CompletedNot Applicable

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

China901 participantsStarted 2024-10-17

Plain-language summary

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.

Who can participate

Age range60 Years

SexALL

See this in plain English?

AI-rewrites the medical criteria so a patient or caregiver can understand them. Always confirm with the trial site.

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.

What they're measuring

Postoperative delirium

Timeframe: The day after the operation

Trial details

NCT IDNCT07121309

SponsorSecond Affiliated Hospital of Soochow University

Sponsor typeOTHER

Study typeOBSERVATIONAL

Primary completion2025-03-03

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