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Cardiac health risk stratification system (CHRiSS): A Bayesian-based decision support system for left ventricular assist device (LVAD) therapy

Loghmanpour, NA and Druzdzel, MJ and Antaki, JF (2014) Cardiac health risk stratification system (CHRiSS): A Bayesian-based decision support system for left ventricular assist device (LVAD) therapy. PLoS ONE, 9 (11).

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Abstract

© 2014 Loghmanpour et al. This study investigated the use of Bayesian Networks (BNs) for left ventricular assist device (LVAD) therapy; a treatment for end-stage heart failure that has been steadily growing in popularity over the past decade. Despite this growth, the number of LVAD implants performed annually remains a small fraction of the estimated population of patients who might benefit from this treatment. We believe that this demonstrates a need for an accurate stratification tool that can help identify LVAD candidates at the most appropriate point in the course of their disease. We derived BNs to predict mortality at five endpoints utilizing the Interagency Registry for Mechanically Assisted Circulatory Support (INTERMACS) database: containing over 12,000 total enrolled patients from 153 hospital sites, collected since 2006 to the present day, and consisting of approximately 230 pre-implant clinical variables. Synthetic minority oversampling technique (SMOTE) was employed to address the uneven proportion of patients with negative outcomes and to improve the performance of the models. The resulting accuracy and area under the ROC curve (%) for predicted mortality were 30 day: 94.9 and 92.5; 90 day: 84.2 and 73.9; 6 month: 78.2 and 70.6; 1 year: 73.1 and 70.6; and 2 years: 71.4 and 70.8. To foster the translation of these models to clinical practice, they have been incorporated into a web-based application, the Cardiac Health Risk Stratification System (CHRiSS). As clinical experience with LVAD therapy continues to grow, and additional data is collected, we aim to continually update these BN models to improve their accuracy and maintain their relevance. Ongoing work also aims to extend the BN models to predict the risk of adverse events post-LVAD implant as additional factors for consideration in decision making.


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Details

Item Type: Article
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Loghmanpour, NA
Druzdzel, MJdruzdzel@pitt.eduDRUZDZEL
Antaki, JF
Date: 14 November 2014
Date Type: Publication
Journal or Publication Title: PLoS ONE
Volume: 9
Number: 11
DOI or Unique Handle: 10.1371/journal.pone.0111264
Schools and Programs: School of Information Sciences > Information Science
Refereed: Yes
Date Deposited: 02 Jul 2015 14:31
Last Modified: 13 Oct 2017 21:56
URI: http://d-scholarship.pitt.edu/id/eprint/25528

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