Research on Multimodal Multi-objective Integrated Machine Algorithm for Hip Replacement Surgery

Jingkun Liu

Status: Active, not recruiting

Conditions: Hip Replacement Surgery

Treatments: Other: Multimodal Data Integration and Multi-Task Learning

Study type: Observational

Funder types: Other

Identifiers

NCT06689059

Xi'anHongHuiH

Details and patient eligibility

About

Purpose:

The aim of this study is to develop the Holistic Predictive Multi-Tasking Platform for Clinical Data Analysis (HoPreM) to accurately predict perioperative events following hip replacement surgery by integrating various types of data, including demographic, surgical, medical history, and laboratory information. The events targeted for prediction include acute kidney injury (AKI), blood transfusion requirements, 48-hour postoperative discharge (48hPOD), Intensive Care Unit (ICU) transfer, and length of hospital stay (LOS).

Key Questions:

Can the HoPreM platform reduce the risk of complications after hip replacement surgery? How accurate is the platform in predicting the specified perioperative events?

Participants:

Participants will include patients undergoing hip replacement surgery, aged 18 and above, with less than 10% missing values in their medical records. The collected data will be used to train and test the predictive models of the HoPreM platform.

Study Procedures:

Patient data will be collected from Xi'an Honghui Hospital, including creatinine values recorded before and after surgery.

The HoPreM platform will process multimodal data, including demographic, surgical, medical history, and laboratory test data.

Various ensemble learning algorithms (including XGBoost, random forest, LightGBM, and CatBoost) will be applied to predict different perioperative outcomes.

Expected Outcomes:

The HoPreM platform is expected to demonstrate its capability in predicting complications after hip replacement surgery, particularly acute kidney injury and blood transfusion requirements. Through SHAP value analysis, the study aims to reveal relationships between features and clinical outcomes, enhancing the model's interpretability and clinical utility.

Contact Information:

For any questions about this study or for more information, please contact the research team.

Full description

This study aims to develop the Holistic Predictive Multi-Tasking Platform for Clinical Data Analysis (HoPreM) to accurately predict perioperative events following hip replacement surgery. The HoPreM platform integrates various types of patient data, including demographic, surgical, medical history, and laboratory information. Utilizing a multi-task learning framework, the platform is designed to predict multiple perioperative complications, such as acute kidney injury (AKI), blood transfusion requirements, 48-hour postoperative discharge (48hPOD), Intensive Care Unit (ICU) transfer, and length of hospital stay (LOS). To enhance predictive accuracy, feature selection techniques like Lasso regression and random forest models are employed, followed by ensemble learning algorithms, including CatBoost. This predictive platform is expected to support personalized postoperative management, reduce complication rates, and improve clinical outcomes for hip replacement patients.

Enrollment: 6,271 patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• Patients who have undergone hip replacement surgery
• Age 18 years or older
• Missing values in medical records less than 10%
• Logically consistent medical records
• Availability of both preoperative and postoperative creatinine values

Exclusion criteria

• Non-hip replacement surgery patients (patients who did not undergo hip replacement surgery)
• Age less than 18 years
• Missing values greater than 10% in medical records
• Logical inconsistencies in the medical record
• No available preoperative or postoperative creatinine values

Trial design

6,271 participants in 1 patient group

Hip Replacement Cohort

Description:

This cohort includes patients undergoing hip replacement surgery. The HoPreM platform is used for multi-task predictive analysis of perioperative complications, including AKI, blood transfusion requirements, postoperative discharge within 48 hours, ICU transfer, and length of hospital stay (LOS).

Treatment:

Other: Multimodal Data Integration and Multi-Task Learning