Machine Learning Algorithm for Predicting Postoperative Delirium in Elderly Patients After Thoracic Surgery

Postoperative delirium (POD) is a common and severe complication in patients undergoing major surgery, especially in the elderly. POD has been proven to be associated with increased morbidity and mortality, institutionalization, and high healthcare costs. This retrospective cohort study aimed to use machine learning methods to develop clinically meaningful models to support clinical decision making.

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Key information

Conditions

Postoperative Delirium Confusion Delirium Emergence Delirium Mental Disorders Nervous System Diseases Neurobehavioral Manifestations Neurocognitive Disorders Neurologic Manifestations Pathologic Processes Pathological Conditions, Signs and Symptoms Postoperative Complications Signs and Symptoms

Age range

65 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Affiliated Hospital of Nantong University

Nantong, Jiangsu, 226001, China

About this study

The primary outcome was the incidence of POD within 3 days postoperatively. The patients will be randomly split into two datasets with split ratios of 80% and 20%.

Subsequently, 80% of the patients will be used for training, and 20% of the patients will be used for testing. Multiple machine learning algorithms will be used to develop POD risk prediction models. The discrimination ability of the prediction models will be assessed by calculating the area under the receiver operating characteristic curve (AUC). The calibration of the model will be evaluated using the Hosmer-Lemeshow goodness of fit test. Decision curve analysis (DCA) will be used to evaluate the net benefits for each threshold probability. The best model will be selected by comparing the performance between the models. Then the SHapley Additive exPlanations (SHAP) will be used to explain the best one.

Who can participate

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

aged ≥ 65 years
elective segmentectomy, lobectomy, or esophagectomy surgeries
general anesthesia

Exclusion criteria

surgery length less than 1 hour
preoperative cognitive dysfunction
admission to the intensive care unit
second operation within 24 hours
missing data for any variables

Treatment and study plan

Primary outcomes

Number of participants with postoperative delirium

Time frame: The first three postoperative days

The following descriptive words and the derivatives were searched from the medical records: "delirium", "agitation", "hallucinations", "combative behavior", "inattention", "confusion", "mental status change", "disorientation", "drowsy". Medical records with the presence of these keywords were reviewed by an independent researcher, and medical records presented the aforementioned symptoms preoperatively were excluded. Postoperative delirium was diagnosed according to the Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV) criteria.

Sponsors and collaborators

Lead sponsor

Affiliated Hospital of Nantong University

Other

Registry information

Official study title

Development and Validation of a Prediction Model for Postoperative Delirium in Elderly Patients After Thoracic Surgery

Acronym: POD

Important dates

Study start: 2024
Primary completion: 2024
Study completion: 2024
First posted: Jan 26, 2024
Registry last updated: Dec 3, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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Completed