NCT05320965

Prospective Validation of the Model Predicting Postoperative Delirium Occurrence With Machine Learning-based Analysis of Intraoperative Biological Signals During Anesthesia in Cardiac Surgery

Status unknown Last updated 11 April 2022

What this trial tests

trial in Adult Patients Undergoing Open Heart Surgery in 200 participants. Status unknown.

Timeline

17 March 2022

Primary endpoint
31 December 2023

29 February 2024

Quick facts

Lead sponsor	Yonsei University
Status	Status unknown
Study type	OBSERVATIONAL
Enrollment	200
Start date	17 March 2022
Primary completion	31 December 2023
Estimated completion	29 February 2024
Sites	1 location across South Korea

Conditions studied

• Adult Patients Undergoing Open Heart Surgery — all drugs for Adult Patients Undergoing Open Heart Surgery →

Sponsor

Yonsei University

Who can join

Adults 19 to 100, any sex, with Adult Patients Undergoing Open Heart Surgery. Patients with the condition only — healthy volunteers not accepted.

Sponsor's own description

Postoperative delirium (POD) not only increases the length of hospitalization and intensive care unit stay and medical costs, but is also closely associated with negative prognosis, including postoperative mortality, increased morbidity, long-term cognitive decline after surgery, and impaired quality of life and independence. The preoperative risk assessment and early detection of POD are very important in the proper management of POD. This is because drug treatment that can prevent or treat POD is limited, and for its prevention and management, a multidisciplinary approach and resource management covering almost all aspects of patient management are required. Therefore, if there is a model that can predict the occurrence of POD, it can be of great help in managing delirium after cardiac surgery through more accurate risk assessment and early detection. In previous studies, aging and cognitive decline before surgery are known as major risk factors for POD, but identification of risk factors before surgery alone is insufficient to predict the occurrence of POD. Cardiac surgery is highly likely to cause pathophysiological changes that can cause POD, because it is associated with hemodynamic instability, cardiopulmonary use, changes in body temperature, and systemic inflammatory response. These pathophysiological changes can be reflected in the data (biosignals) obtained through various monitoring devices during anesthesia. Most of the events that occur during anesthesia are considered to be correctable risk factors of POD, unlike preoperative risk factors, and there is a potential to reduce the occurrence of POD by actively correcting them. Therefore, it is necessary to analyze the effect of these intraoperative biosignals on POD. In the delirium prediction model development process, rather than simply dividing the already collected data and using it in the model performance validation process, it is better to conduct model performance validation based on patient data prospectively collected to prevent overfitting and achieve higher predictive performance. Therefore, this study aims to collect prospective data to evaluate the performance of the delirium prediction model after cardiac surgery built using machine learning techniques based on the already collected data including biosignals during anesthesia. After reviewing the medical records from the day of surgery to the period of stay in the ICU, if the Intensive Care Delirium Screening Checklist (ICDSC) score is 6 or higher or there is a record of consultation with delirium, it is recorded as POD. After structuring the database through purification, standardization, outlier detection, and sampling of biosignal data generated during surgery, various variables obtained from medical records are collected to construct an evaluation dataset. Using this dataset, the performance of the delirium prediction model built by applying the machine learning algorithm is evaluated through Receiver Operating Characteristic curve analysis.

Publications & conference data

No peer-reviewed publications indexed yet for this trial.