Developing machine learning models to predict primary graft dysfunction after lung transplantation
Copyright © 2023 American Society of Transplantation & American Society of Transplant Surgeons. Published by Elsevier Inc. All rights reserved..
Primary graft dysfunction (PGD) is the leading cause of morbidity and mortality in the first 30 days after lung transplantation. Risk factors for the development of PGD include donor and recipient characteristics, but how multiple variables interact to impact the development of PGD and how clinicians should consider these in making decisions about donor acceptance remain unclear. This was a single-center retrospective cohort study to develop and evaluate machine learning pipelines to predict the development of PGD grade 3 within the first 72 hours of transplantation using donor and recipient variables that are known at the time of donor offer acceptance. Among 576 bilateral lung recipients, 173 (30%) developed PGD grade 3. The cohort underwent a 75% to 25% train-test split, and lasso regression was used to identify 11 variables for model development. A K-nearest neighbor's model showing the best calibration and performance with relatively small confidence intervals was selected as the final predictive model with an area under the receiver operating characteristics curve of 0.65. Machine learning models can predict the risk for development of PGD grade 3 based on data available at the time of donor offer acceptance. This may improve donor-recipient matching and donor utilization in the future.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:24 |
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| Enthalten in: |
American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons - 24(2024), 3 vom: 01. März, Seite 458-467 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Michelson, Andrew P [VerfasserIn] |
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| Links: |
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| Themen: |
Journal Article |
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| Anmerkungen: |
Date Completed 04.03.2024 Date Revised 04.03.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.ajt.2023.07.008 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a Primary graft dysfunction (PGD) is the leading cause of morbidity and mortality in the first 30 days after lung transplantation. Risk factors for the development of PGD include donor and recipient characteristics, but how multiple variables interact to impact the development of PGD and how clinicians should consider these in making decisions about donor acceptance remain unclear. This was a single-center retrospective cohort study to develop and evaluate machine learning pipelines to predict the development of PGD grade 3 within the first 72 hours of transplantation using donor and recipient variables that are known at the time of donor offer acceptance. Among 576 bilateral lung recipients, 173 (30%) developed PGD grade 3. The cohort underwent a 75% to 25% train-test split, and lasso regression was used to identify 11 variables for model development. A K-nearest neighbor's model showing the best calibration and performance with relatively small confidence intervals was selected as the final predictive model with an area under the receiver operating characteristics curve of 0.65. Machine learning models can predict the risk for development of PGD grade 3 based on data available at the time of donor offer acceptance. This may improve donor-recipient matching and donor utilization in the future | ||
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| 700 | 1 | |a Kreisel, Daniel |e verfasserin |4 aut | |
| 700 | 1 | |a Gelman, Andrew E |e verfasserin |4 aut | |
| 700 | 1 | |a Nava, Ruben |e verfasserin |4 aut | |
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| 700 | 1 | |a Payne, Philip R O |e verfasserin |4 aut | |
| 700 | 1 | |a Hachem, Ramsey R |e verfasserin |4 aut | |
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