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

N/AActive Not RecruitingNCT06689059

Jingkun Liu6,271 enrolled

Overview

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.

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.

Interventions

Multimodal Data Integration and Multi-Task LearningOTHER

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.

Eligibility

Sex: ALLMin age: 18 Years

Medical Language ↔ Plain English

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

Outcomes

Primary Outcomes

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.

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

Blood Transfusion Requirements

To evaluate the need for blood transfusion postoperatively.

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.

48-Hour Postoperative Discharge

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

Time frame: 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.

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.

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.

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