Cargando…

Pre-operative Machine Learning for Heart Transplant Patients Bridged with Temporary Mechanical Circulatory Support †

Background: Existing prediction models for post-transplant mortality in patients bridged to heart transplantation with temporary mechanical circulatory support (tMCS) perform poorly. A more reliable model would allow clinicians to provide better pre-operative risk assessment and develop more targete...

Descripción completa

Detalles Bibliográficos
Autores principales: Shou, Benjamin L., Chatterjee, Devina, Russel, Joseph W., Zhou, Alice L., Florissi, Isabella S., Lewis, Tabatha, Verma, Arjun, Benharash, Peyman, Choi, Chun Woo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9500687/
https://www.ncbi.nlm.nih.gov/pubmed/36135456
http://dx.doi.org/10.3390/jcdd9090311
_version_ 1784795281953390592
author Shou, Benjamin L.
Chatterjee, Devina
Russel, Joseph W.
Zhou, Alice L.
Florissi, Isabella S.
Lewis, Tabatha
Verma, Arjun
Benharash, Peyman
Choi, Chun Woo
author_facet Shou, Benjamin L.
Chatterjee, Devina
Russel, Joseph W.
Zhou, Alice L.
Florissi, Isabella S.
Lewis, Tabatha
Verma, Arjun
Benharash, Peyman
Choi, Chun Woo
author_sort Shou, Benjamin L.
collection PubMed
description Background: Existing prediction models for post-transplant mortality in patients bridged to heart transplantation with temporary mechanical circulatory support (tMCS) perform poorly. A more reliable model would allow clinicians to provide better pre-operative risk assessment and develop more targeted therapies for high-risk patients. Methods: We identified adult patients in the United Network for Organ Sharing database undergoing isolated heart transplantation between 01/2009 and 12/2017 who were supported with tMCS at the time of transplant. We constructed a machine learning model using extreme gradient boosting (XGBoost) with a 70:30 train:test split to predict 1-year post-operative mortality. All pre-transplant variables available in the UNOS database were included to train the model. Shapley Additive Explanations was used to identify and interpret the most important features for XGBoost predictions. Results: A total of 1584 patients were included, with a median age of 56 (interquartile range: 46–62) and 74% male. Actual 1-year mortality was 12.1%. Out of 498 available variables, 43 were selected for the final model. The area under the receiver operator characteristics curve (AUC) for the XGBoost model was 0.71 (95% CI: 0.62–0.78). The most important variables predictive of 1-year mortality included recipient functional status, age, pulmonary capillary wedge pressure (PCWP), cardiac output, ECMO usage, and serum creatinine. Conclusions: An interpretable machine learning model trained on a large clinical database demonstrated good performance in predicting 1-year mortality for patients bridged to heart transplantation with tMCS. Machine learning may be used to enhance clinician judgement in the care of markedly high-risk transplant recipients.
format Online
Article
Text
id pubmed-9500687
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-95006872022-09-24 Pre-operative Machine Learning for Heart Transplant Patients Bridged with Temporary Mechanical Circulatory Support † Shou, Benjamin L. Chatterjee, Devina Russel, Joseph W. Zhou, Alice L. Florissi, Isabella S. Lewis, Tabatha Verma, Arjun Benharash, Peyman Choi, Chun Woo J Cardiovasc Dev Dis Article Background: Existing prediction models for post-transplant mortality in patients bridged to heart transplantation with temporary mechanical circulatory support (tMCS) perform poorly. A more reliable model would allow clinicians to provide better pre-operative risk assessment and develop more targeted therapies for high-risk patients. Methods: We identified adult patients in the United Network for Organ Sharing database undergoing isolated heart transplantation between 01/2009 and 12/2017 who were supported with tMCS at the time of transplant. We constructed a machine learning model using extreme gradient boosting (XGBoost) with a 70:30 train:test split to predict 1-year post-operative mortality. All pre-transplant variables available in the UNOS database were included to train the model. Shapley Additive Explanations was used to identify and interpret the most important features for XGBoost predictions. Results: A total of 1584 patients were included, with a median age of 56 (interquartile range: 46–62) and 74% male. Actual 1-year mortality was 12.1%. Out of 498 available variables, 43 were selected for the final model. The area under the receiver operator characteristics curve (AUC) for the XGBoost model was 0.71 (95% CI: 0.62–0.78). The most important variables predictive of 1-year mortality included recipient functional status, age, pulmonary capillary wedge pressure (PCWP), cardiac output, ECMO usage, and serum creatinine. Conclusions: An interpretable machine learning model trained on a large clinical database demonstrated good performance in predicting 1-year mortality for patients bridged to heart transplantation with tMCS. Machine learning may be used to enhance clinician judgement in the care of markedly high-risk transplant recipients. MDPI 2022-09-19 /pmc/articles/PMC9500687/ /pubmed/36135456 http://dx.doi.org/10.3390/jcdd9090311 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Shou, Benjamin L.
Chatterjee, Devina
Russel, Joseph W.
Zhou, Alice L.
Florissi, Isabella S.
Lewis, Tabatha
Verma, Arjun
Benharash, Peyman
Choi, Chun Woo
Pre-operative Machine Learning for Heart Transplant Patients Bridged with Temporary Mechanical Circulatory Support †
title Pre-operative Machine Learning for Heart Transplant Patients Bridged with Temporary Mechanical Circulatory Support †
title_full Pre-operative Machine Learning for Heart Transplant Patients Bridged with Temporary Mechanical Circulatory Support †
title_fullStr Pre-operative Machine Learning for Heart Transplant Patients Bridged with Temporary Mechanical Circulatory Support †
title_full_unstemmed Pre-operative Machine Learning for Heart Transplant Patients Bridged with Temporary Mechanical Circulatory Support †
title_short Pre-operative Machine Learning for Heart Transplant Patients Bridged with Temporary Mechanical Circulatory Support †
title_sort pre-operative machine learning for heart transplant patients bridged with temporary mechanical circulatory support †
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9500687/
https://www.ncbi.nlm.nih.gov/pubmed/36135456
http://dx.doi.org/10.3390/jcdd9090311
work_keys_str_mv AT shoubenjaminl preoperativemachinelearningforhearttransplantpatientsbridgedwithtemporarymechanicalcirculatorysupport
AT chatterjeedevina preoperativemachinelearningforhearttransplantpatientsbridgedwithtemporarymechanicalcirculatorysupport
AT russeljosephw preoperativemachinelearningforhearttransplantpatientsbridgedwithtemporarymechanicalcirculatorysupport
AT zhoualicel preoperativemachinelearningforhearttransplantpatientsbridgedwithtemporarymechanicalcirculatorysupport
AT florissiisabellas preoperativemachinelearningforhearttransplantpatientsbridgedwithtemporarymechanicalcirculatorysupport
AT lewistabatha preoperativemachinelearningforhearttransplantpatientsbridgedwithtemporarymechanicalcirculatorysupport
AT vermaarjun preoperativemachinelearningforhearttransplantpatientsbridgedwithtemporarymechanicalcirculatorysupport
AT benharashpeyman preoperativemachinelearningforhearttransplantpatientsbridgedwithtemporarymechanicalcirculatorysupport
AT choichunwoo preoperativemachinelearningforhearttransplantpatientsbridgedwithtemporarymechanicalcirculatorysupport