Predictive modeling for post operative delirium in elderly

Abstract

Delirium, a complex neurocognitive syndrome, frequently emerges following surgery, presenting diverse manifestations and considerable obstacles, especially among the elderly. This editorial delves into the intricate phenomenon of postoperative delirium (POD), shedding light on a study that explores POD in elderly individuals undergoing abdominal malignancy surgery. The study examines pathophysiology and predictive determinants, offering valuable insights into this challenging clinical scenario. Employing the synthetic minority oversampling technique, a predictive model is developed, incorporating critical risk factors such as comorbidity index, anesthesia grade, and surgical duration. There is an urgent need for accurate risk factor identification to mitigate POD incidence. While specific to elderly patients with abdominal malignancies, the findings contribute significantly to understanding delirium pathophysiology and prediction. Further research is warranted to establish standardized predictive for enhanced generalizability.

Keywords: Post-operative delirium; Elderly delirium; Neurocognitive syndrome; Neurotransmitters; Abdominal malignancy; Predictive model; Synthetic minority oversampling technique

Core Tip: Postoperative delirium (POD) presents significant challenges in elderly patients, with no current gold standard for prevention. This editorial sheds light on a study that introduces a predictive model utilizing synthetic minority oversampling technique (SMOTE) to identify high-risk patients. Key risk factors include comorbidity index, anesthesia grade, and surgical duration. The editorial discusses that standardizing predictive models across surgical subspecialties is crucial for effective POD management. Further advancements in SMOTE algorithms offer promising avenues for handling unbalanced datasets prevalent in research.