Machine Learning Algorithms Identify Distinct Phenotypes of Right Heart Failure After Left Ventricular Assist Device Implant

Nayak, Aditi; Hu, Yingtian; Patel, Kritika; Ko, Y.; Okoh, Alexis; Wang, J.; Mehta, Akanksha; Liu, C.; Pennington, John; Xie, Rongbing; Kirklin, James K.; Kormos, R.L.; Simon, M.A.; Cowger, Jennifer; Morris, Alanna A.

doi:10.1016/j.healun.2022.01.089

Cited by 2 publications

(1 citation statement)

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“…A total of 2550 patient data from the International Registry for Mechanically Assisted Circulatory Support (IMACS) database were utilized by Nayak et al in 2022 in order to analyze 41 pre-implant variables of patients with acute post-LVAD RHF [63]. An unsupervised ML model was used in their work, identifying four RHF phenotypes, with the severe shock phenotype having the worst clinical outcomes.…”

Section: Ai-based Studies/risk Scoresmentioning

confidence: 99%

The Role of Artificial Intelligence and Machine Learning in the Prediction of Right Heart Failure after Left Ventricular Assist Device Implantation: A Comprehensive Review

Balcioglu,

Ozgocmen,

Ozsahin

et al. 2024

Diagnostics

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One of the most challenging and prevalent side effects of LVAD implantation is that of right heart failure (RHF) that may develop afterwards. The purpose of this study is to review and highlight recent advances in the uses of AI in evaluating RHF after LVAD implantation. The available literature was scanned using certain key words (artificial intelligence, machine learning, left ventricular assist device, prediction of right heart failure after LVAD) was scanned within Pubmed, Web of Science, and Google Scholar databases. Conventional risk scoring systems were also summarized, with their pros and cons being included in the results section of this study in order to provide a useful contrast with AI-based models. There are certain interesting and innovative ML approaches towards RHF prediction among the studies reviewed as well as more straightforward approaches that identified certain important predictive clinical parameters. Despite their accomplishments, the resulting AUC scores were far from ideal for these methods to be considered fully sufficient. The reasons for this include the low number of studies, standardized data availability, and lack of prospective studies. Another topic briefly discussed in this study is that relating to the ethical and legal considerations of using AI-based systems in healthcare. In the end, we believe that it would be beneficial for clinicians to not ignore these developments despite the current research indicating more time is needed for AI-based prediction models to achieve a better performance.

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Section: Ai-based Studies/risk Scoresmentioning

confidence: 99%

The Role of Artificial Intelligence and Machine Learning in the Prediction of Right Heart Failure after Left Ventricular Assist Device Implantation: A Comprehensive Review

Balcioglu,

Ozgocmen,

Ozsahin

et al. 2024

Diagnostics

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The Role of Artificial Intelligence and Machine Learning in Prediction of Right Heart Failure After LVAD Implantation: Comprehensive Review

Balcioglu,

Ozgocmen,

Ozsahin

et al. 2024

Preprint

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Especially in the last two decades, due to the developments in durable mechanical circulatory support (MCS) technology and applications, left ventricular assist (LVAD) implantation has become used in more fragile end-stage heart failure patients with higher risk. Despite advances in technology and patient care processes, right heart failure remains the leading cause of morbidity and mortality after durable LVAD implantation. It is difficult to make decisions about the type and duration of MCS during preoperative evaluation, and many clinical, hemodynamic, biochemical and echocardiographic criteria must be taken into account when evaluating the right ventricle. Complex quantitative scoring systems, including measurements of different risk factors for postimplant right ventricular failure, and composite variables, including the anatomical and functional status of the right ventricle, have been used to decide between left or biventricular assist device implantation. The successful use of artificial intelligence (AI) and machine learning (ML) in various applications in the medical field has paved the way for the idea of using it to better predict possible conditions such as right heart failure after LVAD implantation. This review aims to evaluate the current situation of risk prediction models using AI and ML technology, which have become popular in recent years, and possible future developments in predicting right heart failure after the application of durable MCS systems.

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Machine Learning Algorithms Identify Distinct Phenotypes of Right Heart Failure After Left Ventricular Assist Device Implant

Cited by 2 publications

References 0 publications

The Role of Artificial Intelligence and Machine Learning in the Prediction of Right Heart Failure after Left Ventricular Assist Device Implantation: A Comprehensive Review

The Role of Artificial Intelligence and Machine Learning in the Prediction of Right Heart Failure after Left Ventricular Assist Device Implantation: A Comprehensive Review

The Role of Artificial Intelligence and Machine Learning in Prediction of Right Heart Failure After LVAD Implantation: Comprehensive Review

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