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

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.

Study Overview

• Status: Active, not recruiting
• Conditions: Adult Patients Undergoing Open Heart Surgery

• Study Type: Observational
• Enrollment (Anticipated): 200

Contacts and Locations

Study Locations

• Korea, Republic of
  • Seoul, Korea, Republic of
    • Yonsei University Health System, Severance Hospital

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 19 years to 100 years (Adult, Older Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

Adult patients scheduled for heart/aorta surgery at a tertiary hospital

Description

Inclusion Criteria:

1. Adults between 19 and 100 years of age
2. Patients scheduled for cardiac or aortic surgery

Exclusion Criteria:

1. Patients with a history of major neurocognitive disorder, major depressive disorder, and alcohol or drug dependence before surgery
2. Patients who have already lost consciousness before surgery

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Postoperative delirium	The Intensive Care Delirium Screening Checklist (ICDSC) score is 6 or higher	up to 7 days post-surgery

Collaborators and Investigators

• Sponsor: Yonsei University
• Investigators: Principal Investigator: Sarah Soh, MD. PhD., Department of Anesthesiology and Pain Medicine, Yonsei Cardiovascular Hospital, Yonsei University College of Medicine

Study record dates

Study Major Dates

• Study Start (Actual): March 17, 2022
• Primary Completion (Anticipated): December 31, 2023
• Study Completion (Anticipated): February 29, 2024

Study Registration Dates

• First Submitted: April 3, 2022
• First Submitted That Met QC Criteria: April 3, 2022
• First Posted (Actual): April 11, 2022

Study Record Updates

• Last Update Posted (Actual): April 11, 2022
• Last Update Submitted That Met QC Criteria: April 3, 2022
• Last Verified: April 1, 2022

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Mental Disorders; Nervous System Diseases; Neurologic Manifestations; Confusion; Neurobehavioral Manifestations; Neurocognitive Disorders; Delirium
• Other Study ID Numbers: 4-2022-0093

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No