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

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.

Active, Not Recruiting

This study is active but is not currently recruiting participants.

Key information

Conditions

Hip Replacement Surgery

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

Multimodal Data Integration and Multi-Task Learning

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.

Primary outcomes

Acute Kidney Injury (AKI) Incidence

Time frame: From Day 1 to Day 7 post-surgery.

AKI incidence will be assessed daily by comparing serum creatinine levels with the preoperative baseline. AKI incidence is determined by a ≥0.3 mg/dL increase in creatinine within 48 hours or a ≥50% increase within 7 days from baseline.
Blood Transfusion Requirements

Time frame: From post-surgery Day 1 until discharge, up to a maximum of 40 days, assessed based on whether a blood transfusion was recorded during the hospital stay.

To evaluate the need for blood transfusion postoperatively.
48-Hour Postoperative Discharge

Time frame: Within 48 hours post-surgery, assessed based on whether the patient was discharged from the hospital within this 48-hour period.

This outcome measure assesses whether the patient was discharged from the hospital within 48 hours following surgery.
ICU Transfer

Time frame: From post-surgery Day 1 until discharge, up to a maximum of 40 days, assessed based on whether an ICU transfer occurred during the hospital stay.

This outcome measure records whether the patient was transferred to the Intensive Care Unit (ICU) at any point during the hospital stay from post-surgery Day 1 until discharge, with a maximum observation period of 40 days.
Length of Hospital Stay

Time frame: Total duration of hospital stay from admission to discharge, with a maximum observation period of 40 days.

This outcome measure calculates the total number of days the patient spends in the hospital from the time of admission until discharge, up to a maximum of 40 days.

Sponsors and collaborators

Lead sponsor

Jingkun Liu

Other

Registry information

Official study title

HoPreM Platform: Efficient Multimodal Multi-Task Prediction of Perioperative Events Following Hip Replacement Surgery

Important dates

Study start: 2024
Primary completion: 2024
Study completion: 2025
First posted: Nov 14, 2024
Registry last updated: Nov 18, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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Completed