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A new Bayesian network-based risk stratification model for prediction of short-term and long-term LVAD mortality

Loghmanpour, NA and Kanwar, MK and Druzdzel, MJ and Benza, RL and Murali, S and Antaki, JF (2015) A new Bayesian network-based risk stratification model for prediction of short-term and long-term LVAD mortality. ASAIO Journal, 61 (3). 313 - 323. ISSN 1058-2916

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Abstract

© 2015 by the American Society for Artificial Internal Organs. Existing risk assessment tools for patient selection for left ventricular assist devices (LVADs) such as the Destination Therapy Risk Score and HeartMate II Risk Score (HMRS) have limited predictive ability. This study aims to overcome the limitations of traditional statistical methods by performing the first application of Bayesian analysis to the comprehensive Interagency Registry for Mechanically Assisted Circulatory Support dataset and comparing it to HMRS. We retrospectively analyzed 8,050 continuous flow LVAD patients and 226 preimplant variables. We then derived Bayesian models for mortality at each of five time end-points postimplant (30 days, 90 days, 6 month, 1 year, and 2 years), achieving accuracies of 95%, 90%, 90%, 83%, and 78%, Kappa values of 0.43, 0.37, 0.37, 0.45, and 0.43, and area under the receiver operator characteristic (ROC) of 91%, 82%, 82%, 80%, and 81%, respectively. This was in comparison to the HMRS with an ROC of 57% and 60% at 90 days and 1 year, respectively. Preimplant interventions, such as dialysis, ECMO, and ventilators were major contributing risk markers. Bayesian models have the ability to reliably represent the complex causal relations of multiple variables on clinical outcomes. Their potential to develop a reliable risk stratification tool for use in clinical decision making on LVAD patients encourages further investigation.


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Details

Item Type: Article
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Loghmanpour, NA
Kanwar, MK
Druzdzel, MJdruzdzel@pitt.eduDRUZDZEL
Benza, RL
Murali, S
Antaki, JF
Date: 1 January 2015
Date Type: Publication
Journal or Publication Title: ASAIO Journal
Volume: 61
Number: 3
Page Range: 313 - 323
DOI or Unique Handle: 10.1097/mat.0000000000000209
Schools and Programs: School of Information Sciences > Information Science
Refereed: Yes
ISSN: 1058-2916
Date Deposited: 02 Jul 2015 14:31
Last Modified: 13 Oct 2017 21:56
URI: http://d-scholarship.pitt.edu/id/eprint/25529

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