Patient screening for early detection of aortic stenosis (AS)-review of current practice and future perspectives

Optimizing encoding strategies for 4D Flow MRI of mean and turbulent flow

For 4D Flow MRI of mean and turbulent flow a compromise between spatiotemporal undersampling and velocity encodings needs to be found. Assuming a fixed scan time budget, the impact of trading off spatiotemporal undersampling versus velocity encodings on quantification of velocity and turbulence for aortic 4D Flow MRI was investigated. For this purpose, patient-specific mean and turbulent aortic flow data were generated using computational fluid dynamics which were embedded into the patient-specific background image data to generate synthetic MRI data with corresponding ground truth flow. Cardiac and respiratory motion were included. Using the synthetic MRI data as input, 4D Flow MRI was subsequently simulated with undersampling along pseudo-spiral Golden angle Cartesian trajectories for various velocity encoding schemes. Data were reconstructed using a locally low rank approach to obtain mean and turbulent flow fields to be compared to ground truth. Results show that, for a 15-min scan, velocity magnitudes can be reconstructed with good accuracy relatively independent of the velocity encoding scheme ( S S I M U = 0.938 ± 0.003 ) , good accuracy ( S S I M U ≥ 0.933 ) and with peak velocity errors limited to 10%. Turbulence maps on the other hand suffer from both lower reconstruction quality ( S S I M TKE ≥ 0.323 ) and larger sensitivity to undersampling, motion and velocity encoding strengths ( S S I M TKE = 0.570 ± 0.110 ) when compared to velocity maps. The best compromise to measure unwrapped velocity maps and turbulent kinetic energy given a fixed 15-min scan budget was found to be a 7-point multi- V enc acquisition with a low V enc tuned for best sensitivity to the range of expected intra-voxel standard deviations and a high V enc larger than the expected peak velocity.

[Anesthesia for aortic valve stenosis : Anesthesiological management of patients with aortic valve stenosis during noncardiac surgery]

Aortic valve stenosis is a common condition that requires an anesthesiologist's in-depth knowledge of the pathophysiology, diagnostics and perioperative features of the disease. A newly diagnosed aortic valve stenosis is often initially identified from the anamnesis (dyspnea, syncope, angina pectoris) or a suspicious auscultation finding during the anesthesiologist's preoperative assessment. Interdisciplinary collaboration is essential to ensure the optimal management of these patients in the perioperative setting. An accurate anamnesis and examination during the preoperative assessment are crucial to select the most suitable anesthetic approach. Additionally, a precise understanding of the hemodynamic peculiarities associated with aortic valve stenosis is necessary. After a short summary of the overall pathophysiology of aortic valve stenosis, this review article focuses on the specific anesthetic considerations, risk factors for complications, and the perioperative management for noncardiac surgery in patients with aortic valve stenosis.

Artificial Intelligence in the Screening, Diagnosis, and Management of Aortic Stenosis

The integration of artificial intelligence (AI) into clinical management of aortic stenosis (AS) has redefined our approach to the assessment and management of this heterogenous valvular heart disease (VHD). While the large-scale early detection of valvular conditions is limited by socioeconomic constraints, AI offers a cost-effective alternative solution for screening by utilizing conventional tools, including electrocardiograms and community-level auscultations, thereby facilitating early detection, prevention, and treatment of AS. Furthermore, AI sheds light on the varied nature of AS, once considered a uniform condition, allowing for more nuanced, data-driven risk assessments and treatment plans. This presents an opportunity to re-evaluate the complexity of AS and to refine treatment using data-driven risk stratification beyond traditional guidelines. AI can be used to support treatment decisions including device selection, procedural techniques, and follow-up surveillance of transcatheter aortic valve replacement (TAVR) in a reproducible manner. While recognizing notable AI achievements, it is important to remember that AI applications in AS still require collaboration with human expertise due to potential limitations such as its susceptibility to bias, and the critical nature of healthcare. This synergy underpins our optimistic view of AI's promising role in the AS clinical pathway.

Single-Stage Procedure of Transcatheter Aortic Valve Replacement and Endovascular Aneurysm Repair Under Local Anaesthesia and Percutaneous Access

PURPOSE

Abdominal aortic aneurysms (AAA) are observed in 6% of patients with concomitant aortic valve stenosis (AS) requiring aortic valve replacement. Optimal management of these concomitant pathologies is still debated.

CASE REPORT

An 80-year-old man presented with acute heart failure due to a severe AS. Past medical history included AAA under regular surveillance. A thoracic and abdominal computed tomography angiography (CTA) confirmed a 6 mm increase of AAA over an 8-month period (max 55 mm). A multidisciplinary team prescribed a simultaneous endovascular approach of transcatheter aortic valve implantation (TAVI) followed by endovascular aneurysm repair (EVAR) under local anaesthesia with bilateral femoral percutaneous access. No intra or post-procedural complications were registered; technical success was confirmed by completion angiography and post-operative ultrasound. The patient was discharged on postoperative day 5. A 2-month post-operative CTA confirmed ongoing technical success.

CONCLUSION

Combined TAVI and EVAR under local anaesthesia for AS and AAA was associated with reduced hospital stay and technical success at 2 months from intervention in this case report.

