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Development of Predictive Models for Continuous Flow Left Ventricular Assist Device Patients using Bayesian Networks
Journal article   Open access

Development of Predictive Models for Continuous Flow Left Ventricular Assist Device Patients using Bayesian Networks

Natasha A. Loghmanpour, Manreet K. Kanwar, Raymond L. Benza, Srinivas Murali and James F. Antaki
International journal of statistics in medical research, v 3(4), pp 423-434
06 Nov 2014
DOI: https://doi.org/10.6000/1929-6029.2014.03.04.11
url
https://lifescienceglobal.com/pms/index.php/ijsmr/article/view/2472View
Published, Version of Record (VoR)Open Access (License Unspecified),  Open
url
https://doi.org/10.6000/1929-6029.2014.03.04.11View
Published, Version of Record (VoR) Open

Abstract

Background: Existing prognostic tools for patient selection for ventricular assist devices (VADs) such as the Destination Therapy Risk Score (DTRS) and newly published HeartMate II Risk Score (HMRS) have limited predictive ability, especially with the current generation of continuous flow VADs (cfVADs). This study aims to use a modern machine learning approach, employing Bayesian Networks (BNs), which overcomes some of the limitations of traditional statistical methods. Methods: Retrospective data from 144 patients at Allegheny General Hospital and Integris Health System from 2007 to 2011 were analyzed. 43 data elements were grouped into four sets: demographics, laboratory tests, hemodynamics, and medications. Patients were stratified by survival at 90 days post LVAD. Results: The independent variables were ranked based on their predictive power and reduced to an optimal set of 10: hematocrit, aspartate aminotransferase, age, heart rate, transpulmonary gradient, mean pulmonary artery pressure, use of diuretics, platelet count, blood urea nitrogen and hemoglobin. Two BNs, Naïve Bayes (NB) and Tree-Augmented Naïve Bayes (TAN) outperformed the DTRS in identifying low risk patients (specificity: 91% and 93% vs. 78%) and outperformed HMRS predictions of high risk patients (sensitivity: 80% and 60% vs. 25%). Both models were more accurate than DTRS and HMRS (90% vs. 73% and 84%), Kappa (NB: 0.56 TAN: 0.48, DTRS: 0.14, HMRS: 0.22), and AUC (NB: 80%, TAN: 84%, DTRS: 59%, HMRS: 59%). Conclusion: The Bayesian Network models developed in this study consistently outperformed the DTRS and HMRS on all metrics. An added advantage is their intuitive graphical structure that closely mimics natural reasoning patterns. This warrants further investigation with an expanded patient cohort, and inclusion of adverse event outcomes.

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