Literature DB >> 22334033

Prognosis of right ventricular failure in patients with left ventricular assist device based on decision tree with SMOTE.

Yajuan Wang1, Marc Simon, Pramod Bonde, Bronwyn U Harris, Jeffrey J Teuteberg, Robert L Kormos, James F Antaki.   

Abstract

Right ventricular failure is a significant complication following implantation of a left ventricular assist device (LVAD), which increases morbidity and mortality. Consequently, researchers have sought predictors that may identify patients at risk. However, they have lacked sensitivity and/or specificity. This study investigated the use of a decision tree technology to explore the preoperative data space for combinatorial relationships that may be more accurate and precise. We retrospectively analyzed the records of 183 patients with initial LVAD implantation at the Artificial Heart Program, University of Pittsburgh Medical Center, between May 1996 and October 2009. Among those patients, 27 later required a right ventricular assist device (RVAD+) and 156 remained on LVAD (RVAD-) until the time of transplantation or death. A synthetic minority oversampling technique (SMOTE) was applied to the RVAD+ group to compensate for the disparity of sample size. Twenty-one resampling levels were evaluated, with decision tree model built for each. Among these models, the top six predictors of the need for an RVAD were transpulmonary gradient (TPG), age, international normalized ratio (INR), heart rate (HR), aspartate aminotransferase (AST), prothrombin time, and right ventricular systolic pressure. TPG was identified to be the most predictive variable in 15 out of 21 models, and constituted the first splitting node with 7 mmHg as the breakpoint. Oversampling was shown to improve the senstivity of the models monotonically, although asymptotically, while the specificity was diminished to a lesser degree. The model built upon 5X synthetic RVAD+ oversampling was found to provide the best compromise between sensitivity and specificity, included TPG (layer 1), age (layer 2), right atrial pressure (layer 3), HR (layer 4,7), INR (layer 4, 9), alanine aminotransferase (layer 5), white blood cell count (layer 5,6, &7), the number of inotrope agents (layer 6), creatinine (layer 8), AST (layer 9, 10), and cardiac output (layer 9). It exhibited 85% sensitivity, 83% specificity, and 0.87 area under the receiver operating characteristic curve (RoC), which was found to be greatly improved compared to previously published studies.

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Year:  2012        PMID: 22334033     DOI: 10.1109/TITB.2012.2187458

Source DB:  PubMed          Journal:  IEEE Trans Inf Technol Biomed        ISSN: 1089-7771


  9 in total

Review 1.  Right Ventricular Strain to Assess Early Right Heart Failure in the Left Ventricular Assist Device Candidate.

Authors:  Fatih Gumus; Cahit Sarıcaoglu; Mustafa Bahadir Inan; Ahmet Ruchan Akar
Journal:  Curr Heart Fail Rep       Date:  2019-12

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

Authors:  Natasha A Loghmanpour; Manreet K Kanwar; Marek J Druzdzel; Raymond L Benza; Srinivas Murali; James F Antaki
Journal:  ASAIO J       Date:  2015 May-Jun       Impact factor: 2.872

3.  A Bayesian Model to Predict Right Ventricular Failure Following Left Ventricular Assist Device Therapy.

Authors:  Natasha A Loghmanpour; Robert L Kormos; Manreet K Kanwar; Jeffrey J Teuteberg; Srinivas Murali; James F Antaki
Journal:  JACC Heart Fail       Date:  2016-06-08       Impact factor: 12.035

4.  Wave Intensity Analysis of Right Ventricular Function during Pulsed Operation of Rotary Left Ventricular Assist Devices.

Authors:  J Christopher Bouwmeester; Jiheum Park; John Valdovinos; Pramod Bonde
Journal:  ASAIO J       Date:  2019-07       Impact factor: 2.872

5.  Prediction of preterm deliveries from EHG signals using machine learning.

Authors:  Paul Fergus; Pauline Cheung; Abir Hussain; Dhiya Al-Jumeily; Chelsea Dobbins; Shamaila Iram
Journal:  PLoS One       Date:  2013-10-28       Impact factor: 3.240

6.  Cardiac Health Risk Stratification System (CHRiSS): a Bayesian-based decision support system for left ventricular assist device (LVAD) therapy.

Authors:  Natasha A Loghmanpour; Marek J Druzdzel; James F Antaki
Journal:  PLoS One       Date:  2014-11-14       Impact factor: 3.240

7.  Automatic epileptic seizure detection using scalp EEG and advanced artificial intelligence techniques.

Authors:  Paul Fergus; David Hignett; Abir Hussain; Dhiya Al-Jumeily; Khaled Abdel-Aziz
Journal:  Biomed Res Int       Date:  2015-01-29       Impact factor: 3.411

8.  Classification of caesarean section and normal vaginal deliveries using foetal heart rate signals and advanced machine learning algorithms.

Authors:  Paul Fergus; Abir Hussain; Dhiya Al-Jumeily; De-Shuang Huang; Nizar Bouguila
Journal:  Biomed Eng Online       Date:  2017-07-06       Impact factor: 2.819

9.  A Real-Time Artificial Intelligence-Assisted System to Predict Weaning from Ventilator Immediately after Lung Resection Surgery.

Authors:  Ying-Jen Chang; Kuo-Chuan Hung; Li-Kai Wang; Chia-Hung Yu; Chao-Kun Chen; Hung-Tze Tay; Jhi-Joung Wang; Chung-Feng Liu
Journal:  Int J Environ Res Public Health       Date:  2021-03-08       Impact factor: 3.390

  9 in total

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