Improving access to transcatheter aortic valve implantation across Europe by restructuring cardiovascular services: An expert council consensus statement

Transcatheter aortic valve implantation (TAVI) is recommended for a growing range of patients with severe aortic stenosis in the European Society of Cardiology and European Association for Cardio-Thoracic Surgery (ESC/EACTS) 2021 Guidelines update. However, guideline implementation programs are needed to ensure the application of clinical recommendations which will favorably influence disease outcomes. An Expert Council was convened to identify whether cardiology services across Europe are set up to address the growing needs of patients with severe aortic stenosis for increased access to TAVI by identifying the key challenges faced in growing TAVI programs and mapping associated solutions. Wide variation exists across Europe in terms of TAVI availability and capacity to deliver the increased demand for TAVI in different countries. The recommendations of this Expert Council focus on the short-to-medium-term aspects where the most immediate, actionable impact can be achieved. The focus on improving procedural efficiency and optimizing the patient pathway via clinical practice and patient management demonstrates how to mitigate the current major issues of shortfall in catheterization laboratory, workforce, and bed capacity. Procedural efficiencies may be achieved through steps including streamlined patient assessment, the benchmarking of standards for minimalist procedures, standardized approaches around patient monitoring and conduction issues, and the implementation of nurse specialists and dedicated TAVI coordinators to manage organization, logistics, and early mobilization. Increased collaboration with wider stakeholders within institutions will support successful TAVI uptake and improve patient and economic outcomes. Further, increased education, collaboration, and partnership between cardiology centers will facilitate sharing of expertise and best clinical practice.

Systolic Time Interval Extraction in Hypertensive and Hypotensive Pig Models Using Wearable Near-Field Radio-Frequency Sensors

Screening and monitoring for cardiovascular diseases (CVDs) can be enabled by analyzing systolic time intervals (STIs). As CVDs have a strong causal correlation with hypertension, it is important to validate STI sensor accuracy in hypertensive hearts to ensure consistent performance in this prevalent cardiac disease state. This work presents STI extraction using a non-invasive near-field radio-frequency (RF) sensor during normotension, hypertension, and hypotension in a pig model. Waveform features of semilunar and atrioventricular valve dynamics during systole were extracted to derive isovolumic contraction time (ICT) and left ventricular ejection time (LVET), benchmarked by a phonocardiogram and aortic catheterization. Study-wide mean relative ICT and LVET errors were -4.4ms and -3.6ms, respectively, demonstrating high accuracy during both normal and abnormal systemic pressures.Clinical relevance- This work demonstrates accurate STI extraction with relative error less than 5 ms from a non-invasive near-field RF sensor during normotensive, hypotensive, and hypertensive systemic pressures, validating the sensor's accuracy as a screening tool during this disease state.

Budget impact analysis of a bovine pericardial aortic bioprosthesis versus mechanical aortic valve replacement in adult patients with aortic stenosis in Romania

AIMS

An analysis of the budget impact of using a bovine pericardial aortic bioprosthesis (BPAB) or a mechanical valve (MV) in aortic stenosis (AS) patients in Romania.

MATERIALS AND METHODS

A decision-tree with a partitioned survival model was used to predict the financial outcomes of using either a BPAB (the Carpentier-Edwards Perimount Magna Ease Valve) or MV in aortic valve replacement (AVR) procedure over a 5-year period. The budget impact of various resource consumption including disabling strokes, reoperations, minor thromboembolic events, major bleeding, endocarditis, anticoagulation treatment and monitoring, and echocardiogram assessments were compared for both types of valves. One-way sensitivity analyses (OWSA) were conducted on the input costs and probabilities.

RESULTS

The use of BPAB compared to MV approaches budget neutrality due to incremental savings year-on-year. The initial surgical procedure and reoperation costs for BPAB are offset by savings in acenocoumarol use, disabling strokes, major bleeding, minor thromboembolic events, and anticoagulation complications. The cost of the initial procedure per patient is 460 euros higher for a BPAB due to the higher valve acquisition cost, although this is partially offset by a shorter hospital stay. The OWSA shows that the total procedure costs, including the hospital stay, are the primary cost drivers in the model.

LIMITATIONS

Results are limited by cost data aggregation in the DRG system, exclusion of costs for consumables and capital equipment use, possible underestimation of outpatient complication costs, age-related variations of event rates, and valve durability.

CONCLUSIONS

Adopting BPAB as a treatment option for AS patients in Romania can lead to cost savings and long-term economic benefits. By mitigating procedure costs and increasing anticoagulation treatment costs, BPAB offers a budget-neutral option that can help healthcare providers, policymakers, and patients alike manage the growing burden of AS in Romania.

Artificial intelligence for the echocardiographic assessment of valvular heart disease

Developments in artificial intelligence (AI) have led to an explosion of studies exploring its application to cardiovascular medicine. Due to the need for training and expertise, one area where AI could be impactful would be in the diagnosis and management of valvular heart disease. This is because AI can be applied to the multitude of data generated from clinical assessments, imaging and biochemical testing during the care of the patient. In the area of valvular heart disease, the focus of AI has been on the echocardiographic assessment and phenotyping of patient populations to identify high-risk groups. AI can assist image acquisition, view identification for review, and segmentation of valve and cardiac structures for automated analysis. Using image recognition algorithms, aortic and mitral valve disease states have been directly detected from the images themselves. Measurements obtained during echocardiographic valvular assessment have been integrated with other clinical data to identify novel aortic valve disease subgroups and describe new predictors of aortic valve disease progression. In the future, AI could integrate echocardiographic parameters with other clinical data for precision medical management of patients with valvular heart disease.

