Clinical trial NCT06689059: Research on Multimodal Multi-objective Integrated Machine Algorithm for Hip Replacement Surgery
Official title: HoPreM Platform: Efficient Multimodal Multi-Task Prediction of Perioperative Events Following Hip Replacement Surgery
Study type: OBSERVATIONAL
Overall status: ACTIVE_NOT_RECRUITING
Conditions: Hip Replacement Surgery
Lead sponsor: Xi'an Honghui Hospital
Sponsor class: OTHER
First posted: 2024-11-14
Enrollment: 6271
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
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.

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.

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

Primary outcomes:
measure: Acute Kidney Injury (AKI) Incidence
timeFrame: From Day 1 to Day 7 post-surgery.
measure: Blood Transfusion Requirements
timeFrame: 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.
measure: 48-Hour Postoperative Discharge
timeFrame: Within 48 hours post-surgery, assessed based on whether the patient was discharged from the hospital within this 48-hour period.
measure: ICU Transfer
timeFrame: 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.
measure: Length of Hospital Stay
timeFrame: Total duration of hospital stay from admission to discharge, with a maximum observation period of 40 days.
Secondary outcomes:
Other outcomes:
Locations:
Study officials:
affiliation: Honghui hospital, Xi'an Jiaotong University
name: Jingkun Liu
role: PRINCIPAL_INVESTIGATOR
References: