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

HoPreM Platform: Efficient Multimodal Multi-Task Prediction of Perioperative Events Following 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.

Study Overview

• Status: Active, not recruiting
• Conditions: Hip Replacement Surgery
• Intervention / Treatment: Other: Multimodal Data Integration and Multi-Task Learning

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.

• Study Type: Observational
• Enrollment (Actual): 6271

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

This study includes adult patients who have undergone hip replacement surgery at Xi'an Honghui Hospital. The study population is characterized by a diverse range of gender, age, and medical history, including patients with hypertension, diabetes, and other comorbidities. The inclusion criteria are:

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 The goal is to create a comprehensive dataset that represents the typical demographic of patients seen in clinical practice for hip replacement surgery.

Description

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

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort

Intervention / Treatment

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).

Other: 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Blood Transfusion Requirements	To evaluate the need for blood transfusion postoperatively.	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.
48-Hour Postoperative Discharge	This outcome measure assesses whether the patient was discharged from the hospital within 48 hours following surgery.	Within 48 hours post-surgery, assessed based on whether the patient was discharged from the hospital within this 48-hour period.
ICU Transfer	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.	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.
Length of Hospital Stay	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.	Total duration of hospital stay from admission to discharge, with a maximum observation period of 40 days.
Acute Kidney Injury (AKI) Incidence	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.	From Day 1 to Day 7 post-surgery.

Collaborators and Investigators

• Sponsor: Jingkun Liu
• Investigators: Principal Investigator: Jingkun Liu, Honghui hospital, Xi'an Jiaotong University

Study record dates

Study Major Dates

• Study Start (Actual): October 24, 2024
• Primary Completion (Actual): October 31, 2024
• Study Completion (Estimated): December 31, 2025

Study Registration Dates

• First Submitted: November 6, 2024
• First Submitted That Met QC Criteria: November 12, 2024
• First Posted (Actual): November 14, 2024

Study Record Updates

• Last Update Posted (Estimated): November 18, 2024
• Last Update Submitted That Met QC Criteria: November 13, 2024
• Last Verified: November 1, 2024

More Information

Terms related to this study

• Other Study ID Numbers: Xi'anHongHuiH

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO