Prediction of time dependent survival in HF patients after VAD implantation using pre- and post-operative data. (1st March 2016)

• Record Type: Journal Article
• Authors: Kourou, Konstantina; Rigas, George; Exarchos, Konstantinos P.; Goletsis, Yorgos; Exarchos, Themis P.; Jacobs, Steven; Meyns, Bart; Trivella, Maria-Giovanna; Fotiadis, Dimitrios I.
• Is Part Of: Computers in biology and medicine. Volume 70(2016)
• Page Start: 99
• Page End: 105
• Publication Date: 2016-03-01
• Subjects: Heart failure disease -- Ventricular assist device -- Survival prediction -- Data mining -- Feature selection -- Classification; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2016.01.005
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Heart failure is one of the most common diseases worldwide. In recent years, Ventricular Assist Devices (VADs) have become a valuable option for patients with advanced HF. Although it has been shown that VADs improve patient survival rates, several complications persist during left VAD (LVAD) support. The stratification scores currently employed are mostly generic, i.e. not specifically built for LVAD patients, and are based on pre-implantation patient data. In this work we apply data mining approaches for the prediction of time dependent survival in patients after LVAD implantation. Moreover, the predictions acquired with the use of pre-implantation data are enriched by employing post-implantation data, i.e. follow-up data. Different clinical scenarios have been depicted and the subsequent conditions are tested in order to identify the optimal set of pre- and post-implant features, as well as the most suitable algorithms for feature selection and prediction. The proposed approach is applied to a real dataset of 71 patients, reporting an accuracy of 84.5%, sensitivity of 87% and specificity of 82%. Based on the reported results, expert cardio-surgeons can be supported in planning the treatment of VAD patients. Highlights: A methodology for the prediction of time dependent survival in patients after LVAD implantation. Data mining techniques applied. Pre-implantation data are exploited in order to estimate their predictive power. Prediction accuracy enhancement with the integration of post-implantation data. An optimal subset of features highly correlated with patient survival is identified.