AI-Based LOS Prediction in Hip Fracture Patients

NCT06392048 | Completed

• 1 other identifier: GOKAEK-2024/06.22
• Study Type: observational
• Target: 366
• Locations: 1 country
• Sites: 1

Brief Summary

With increasing life expectancy, the elderly population is growing. Hip fractures significantly increase morbidity and mortality, particularly within the first year, among elderly patients. Managing anesthesia in these elderly patients, who often have multiple comorbidities, is challenging. Identifying perioperative factors that can reduce mortality will benefit the perioperative management of these patients. The aim of this study is to develop and validate a machine learning based model to predict the length of hospital stay for hip fracture patients after PACU. Different machine learning algorithms such as R language Gradient Boosting, Random Forest, Artificial Neural Networks and Logistic Regression will be used in the study and the best performing model will be determined. In addition, the prediction mechanism of the model will be examined with SHAP analysis and its applicability in clinical decision processes will be evaluated. Thus, by predicting the length of hospital stay, clinicians will be enabled to manage patient care processes more effectively.

Conditions

Hip Fractures

Outcome Measures

Primary Outcomes (1)

• Prediction of Length of Hospital Stay in Hip Fracture Patients After Post-Anesthesia Care Unit Using Artificial Intelligence

  Unit of Measure: Days * Definition: Absolute difference between predicted and actual length of stay * Target: ±7 days accuracy

  Assessed up to 30 days from PACU admission to hospital discharge

Study Arms (2)

> 7 days LOS

This cohort includes patients whose postoperative hospital length of stay exceeded 7 days. The group was formed based on the median LOS determined in the overall study population. No intervention was administered. The group is used for training and evaluating a machine learning model aimed at predicting prolonged hospitalization (\>7 days) based on preoperative and intraoperative clinical features.

<= 7 days LOS

This cohort includes patients whose postoperative hospital length of stay was 7 days or less. The grouping was based on the median LOS observed in the total sample to ensure balanced classification for the machine learning model. No intervention was administered. Clinical data were used to train and test an AI algorithm for hospital LOS prediction.

Eligibility Criteria

• Age: 65 Years - 100 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patient data will be accessed retrospectively via the hospital record system. Consistent with the literature, in elderly patients with hip fractures, risk factors for mortality will be assessed preoperatively and postoperatively as well as postoperative complications Patients with and without mortality will be examined in two separate subgroups. All studies for machine learning classification will be conducted at the Artificial Intelligence and Simulation Systems Research and Development Laboratory at Kocaeli University's Faculty of Engineering and will be supervised by a faculty member specializing in artificial intelligence and machine learning.

You may qualify if:

• Patients who underwent hip fracture surgery at our institution between 2017 and 2024
• Patients aged 65 years or older
• Patients with hip fractures resulting from a low-energy trauma (simple fall from standing height)

You may not qualify if:

• Patients with pathological hip fractures due to malignancy
• Cancer patients with multiple organ metastases
• Patients who underwent revision hip fracture surgery

Sponsors & Collaborators

• Kocaeli University: lead

Study Sites (1)

Kocaeli University

İzmit, Kocaeli̇, 41100, Turkey (Türkiye)

Location

MeSH Terms

Conditions

Hip Fractures

Condition Hierarchy (Ancestors)

Femoral Fractures; Fractures, Bone; Wounds and Injuries; Hip Injuries; Leg Injuries

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Asist. Prof. M.D

Study Record Dates

• First Submitted: April 17, 2024
• First Posted: April 30, 2024
• Study Start: May 25, 2024
• Primary Completion: April 30, 2025
• Study Completion: May 7, 2025
• Last Updated: May 11, 2025

Record last verified: 2025-05

Locations

TU (1)