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Sandeep B, Liu X, Wu Q, Gao K, Xiao Z. Recent updates on asymptomatic and symptomatic aortic valve stenosis its diagnosis, pathogenesis, management and future perspectives. Curr Probl Cardiol 2024; 49:102631. [PMID: 38729278 DOI: 10.1016/j.cpcardiol.2024.102631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2024] [Accepted: 05/06/2024] [Indexed: 05/12/2024]
Abstract
Aortic stenosis (AS) is very common in mid-aged and elderly patients, and it has been reported to have a negative impact on both short and long-term survival with a high mortality rate. The current study identified methods of diagnosis, incidence, and causes of AS, pathogenesis, intervention and management and future perspectives of Asymptomatic and Symptomatic Aortic stenosis. A systematic literature search was conducted using PubMed, Scopus and CINAHL, using the Mesh terms and key words "Aortic stenosis", "diagnostic criteria", "pathogenesis", "incidence and causes of AS" and" intervention and management strategies". Studies were retained for review after meeting strict inclusion criteria that included studies evaluating Asymptomatic and Symptomatic AS. Studies were excluded if duplicate publication, overlap of patients, subgroup studies of a main study, lack of data on AS severity, case reports and letters to editors. Forty-five articles were selected for inclusion. Incidence of AS across the studies ranged from 3 % to 7 %. Many factors have been associated with incidence and increased risk of AS, highest incidence of AS was described after aortic valve calcification, rheumatic heart disease, degenerative aortic valve disease, bicuspid aortic valve and other factors. AS is common and can be predicted by aortic root calcification volume, rheumatic heart disease, degenerative aortic valve disease, bicuspid aortic valve. Intervention and management for AS patients is a complex decision that takes into consideration multiple factors. On the other hand, there is not enough progress in preventive pharmacotherapy to slow the progression of AS.
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Affiliation(s)
- Bhushan Sandeep
- Department of Cardio-Thoracic Surgery, Chengdu Second People's Hospital, Chengdu, Sichuan 610017, China.
| | - Xian Liu
- Department of Cardio-Thoracic Surgery, Chengdu Second People's Hospital, Chengdu, Sichuan 610017, China
| | - Qinghui Wu
- Department of Cardio-Thoracic Surgery, Chengdu Second People's Hospital, Chengdu, Sichuan 610017, China
| | - Ke Gao
- Department of Cardio-Thoracic Surgery, Chengdu Second People's Hospital, Chengdu, Sichuan 610017, China
| | - Zongwei Xiao
- Department of Cardio-Thoracic Surgery, Chengdu Second People's Hospital, Chengdu, Sichuan 610017, China
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Cohen-Shelly M, Attia ZI, Friedman PA, Ito S, Essayagh BA, Ko WY, Murphree DH, Michelena HI, Enriquez-Sarano M, Carter RE, Johnson PW, Noseworthy PA, Lopez-Jimenez F, Oh JK. Electrocardiogram screening for aortic valve stenosis using artificial intelligence. Eur Heart J 2021; 42:2885-2896. [PMID: 33748852 DOI: 10.1093/eurheartj/ehab153] [Citation(s) in RCA: 126] [Impact Index Per Article: 31.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2020] [Revised: 12/22/2020] [Accepted: 03/04/2021] [Indexed: 12/19/2022] Open
Abstract
AIMS Early detection of aortic stenosis (AS) is becoming increasingly important with a better outcome after aortic valve replacement in asymptomatic severe AS patients and a poor outcome in moderate AS. We aimed to develop artificial intelligence-enabled electrocardiogram (AI-ECG) using a convolutional neural network to identify patients with moderate to severe AS. METHODS AND RESULTS Between 1989 and 2019, 258 607 adults [mean age 63 ± 16.3 years; women 122 790 (48%)] with an echocardiography and an ECG performed within 180 days were identified from the Mayo Clinic database. Moderate to severe AS by echocardiography was present in 9723 (3.7%) patients. Artificial intelligence training was performed in 129 788 (50%), validation in 25 893 (10%), and testing in 102 926 (40%) randomly selected subjects. In the test group, the AI-ECG labelled 3833 (3.7%) patients as positive with the area under the curve (AUC) of 0.85. The sensitivity, specificity, and accuracy were 78%, 74%, and 74%, respectively. The sensitivity increased and the specificity decreased as age increased. Women had lower sensitivity but higher specificity compared with men at any age groups. The model performance increased when age and sex were added to the model (AUC 0.87), which further increased to 0.90 in patients without hypertension. Patients with false-positive AI-ECGs had twice the risk for developing moderate or severe AS in 15 years compared with true negative AI-ECGs (hazard ratio 2.18, 95% confidence interval 1.90-2.50). CONCLUSION An AI-ECG can identify patients with moderate or severe AS and may serve as a powerful screening tool for AS in the community.
