Retrospective Study
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Oncol. Apr 15, 2024; 16(4): 1227-1235
Published online Apr 15, 2024. doi: 10.4251/wjgo.v16.i4.1227
Predictive modeling for postoperative delirium in elderly patients with abdominal malignancies using synthetic minority oversampling technique
Wen-Jing Hu, Gang Bai, Yan Wang, Dong-Mei Hong, Jin-Hua Jiang, Jia-Xun Li, Yin Hua, Xin-Yu Wang, Ying Chen
Wen-Jing Hu, Intensive Care Unit, Shanghai Fourth People’s Hospital Affiliated to Tongji University School of Medicine, Shanghai 200434, China
Gang Bai, Department of Anesthesia and Perioperative Medicine, Shanghai Fourth People’s Hospital Affiliated to Tongji University School of Medicine, Shanghai 200434, China
Yan Wang, Dong-Mei Hong, Jin-Hua Jiang, Jia-Xun Li, Yin Hua, Ying Chen, Department of Nursing, Shanghai Fourth People’s Hospital Affiliated to Tongji University School of Medicine, Shanghai 200434, China
Xin-Yu Wang, Department of Thyroid, Breast and Vascular Surgery, Shanghai Fourth People’s Hospital Affiliated to Tongji University School of Medicine, Shanghai 200434, China
Co-first authors: Wen-Jing Hu and Gang Bai.
Author contributions: Hu WJ and Bai G contributed equally in analysis of the data and writing of the manuscript; Wang Y, Hong DM, Jiang JH, Li JX, Hua Y, Wang XY, and Chen Y collected the data and revised the paper; and all authors have read and approved the final manuscript.
Supported by Discipline Advancement Program of Shanghai Fourth People’s Hospital, No. SY-XKZT-2020-2013.
Institutional review board statement: The study underwent review and received approval from the Committee on the Clinical Application of Medicine and Medical Technology at Shanghai Fourth People’s Hospital (No. 202006-013).
Informed consent statement: All patients provided informed consent for the surgical procedures.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: No additional data are available.
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: Ying Chen, MBBS, Chief Nurse, Department of Nursing, Shanghai Fourth People’s Hospital Affiliated to Tongji University School of Medicine, No. 1279 Sanmen Road, Hongkou District, Shanghai 200434, China. hcy0812@163.com
Received: October 1, 2023
Peer-review started: October 1, 2023
First decision: January 2, 2024
Revised: January 12, 2024
Accepted: February 20, 2024
Article in press: February 20, 2024
Published online: April 15, 2024
Processing time: 192 Days and 22.6 Hours
Abstract
BACKGROUND

Postoperative delirium, particularly prevalent in elderly patients after abdominal cancer surgery, presents significant challenges in clinical management.

AIM

To develop a synthetic minority oversampling technique (SMOTE)-based model for predicting postoperative delirium in elderly abdominal cancer patients.

METHODS

In this retrospective cohort study, we analyzed data from 611 elderly patients who underwent abdominal malignant tumor surgery at our hospital between September 2020 and October 2022. The incidence of postoperative delirium was recorded for 7 d post-surgery. Patients were divided into delirium and non-delirium groups based on the occurrence of postoperative delirium or not. A multivariate logistic regression model was used to identify risk factors and develop a predictive model for postoperative delirium. The SMOTE technique was applied to enhance the model by oversampling the delirium cases. The model’s predictive accuracy was then validated.

RESULTS

In our study involving 611 elderly patients with abdominal malignant tumors, multivariate logistic regression analysis identified significant risk factors for postoperative delirium. These included the Charlson comorbidity index, American Society of Anesthesiologists classification, history of cerebrovascular disease, surgical duration, perioperative blood transfusion, and postoperative pain score. The incidence rate of postoperative delirium in our study was 22.91%. The original predictive model (P1) exhibited an area under the receiver operating characteristic curve of 0.862. In comparison, the SMOTE-based logistic early warning model (P2), which utilized the SMOTE oversampling algorithm, showed a slightly lower but comparable area under the curve of 0.856, suggesting no significant difference in performance between the two predictive approaches.

CONCLUSION

This study confirms that the SMOTE-enhanced predictive model for postoperative delirium in elderly abdominal tumor patients shows performance equivalent to that of traditional methods, effectively addressing data imbalance.

Keywords: Elderly patients, Abdominal cancer, Postoperative delirium, Synthetic minority oversampling technique, Predictive modeling, Surgical outcomes

Core Tip: Our study develops a predictive model for postoperative delirium in elderly patients with abdominal malignancies, using the synthetic minority oversampling technique. This model highlights key risk factors including the Charlson comorbidity index, anesthesia grade, cerebrovascular disease history, surgical duration, perioperative transfusion, and postoperative pain. Its validation demonstrates effectiveness in clinical settings, enhancing care and outcomes for this high-risk group.