Cargando…
Prediction of Postoperative Pulmonary Edema Risk Using Machine Learning
Postoperative pulmonary edema (PPE) is a well-known postoperative complication. We hypothesized that a machine learning model could predict PPE risk using pre- and intraoperative data, thereby improving postoperative management. This retrospective study analyzed the medical records of patients aged...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10003313/ https://www.ncbi.nlm.nih.gov/pubmed/36902590 http://dx.doi.org/10.3390/jcm12051804 |
_version_ | 1784904576729612288 |
---|---|
author | Kim, Jong Ho Kim, Youngmi Yoo, Kookhyun Kim, Minguan Kang, Seong Sik Kwon, Young-Suk Lee, Jae Jun |
author_facet | Kim, Jong Ho Kim, Youngmi Yoo, Kookhyun Kim, Minguan Kang, Seong Sik Kwon, Young-Suk Lee, Jae Jun |
author_sort | Kim, Jong Ho |
collection | PubMed |
description | Postoperative pulmonary edema (PPE) is a well-known postoperative complication. We hypothesized that a machine learning model could predict PPE risk using pre- and intraoperative data, thereby improving postoperative management. This retrospective study analyzed the medical records of patients aged > 18 years who underwent surgery between January 2011 and November 2021 at five South Korean hospitals. Data from four hospitals (n = 221,908) were used as the training dataset, whereas data from the remaining hospital (n = 34,991) were used as the test dataset. The machine learning algorithms used were extreme gradient boosting, light-gradient boosting machine, multilayer perceptron, logistic regression, and balanced random forest (BRF). The prediction abilities of the machine learning models were assessed using the area under the receiver operating characteristic curve, feature importance, and average precisions of precision-recall curve, precision, recall, f1 score, and accuracy. PPE occurred in 3584 (1.6%) and 1896 (5.4%) patients in the training and test sets, respectively. The BRF model exhibited the best performance (area under the receiver operating characteristic curve: 0.91, 95% confidence interval: 0.84–0.98). However, its precision and f1 score metrics were not good. The five major features included arterial line monitoring, American Society of Anesthesiologists physical status, urine output, age, and Foley catheter status. Machine learning models (e.g., BRF) could predict PPE risk and improve clinical decision-making, thereby enhancing postoperative management. |
format | Online Article Text |
id | pubmed-10003313 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-100033132023-03-11 Prediction of Postoperative Pulmonary Edema Risk Using Machine Learning Kim, Jong Ho Kim, Youngmi Yoo, Kookhyun Kim, Minguan Kang, Seong Sik Kwon, Young-Suk Lee, Jae Jun J Clin Med Article Postoperative pulmonary edema (PPE) is a well-known postoperative complication. We hypothesized that a machine learning model could predict PPE risk using pre- and intraoperative data, thereby improving postoperative management. This retrospective study analyzed the medical records of patients aged > 18 years who underwent surgery between January 2011 and November 2021 at five South Korean hospitals. Data from four hospitals (n = 221,908) were used as the training dataset, whereas data from the remaining hospital (n = 34,991) were used as the test dataset. The machine learning algorithms used were extreme gradient boosting, light-gradient boosting machine, multilayer perceptron, logistic regression, and balanced random forest (BRF). The prediction abilities of the machine learning models were assessed using the area under the receiver operating characteristic curve, feature importance, and average precisions of precision-recall curve, precision, recall, f1 score, and accuracy. PPE occurred in 3584 (1.6%) and 1896 (5.4%) patients in the training and test sets, respectively. The BRF model exhibited the best performance (area under the receiver operating characteristic curve: 0.91, 95% confidence interval: 0.84–0.98). However, its precision and f1 score metrics were not good. The five major features included arterial line monitoring, American Society of Anesthesiologists physical status, urine output, age, and Foley catheter status. Machine learning models (e.g., BRF) could predict PPE risk and improve clinical decision-making, thereby enhancing postoperative management. MDPI 2023-02-23 /pmc/articles/PMC10003313/ /pubmed/36902590 http://dx.doi.org/10.3390/jcm12051804 Text en © 2023 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 Kim, Jong Ho Kim, Youngmi Yoo, Kookhyun Kim, Minguan Kang, Seong Sik Kwon, Young-Suk Lee, Jae Jun Prediction of Postoperative Pulmonary Edema Risk Using Machine Learning |
title | Prediction of Postoperative Pulmonary Edema Risk Using Machine Learning |
title_full | Prediction of Postoperative Pulmonary Edema Risk Using Machine Learning |
title_fullStr | Prediction of Postoperative Pulmonary Edema Risk Using Machine Learning |
title_full_unstemmed | Prediction of Postoperative Pulmonary Edema Risk Using Machine Learning |
title_short | Prediction of Postoperative Pulmonary Edema Risk Using Machine Learning |
title_sort | prediction of postoperative pulmonary edema risk using machine learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10003313/ https://www.ncbi.nlm.nih.gov/pubmed/36902590 http://dx.doi.org/10.3390/jcm12051804 |
work_keys_str_mv | AT kimjongho predictionofpostoperativepulmonaryedemariskusingmachinelearning AT kimyoungmi predictionofpostoperativepulmonaryedemariskusingmachinelearning AT yookookhyun predictionofpostoperativepulmonaryedemariskusingmachinelearning AT kimminguan predictionofpostoperativepulmonaryedemariskusingmachinelearning AT kangseongsik predictionofpostoperativepulmonaryedemariskusingmachinelearning AT kwonyoungsuk predictionofpostoperativepulmonaryedemariskusingmachinelearning AT leejaejun predictionofpostoperativepulmonaryedemariskusingmachinelearning |