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Affiliation(s)
- Michal Cohen-Shelly
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA
| | - Zachi I Attia
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA
| | - Paul A Friedman
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA
| | - Saki Ito
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA
| | - Benjamin A Essayagh
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA
| | - Wei-Yin Ko
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA
| | - Dennis H Murphree
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA
| | - Hector I Michelena
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA
| | - Maurice Enriquez-Sarano
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA
| | - Rickey E Carter
- Health Sciences Research, Mayo Clinic, 4500 San Pablo Rd S, Jacksonville, FL 32224, USA
| | - Patrick W Johnson
- Health Sciences Research, Mayo Clinic, 4500 San Pablo Rd S, Jacksonville, FL 32224, USA
| | - Peter A Noseworthy
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA
| | - Francisco Lopez-Jimenez
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA
| | - Jae K Oh
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA
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Efthimiadis G, Zegkos T, Parcharidou D, Ntelios D, Panagiotidis T, Gossios T, Karvounis H. A simple algorithm for a clinical step-by-step approach in the management of hypertrophic cardiomyopathy. Future Cardiol 2021; 17:1395-1405. [PMID: 33615852 DOI: 10.2217/fca-2020-0230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Hypertrophic cardiomyopathy (HCM) is the most common inherited heart disease with an autosomal dominant pattern and a reported prevalence of about 0.2%. In this review, we present a simple algorithm for the management of first diagnosed HCM patients. Initially, the clinical examination, medical and detailed family history and the ECG are essential. The etiological diagnosis of left ventricular hypertrophy is important in order to differentiate HCM due to sarcomeric genes mutation from other phenocopies, such as cardiac amyloidosis. The next step consists of the cardiovascular imaging and ambulatory electrocardiography. Cardiopulmonary exercise testing may also be considered if available. All of the above provide evidence for the critical step of the risk stratification of patients for sudden cardiac death. The therapeutic strategy, with respect to obstructive and nonobstructive disease, arrhythmias and end-stage HCM is also described.
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Affiliation(s)
- Georgios Efthimiadis
- 1st Cardiology department, Laboratory of Cardiomyopathies and Inherited Cardiac Diseases, AHEPA University hospital, Thessaloniki 54636, Greece
| | - Thomas Zegkos
- 1 Cardiology department, Laboratory of Cardiomyopathies and Inherited Cardiac Diseases, AHEPA University hospital, Thessaloniki 54636, Greece
| | - Despoina Parcharidou
- 1 Cardiology department, Laboratory of Cardiomyopathies and Inherited Cardiac Diseases, AHEPA University hospital, Thessaloniki 54636, Greece
| | - Dimitris Ntelios
- 1 Cardiology department, Laboratory of Cardiomyopathies and Inherited Cardiac Diseases, AHEPA University hospital, Thessaloniki 54636, Greece
| | - Theofilos Panagiotidis
- 1 Cardiology department, Laboratory of Cardiomyopathies and Inherited Cardiac Diseases, AHEPA University hospital, Thessaloniki 54636, Greece
| | - Thomas Gossios
- Barts Heart Centre, St Bartholomew's Hospital, W Smithfield, London, EC1A 7BE, UK
| | - Haralambos Karvounis
- 1 Cardiology department, Laboratory of Cardiomyopathies and Inherited Cardiac Diseases, AHEPA University hospital, Thessaloniki 54636, Greece
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Abstract
The progress in cardiology during the last 50 years can best be studied by looking at the diagnostics and treatment of patients with aortic valve stenosis. Previously, the clinical examination, electrocardiography (ECG) and chest X‑ray were used before heart catheterization, which included a transseptal puncture to complete the indications for surgery in young patients. Nowadays, echocardiography, often combined with a dobutamine stress test, is the primary diagnostic tool to which computed tomography for quantification of valve calcification and cardiac magnetic resonance imaging can be of additive value. The treatment of severe aortic valve stenosis is no longer only treated by aortic valve replacement but transluminal aortic valve implantation also represents a new therapeutic option. The change in the age groups of treated patients is also noteworthy. Surgery is recommended for patients under 75 years old but for older patients, especially those with a high risk, interventional catheter-assisted treatment is preferred.
