Editorial
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Oncol. Sep 15, 2024; 16(9): 3761-3764
Published online Sep 15, 2024. doi: 10.4251/wjgo.v16.i9.3761
Predictive modeling for post operative delirium in elderly
Chris B Lamprecht, Abeer Dagra, Brandon Lucke-Wold
Chris B Lamprecht, Abeer Dagra, Brandon Lucke-Wold, Lillian S. Wells Department of Neurosurgery, University of Florida, Gaineville, FL 32610, United States
Author contributions: Lamprecht CB and Dagra A contributed to literature research, manuscript composition and editing; Lucke-Wold B contributed to conceptualization and editing the manuscript.
Conflict-of-interest statement: There are no conflict of interests to disclose for all authors.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Abeer Dagra, BSc, Research Assistant, Lillian S. Wells Department of Neurosurgery, University of Florida, Newell Drive, Gainesville, FL 32610, United States. abeer.dagra@ufl.edu
Received: March 19, 2024
Revised: May 9, 2024
Accepted: June 3, 2024
Published online: September 15, 2024
Processing time: 173 Days and 11.7 Hours
Core Tip

Core Tip: Postoperative delirium (POD) presents significant challenges in elderly patients, with no current gold standard for prevention. This editorial sheds light on a study that introduces a predictive model utilizing synthetic minority oversampling technique (SMOTE) to identify high-risk patients. Key risk factors include comorbidity index, anesthesia grade, and surgical duration. The editorial discusses that standardizing predictive models across surgical subspecialties is crucial for effective POD management. Further advancements in SMOTE algorithms offer promising avenues for handling unbalanced datasets prevalent in research.