Synthesis of patient-specific multipoint 4D flow MRI data of turbulent aortic flow downstream of stenotic valves

We propose to synthesize patient-specific 4D flow MRI datasets of turbulent flow paired with ground truth flow data to support training of inference methods. Turbulent blood flow is computed based on the Navier-Stokes equations with moving domains using realistic boundary conditions for aortic shapes, wall displacements and inlet velocities obtained from patient data. From the simulated flow, synthetic multipoint 4D flow MRI data is generated with user-defined spatiotemporal resolutions and reconstructed with a Bayesian approach to compute time-varying velocity and turbulence maps. For MRI data synthesis, a fixed hypothetical scan time budget is assumed and accordingly, changes to spatial resolution and time averaging result in corresponding scaling of signal-to-noise ratios (SNR). In this work, we focused on aortic stenotic flow and quantification of turbulent kinetic energy (TKE). Our results show that for spatial resolutions of 1.5 and 2.5 mm and time averaging of 5 ms as encountered in 4D flow MRI in practice, peak total turbulent kinetic energy downstream of a 50, 75 and 90% stenosis is overestimated by as much as 23, 15 and 14% (1.5 mm) and 38, 24 and 23% (2.5 mm), demonstrating the importance of paired ground truth and 4D flow MRI data for assessing accuracy and precision of turbulent flow inference using 4D flow MRI exams.

Acute Decompensated Aortic Stenosis: State of the Art Review

Aortic stenosis (AS) is a progressive disease that carries a poor prognosis. Patients are managed conservatively until satisfying an indication for transcatheter aortic valve implantation (TAVI) or surgical aortic valve replacement (SAVR) based on AS severity and the presence of symptoms or adverse impact on the myocardium. Up to 1 in 3 TAVIs are performed for patients with acute symptoms of dyspnoea at rest, angina, and/or syncope - termed acute decompensated aortic stenosis (ADAS) and require urgent aortic valve replacement. These patients have longer hospital length of stay, undergo physical deconditioning, have a higher rate of acute kidney injury and mortality compared to stable patients with less severe symptoms. There is an urgent need to prevent ADAS and to deliver pathways to manage and improve ADAS-related outcomes. We provide here a contemporary review on epidemiological and pathophysiological aspects of ADAS, with a focus on the impact of ADAS from clinical and economic perspectives. We will offer also a global overview of the available evidence for treatment of ADAS and with priorities suggested for addressing current gaps in the literature and unmet clinical needs to improve outcomes for AS patients.

Artificial Intelligence-Based Stethoscope for the Diagnosis of Aortic Stenosis

BACKGROUND

The diagnostic accuracy of the stethoscope is limited and highly dependent on clinical expertise. Our purpose was to develop an electronic stethoscope, based on artificial intelligence (AI) and infrasound, for the diagnosis of aortic stenosis (AS).

METHODS

We used an electronic stethoscope (VoqX; Sanolla, Nesher, Israel) with subsonic capabilities and acoustic range of 3-2000 Hz. The study had 2 stages. In the first stage, using the VoqX, we recorded heart sounds from 100 patients referred for echocardiography (derivation group), 50 with moderate or severe AS and 50 without valvular disease. An AI-based supervised learning model was applied to the auscultation data from the first 100 patients used for training, to construct a diagnostic algorithm that was then tested on a validation group (50 other patients, 25 with AS and 25 without AS). In the second stage, conducted at a different medical center, we tested the device on 106 additional patients referred for echocardiography, which included patients with other valvular diseases.

RESULTS

Using data collected at the aortic and pulmonic auscultation points from the derivation group, the AI-based algorithm identified moderate or severe AS with 86% sensitivity and 100% specificity. When applied to the validation group, the sensitivity was 84% and specificity 92%; and in the additional testing group, 90% and 84%, respectively. The sensitivity was 55% for mild, 76% for moderate, and 93% for severe AS.

CONCLUSION

Our initial findings show that an AI-based stethoscope with infrasound capabilities can accurately diagnose AS. AI-based electronic auscultation is a promising new tool for automatic screening and diagnosis of valvular heart disease.

Physiological Features of Cardiac Ventricle and Valve Dynamics from Wearable Radio-Frequency Sensors

Early detection of cardiovascular diseases via non-invasive, convenient, and continuous monitoring is crucial to reducing preventable deaths. This paper illustrates such monitoring using wearable near-field radio-frequency sensors to analyze ventricle and valve transients, which can be used as indicators of myriad cardiac disorders. We applied a novel vector injection signal processing method to improve timing consistency in ventricular contraction, ventricular relaxation, and valve opening extraction. The median relative timing error in valve opening detection was 14.7ms and 37.8ms for semilunar and atrioventricular valves, respectively, as benchmarked by the S1 and S2 heart sounds from a synchronous phonocardiogram. Clinical Relevance- No wearable sensor currently exists to conveniently and reliably evaluate ventricular and valvular dynamics, specifically valvular opening. Beyond extraction of the heart rate and its variation, the method in this paper has the potential to enable non-invasive measurements of detailed cardiac cycle timing features including valve openings, isovolumetric contraction/relaxation times, and ejection periods, improving the monitoring of patient health away from clinical healthcare centers.

