AI-Based LOS Prediction in Hip Fracture Patients

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

Status

Completed

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT06392048

Enrollment

366

Registered

2024-04-30

Start date

2024-05-25

Completion date

2025-05-07

Last updated

2025-05-11

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Hip Fractures

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.

Interventions

None listed

Study design

Observational model

COHORT

Time perspective

RETROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

65 Years to 100 Years

Healthy volunteers

Yes

Inclusion criteria

* 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)

Exclusion criteria

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

Design outcomes

Primary

Measure	Time frame	Description
Prediction of Length of Hospital Stay in Hip Fracture Patients After Post-Anesthesia Care Unit Using Artificial Intelligence	Assessed up to 30 days from PACU admission to hospital discharge	Unit of Measure: Days * Definition: Absolute difference between predicted and actual length of stay * Target: ±7 days accuracy

Countries

Turkey (Türkiye)

Outcome results