With the Development of Research, New Algorithms and Technologies Have Emerged, One of Which is Machine Learning. Machine Learning Can Extract Key Factors From Vast Amounts of Data, Identify Underlying Patterns, and Predict Future Trends. In Recent Years, Machine Learning Has Been Widely Used in (POD)

Soochow University

Status: Completed

Conditions: Delirium

Study type: Observational

Funder types: Other

Identifiers

NCT07121309

JD-HG-2025-022 (Other Identifier)

Details and patient eligibility

About

The aim of this study is to construct a predictive model for postoperative delirium in elderly patients with hip fractures. The main question it answers is to construct a risk prediction model for hip fractures in the elderly through six machine learning methods, compare which method's model is better, and conduct external validation of the model's stability to provide a reference for the early clinical detection of postoperative delirium in elderly hip fracture patients.

The clinical data of elderly patients with hip fractures have been collected in clinical practice and the model has been constructed.

Enrollment: 901 patients

Sex: All

Ages: 60+ years old

Volunteers: Accepts Healthy Volunteers

Inclusion criteria

• Age ≥ 60 years; diagnosed with hip fracture by X-ray; patients who underwent surgical treatment.

Exclusion criteria

• Patients with other severe diseases (Patients who reach grade IV or higher according to the American Society of Anesthesiologists (ASA) health status classification;Suffer from end-stage diseases;there is multiple organ dysfunction syndrome (MODS) or single organ failure); patients with mental disorders; patients participating in other studies.