Impact of Managing Provider Type on Severe Aortic Stenosis Management and Mortality

Background Many patients with symptomatic severe aortic stenosis do not undergo aortic valve replacement (AVR) despite clinical guidelines. This study analyzed the association of managing provider type with cardiac specialist follow-up, AVR, and mortality for patients with newly diagnosed severe aortic stenosis (sAS). Methods and Results We identified adults with newly diagnosed sAS per echocardiography performed between January 2017 and March 2019 using Optum electronic health record data. We then selected from those meeting all eligibility criteria patients managed by a primary care provider (n=1707 [25%]) or cardiac specialist (n=5039 [75%]). We evaluated the association of managing provider type with cardiac specialist follow-up, AVR, and mortality, as well as the independent association of cardiac specialist follow-up and AVR with mortality, within 1 year of echocardiography detecting sAS. A subgroup analysis was limited to patients with symptomatic sAS. Patient characteristics and comorbidities at baseline were used for covariate-adjusted cause-specific and multivariable Cox proportional hazard models assessing group differences in outcomes by managing provider type. An adjusted Cox proportional hazard model with additional time-dependent covariates for follow-up and AVR was used to assess these practices' association with mortality. Within 1 year of echocardiography detecting sAS, data revealed that primary care provider management was associated with lower rates of cardiac specialist follow-up (hazard ratio [HR], 0.47 [95% CI, 0.43-0.50], P<0.0001) and AVR (HR, 0.58 [95% CI, 0.53-0.64], P<0.0001) and with higher 1-year mortality (HR, 1.45 [95% CI, 1.26-1.66], P<0.0001). Cardiac specialist follow-up and AVR were independently associated with lower mortality (follow-up: HR, 0.55 [95% CI, 0.48-0.63], P<0.0001; AVR: HR, 0.70 [95% CI, 0.60-0.83], P<0.0001). Results were similar for patients with symptomatic sAS. All analyses were adjusted for baseline patient characteristics and comorbidities. Conclusions For patients newly diagnosed with sAS, we observed differences in rates of cardiac specialist follow-up and AVR and risk of mortality between primary care provider- versus cardiologist-managed patients with sAS. In addition, a lower likelihood of receiving follow-up and AVR was independently associated with higher mortality.

S2 Heart Sound Detects Aortic Valve Calcification Independent of Hemodynamic Changes in Mice

Background

Calcific aortic valve disease (CAVD) is often undiagnosed in asymptomatic patients, especially in underserved populations. Although artificial intelligence has improved murmur detection in auscultation exams, murmur manifestation depends on hemodynamic factors that can be independent of aortic valve (AoV) calcium load and function. The aim of this study was to determine if the presence of AoV calcification directly influences the S2 heart sound.

Methods

Adult C57BL/6J mice were assigned to the following 12-week-long diets: (1) Control group (n = 11) fed a normal chow, (2) Adenine group (n = 4) fed an adenine-supplemented diet to induce chronic kidney disease (CKD), and (3) Adenine + HP (n = 9) group fed the CKD diet for 6 weeks, then supplemented with high phosphate (HP) for another 6 weeks to induce AoV calcification. Phonocardiograms, echocardiogram-based valvular function, and AoV calcification were assessed at endpoint.

Results

Mice on the Adenine + HP diet had detectable AoV calcification (9.28 ± 0.74% by volume). After segmentation and dimensionality reduction, S2 sounds were labeled based on the presence of disease: Healthy, CKD, or CKD + CAVD. The dataset (2,516 S2 sounds) was split subject-wise, and an ensemble learning-based algorithm was developed to classify S2 sound features. For external validation, the areas under the receiver operating characteristic curve of the algorithm to classify mice were 0.9940 for Healthy, 0.9717 for CKD, and 0.9593 for CKD + CAVD. The algorithm had a low misclassification performance of testing set S2 sounds (1.27% false positive, 1.99% false negative).

Conclusion

Our ensemble learning-based algorithm demonstrated the feasibility of using the S2 sound to detect the presence of AoV calcification. The S2 sound can be used as a marker to identify AoV calcification independent of hemodynamic changes observed in echocardiography.

Trends in Utilization of Aortic Valve Replacement for Severe Aortic Stenosis

BACKGROUND

Despite the rapid growth of aortic valve replacement (AVR) for aortic stenosis (AS), limited data suggest symptomatic severe AS remains undertreated.

OBJECTIVES

This study sought to investigate temporal trends in AVR utilization among patients with a clinical indication for AVR.

METHODS

Patients with severe AS (aortic valve area <1 cm²) on transthoracic echocardiograms from 2000 to 2017 at 2 large academic medical centers were classified based on clinical guideline indications for AVR and divided into 4 AS subgroups: high gradient with normal left ventricular ejection fraction (LVEF) (HG-NEF), high gradient with low LVEF (HG-LEF), low gradient with normal LVEF (LG-NEF), and low gradient with low LVEF (LG-LEF). Utilization of AVR was examined and predictors identified.

RESULTS

Of 10,795 patients, 6,150 (57%) had an indication or potential indication for AVR, of whom 2,977 (48%) received AVR. The frequency of AVR varied by AS subtype with LG groups less likely to receive an AVR (HG-NEF: 70%, HG-LEF: 53%, LG-NEF: 32%, LG-LEF: 38%, P < 0.001). AVR volumes grew over the 18-year study period but were paralleled by comparable growth in the number of patients with an indication for AVR. In patients with a Class I indication, younger age, coronary artery disease, smoking history, higher hematocrit, outpatient index transthoracic echocardiogram, and LVEF ≥0.5 were independently associated with an increased likelihood of receiving an AVR. AVR was associated with improved survival in each AS-subgroup.

CONCLUSIONS

Over an 18-year period, the proportion of patients with an indication for AVR who did not receive AVR has remained substantial despite the rapid growth of AVR volumes.

Can cardiac auscultation accuracy be improved with an additional app-based learning tool?

Introduction

Many institutions use simulation ‘events’ to instruct cardiac auscultation. Research shows that these ‘one and done’ events limit repetition, are costly and do not incorporate learning science techniques, such as spaced learning and retrieval practice. The Littmann Learning™ mobile app, which has unlimited access to a large library of real patient heart sounds, is a cost‐effective tool that we considered could be leveraged by educators to provide this training.

