Preventing postoperative pulmonary complications by establishing a machine-learning assisted approach (PEPPERMINT): Study protocol for the creation of a risk prediction model

Britta Trautwein, Meinrad Beer, Manfred Blobner, Bettina Jungwirth, Simone Maria Kagerbauer and Michael Götz

PLOS ONE, 2025, vol. 20, issue 8, 1-19

Abstract: Background: 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. Methods: 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. Discussion: 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. Trial registration: ClinicalTrials.gov ID: NCT05789953 (29th of March 2023)

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0329076 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 29076&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0329076

DOI: 10.1371/journal.pone.0329076

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().