Evaluation of Bedside Tests of Attention and Arousal Assessing Delirium in Parkinson's Disease, Dementia, and Older Adults

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.

Cognitive disorders in advanced Parkinson's disease: challenges in the diagnosis of delirium

Parkinson's disease (PD) is a neurodegenerative condition that is frequently associated with cognitive disorders. These can arise directly from the primary disease, or be triggered by external factors in susceptible individuals due to PD or other predisposing factors. The cognitive disorders encompass PD-associated cognitive impairment (PD-CI), delirium, PD treatment-associated cognitive side effects, cognitive non-motor fluctuations, and PD-associated psychosis. Accurate diagnosis of delirium is crucial because it often stems from an underlying disease that may be severe and require specific treatment. However, overlapping molecular mechanisms are thought to be involved in both delirium and PD, leading to similar clinical symptoms. Additionally, there is a bidirectional interaction between delirium and PD-CI, resulting in frequent concurrent processes that further complicate diagnosis. No reliable biomarker is currently available for delirium, and the diagnosis is primarily based on clinical criteria. However, the screening tools validated for diagnosing delirium in the general population have not been specifically validated for PD. Our review addresses the current challenges in the diagnosis of these cognitive disorders and highlights existing gaps within this field.