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

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Total Enrollment

901 participants

Study Classification

OBSERVATIONAL

Study Start Date

2024-10-17

Study Completion Date

2025-03-03

Brief Summary

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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.

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Detailed Description

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Conditions

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Delirium

Study Design

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Observational Model Type

CASE_CONTROL

Study Time Perspective

RETROSPECTIVE

Eligibility Criteria

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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.

Minimum Eligible Age

60 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes