A New Bayesian Network-Based Risk Stratification Model for Prediction of Short-Term and Long-Term LVAD Mortality

Loghmanpour, Natasha A.; Kanwar, Manreet; Drużdżel, Marek J.; Benza, Raymond L.; Murali, Srinivas; Antaki, James F.

doi:10.1097/mat.0000000000000209

Cited by 45 publications

(30 citation statements)

References 25 publications

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“…In an article by Loghmanpour et al, BNs were created to predict mortality at five time points among patients receiving left ventricular assist devices. 21 The networks were constructed using three different machine-learning algorithms and evaluated the dependencies between 226 preimplant variables recorded on 8050 subjects. The BN built by Loghmanpour et al modeled the risk of failure of left ventricular assist devices.…”

Section: Other Featuresmentioning

confidence: 99%

Bayesian Networks for Risk Prediction Using Real-World Data: A Tool for Precision Medicine

Arora

Boyne

Slater³

et al. 2019

Value in Health

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124

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Objective: The fields of medicine and public health are undergoing a data revolution. An increasing availability of data has brought about a growing interest in machine-learning algorithms. Our objective is to present the reader with an introduction to a knowledge representation and machine-learning tool for risk estimation in medical science known as Bayesian networks (BNs). Study Design: In this article we review how BNs are compact and intuitive graphical representations of joint probability distributions (JPDs) that can be used to conduct causal reasoning and risk estimation analysis and offer several advantages over regression-based methods. We discuss how BNs represent a different approach to risk estimation in that they are graphical representations of JPDs that take the form of a network representing model random variables and the influences between them, respectively. Methods: We explore some of the challenges associated with traditional risk prediction methods and then describe BNs, their construction, application, and advantages in risk prediction based on examples in cancer and heart disease.Results: Risk modeling with BNs has advantages over regressionbased approaches, and in this article we focus on three that are relevant to health outcomes research: (1) the generation of network structures in which relationships between variables can be easily communicated; (2) their ability to apply Bayes's theorem to conduct individual-level risk estimation; and (3) their easy transformation into decision models. Conclusions: Bayesian networks represent a powerful and flexible tool for the analysis of health economics and outcomes research data in the era of precision medicine.

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Section: Other Featuresmentioning

confidence: 99%

Bayesian Networks for Risk Prediction Using Real-World Data: A Tool for Precision Medicine

Arora

Boyne

Slater³

et al. 2019

Value in Health

Self Cite

124

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“…While several methods for multivariable modeling have been applied to the development of clinical prediction models, few comparisons of these methods have been reported. Our secondary aim was to compare three multivariable methods—stepwise Cox proportional hazard procedure, regularized Cox proportional hazard procedure, and Bayesian network—and to determine which method has the optimal performance.…”

Section: Introductionmentioning

confidence: 99%

Prediction model for cardiac allograft vasculopathy: Comparison of three multivariable methods

Kransdorf

Loghmanpour

Kanwar

et al. 2017

Clinical Transplantation

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“…12 The Bayesian method clearly outperforms conventional risk scores in predictive accuracy and we believe that this method could allow clinicians to reliably select patients who are likely to achieve “optimal” clinical outcomes with LVAD implantation. This undoubtedly will go a long way towards cost-effectively using this technology in the management of advanced HF.…”

Section: Discussionmentioning

confidence: 88%

Low Accuracy of the HeartMate Risk Score for Predicting Mortality Using the INTERMACS Registry Data

et al. 2017

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Patient selection is a key determinant of clinical outcomes after LVAD placement in patients with end stage heart failure. The HMRS has been proposed to facilitate risk stratification and patient selection for continuous flow pumps. This study retrospectively assessed the performance of HMRS in predicting 90-day and 1-year mortality in patients within the Interagency Registry for Mechanically Assisted Circulatory Support (INTERMACS). 11,523 INTERMACS patients who received a continuous flow LVAD between 2010 and 2015 were retrospectively categorized per their calculated HMRS to predict their 90-day and 1-year risk of mortality. The performance of the score was evaluated by the area under curve (AUC) of the receiver operator characteristic. We also performed multiple regression analysis using variables from the HMRS calculation on the INTERMACS data. The HMRS model showed moderate discrimination for both 90-day and 1-year mortality prediction with AUCs of 61% and 59%, respectively. The predictions had similar accuracy irrespective of whether the pump was axial or centrifugal flow. Multivariable analysis using independent variables used in the original HMRS analysis revealed different set of variables to be predictive of 90-day mortality than those used to calculate HMRS. HMRS predicts both 90-day and 1-year mortality with poor discrimination when applied to a large cohort of LVAD patients. Newer risk prediction models are therefore needed to optimize the therapeutic application of LVAD therapy.

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A New Bayesian Network-Based Risk Stratification Model for Prediction of Short-Term and Long-Term LVAD Mortality

Cited by 45 publications

References 25 publications

Bayesian Networks for Risk Prediction Using Real-World Data: A Tool for Precision Medicine

Bayesian Networks for Risk Prediction Using Real-World Data: A Tool for Precision Medicine

Prediction model for cardiac allograft vasculopathy: Comparison of three multivariable methods

Low Accuracy of the HeartMate Risk Score for Predicting Mortality Using the INTERMACS Registry Data

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