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

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.