Methods

This was a quasi‐experimental pre‐ and post‐design consisting of an intervention group (PA22) and a non‐equivalent comparator group (PA21). The intervention group used a novel mobile app cardiac auscultation curriculum (MACAC), while the comparator group received standard didactic instruction. One‐way analyses of variance were used to analyse the data.

Results

A total of 174 PA students participated in the study. There was a significant (p < 0.001) difference in knowledge and auscultation scores between those who did and did not complete the MACAC. PA22 didactic year knowledge scores were 4.11 and 2.96 points higher than PA21 didactic and clinical year knowledge scores (p < 0.001, d = 1.61 and p < 0.001, d = 1.32), respectively. On average, PA22 didactic year auscultation scores were 0.83 points higher than PA21 clinical year scores (p < 0.001, d = 0.6).

Conclusion

Results indicate that students in their didactic year achieved proficiency in clinically identifying heart sounds, despite not having access to a mannequin simulator and not having an opportunity to identify these sounds bedside. Overall, a MACAC may be an effective method to teach cardiac auscultation to medical learners.

Evaluating Medical Therapy for Calcific Aortic Stenosis: JACC State-of-the-Art Review

Despite numerous promising therapeutic targets, there are no proven medical treatments for calcific aortic stenosis (AS). Multiple stakeholders need to come together and several scientific, operational, and trial design challenges must be addressed to capitalize on the recent and emerging mechanistic insights into this prevalent heart valve disease. This review briefly discusses the pathobiology and most promising pharmacologic targets, screening, diagnosis and progression of AS, identification of subgroups that should be targeted in clinical trials, and the need to elicit the patient voice earlier rather than later in clinical trial design and implementation. Potential trial end points and tools for assessment and approaches to implementation and design of clinical trials are reviewed. The efficiencies and advantages offered by a clinical trial network and platform trial approach are highlighted. The objective is to provide practical guidance that will facilitate a series of trials to identify effective medical therapies for AS resulting in expansion of therapeutic options to complement mechanical solutions for late-stage disease.

Artificial intelligence-based detection of aortic stenosis from chest radiographs

Aims

We aimed to develop models to detect aortic stenosis (AS) from chest radiographs-one of the most basic imaging tests-with artificial intelligence.

Methods and results

We used 10 433 retrospectively collected digital chest radiographs from 5638 patients to train, validate, and test three deep learning models. Chest radiographs were collected from patients who had also undergone echocardiography at a single institution between July 2016 and May 2019. These were labelled from the corresponding echocardiography assessments as AS-positive or AS-negative. The radiographs were separated on a patient basis into training [8327 images from 4512 patients, mean age 65 ±  (standard deviation) 15 years], validation (1041 images from 563 patients, mean age 65 ± 14 years), and test (1065 images from 563 patients, mean age 65 ± 14 years) datasets. The soft voting-based ensemble of the three developed models had the best overall performance for predicting AS with an area under the receiver operating characteristic curve, sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of 0.83 (95% confidence interval 0.77-0.88), 0.78 (0.67-0.86), 0.71 (0.68-0.73), 0.71 (0.68-0.74), 0.18 (0.14-0.23), and 0.97 (0.96-0.98), respectively, in the validation dataset and 0.83 (0.78-0.88), 0.83 (0.74-0.90), 0.69 (0.66-0.72), 0.71 (0.68-0.73), 0.23 (0.19-0.28), and 0.97 (0.96-0.98), respectively, in the test dataset.

Conclusion

Deep learning models using chest radiographs have the potential to differentiate between radiographs of patients with and without AS.

Lay Summary

We created artificial intelligence (AI) models using deep learning to identify aortic stenosis (AS) from chest radiographs. Three AI models were developed and evaluated with 10 433 retrospectively collected radiographs and labelled from echocardiography reports. The ensemble AI model could detect AS in a test dataset with an area under the receiver operating characteristic curve of 0.83 (95% confidence interval 0.78-0.88). Since chest radiography is a cost-effective and widely available imaging test, our model can provide an additive resource for the detection of AS.

Prevalence, Association Relation, and Dynamic Evolution Analysis of Critical Values in Health Checkup in China: A Retrospective Study

Objective: The critical values in health checkup play a key role in preventing chronic diseases and different types of cancer. This study aimed to analyze the prevalence, association relation, and dynamic evolution of critical values in health checkups at a large physical examination center in China.

Methods: Herein, we chose 33,639 samples of physical examiners from January 2017 to December 2019. After strict exclusion processes, combined with the critical values in health checkup reporting data, 4,721 participants with at least one critical value were included. We first defined a critical value list for laboratory test, imaging, cervical cancer screening, electrocardiogram, and health checkup informed on site, and then performed a cross-sectional study to analyze the distribution and significance of critical values of 4,721 participants from different views and the association relation of 628 participants with more than one critical value and a retrospective cohort study to analyze the incidence and dynamic evolution of critical values based on 2,813 participants attending the physical examination from 2017 to 2019.

