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

NCT ID: NCT06689059

Last Updated: 2024-11-18

Basic Information

• Recruitment Status: ACTIVE_NOT_RECRUITING
• Total Enrollment: 6271 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2024-10-24
• Study Completion Date: 2025-12-31

Brief Summary

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.

Detailed 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.

Conditions

Hip Replacement Surgery

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: RETROSPECTIVE

Study Groups

Hip Replacement Cohort

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).

Multimodal Data Integration and Multi-Task Learning

Intervention Type: OTHER

This study utilizes a multimodal data integration and multi-task learning approach to predict perioperative events after hip replacement surgery. By combining various data types, including demographics, surgical details, medical history, and lab results, the model enhances prediction accuracy for outcomes like AKI, blood transfusion needs, and ICU transfers. The use of ensemble learning algorithms such as CatBoost optimizes the platform's performance, offering a unique method for clinical decision support.

Interventions

Multimodal Data Integration and Multi-Task Learning

This study utilizes a multimodal data integration and multi-task learning approach to predict perioperative events after hip replacement surgery. By combining various data types, including demographics, surgical details, medical history, and lab results, the model enhances prediction accuracy for outcomes like AKI, blood transfusion needs, and ICU transfers. The use of ensemble learning algorithms such as CatBoost optimizes the platform's performance, offering a unique method for clinical decision support.

Intervention Type: OTHER

Eligibility Criteria

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

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Jingkun Liu

OTHER

Sponsor Role: lead

Responsible Party

Jingkun Liu

Director

Responsibility Role: SPONSOR_INVESTIGATOR

Principal Investigators

Jingkun Liu

Role: PRINCIPAL_INVESTIGATOR

Honghui hospital, Xi'an Jiaotong University

Provided Documents

Document Type: Study Protocol and Statistical Analysis Plan

View Document

Other Identifiers

Xi'anHongHuiH

Identifier Type: -

Identifier Source: org_study_id