology. We found that pre-training with large video datasets (Kinetics-600) was critical for model performance53. The same could not be said for pre-training from the Echo-Net Dynamic dataset despite sharing the same imaging modality. While beyond the scope of this current manuscript, future attempts at self-supervised pre-training with cardiovascular imaging datasets may yield superior results55. Finally, unlike hard radiological or histopathological ground truth labels, the clinical ground truth for post-operative RV failure leaves room for subjectivity despite the MCS-ARC guidelines. As part of additional experiments, we evaluated the incorporation of certain baseline demographic and clinical features  into the training loop. We did not see a meaningful improvement over our best performing video-based system, although the improvements were more pronounced in some of the shallower neural networks evaluated , Spectrum Health (MI), and the Houston Methodist Hospital (TX); (IRB 52440) with a waiver of consent owing to the retrospective nature of the research. All patients aged 18 years or older with at least one pre-operative trans-thoracic echocardiogram as well as a complete pre-operative and post-operative assessment of RV failure during index-hospitalization as per the MCS-ARC consensus definitions  were included ; the remainder were kept as controls (n = 541). Multiple scans were available for each patient (501 RV failure (26.24%); 1408 controls).The primary outcome of the study was the ability of the AI system to identify and predict the likelihood of post-operative RV failure using only pre-operative trans-thoracic echocardiograms as the input. All patients with MCS-ARC defined post-operative RV failure were included in the ‘RV failure’ group  from file metadata and by obscuring any sensitive information in the videos. The complete removal of all sensitive information was verified manually on all videos before proceeding to downstream postprocessing. Areas outside of the scanning sector were masked to remove any miscellaneous markings in the video frames that may otherwise influence the neural networks. The videos were then normalized by dividing each pixel value by the pixel of maximal intensity. The frames of the processed videos were additionally downsampled by bi-linear interpolation to a 112 × 112 resolution, and all videos were temporally normalized to framerate prior to training and evaluation. Additional experiments with an input resolution of 224 × 224 pixels were also conducted on shallower residual networks. Optical flow was calculated prior to model training using an OpenCV implementation of the Gunnar Farnebäck method based on polynomial expansion58, built using the Keras Framework with a TensorFlow 2.1  backend and Python, that tracks motion features and structural features in blocks of 32 consecutive frames. We make use of bottlenecked residual blocks expanded to three-dimensions. We used validation data to do model selection over a range of architectures and tested multiple 3D neural network architectures before selecting a two-stream fusion 152-layer 3D Residual Network with bottlenecks incorporated within the residual blocks , opencv-python (4.5.1), scikit-learn (0.22.2), and pROC package (1.17) and dplyr (1.0.7) in R.We use a three-dimensional convolutional neural network53. Videos in the Kinetics-600 dataset were converted to greyscale and optical flow and subsampled for 32 consecutive frames prior to pre-training. Pre-training on Kinetics-600 was performed on servers, each with eight NVIDIA V100 GPUs, on the Stanford Sherlock Supercomputing Cluster. We additionally experimented with transfer learning from networks initialized on the EchoNet Dynamic dataset for Ejection Fraction prediction with over 10,000 Apical 4-Chamber echocardiography videos30. The results were, however, similar to those of randomly initialized networks. Training was stopped when validation loss did not improve, and the model weights were saved. The networks for RV failure prediction were finally initialized with these weights and the terminal linear activation function was replaced by a sigmoid function. In our experiments we find that pre-training significantly improves training convergence, with higher validation AUC and lower cross-entropy losses. This corroborates findings from a number of groups working on both medical imaging and general purpose video action recognition problems48.All candidate networks were pre-trained on the Kinetics-600 dataset for video action recognition41.We used an implementation of Gradient backpropagation to generate saliency maps for the AI system as it makes predictions of RV failure outcome when passed through the three-dimensional convolutional neural networkl and setting the negative gradients to zero:Visualization of representation learned by higher layers of the network are generated by propagating the output activation back through the ReLU function in each layer Alternative visualization techniques such as layer wise relevance propagation were also considered. The neurons that contribute the most to the higher layers receive the most ‘relevance’ from it. The relative contribution of each pixel towards the final predicted value is quantified to satisfy the following equation:R is conserved between layers l. During each forward pass, neuron i inputs aiwik to the next connected neuron k. The messages k, onto the preceding neurons that feed into it at layer l. The presence of skip connections in 3D residual networks violates the assumptions of relevance conservation, limiting us to Gradient backpropagation.The total relevance 6. Missing data prevented the calculation of clinical risk scores for as many as 37% of all patients in our total dataset. For this reason, we elected to use a multiple imputation strategy following Rubin’s rules to pool and calculate AUCs64. No significant difference in AUC was noted in the original incomplete dataset vs the imputed dataset . Imputation diagnostic plots are shown in Supplementary Fig The US dataset was split in an approximate 66:17:17 ratio into a training, validation, and test dataset. The stratified split ensured proportional numbers of unique patients with and without RV failure in each group. The validation set was used for hyperparameter tuning and an ensemble of three models trained with identical settings were used to generate final predictions at the scan level. On freezing the model weights, model performance was evaluated on the testing dataset using the area under curve (AUC) of the receiver-operator characteristic and AUC of the precision-recall curve. We further compared the predictive performance of our AI system against clinicians equipped with two contemporary risk scores used in for predicting post-operative RV failure—the CRITT score and Penn score. The variables used for calculating these scores are described in Supplementary Table 2n = 161 controls; n = 46 with RV failure) in our dataset, using metrics of RV function previously described38. The echocardiography scans were hosted on a secure Google Cloud Bucket at full resolution, ranging from a resolution of (422 × 636) to (768 × 1024) pixels. A custom cloud-based annotation system  was used to allow the clinical heart failure team to remotely take measurements, perform quality control, and adjudicate metrics of RV failure 68. The pooled AUC for the imputed datasets  was calculated using Rubin’s Rules by log transforming the AUC prior to pooling63. Statistical analyses were conducted in R (v3.6.2).No statistical methods were used to predetermine sample size. To evaluate the stand-alone performance of the AI system, ROC curves were calculated as empirical curves in the sensitivity and specificity space. AUCs for ROC curves were computed with trapezoids using the pROC packageFurther information on research design is available in the Supplementary InformationReporting SummaryPeer Review File