Results: A total of 4,721 participants were included in the retrospective study. The prevalence of 10 critical values from 33,639 participants was over 0.6%. The critical values of obesity, hypertension, Glucose_T, Liver_T, Kidney_T, Lipid_T, Urine_T, and Head_CT were significantly increased in men (P < 0.05), whereas the results were the opposite for the Blood_T and Thyroid_US (P < 0.01). The prevalence trend of critical values increased along with age, where the prevalence of men was higher than that of women under 60 years old (P < 0.01), while the prevalence of women increased by four times and exceeded the prevalence of men above 70 years old. Association relation analysis identified 16 and 6 effective rules for men and women, respectively, where the critical values of Urine_T and Glucose_T played the central roles. Furthermore, a retrospective dynamic evolution analysis found that the incidence of new critical values was about 10%, the incidence of persistent critical values was about 50%, and that most of the effective evolution paths tended to no critical values for men and women.

Conclusion: In conclusion, this study provides a new perspective to explore the population health status using the critical value reporting data in a physical examination center, which can assist in decision-making by health management at the population level and in the prevention and treatment of various types of cancer and chronic diseases at the individual level.

Management of aortic stenosis: a systematic review of clinical practice guidelines and recommendations

Multiple guidelines exist for the management of aortic stenosis (AS). We systematically reviewed current guidelines and recommendations, developed by national or international medical organizations, on management of AS to aid clinical decision-making. Publications in MEDLINE and EMBASE between 1 June 2010 and 15 January 2021 were identified. Additionally, the International Guideline Library, National Guideline Clearinghouse, National Library for Health Guidelines Finder, Canadian Medical Association Clinical Practice Guidelines Infobase, and websites of relevant organizations were searched. Two reviewers independently screened titles and abstracts. Two reviewers assessed rigour of guideline development and extracted the recommendations. Of the seven guidelines and recommendations retrieved, five showed considerable rigour of development. Those rigourously developed, agreed on the definition of severe AS and diverse haemodynamic phenotypes, indications and contraindications for intervention in symptomatic severe AS, surveillance intervals in asymptomatic severe AS, and the importance of multidisciplinary teams (MDTs) and shared decision-making. Discrepancies exist in age and surgical risk cut-offs for recommending surgical aortic valve replacement (SAVR) vs. transcatheter aortic valve implantation (TAVI), the use of biomarkers and complementary multimodality imaging for decision-making in asymptomatic patients and surveillance intervals for non-severe AS. Contemporary guidelines for AS management agree on the importance of MDT involvement and shared decision-making for individualized treatment and unanimously indicate valve replacement in severe, symptomatic AS. Discrepancies exist in thresholds for age and procedural risk used in choosing between SAVR and TAVI, role of biomarkers and complementary imaging modalities to define AS severity and risk of progression in asymptomatic patients.

Community-based aortic stenosis detection: clinical and echocardiographic screening during influenza vaccination

Background

Degenerative aortic stenosis (AS), the most common valvular heart disease in the Western world, is often diagnosed late when the mortality risk becomes substantial. We determined the feasibility of AS screening during influenza vaccination at general practitioner (GP) surgeries in the UK.

Methods

Consecutive subjects aged >65 years presenting to a GP for influenza vaccination underwent heart auscultation and 2D echocardiography (V-scan). Based on these findings, a patient management strategy was determined (referral to cardiologist, review within own practice or no follow-up measures) and status at 3 months was determined.

Results

167 patients were enrolled with a mean age of 75 years. On auscultation, a heart murmur was detected in 30 of 167 (18%) patients (6 subjects with an AS-specific and 24 with a non-specific murmur). 75.2% of those with no murmur had a negative V-scan finding. Conversely, 16 of 30 (53%) patients with any murmur had an abnormal V-scan finding that was largely related to the aortic valve. Using clinical auscultation and V-scan screening, a decision not to pursue follow-up measures was taken in 147 (88%) cases, whereas 18 (10.8%) subjects were referred onward; with 5 of 18 (27.8%) and 3 of 18 (16.7%) being diagnosed with mild and moderate AS.

Conclusions

Our pilot study confirms feasibility of valvular heart disease screening in the elderly in a primary care setting. Using simple and inexpensive diagnostic measures and 7.3 million UK inhabitants undergoing influenza vaccination, nationwide screening could potentially identify 130 000 patients with moderate AS and a significant number of patients with severe AS.

Risk factors and comorbidities associated with severe aortic stenosis: a case-control study

BACKGROUND AND OBJECTIVE

Aortic stricture (AS) is one of the most prevalent cardiovascular diseases in individuals 65 years of age or older. A number of epidemiological studies have suggested that certain cardiovascular risk factors (CRFs) and comorbidities could be associated with AS. The aim of this study was to evaluate the association between CRFs and comorbidities and severe symptomatic AS in individuals 65 years of age or older in a Spanish healthcare region.

PATIENTS AND METHODS

We conducted an epidemiological case-control study from a single primary care centre. We collected information on exposure to CRFs and comorbidities and determined their association with AS, employing adjusted odds ratios (OR) and multiple logistic regression models.

RESULTS

The study included 102 cases (mean age, 77.6 years) and 221 controls (mean age, 75.5 years). The CRFs significantly associated with severe symptomatic AS were hypercholesterolaemia (OR, 2.67; p<.001), tobacco use (OR, 2.60; p<.001), hypertension (OR, 2.41; p=.010) and low HDL cholesterol readings (OR, 2.20; p=.007). The comorbidities significantly associated with severe symptomatic AS were carotid stenosis (OR, 14.5; p=.017), stroke (OR, 4.14; p=.024), chronic renal failure (OR, 3.78; p<.001) and low haemoglobin levels (OR, 0.76; p<.001).

CONCLUSIONS

Hypercholesterolaemia, tobacco use, arterial hypertension and low HDL cholesterol levels are the CRFs with a greater risk of severe AS. Furthermore, this disease is associated with a number of comorbidities (chronic renal failure, stroke, carotid stenosis and low haemoglobin levels), which could be markers of AS.

