Improving prediction of heart transplantation outcome using deep learning techniques

Scientific Reports
Dennis MedvedJohan Nilsson

Abstract

The primary objective of this study is to compare the accuracy of two risk models, International Heart Transplantation Survival Algorithm (IHTSA), developed using deep learning technique, and Index for Mortality Prediction After Cardiac Transplantation (IMPACT), to predict survival after heart transplantation. Data from adult heart transplanted patients between January 1997 to December 2011 were collected from the UNOS registry. The study included 27,860 heart transplantations, corresponding to 27,705 patients. The study cohorts were divided into patients transplanted before 2009 (derivation cohort) and from 2009 (test cohort). The receiver operating characteristic (ROC) values, for the validation cohort, computed for one-year mortality, were 0.654 (95% CI: 0.629-0.679) for IHTSA and 0.608 (0.583-0.634) for the IMPACT model. The discrimination reached a C-index for long-term survival of 0.627 (0.608-0.646) for IHTSA, compared with 0.584 (0.564-0.605) for the IMPACT model. These figures correspond to an error reduction of 12% for ROC and 10% for C-index by using deep learning technique. The predicted one-year mortality rates for were 12% and 22% for IHTSA and IMPACT, respectively, versus an actual mortality rate of 10%. The IHTS...Continue Reading

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Citations

Mar 1, 2019·European Heart Journal·Chayakrit KrittanawongSanjiv M Narayan
Feb 11, 2020·Frontiers in Cardiovascular Medicine·Antonio de MarvaoDeclan P O'Regan
Nov 17, 2019·American Journal of Transplantation : Official Journal of the American Society of Transplantation and the American Society of Transplant Surgeons·Natasha AleksovaJuan G Duero Posada
Aug 23, 2020·Transplantation·Katie L ConnorEwen M Harrison
Apr 14, 2021·The Journal of Surgical Research·Omar ElfanagelyJohn P Fischer
Jun 15, 2020·JACC. Heart Failure·Eileen M HsichHemant Ishwaran
Aug 21, 2021·Journal of Cardiac Surgery·Brian AyersArman Kilic
Oct 7, 2021·Big Data·Hamidreza Ahady DolatsaraL Allison Jones-Farmer

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Software Mentioned

SPARQL
R
Stata MP
IHTSA
Matlab
IMPACT

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