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Paravalvular leak (PVL) is a shortcoming that can erode the clinical benefits of transcatheter valve replacement (TAVR) and therefore a readily applicable method (aortography) to quantitate PVL objectively and accurately in the interventional suite is appealing to all operators. The ratio between the areas of the time-density curves in the aorta and left ventricular outflow tract (LVOT-AR) defines the regurgitation fraction (RF). This technique has been validated in a mock circulation; a single injection in diastole was further tested in porcine and ovine models. In the clinical setting, LVOT-AR was compared with trans-thoracic and trans-oesophageal echocardiography and cardiac magnetic resonance imaging. LVOT-AR > 17% discriminates mild from moderate aortic regurgitation on echocardiography and confers a poor prognosis in multiple registries, and justifies balloon post-dilatation. The LVOT-AR differentiates the individual performances of many old and novel devices and is being used in ongoing randomized trials and registries.

Abstract Wrist-worn devices with heart rate monitoring have become increasingly popular. Although current guidelines advise to consider clinical symptoms and exercise tolerance during decision-making in heart disease, it remains unknown to which extent wearables can help to determine such functional capacity measures. In clinical settings, the 6-minute walk test has become a standardized diagnostic and prognostic marker. We aimed to explore, whether 6-minute walk distances can be predicted by wrist-worn devices in patients with different stages of mitral and aortic valve disease. A total of n = 107 sensor datasets with 1,019,748 min of recordings were analysed. Based on heart rate recordings and literature information, activity levels were determined and compared to results from a 6-minute walk test. The percentage of time spent in moderate activity was a predictor for the achievement of gender, age and body mass index-specific 6-minute walk distances (p

Background: Quantification of coronary blood flow is used to evaluate coronary artery disease, but our understanding of flow through branched systems is poor. Murray’s law defines coronary morphometric scaling, the relationship between flow (Q) and vessel diameter (D) and is the basis for minimum lumen area targets when intervening on bifurcation lesions. Murray’s original law (Q α DP) dictates that the exponent (P) is 3.0, whilst constant blood velocity throughout the system would suggest an exponent of 2.0. In human coronary arteries, the value of Murray’s exponent remains unknown.Aim: To establish the exponent in Murray’s power law relationship that best reproduces coronary blood flows (Q) and microvascular resistances (Rmicro) in a bifurcating coronary tree.Methods and Results: We screened 48 cases, and were able to evaluate inlet Q and Rmicro in 27 branched coronary arteries, taken from 20 patients, using a novel computational fluid dynamics (CFD) model which reconstructs 3D coronary anatomy from angiography and uses pressure-wire measurements to compute Q and Rmicro distribution in the main- and side-branches. Outputs were validated against invasive measurements using a Rayflow™ catheter. A Murray’s power law exponent of 2.15 produced the strongest correlation and closest agreement with inlet Q (zero bias, r = 0.47, p = 0.006) and an exponent of 2.38 produced the strongest correlation and closest agreement with Rmicro (zero bias, r = 0.66, p = 0.0001).Conclusions: The optimal power law exponents for Q and Rmicro were not 3.0, as dictated by Murray’s Law, but 2.15 and 2.38 respectively. These data will be useful in assessing patient-specific coronary physiology and tailoring revascularisation decisions.

Objective To review, inventory and compare available diagnostic tools and investigate which tool has the best performance for prehospital risk assessment in patients suspected of non-ST-segment elevation acute coronary syndrome (NSTE-ACS).Methods Systematic review and meta-analysis. Medline and Embase were searched up till 1 April 2021. Prospective studies with patients, suspected of NSTE-ACS, presenting in the primary care setting or by emergency medical services (EMS) were included. The most important exclusion criteria were studies including only patients with ST-elevation myocardial infarction and studies before 1995, the pretroponin era. The primary end point was the final hospital discharge diagnosis of NSTE-ACS or major adverse cardiac events (MACE) within 6 weeks. Risk of bias was evaluated by the Quality Assessment of Diagnostic Accuracy Studies Criteria.Main outcome and measures Sensitivity, specificity and likelihood ratio of findings for risk stratification in patients suspected of NSTE-ACS.Results In total, 15 prospective studies were included; these studies reflected in total 26 083 patients. No specific variables related to symptoms, physical examination or risk factors were useful in risk stratification for NSTE-ACS diagnosis. The most useful electrocardiographic finding was ST-segment depression (LR+3.85 (95% CI 2.58 to 5.76)). Point-of-care troponin was found to be a strong predictor for NSTE-ACS in primary care (LR+14.16 (95% CI 4.28 to 46.90) and EMS setting (LR+6.16 (95% CI 5.02 to 7.57)). Combined risk scores were the best for risk assessment in an NSTE-ACS. From the combined risk scores that can be used immediately in a prehospital setting, the PreHEART score, a validated combined risk score for prehospital use, derived from the HEART score (History, ECG, Age, Risk factors, Troponin), was most useful for risk stratification in patients with NSTE-ACS (LR+8.19 (95% CI 5.47 to 12.26)) and for identifying patients without ACS (LR−0.05 (95% CI 0.02 to 0.15)).Discussion Important study limitations were verification bias and heterogeneity between studies. In the prehospital setting, several diagnostic tools have been reported which could improve risk stratification, triage and early treatment in patients suspected for NSTE-ACS. On-site assessment of troponin and combined risk scores derived from the HEART score are strong predictors. These results support further studies to investigate the impact of these new tools on logistics and clinical outcome.Funding This study is funded by ZonMw, the Dutch Organisation for Health Research and Development.Trial registration number This meta-analysis was published for registration in PROSPERO prior to starting (CRD York, CRD42021254122).