Genetic and Developmental Contributors to Aortic Stenosis

Aortic stenosis (AS) remains one of the most common forms of valve disease, with significant impact on patient survival. The disease is characterized by left ventricular outflow obstruction and encompasses a series of stenotic lesions starting from the left ventricular outflow tract to the descending aorta. Obstructions may be subvalvar, valvar, or supravalvar and can be present at birth (congenital) or acquired later in life. Bicuspid aortic valve, whereby the aortic valve forms with two instead of three cusps, is the most common cause of AS in younger patients due to primary anatomic narrowing of the valve. In addition, the secondary onset of premature calcification, likely induced by altered hemodynamics, further obstructs left ventricular outflow in bicuspid aortic valve patients. In adults, degenerative AS involves progressive calcification of an anatomically normal, tricuspid aortic valve and is attributed to lifelong exposure to multifactoral risk factors and physiological wear-and-tear that negatively impacts valve structure-function relationships. AS continues to be the most frequent valvular disease that requires intervention, and aortic valve replacement is the standard treatment for patients with severe or symptomatic AS. While the positive impacts of surgical interventions are well documented, the financial burden, the potential need for repeated procedures, and operative risks are substantial. In addition, the clinical management of asymptomatic patients remains controversial. Therefore, there is a critical need to develop alternative approaches to prevent the progression of left ventricular outflow obstruction, especially in valvar lesions. This review summarizes our current understandings of AS cause; beginning with developmental origins of congenital valve disease, and leading into the multifactorial nature of AS in the adult population.

Minimising permanent pacemaker implantation (PPI) after TAVI

Increased demand for transcatheter aortic valve implantation (TAVI) procedures for patients with severe aortic stenosis has not been matched with a proportional increase in available resources in recent years. This article highlights the importance of developing integrated care pathways for TAVI, which incorporate standardised protocols for permanent pacemaker implantation (PPI) to ensure best practice, increase service efficiency and reduce rates of PPI post-TAVI.

Risk factors and comorbidities associated with severe aortic stenosis: A case-control study

BACKGROUND AND OBJECTIVE

Aortic stricture (AS) is one of the most prevalent cardiovascular diseases in individuals 65 years of age or older. A number of epidemiological studies have suggested that certain cardiovascular risk factors (CRFs) and comorbidities could be associated with AS. The aim of this study was to evaluate the association between CRFs and comorbidities and severe symptomatic AS in individuals 65 years of age or older in a Spanish healthcare region.

PATIENTS AND METHODS

We conducted an epidemiological case-control study from a single primary care centre. We collected information on exposure to CRFs and comorbidities and determined their association with AS, employing adjusted odds ratios (OR) and multiple logistic regression models.

RESULTS

The study included 102 cases (mean age, 77.6 years) and 221 controls (mean age, 75.5 years). The CRFs significantly associated with severe symptomatic AS were hypercholesterolaemia (OR, 2.67; p < .001), tobacco use (OR, 2.60; p < .001), hypertension (OR, 2.41; p = .010) and low HDL cholesterol readings (OR, 2.20; p = .007). The comorbidities significantly associated with severe symptomatic AS were carotid stenosis (OR, 14.5; p = .017), stroke (OR, 4.14; p = .024), chronic renal failure (OR, 3.78; p < .001) and low haemoglobin levels (OR, 0.76; p < .001).

CONCLUSIONS

Hypercholesterolaemia, tobacco use, arterial hypertension and low HDL cholesterol levels are the CRFs with a greater risk of severe AS. Furthermore, this disease is associated with a number of comorbidities (chronic renal failure, stroke, carotid stenosis and low haemoglobin levels), which could be markers of AS.

Narrative review of the role of artificial intelligence to improve aortic valve disease management

Valvular heart disease (VHD) is a chronic progressive condition with an increasing prevalence in the Western world due to aging populations. VHD is often diagnosed at a late stage when patients are symptomatic and the outcomes of therapy, including valve replacement, may be sub-optimal due the development of secondary complications, including left ventricular (LV) dysfunction. The clinical application of artificial intelligence (AI), including machine learning (ML), has promise in supporting not only early and more timely diagnosis, but also hastening patient referral and ensuring optimal treatment of VHD. As physician auscultation lacks accuracy in diagnosis of significant VHD, computer-aided auscultation (CAA) with the help of a commercially available digital stethoscopes improves the detection and classification of heart murmurs. Although used little in current clinical practice, CAA can screen large populations at low cost with high accuracy for VHD and faciliate appropriate patient referral. Echocardiography remains the next step in assessment and planning management and AI is delivering major changes in speeding training, improving image quality by pattern recognition and image sorting, as well as automated measurement of multiple variables, thereby improving accuracy. Furthermore, AI then has the potential to hasten patient disposal, by automated alerts for red-flag findings, as well as decision support in dealing with results. In management, there is great potential in ML-enabled tools to support comprehensive disease monitoring and individualized treatment decisions. Using data from multiple sources, including demographic and clinical risk data to image variables and electronic reports from electronic medical records, specific patient phenotypes may be identified that are associated with greater risk or modeled to the estimate trajectory of VHD progression. Finally, AI algorithms are of proven value in planning intervention, facilitating transcatheter valve replacement by automated measurements of anatomical dimensions derived from imaging data to improve valve selection, valve size and method of delivery.

