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Accepted (AM)Open Access (License Unspecified), Open
Journal article
A Bayesian Model to Predict Right Ventricular Failure Following Left Ventricular Assist Device Therapy
JACC. Heart failure, v 4(9), pp 711-721
01 Sep 2016
PMID: 27289403
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
This study investigates the use of a Bayesian statistical model to address the limited predictive capacity of existing risk scores derived from multivariate analyses. This is based on the hypothesis that it is necessary to consider the interrelationships and conditional probabilities among independent variables to achieve sufficient statistical accuracy.
Right ventricular failure (RVF) continues to be a major adverse event following left ventricular assist device (LVAD) implantation.
Data used for this study were derived from 10,909 adult patients from the Inter-Agency Registry for Mechanically Assisted Circulatory Support (INTERMACS) who had a primary LVAD implanted between December 2006 and March 2014. An initial set of 176 pre-implantation variables were considered. RVF post-implant was categorized as acute (<48 h), early (48 h to 14 daysays), and late (>14 days) in onset. For each of these endpoints, a separate tree-augmented naïve Bayes model was constructed using the most predictive variables employing an open source Bayesian inference engine.
The acute RVF model consisted of 33 variables including systolic pulmonary artery pressure (PAP), white blood cell count, left ventricular ejection fraction, cardiac index, sodium levels, and lymphocyte percentage. The early RVF model consisted of 34 variables, including systolic PAP, pre-albumin, lactate dehydrogenase level, INTERMACS profile, right ventricular ejection fraction, pro-B-type natriuretic peptide, age, heart rate, tricuspid regurgitation, and body mass index. The late RVF model included 33 variables and was predicted mostly by peripheral vascular resistance, model for end-stage liver disease score, albumin level, lymphocyte percentage, and mean and diastolic PAP. The accuracy of all Bayesian models was between 91% and 97%, with an area under the receiver operator characteristics curve between 0.83 and 0.90, sensitivity of 90%, and specificity between 98% and 99%, significantly outperforming previously published risk scores.
A Bayesian prognostic model of RVF based on the large, multicenter INTERMACS registry provided highly accurate predictions of acute, early, and late RVF on the basis of pre-operative variables. These models may facilitate clinical decision making while screening candidates for LVAD therapy.
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Details
- Title
- A Bayesian Model to Predict Right Ventricular Failure Following Left Ventricular Assist Device Therapy
- Creators
- Natasha A. Loghmanpour - Carnegie Mellon UniversityRobert L. Kormos - University of Pittsburgh Medical CenterManreet K. Kanwar - Allegheny Health NetworkJeffrey J. Teuteberg - University of Pittsburgh Medical CenterSrinivas Murali - Allegheny Health NetworkJames F. Antaki - Carnegie Mellon University
- Publication Details
- JACC. Heart failure, v 4(9), pp 711-721
- Publisher
- Elsevier
- Number of pages
- 11
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Medicine (Graduate); Cardiology
- Web of Science ID
- WOS:000382905800004
- Scopus ID
- 2-s2.0-84991387192
- Other Identifier
- 991021932195604721
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Cardiac & Cardiovascular Systems