Background: Machine learning models have been developed for numerous medical prognostic purposes. These models are commonly developed using data from single centers or regional registries. Including data from multiple centers improves robustness and accuracy of prognostic models. However, data sharing between multiple centers is complex, mainly because of regulations and patient privacy issues.Objective: We aim to overcome data sharing impediments by using distributed ML and local learning followed by model integration. We applied these techniques to develop 1-year TAVI mortality estimation models with data from two centers without sharing any data.Methods: A distributed ML technique and local learning followed by model integration was used to develop models to predict 1-year mortality after TAVI. We included two populations with 1,160 (Center A) and 631 (Center B) patients. Five traditional ML algorithms were implemented. The results were compared to models created individually on each center.Results: The combined learning techniques outperformed the mono-center models. For center A, the combined local XGBoost achieved an AUC of 0.67 (compared to a mono-center AUC of 0.65) and, for center B, a distributed neural network achieved an AUC of 0.68 (compared to a mono-center AUC of 0.64).Conclusion: This study shows that distributed ML and combined local models techniques, can overcome data sharing limitations and result in more accurate models for TAVI mortality estimation. We have shown improved prognostic accuracy for both centers and can also be used as an alternative to overcome the problem of limited amounts of data when creating prognostic models.

Background Global fractional flow reserve (FFR) (ie, the sum of the FFR values in the 3 major coronary arteries) is a physiologic correlate of global atherosclerotic burden. The objective of the present study was to investigate the value of global FFR in predicting long‐term clinical outcome of patients with stable coronary artery disease but no ischemia‐inducing stenosis. Methods and Results We studied major adverse cardiovascular events (MACEs: all‐cause death, myocardial infarction, and any revascularization) after 5 years in 1122 patients without significant stenosis (all FFR >0.80; n=275) or with at least 1 significant stenosis successfully treated by percutaneous coronary intervention (ie, post–percutaneous coronary intervention FFR >0.80; n=847). The patients were stratified into low, mid, or high tertiles of global FFR (≤2.80, 2.80–2.88, and ≥2.88). Patients in the lowest tertile of global FFR showed the highest 5‐year MACE rate compared with those in the mid or high tertile of global FFR (27.5% versus 22.0% and 20.9%, respectively; log‐rank P=0.040). The higher 5‐year MACE rate was mainly driven by a higher rate of revascularization in the low global FFR group (16.4% versus 11.3% and 11.8%, respectively; log‐rank P=0.038). In a multivariable model, an increase in global FFR of 0.1 unit was associated with a significant reduction in the rates of MACE (hazard ratio [HR], 0.988; 95% CI, 0.977–0.998; P=0.023), myocardial infarction (HR, 0.982; 95% CI, 0.966–0.998; P=0.032), and revascularization (HR, 0.985; 95% CI, 0.972–0.999; P=0.040). Conclusions Even in the absence of ischemia‐producing stenoses, patients with a low global FFR, physiologic correlate of global atherosclerotic burden, present a higher risk of MACE at 5‐year follow‐up.

