Lamprecht CB, Dagra A, Lucke-Wold B. Predictive modeling for post operative delirium in elderly. World J Gastrointest Oncol 2024; 16(9): 3761-3764 [DOI: 10.4251/wjgo.v16.i9.3761]
Corresponding Author of This Article
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
Research Domain of This Article
Oncology
Article-Type of This Article
Editorial
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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: http://creativecommons.org/licenses/by-nc/4.0/
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 6.4 Hours
Abstract
Delirium, a complex neurocognitive syndrome, frequently emerges following surgery, presenting diverse manifestations and considerable obstacles, especially among the elderly. This editorial delves into the intricate phenomenon of postoperative delirium (POD), shedding light on a study that explores POD in elderly individuals undergoing abdominal malignancy surgery. The study examines pathophysiology and predictive determinants, offering valuable insights into this challenging clinical scenario. Employing the synthetic minority oversampling technique, a predictive model is developed, incorporating critical risk factors such as comorbidity index, anesthesia grade, and surgical duration. There is an urgent need for accurate risk factor identification to mitigate POD incidence. While specific to elderly patients with abdominal malignancies, the findings contribute significantly to understanding delirium pathophysiology and prediction. Further research is warranted to establish standardized predictive for enhanced generalizability.
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.