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Kwon JM, Lee SY, Jeon KH, Lee Y, Kim KH, Park J, Oh BH, Lee MM. Deep Learning-Based Algorithm for Detecting Aortic Stenosis Using Electrocardiography. J Am Heart Assoc 2020; 9:e014717. [PMID: 32200712 PMCID: PMC7428650 DOI: 10.1161/jaha.119.014717] [Citation(s) in RCA: 122] [Impact Index Per Article: 24.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Background Severe, symptomatic aortic stenosis (AS) is associated with poor prognoses. However, early detection of AS is difficult because of the long asymptomatic period experienced by many patients, during which screening tools are ineffective. The aim of this study was to develop and validate a deep learning–based algorithm, combining a multilayer perceptron and convolutional neural network, for detecting significant AS using ECGs. Methods and Results This retrospective cohort study included adult patients who had undergone both ECG and echocardiography. A deep learning–based algorithm was developed using 39 371 ECGs. Internal validation of the algorithm was performed with 6453 ECGs from one hospital, and external validation was performed with 10 865 ECGs from another hospital. The end point was significant AS (beyond moderate). We used demographic information, features, and 500‐Hz, 12‐lead ECG raw data as predictive variables. In addition, we identified which region had the most significant effect on the decision‐making of the algorithm using a sensitivity map. During internal and external validation, the areas under the receiver operating characteristic curve of the deep learning–based algorithm using 12‐lead ECG for detecting significant AS were 0.884 (95% CI, 0.880–0.887) and 0.861 (95% CI, 0.858–0.863), respectively; those using a single‐lead ECG signal were 0.845 (95% CI, 0.841–0.848) and 0.821 (95% CI, 0.816–0.825), respectively. The sensitivity map showed the algorithm focused on the T wave of the precordial lead to determine the presence of significant AS. Conclusions The deep learning–based algorithm demonstrated high accuracy for significant AS detection using both 12‐lead and single‐lead ECGs.
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Affiliation(s)
- Joon-Myoung Kwon
- Department of Emergency Medicine Mediplex Sejong Hospital Incheon Korea.,Artificial Intelligence and Big Data Center Sejong Medical Research Institute Bucheon Korea
| | - Soo Youn Lee
- Department of Cardiology Sejong General Hospital Bucheon Korea
| | - Ki-Hyun Jeon
- Division of Cardiology Cardiovascular Center Incheon Korea.,Artificial Intelligence and Big Data Center Sejong Medical Research Institute Bucheon Korea
| | | | - Kyung-Hee Kim
- Division of Cardiology Cardiovascular Center Incheon Korea
| | - Jinsik Park
- Division of Cardiology Cardiovascular Center Incheon Korea
| | - Byung-Hee Oh
- Division of Cardiology Cardiovascular Center Incheon Korea
| | - Myong-Mook Lee
- Department of Cardiology Sejong General Hospital Bucheon Korea
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Vranic II. Electrocardiographic appearance of aortic stenosis before and after aortic valve replacement. Ann Noninvasive Electrocardiol 2017; 22. [PMID: 28429500 DOI: 10.1111/anec.12457] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2016] [Accepted: 03/13/2017] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND So far, the specific appearance of QRS complex, ST-segment, and T wave was observed in aortic stenosis (AS). S-wave dynamic change in leads V1 -V3 was not reported in AS. METHODS In a single-center, prospective study, we included a total number of 1.175 patients who underwent surgical aortic valve replacement (AVR). We conducted 3-year gathering of patients with symptomatic and asymptomatic severe AS, and separated them by hemodynamic stability into groups A and B, through EFLV (of more or less than 50%), AVA (of more or less than 0.9 cm2 ), PG (between 55 and 75 mm Hg or over 75 mm Hg), and end-diastolic LV dimension (of more or less than 56 mm). We evaluated the impact of S-wave magnitude in right precordial leads before and after AVR in all patients. We followed S-wave changes in electrocardiogram altogether with hemodynamic measurements derived from echocardiography. RESULTS Analysis of echocardiographic parameters, measured in patients before surgery, did not show statistical significance between asymptomatic and symptomatic group. The statistical significance was observed in the change in S-wave magnitude in the right precordial leads in both subsets of patients before AVR. We found statistically significant predictive value of S-wave magnitude in leads V2 -V3 for dependent variables PG and end-diastolic LV dimension. CONCLUSIONS S-wave changes in right precordial leads can predict increase in PG and critical narrowing of AVA, suggestive of timely referral for AVR.
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Affiliation(s)
- Ivana I Vranic
- Department of Cardiology, Clinical Center of Serbia, Belgrade, Serbia
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