Cardiac Imaging of Aortic Valve Area From 34 287 UK Biobank Participants Reveals Novel Genetic Associations and Shared Genetic Comorbidity With Multiple Disease Phenotypes

Background:

The aortic valve is an important determinant of cardiovascular physiology and anatomic location of common human diseases.

Methods:

From a sample of 34 287 white British ancestry participants, we estimated functional aortic valve area by planimetry from prospectively obtained cardiac magnetic resonance imaging sequences of the aortic valve. Aortic valve area measurements were submitted to genome-wide association testing, followed by polygenic risk scoring and phenome-wide screening, to identify genetic comorbidities.

Results:

A genome-wide association study of aortic valve area in these UK Biobank participants showed 3 significant associations, indexed by rs71190365 (chr13:50764607, DLEU1 , P =1.8×10 −9 ), rs35991305 (chr12:94191968, CRADD , P =3.4×10 −8 ), and chr17:45013271:C:T ( GOSR2 , P =5.6×10 −8 ). Replication on an independent set of 8145 unrelated European ancestry participants showed consistent effect sizes in all 3 loci, although rs35991305 did not meet nominal significance. We constructed a polygenic risk score for aortic valve area, which in a separate cohort of 311 728 individuals without imaging demonstrated that smaller aortic valve area is predictive of increased risk for aortic valve disease (odds ratio, 1.14; P =2.3×10 −6 ). After excluding subjects with a medical diagnosis of aortic valve stenosis (remaining n=308 683 individuals), phenome-wide association of >10 000 traits showed multiple links between the polygenic score for aortic valve disease and key health-related comorbidities involving the cardiovascular system and autoimmune disease. Genetic correlation analysis supports a shared genetic etiology with between aortic valve area and birth weight along with other cardiovascular conditions.

Conclusions:

These results illustrate the use of automated phenotyping of cardiac imaging data from the general population to investigate the genetic etiology of aortic valve disease, perform clinical prediction, and uncover new clinical and genetic correlates of cardiac anatomy.

Myval transcatheter heart valve system in the treatment of severe symptomatic aortic stenosis

The transcatheter aortic valve replacement (TAVR) is an established treatment for patients with severe symptomatic aortic stenosis (AS) at prohibitive risk for surgery. It is an alternative treatment to surgical aortic valve replacement in patients with AS at intermediate- and high-surgical risk. Although regulatory authorities extend the indications of TAVR to treat patients at low-surgical risk, the limitations of earlier-generation transcatheter heart valve (THV) systems accelerate the development of improved newer generation of THV systems. Myval™ THV (Meril Life Sciences Pvt. Ltd., Vapi, Gujarat, India) is a newer-generation, balloon-expandable TAVR system with features that facilitate accurate positioning of the bioprosthetic valve and favorable procedural and clinical outcomes. This review summarizes existing preclinical and clinical data on Myval THV for the intervention of symptomatic native AS and lays out the plan for future research program.

National Trends of Outcomes in Transcatheter Aortic Valve Replacement (TAVR) Through Transapical Versus Endovascular Approach: From the National Inpatient Sample (NIS)

BACKGROUND

To evaluate the trends in complication rates following transcatheter aortic valve replacement (TAVR) procedures according to the type of vascular approach (endovascular vs. transapical) in a large US population sample.

METHODS

The National Inpatient Sample (NIS) was queried for all patients diagnosed with aortic stenosis who underwent a TAVR procedure in the United States during the years 2012-2016. Outcomes assessed were peri-procedural mortality, cardiac, and non-cardiac complications. Hospitalization outcomes were modeled using logistic regression for binary outcomes and generalized linear models for continuous outcomes.

RESULTS

There were 97,320 endovascular-TAVR patients and 11,140 transapical-TAVR patients. The mean age was 80.8 years (standard error of the mean: ± 0.1). Most patients were males (53.7%) and Caucasian (87.1%). On multivariate analysis, after adjusting for age, gender, comorbidities, as well as hospital factors, patients with the transapical approach had a higher risk for mortality and adverse outcomes. Among the endovascular-TAVR group, national trends showed a diminishing incidence of procedural mortality (incidence rate ratio [IRR] 0.77; 95% CI: 0.72-0.84, p < 0.001), stroke (IRR 0.80; 95% CI: 0.73-0.87, p < 0.001), and all secondary outcomes, but no significant change in myocardial infarction. In contrast, most transapical-TAVR related procedural complications remained unchanged over time, except for a significant decrease in stroke, acute respiratory failure and need for pacemaker insertion.

CONCLUSION

National trends show a steady increase in the number of endovascular-TAVR procedures with a concurrent decrease in procedural complications.

Aortic valve stenosis awareness in Austria-results of a nationwide survey in 1001 subjects

Despite the prognostic significance of severe aortic valve stenosis, knowledge is limited in the general population. To document the status quo for Austria, knowledge about valvular heart disease/aortic valve stenosis was documented in 1001 participants >60 years of age. 6.7% of respondents were knowledgeable of aortic valve stenosis, with 1.6% being concerned about the condition (24.1% cancer, 18.8% Alzheimer’s disease, 15.1% stroke). 29.5% were familiar with valvular heart disease (76.7% heart attack, 36.9% stroke). Only 1/3 reported auscultation by their general practitioner (GP) at least every third visit. Typical symptoms of aortic valve stenosis were likely to be reported by 50%. After exposure to further information on aortic valve stenosis, only 20% reported to be more concerned and ready to obtain more disease-related information. Awareness of surgical and catheter-based treatment options was claimed by 77% of respondents. Awareness campaigns on valvular heart disease are warranted to improve patient care in Austria.