Hip FracturesNCT06392048
AI-Based LOS Prediction in Hip Fracture Patients
An observational study of Hip Fractures, run by Kocaeli University at 1 site in Turkey (Türkiye).
Trial 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. Read more about this study
- Who is it for?
- Ages 65 to 100 yearsHealthy volunteers can take part
- Conditions the study is for
- Hip Fractures
- How many people
- 366 participants
- Where
- 1 siteTurkey (Türkiye)
- Study dates
- 25 May 2024to 7 May 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 65 to 100 years
- All sexes
- Healthy volunteers accepted
- Older adult
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.
Who may be able to join
- 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)
Who cannot join
- Patients with pathological hip fractures due to malignancy
- Cancer patients with multiple organ metastases
- Patients who underwent revision hip fracture surgery
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Prediction of Length of Hospital Stay in Hip Fracture Patients After Post-Anesthesia Care Unit Using Artificial Intelligence
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
- Medical subject headings
- Hip Fractures
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| > 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. |
What is being measured
The primary outcome is the main question the study is designed to answer.
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
Measured: Assessed up to 30 days from PACU admission to hospital discharge
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted30 Apr 2024
- Study start25 May 2024
- Primary completion30 Apr 2025
- Study completion7 May 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Hip FracturesStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 366 participants (actual)
Sponsor and investigators
- Lead sponsor
- Kocaeli University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Volkan Alparslan, Kocaeli University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06392048
- Sponsor's protocol ID
- GOKAEK-2024/06.22
- Status last verified
- May 2025
- First submitted
- 17 Apr 2024
- Last updated
- 11 May 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 11 May 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Prediction of Length of Hospital Stay in Hip Fracture Patients After Post-Anesthesia Care Unit Using Artificial Intelligence
- ClinicalTrials.gov ID
- NCT06392048
- Acronym / sponsor ID
- GOKAEK-2024/06.22
- Conditions
- Hip Fractures
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 366 participants (actual)
- Lead sponsor
- Kocaeli UniversityUniversity, hospital or other
- Study start
- 25 May 2024 (actual)
- Primary completion
- 30 Apr 2025 (actual)
- Study completion
- 7 May 2025 (actual)
- First posted
- 30 Apr 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| > 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. |
Who may qualify
- Ages
- 65 to 100 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Older adult
- 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.
Key inclusion criteria (3 listed)
- 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)
Key exclusion criteria (3 listed)
- Patients with pathological hip fractures due to malignancy
- Cancer patients with multiple organ metastases
- Patients who underwent revision hip fracture surgery
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Prediction of Length of Hospital Stay in Hip Fracture Patients After Post-Anesthesia Care Unit Using Artificial Intelligence
Time frame: 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
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 11 May 2025.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Hip Fractures, run by Kocaeli University at 1 site in Turkey (Türkiye).
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 11 May 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Turkey (Türkiye). Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Prediction of Length of Hospital Stay in Hip Fracture Patients After Post-Anesthesia Care Unit Using Artificial Intelligence (time frame: Assessed up to 30 days from PACU admission to hospital discharge). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 11 May 2025.