Postoperative delirium is one of the most prevalent postoperative complications, affecting mostly older adults. Its incidence is expected to rise because of surgical advances, shifting demographics, and increased life expectancy. Although an acute alteration in brain function, postoperative delirium is associated with adverse outcomes, including progressive cognitive decline and dementia, that place significant burdens on patients' lives and healthcare systems. This has prompted efforts to understand the mechanisms of postoperative delirium to provide effective prevention and treatment. There are multiple mechanisms involved in the etiology of postoperative delirium that share similarities with the physiological changes associated with the aging brain. In addition, older patients often have multiple comorbidities including increased cognitive impairment that is also implicated in the genesis of delirium. These tangled connections pinpointed a shift toward creation of a holistic model of the pathophysiology of postoperative delirium. Scientific advancements integrating clinical risk factors, possible postoperative delirium biomarkers, genetic features, digital platforms, and other biotechnical and information technological innovations, will become available in the near future. Advances in artificial intelligence, for example, will aggregate cognitive testing platforms with patient-specific postoperative delirium risk stratification studies, panels of serum and cerebrospinal fluid molecules, electroencephalogram signatures, and gut microbiome features, along with the integration of novel polygenetic variants of sleep and cognition. These advances will allow for the enrollment of high-risk patients into prevention programs and help uncover new pharmacologic targets.
Keyphrases
- patients undergoing
- cardiac surgery
- hip fracture
- artificial intelligence
- cognitive decline
- risk factors
- end stage renal disease
- healthcare
- mild cognitive impairment
- cognitive impairment
- chronic kidney disease
- newly diagnosed
- machine learning
- ejection fraction
- physical activity
- white matter
- cerebrospinal fluid
- multiple sclerosis
- peritoneal dialysis
- deep learning
- depressive symptoms
- resting state
- sleep quality
- young adults
- blood brain barrier
- big data
- acute respiratory distress syndrome
- patient reported