Preventing postoperative pulmonary complications by establishing a machine-learning assisted approach (PEPPERMINT): Study protocol for the creation of a risk prediction model.
<h4>Background</h4>Postoperative pulmonary complications (POPC) are common after general anaesthesia and are a major cause of increased morbidity and mortality in surgical patients. However, prevention and treatment methods for POPC that are considered effective tie up human and technica...
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2025-01-01
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| Online Access: | https://doi.org/10.1371/journal.pone.0329076 |
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| author | Britta Trautwein Meinrad Beer Manfred Blobner Bettina Jungwirth Simone Maria Kagerbauer Michael Götz |
| author_facet | Britta Trautwein Meinrad Beer Manfred Blobner Bettina Jungwirth Simone Maria Kagerbauer Michael Götz |
| author_sort | Britta Trautwein |
| collection | DOAJ |
| description | <h4>Background</h4>Postoperative pulmonary complications (POPC) are common after general anaesthesia and are a major cause of increased morbidity and mortality in surgical patients. However, prevention and treatment methods for POPC that are considered effective tie up human and technical resources. Therefore, the planned research project aims to create a prediction model that enables the reliable identification of high-risk patients immediately after surgery based on a tailored machine learning algorithm.<h4>Methods</h4>This clinical cohort study will follow the TRIPOD statement for multivariable prediction model development. Development of the prognostic model will require 512 patients undergoing elective surgery under general anaesthesia. Besides the collection of perioperative routine data, standardised lung sonography will be performed postoperatively in the recovery room on each patient. During the postoperative course, patients will be examined in a structured manner on postoperative days 1,3 and 7 to detect POPC. The endpoints determined in this way, together with the clinical and imaging data collected, are then used to train a machine learning model based on neural networks and ensemble methods to predict POPC in the early postoperative phase.<h4>Discussion</h4>In the perioperative setting, detecting POPC before they become clinically manifest is desirable. This would ensure optimal patient care and resource allocation and help initiate adequate patient treatment after being transferred from the recovery room to the ward. A reliable prediction algorithm based on machine learning holds great potential to improve postoperative outcomes.<h4>Trial registration</h4>ClinicalTrials.gov ID: NCT05789953 (29th of March 2023). |
| format | Article |
| id | doaj-art-70d38c97e45242608a0281a5dd4a3a96 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
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| series | PLoS ONE |
| spelling | doaj-art-70d38c97e45242608a0281a5dd4a3a962025-08-24T05:31:07ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01208e032907610.1371/journal.pone.0329076Preventing postoperative pulmonary complications by establishing a machine-learning assisted approach (PEPPERMINT): Study protocol for the creation of a risk prediction model.Britta TrautweinMeinrad BeerManfred BlobnerBettina JungwirthSimone Maria KagerbauerMichael Götz<h4>Background</h4>Postoperative pulmonary complications (POPC) are common after general anaesthesia and are a major cause of increased morbidity and mortality in surgical patients. However, prevention and treatment methods for POPC that are considered effective tie up human and technical resources. Therefore, the planned research project aims to create a prediction model that enables the reliable identification of high-risk patients immediately after surgery based on a tailored machine learning algorithm.<h4>Methods</h4>This clinical cohort study will follow the TRIPOD statement for multivariable prediction model development. Development of the prognostic model will require 512 patients undergoing elective surgery under general anaesthesia. Besides the collection of perioperative routine data, standardised lung sonography will be performed postoperatively in the recovery room on each patient. During the postoperative course, patients will be examined in a structured manner on postoperative days 1,3 and 7 to detect POPC. The endpoints determined in this way, together with the clinical and imaging data collected, are then used to train a machine learning model based on neural networks and ensemble methods to predict POPC in the early postoperative phase.<h4>Discussion</h4>In the perioperative setting, detecting POPC before they become clinically manifest is desirable. This would ensure optimal patient care and resource allocation and help initiate adequate patient treatment after being transferred from the recovery room to the ward. A reliable prediction algorithm based on machine learning holds great potential to improve postoperative outcomes.<h4>Trial registration</h4>ClinicalTrials.gov ID: NCT05789953 (29th of March 2023).https://doi.org/10.1371/journal.pone.0329076 |
| spellingShingle | Britta Trautwein Meinrad Beer Manfred Blobner Bettina Jungwirth Simone Maria Kagerbauer Michael Götz Preventing postoperative pulmonary complications by establishing a machine-learning assisted approach (PEPPERMINT): Study protocol for the creation of a risk prediction model. PLoS ONE |
| title | Preventing postoperative pulmonary complications by establishing a machine-learning assisted approach (PEPPERMINT): Study protocol for the creation of a risk prediction model. |
| title_full | Preventing postoperative pulmonary complications by establishing a machine-learning assisted approach (PEPPERMINT): Study protocol for the creation of a risk prediction model. |
| title_fullStr | Preventing postoperative pulmonary complications by establishing a machine-learning assisted approach (PEPPERMINT): Study protocol for the creation of a risk prediction model. |
| title_full_unstemmed | Preventing postoperative pulmonary complications by establishing a machine-learning assisted approach (PEPPERMINT): Study protocol for the creation of a risk prediction model. |
| title_short | Preventing postoperative pulmonary complications by establishing a machine-learning assisted approach (PEPPERMINT): Study protocol for the creation of a risk prediction model. |
| title_sort | preventing postoperative pulmonary complications by establishing a machine learning assisted approach peppermint study protocol for the creation of a risk prediction model |
| url | https://doi.org/10.1371/journal.pone.0329076 |
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