A Machine-Learning Framework to Identify Distinct Phenotypes of Aortic Stenosis Severity

Sengupta, Partho P.; Shrestha, Sirish; Kagiyama, Nobuyuki; Hamirani, Yasmin S.; Kulkarni, Hemant; Yanamala, Naveena; Bing, Rong; Chin, Calvin Woon Loong; Pawade, Tania; Messika–Zeitoun, David; Tastet, Lionel; Shen, Mylène; Newby, David E.; Clavel, Marie‐Annick; Pibarot, Phillippe; Dweck, Marc R; Larose, Éric; Guzzetti, Ezéquiel; Bernier, Martin; Beaudoin, Jonathan; Arsenault, Marie; Côté, Nancy; Everett, Russell J.; Jenkins, William; Tribouilloy, Christophe; Dreyfus, Julien; Mathieu, Tiffany; Renard, C.; Gun, Mesut; Macron, Laurent; Sechrist, Jacob W.; Lacomis, Joan M.; Nguyen, Virginia; Gay, Laura Galian; Calabria, Hug Cuéllar; Ntalas, Ioannis; Prendergast, Bernard; Rajani, Ronak; Evangelista, Arturo; Cavalcante, João L.

doi:10.1016/j.jcmg.2021.03.020

Cited by 56 publications

(30 citation statements)

References 27 publications

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“…Sengupta et al created a model which can provide the same amount of information about aortic stenosis severity as if both echocardiography and cardiac CT/MR were performed using only echocardiography and the model. Such solutions can be widely implemented in less-developed areas where sophisticated diagnostic methods are not available [ 62 ]. Another field that represents great potential is predicting pathology from seemingly physiological findings; for instance, there are successful attempts to identify patients with a history of atrial fibrillation from the ECG performed during the sinus rhythm [ 63 ].…”

Section: Discussionmentioning

confidence: 99%

An Artificial Intelligence Approach to Guiding the Management of Heart Failure Patients Using Predictive Models: A Systematic Review

et al. 2022

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Heart failure (HF) is one of the leading causes of mortality and hospitalization worldwide. The accurate prediction of mortality and readmission risk provides crucial information for guiding decision making. Unfortunately, traditional predictive models reached modest accuracy in HF populations. We therefore aimed to present predictive models based on machine learning (ML) techniques in HF patients that were externally validated. We searched four databases and the reference lists of the included papers to identify studies in which HF patient data were used to create a predictive model. Literature screening was conducted in Academic Search Ultimate, ERIC, Health Source Nursing/Academic Edition and MEDLINE. The protocol of the current systematic review was registered in the PROSPERO database with the registration number CRD42022344855. We considered all types of outcomes: mortality, rehospitalization, response to treatment and medication adherence. The area under the receiver operating characteristic curve (AUC) was used as the comparator parameter. The literature search yielded 1649 studies, of which 9 were included in the final analysis. The AUCs for the machine learning models ranged from 0.6494 to 0.913 in independent datasets, whereas the AUCs for statistical predictive scores ranged from 0.622 to 0.806. Our study showed an increasing number of ML predictive models concerning HF populations, although external validation remains infrequent. However, our findings revealed that ML approaches can outperform conventional risk scores and may play important role in HF management.

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Section: Discussionmentioning

confidence: 99%

An Artificial Intelligence Approach to Guiding the Management of Heart Failure Patients Using Predictive Models: A Systematic Review

et al. 2022

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“…This superior stratification supports the use of changes in LV and AV function along a continuum in disease management. In Sengupta et al, 41 the investigators sought to identify a high-risk group among a cohort of 1052 patients with mild or moderate AS and a discordant AS group which is the traditional low-flow, low-gradient group. Topological data analysis based on echocardiographic parameters derived a high-risk type which had higher AV calcium scores, more late gadolinium enhancement, higher brain natriuretic protein and troponin levels, greater incidences of AVR, and death before and after AVR.…”

Section: Reviewmentioning

confidence: 99%

Artificial intelligence for the echocardiographic assessment of valvular heart disease

Nedadur

Wang

Tsang

2022

Heart

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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.

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“…Sengupta et al utilized a supervised ML model to augment echocardiographic stratification of aortic stenosis (AS) severity. They showed high-severity and low-severity AS phenotypes which were compared to markers of disease severity in CT and cardiac magnetic resonance (CMR) imaging and major clinical outcomes such as aortic valve replacement (AVR) and mortality [ 20 ]. Close to 70% of the 1964 patients were classified as having non-severe or discordant AS, but the ML model showed 1117 (57%) patients having high-severity AS and 847 (43%) patients having low-severity AS.…”

Section: Application Of ML In Cardiovascular Imagingmentioning

confidence: 99%

“…High-severity groups had a higher incidence of elevated calcium scores and left ventricular fibrosis. In relation to current classification approaches, ML-derived classification had enhanced discrimination (integrated discrimination improvement 0.17, CI 0.02–0.12) and reclassification (net reclassification improvement 0.17, CI 0.11–0.23) for aortic valve replacement (AVR) outcomes at 5 years [ 20 ]. Current recommendations for evaluating AS are hindered by diagnostic ambiguity as many complications in AS arise secondary to valvular obstruction and ventricular decompensation [ 21 ].…”

Section: Application Of ML In Cardiovascular Imagingmentioning

confidence: 99%

Applications of Machine Learning in Cardiology

et al. 2022

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In this digital era, artificial intelligence (AI) is establishing a strong foothold in commercial industry and the field of technology. These effects are trickling into the healthcare industry, especially in the clinical arena of cardiology. Machine learning (ML) algorithms are making substantial progress in various subspecialties of cardiology. This will have a positive impact on patient care and move the field towards precision medicine. In this review article, we explore the progress of ML in cardiovascular imaging, electrophysiology, heart failure, and interventional cardiology.

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A Machine-Learning Framework to Identify Distinct Phenotypes of Aortic Stenosis Severity

Cited by 56 publications

References 27 publications

An Artificial Intelligence Approach to Guiding the Management of Heart Failure Patients Using Predictive Models: A Systematic Review

An Artificial Intelligence Approach to Guiding the Management of Heart Failure Patients Using Predictive Models: A Systematic Review

Artificial intelligence for the echocardiographic assessment of valvular heart disease

Applications of Machine Learning in Cardiology

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