Description

Post-operative delirium (POD) is a serious clinical syndrome associated with increased mortality and morbidity. Old age and Alzheimer's disease are at an increased risk for developing post-operative delirium. POD occurs in 60% of patients after surgery (ref) and is associated with a poor outcome including longer hospitalization and higher rate of mortality (ref). Although the investigators have methods like AD biomarker blood tests, they are expensive and time-consuming.

This study involves collecting exhaled breath containing hundreds of volatile organic compounds (VOCs) that are present in very low quantity (parts per billion). These VOCs are by-products of metabolic activities in healthy and disease states. VOCs in the exhaled gases, which can be extracted in a noninvasive fashion, carry information to indicate individual's risk for POD and its severity. Real-time monitoring of VOCs could potentially produce point-of-care (POC) breathomic signatures capable of rapid prediction during pre-operative phase, as well as early diagnosis and stratification of POD during post-operative phase. However, there is no study on analyzing exhaled gases of patients with surgery to improve their outcome. In this initial project, the investigators propose to determine whether a portable and fully automated gas chromatography (GC) device, which can be attached to an anesthesia machine circuit, is capable of rapidly detecting exhaled VOCs for POD prediction. Our long-term objectives are to identify breathomic patterns for prediction, early detection, and stratification of POD during pre- and post-operative phases. Such POC monitoring has a great potential not only to be transformative in perioperative managing patients with surgery and allowing for precision treatment, but also to elicit the pathophysiological understanding of POD and accelerate drug development for reducing POD.

The investigators hypothesize that patients with POD have a unique pattern of exhaled VOCs and that this pattern is associated with AD-related biomarkers in the blood.

Aim 1: To determine whether patients with POD have a pattern of exhaled VOCs that is different from patients without POD. The exhaled gases of patients with a major spine surgery will be analyzed by a portable GC with an automated GC data processing pipeline for chromatogram peak feature extractions, including baseline removal, denoising, normalization, peak detection, peak modeling, and chromatogram alignment. The VOC pattern of patients with POD will be compared with that of patients without POD.

Aim 2: To determine whether the exhaled gas pattern is associated with AD-related biomarkers in the blood. AD-related biomarkers in the blood will be measured by ELISA. Correlations between the levels of VOCs that contribute to the pattern changes between patients with POD and patients without POD and the levels of AD related biomarkers will be performed. Dysfunction of possible metabolic pathways in patients with POD may be indicated by VOCs that have changes in those patients