BackgroundLoss of high-molecular-weight multimers (HMWMs) of von Willebrand factor (vWF) occurs due to high shear stress in patients with aortic stenosis. As symptoms of aortic stenosis occur during exercise, measurement of vWF during exercise might identify patients with aortic stenosis of clinical importance. The aim of this pilot study is to evaluate whether vWF changes over time as a result of exercise in patients with asymptomatic moderate or severe aortic stenosis.MethodsTen subjects were analysed for changes in vWF by measuring HMWMs and closure time with adenosine diphosphate (CT-ADP). All subjects underwent a full stress test on a bicycle ergometer. At rest and at peak exercise, a transthoracic echocardiogram was performed. HMWMs and CT-ADP were assessed at baseline, during and after exercise.ResultsHMWMs and CT-ADP did not change significantly during exercise, p=0.45 and p=0.65, respectively. HMWMs and CT-ADP correlated well, Spearman’s rho −0.621, p

During the last two decades, there has been a sharp increase in both interest and knowledge about the coronary microcirculation. Since these small vessels are not visible by the human eye, physiologic measurements should be used to characterize their function. The invasive methods presently used (coronary flow reserve (CFR) and index of microvascular resistance (IMR)) are operator-dependent and mandate the use of adenosine to induce hyperemia. In recent years, a new thermodilution-based method for measurement of absolute coronary blood flow and microvascular resistance has been proposed and initial procedural problems have been overcome. Presently, the technique is easy to perform using the Rayflow infusion catheter and the Coroventis software. The method is accurate, reproducible, and completely operator-independent. This method has been validated noninvasively against the current golden standard for flow assessment: Positron Emission Tomography-Computed Tomography (PET-CT). In addition, absolute flow and resistance measurements have proved to be safe, both periprocedurally and at long-term follow-up. With an increasing number of studies being performed, this method has great potential for better understanding and quantification of microvascular disease.

With the increasing prevalence of aortic stenosis (AS) due to a growing elderly population, a proper understanding of its physiology is paramount to guide therapy and define severity. A better understanding of the microvasculature in AS could improve clinical care by predicting left ventricular remodeling or anticipate the interplay between epicardial stenosis and myocardial dysfunction. In this review, we combine five decades of literature regarding microvascular, coronary, and aortic valve physiology with emerging insights from newly developed invasive tools for quantifying microcirculatory function. Furthermore, we describe the coupling between microcirculation and epicardial stenosis, which is currently under investigation in several randomized trials enrolling subjects with concomitant AS and coronary disease. To clarify the physiology explained previously, we present two instructive cases with invasive pressure measurements quantifying coexisting valve and coronary stenoses. Finally, we pose open clinical and research questions whose answers would further expand our knowledge of microvascular dysfunction in AS. These trials were registered with NCT03042104, NCT03094143, and NCT02436655.

Current prognostic risk scores for transcatheter aortic valve implantation (TAVI) do not benefit yet from modern machine learning techniques, which can improve risk stratification of one-year mortality of patients before TAVI. Despite the advancement of machine learning in healthcare, data sharing regulations are very strict and typically prevent exchanging patient data, without the involvement of ethical committees. A very robust validation approach, including 1300 and 631 patients per center, was performed to validate a machine learning model of one center at the other external center with their data, in a mutual fashion. This was achieved without any data exchange but solely by exchanging the models and the data processing pipelines. A dedicated exchange protocol was designed to evaluate and quantify the model’s robustness on the data of the external center. Models developed with the larger dataset offered similar or higher prediction accuracy on the external validation. Logistic regression, random forest and CatBoost lead to areas under curve of the ROC of 0.65, 0.67 and 0.65 for the internal validation and of 0.62, 0.66, 0.68 for the external validation, respectively. We propose a scalable exchange protocol which can be further extended on other TAVI centers, but more generally to any other clinical scenario, that could benefit from this validation approach.

Current prognostic risk scores in cardiac surgery do not benefit yet from machine learning (ML). This research aims to create a machine learning model to predict one-year mortality of a patient after transcatheter aortic valve implantation (TAVI). We adopt a modern gradient boosting on decision trees classifier (GBDTs), specifically designed for categorical features. In combination with a recent technique for model interpretations, we developed a feature analysis and selection stage, enabling the identification of the most important features for the prediction. We base our prediction model on the most relevant features, after interpreting and discussing the feature analysis results with clinical experts. We validated our model on 270 consecutive TAVI cases, reaching a C-statistic of 0.83 with CI [0.82, 0.84]. The model has achieved a positive predictive value ranging from 57% to 64%, suggesting that the patient selection made by the heart team of professionals can be further improved by taking into consideration the clinical data we identified as important and by exploiting ML approaches in the development of clinical risk scores. Our approach has shown promising predictive potential also with respect to widespread prognostic risk scores, such as logistic European system for cardiac operative risk evaluation (EuroSCORE II) and the society of thoracic surgeons (STS) risk score, which are broadly adopted by cardiologists worldwide.