Development of a Brief Cognitive Screening Tool for Predicting Postoperative Delirium in Patients with Parkinson's Disease: A Secondary Analysis

Evolution of Deep Brain Stimulation Techniques for Complication Mitigation

Complication mitigation in deep brain stimulation has been a topic matter of much discussion in the literature. In this article, we examine how neurosurgeons as individuals and as a field generated and adapted techniques to prevent infection, lead fracture/lead migration, and suboptimal outcomes in both the acute period and longitudinally. The authors performed a MEDLINE search inclusive of articles from 1987 to June 2023 including human studies written in English. Using the Rayyan platform, two reviewers (J.P. and R.M.) performed a title screen. Of the 776 articles, 252 were selected by title screen and 172 from abstract review for full-text evaluation. Ultimately, 124 publications were evaluated. We describe the initial complications and inefficiencies at the advent of deep brain stimulation and detail changes instituted by surgeons that reduced them. Furthermore, we discuss the trend in both undesired short-term and long-term outcomes with emphasis on how surgeons recognized and modified their practice to provide safer and better procedures. This scoping review adds to the literature as a guide to both new neurosurgeons and seasoned neurosurgeons alike to understand better what innovations have been trialed over time as we embark on novel targets and neuromodulatory technologies.

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

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.