Usefulness of Semisupervised Machine-Learning-Based Phenogrouping to Improve Risk Assessment for Patients Undergoing Transcatheter Aortic Valve Implantation

Ghffar, Yasir Abdul; Shrestha, Sirish; Shaukat, Faizan; Kagiyama, Nobuyuki; Alkhouli, Mohammed; Raybuck, Bryan; Badhwar, Vinay; Sengupta, Partho P.

doi:10.1016/j.amjcard.2020.08.048

Cited by 10 publications

(5 citation statements)

References 22 publications

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“…Their patient similarity network identified five patient phenogroups, and substantial variations in clinical comorbidities and in-hospital and 30-day outcomes. For 30-day cardiovascular mortality, the use of phenogroup data in conjunction with the STS score was found to improve the overall prediction of mortality, when compared against using the STS scores alone (AUC 0.96 vs. AUC 0.8, p = 0.02) [37]. Similarly, Gomes et al studied 83 features of 451 consecutive patients who underwent TAVR and found machine learning methods were superior to STS and STS/ACC TAVR scores in predicting all-cause intrahospital mortality [38].…”

Section: Predicting Mortality Riskmentioning

confidence: 99%

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Artificial Intelligence in Transcatheter Aortic Valve Replacement: Its Current Role and Ongoing Challenges

Benjamin,

Rabbat

2024

Diagnostics

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Transcatheter aortic valve replacement (TAVR) has emerged as a viable alternative to surgical aortic valve replacement, as accumulating clinical evidence has demonstrated its safety and efficacy. TAVR indications have expanded beyond high-risk or inoperable patients to include intermediate and low-risk patients with severe aortic stenosis. Artificial intelligence (AI) is revolutionizing the field of cardiology, aiding in the interpretation of medical imaging and developing risk models for at-risk individuals and those with cardiac disease. This article explores the growing role of AI in TAVR procedures and assesses its potential impact, with particular focus on its ability to improve patient selection, procedural planning, post-implantation monitoring and contribute to optimized patient outcomes. In addition, current challenges and future directions in AI implementation are highlighted.

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Section: Predicting Mortality Riskmentioning

confidence: 99%

“…Abdul Ghaffar [37] 354 TAVR cases divided into 2 cohorts STS score In-hospital and 30-day CV and all-cause mortality TDA and a cloud-based supervised AutoML platform (OptiML)…”

Section: Predicting Mortality Riskmentioning

confidence: 99%

Artificial Intelligence in Transcatheter Aortic Valve Replacement: Its Current Role and Ongoing Challenges

Benjamin,

Rabbat

2024

Diagnostics

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“…Azzalini applied an ML algorithm in 2648 patients to assess which contrast agent contributed to acute kidney injury (AKI) after PCI; they did not find any type to be significantly linked to AKI [ 55 ]. Abdul Ghffar et al developed a semi-supervised ML model in 344 patients with TAVR to isolate phenotyping groups and assess their relationships with clinical outcomes [ 56 ]. The ML algorithm isolated five phenotype groups that had significant differences in comorbidities and clinical outcomes.…”

Section: Application Of ML In Interventional Cardiologymentioning

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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“…Wang et al applied ML for evaluating mitral inflow and aortic outflow (57). Abdul Ghaffar et al evaluated the role of semisupervised learning for phenogrouping based risk assessment in transcatheter aortic valve replacement (TAVR) (58). Group 5 was associated with significant in-hospital cardiovascular mortality (OR 3.5, p = 0.001).…”

Section: Role Of Ai In Echocardiographymentioning

confidence: 99%

The Role of Artificial Intelligence in Cardiovascular Imaging: State of the Art Review

Seetharam

Brito

Farjo

et al. 2020

Front. Cardiovasc. Med.

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In this current digital landscape, artificial intelligence (AI) has established itself as a powerful tool in the commercial industry and is an evolving technology in healthcare. Cutting-edge imaging modalities outputting multi-dimensional data are becoming increasingly complex. In this era of data explosion, the field of cardiovascular imaging is undergoing a paradigm shift toward machine learning (ML) driven platforms. These diverse algorithms can seamlessly analyze information and automate a range of tasks. In this review article, we explore the role of ML in the field of cardiovascular imaging.

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Usefulness of Semisupervised Machine-Learning-Based Phenogrouping to Improve Risk Assessment for Patients Undergoing Transcatheter Aortic Valve Implantation

Cited by 10 publications

References 22 publications

Artificial Intelligence in Transcatheter Aortic Valve Replacement: Its Current Role and Ongoing Challenges

Artificial Intelligence in Transcatheter Aortic Valve Replacement: Its Current Role and Ongoing Challenges

Applications of Machine Learning in Cardiology

The Role of Artificial Intelligence in Cardiovascular Imaging: State of the Art Review

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