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
ARTICLE HIGHLIGHTS
Research background

Postoperative delirium is a serious complication that disproportionately affects elderly patients undergoing surgery for abdominal malignant tumors, including stomach, colon, liver, gallbladder, and pancreas cancers. This condition challenges patient care and leads to adverse outcomes, such as prolonged hospital stays and increased mortality. Our study focused on developing predictive models using advanced techniques like the synthetic minority oversampling technique (SMOTE) to identify patients at risk, aiming to fill a critical gap in this domain.

Research motivation

There is an urgent need for an accurate predictive model for postoperative delirium in elderly patients after abdominal malignant tumor surgeries. With the high incidence and impact of delirium on this demographic, particularly in prognosis and healthcare burden, an effective predictive tool is paramount. This study enhances early detection and intervention, contributing significantly to geriatric oncology and postoperative care knowledge.

Research objectives

Our primary goal was to create a robust predictive model for postoperative delirium in elderly patients undergoing abdominal malignant tumor surgery. We aimed to identify and validate significant risk factors and assess the model’s accuracy and efficacy. A novel aspect of our research was applying SMOTE to enhance predictive accuracy in imbalanced data sets, offering a validated model for early identification and management of postoperative delirium, and demonstrating SMOTE’s potential in medical research.

Research methods

The study involved a retrospective analysis of 611 elderly patients who underwent surgery for abdominal malignant tumors from September 2020 to October 2022. We used multivariate logistic regression to identify risk factors for postoperative delirium, incorporating SMOTE to address data imbalance. Our validation process ensured the model’s accuracy and reliability.

Research results

We analyzed various risk factors for postoperative delirium in our patient cohort. Factors like the Charlson comorbidity index, anesthesia grade, cerebrovascular disease history, surgical duration, perioperative blood transfusion, and postoperative pain score were significant. Our SMOTE-enhanced predictive model showed superior accuracy over traditional methods, indicating a potential leap in clinical management of postoperative delirium.

Research conclusions

Our study introduces a novel, SMOTE-augmented predictive model for postoperative delirium in elderly patients undergoing abdominal malignant tumor surgery. This model addresses dataset imbalances common in medical research, improving predictive accuracy and offering methodological advancements in medical analytics. It holds promise for early intervention and improved patient care.

Research perspectives

Future research should focus on the prospective validation of this model and its integration into clinical practice. Enhancing predictive accuracy and generalizability is key. Investigations should include larger, more diverse patient cohorts and additional predictive factors to broaden the model’s clinical applicability.