Postoperative delirium: A tragedy for elderly cancer patients

Abstract

In this editorial, we comment on the article by Hu et al entitled “Predictive modeling for postoperative delirium in elderly patients with abdominal malignancies using synthetic minority oversampling technique”. We wanted to draw attention to the general features of postoperative delirium (POD) as well as the areas where there are uncertainties and contradictions. POD can be defined as acute neurocognitive dysfunction that occurs in the first week after surgery. It is a severe postoperative complication, especially for elderly oncology patients. Although the underlying pathophysiological mechanism is not fully understood, various neuroinflammatory mechanisms and neurotransmitters are thought to be involved. Various assessment scales and diagnostic methods have been proposed for the early diagnosis of POD. As delirium is considered a preventable clinical entity in about half of the cases, various early prediction models developed with the support of machine learning have recently become a hot scientific topic. Unfortunately, a model with high sensitivity and specificity for the prediction of POD has not yet been reported. This situation reveals that all health personnel who provide health care services to elderly patients should approach patients with a high level of awareness in the perioperative period regarding POD.

Keywords: Delirium; Anesthesia; Neurocognitive dysfunction; Postoperative cognitive dysfunction; Prevention; Risk management; Synthetic minority oversampling technique; Postoperative delirium; Elderly patients; Abdominal cancer

Core Tip: Postoperative delirium (POD) is a clinical complication with severe adverse consequences that can lead to death, especially in elderly patients. POD can occur at any time after surgery until hospital discharge. Predicting and preventing the disease among the most important clinical goals as the pathophysiology is not fully understood and effective treatment is not available. With this objective, many tools for assessment of delirium have been validated and various models have recently been developed with the help of machine learning using known POD risk factors.