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

doi:10.4251/wjgo.v16.i4.1227

Predictive modeling for postoperative delirium in elderly patients with abdominal malignancies using synthetic minority oversampling technique

World J Gastrointest Oncol. 2024 Apr 15;16(4):1227-1235. doi: 10.4251/wjgo.v16.i4.1227.

Authors

Wen-Jing Hu¹, Gang Bai², Yan Wang³, Dong-Mei Hong³, Jin-Hua Jiang³, Jia-Xun Li³, Yin Hua³, Xin-Yu Wang⁴, Ying Chen⁵

Affiliations

¹ Intensive Care Unit, Shanghai Fourth People's Hospital Affiliated to Tongji University School of Medicine, Shanghai 200434, China.
² Department of Anesthesia and Perioperative Medicine, Shanghai Fourth People's Hospital Affiliated to Tongji University School of Medicine, Shanghai 200434, China.
³ Department of Nursing, Shanghai Fourth People's Hospital Affiliated to Tongji University School of Medicine, Shanghai 200434, China.
⁴ Department of Thyroid, Breast and Vascular Surgery, Shanghai Fourth People's Hospital Affiliated to Tongji University School of Medicine, Shanghai 200434, China.
⁵ Department of Nursing, Shanghai Fourth People's Hospital Affiliated to Tongji University School of Medicine, Shanghai 200434, China. hcy0812@163.com.

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: Abdominal cancer; Elderly patients; Postoperative delirium; Predictive modeling; Surgical outcomes; Synthetic minority oversampling technique.