Polydoros N Kampaktsis1, Aspasia Tzani2, Ilias P Doulamis3, Serafeim Moustakidis4, Anastasios Drosou5, Nikolaos Diakos6, Stavros G Drakos7, Alexandros Briasoulis8,9. 1. Division of Cardiology, New York University Langone Medical Center, New York, New York, USA. 2. Brigham and Women's Hospital Heart and Vascular Center, Harvard Medical School, Boston, Massachusetts, USA. 3. Department of Cardiac Surgery, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA. 4. AIDEAS OÜ, Tallinn, Estonia. 5. Centre for Research & Technology Hellas, Information Technologies Institute (CERTH-ITI), Thessaloniki, Greece. 6. Division of Cardiology, Columbia University Irving Medical Center, New York, New York, USA. 7. Division of Cardiovascular Medicine & Nora Eccles Harrison Cardiovascular Research and Training Institute, University of Utah Health & School of Medicine, Salt Lake, Utah, USA. 8. National and Kapodistrian University of Athens, Athens, Greece. 9. Division of Cardiovascular Medicine, University of Iowa Carver College of Medicine, Iowa City, Iowa, USA.
Abstract
PURPOSE: We sought to develop and validate machine learning (ML) models to increase the predictive accuracy of mortality after heart transplantation (HT). METHODS AND RESULTS: We included adult HT recipients from the United Network for Organ Sharing (UNOS) database between 2010 and 2018 using solely pre-transplant variables. The study cohort comprised 18 625 patients (53 ± 13 years, 73% males) and was randomly split into a derivation and a validation cohort with a 3:1 ratio. At 1-year after HT, there were 2334 (12.5%) deaths. Out of a total of 134 pre-transplant variables, 39 were selected as highly predictive of 1-year mortality via feature selection algorithm and were used to train five ML models. AUC for the prediction of 1-year survival was .689, .642, .649, .637, .526 for the Adaboost, Logistic Regression, Decision Tree, Support Vector Machine, and K-nearest neighbor models, respectively, whereas the Index for Mortality Prediction after Cardiac Transplantation (IMPACT) score had an AUC of .569. Local interpretable model-agnostic explanations (LIME) analysis was used in the best performing model to identify the relative impact of key predictors. ML models for 3- and 5-year survival as well as acute rejection were also developed in a secondary analysis and yielded AUCs of .629, .609, and .610 using 27, 31, and 91 selected variables respectively. CONCLUSION: Machine learning models showed good predictive accuracy of outcomes after heart transplantation.
PURPOSE: We sought to develop and validate machine learning (ML) models to increase the predictive accuracy of mortality after heart transplantation (HT). METHODS AND RESULTS: We included adult HT recipients from the United Network for Organ Sharing (UNOS) database between 2010 and 2018 using solely pre-transplant variables. The study cohort comprised 18 625 patients (53 ± 13 years, 73% males) and was randomly split into a derivation and a validation cohort with a 3:1 ratio. At 1-year after HT, there were 2334 (12.5%) deaths. Out of a total of 134 pre-transplant variables, 39 were selected as highly predictive of 1-year mortality via feature selection algorithm and were used to train five ML models. AUC for the prediction of 1-year survival was .689, .642, .649, .637, .526 for the Adaboost, Logistic Regression, Decision Tree, Support Vector Machine, and K-nearest neighbor models, respectively, whereas the Index for Mortality Prediction after Cardiac Transplantation (IMPACT) score had an AUC of .569. Local interpretable model-agnostic explanations (LIME) analysis was used in the best performing model to identify the relative impact of key predictors. ML models for 3- and 5-year survival as well as acute rejection were also developed in a secondary analysis and yielded AUCs of .629, .609, and .610 using 27, 31, and 91 selected variables respectively. CONCLUSION: Machine learning models showed good predictive accuracy of outcomes after heart transplantation.
Authors: Neta Gotlieb; Amirhossein Azhie; Divya Sharma; Ashley Spann; Nan-Ji Suo; Jason Tran; Ani Orchanian-Cheff; Bo Wang; Anna Goldenberg; Michael Chassé; Heloise Cardinal; Joseph Paul Cohen; Andrea Lodi; Melanie Dieude; Mamatha Bhat Journal: NPJ Digit Med Date: 2022-07-11
Authors: Benjamin L Shou; Devina Chatterjee; Joseph W Russel; Alice L Zhou; Isabella S Florissi; Tabatha Lewis; Arjun Verma; Peyman Benharash; Chun Woo Choi Journal: J Cardiovasc Dev Dis Date: 2022-09-19