Ignite Creation Date:
2025-12-24 @ 2:55 PM
Ignite Modification Date:
2026-01-05 @ 5:44 PM
Study NCT ID:
NCT06689059
Status:
ACTIVE_NOT_RECRUITING
Last Update Posted:
2024-11-18
First Post:
2024-11-06
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Research on Multimodal Multi-objective Integrated Machine Algorithm for Hip Replacement Surgery
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}}, 'documentSection': {'largeDocumentModule': {'largeDocs': [{'date': '2024-11-01', 'size': 123815, 'label': 'Study Protocol and Statistical Analysis Plan', 'hasIcf': False, 'hasSap': True, 'filename': 'Prot_SAP_000.pdf', 'typeAbbrev': 'Prot_SAP', 'uploadDate': '2024-11-12T19:54', 'hasProtocol': True}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'RETROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 6271}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'ACTIVE_NOT_RECRUITING', 'startDateStruct': {'date': '2024-10-24', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2024-11', 'completionDateStruct': {'date': '2025-12-31', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2024-11-13', 'studyFirstSubmitDate': '2024-11-06', 'studyFirstSubmitQcDate': '2024-11-12', 'lastUpdatePostDateStruct': {'date': '2024-11-18', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2024-11-14', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2024-10-31', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Acute Kidney Injury (AKI) Incidence', 'timeFrame': 'From Day 1 to Day 7 post-surgery.', 'description': '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.'}, {'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.', 'description': 'To evaluate the need for blood transfusion postoperatively.'}, {'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.', 'description': 'This outcome measure assesses whether the patient was discharged from the hospital within 48 hours following surgery.'}, {'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.', 'description': '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.'}, {'measure': 'Length of Hospital Stay', 'timeFrame': 'Total duration of hospital stay from admission to discharge, with a maximum observation period of 40 days.', 'description': '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.'}]}, 'conditionsModule': {'conditions': ['Hip Replacement Surgery']}, 'descriptionModule': {'briefSummary': "Purpose:\n\nThe 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).\n\nKey Questions:\n\nCan the HoPreM platform reduce the risk of complications after hip replacement surgery? How accurate is the platform in predicting the specified perioperative events?\n\nParticipants:\n\nParticipants 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.\n\nStudy Procedures:\n\nPatient data will be collected from Xi'an Honghui Hospital, including creatinine values recorded before and after surgery.\n\nThe HoPreM platform will process multimodal data, including demographic, surgical, medical history, and laboratory test data.\n\nVarious ensemble learning algorithms (including XGBoost, random forest, LightGBM, and CatBoost) will be applied to predict different perioperative outcomes.\n\nExpected Outcomes:\n\nThe 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.\n\nContact Information:\n\nFor any questions about this study or for more information, please contact the research team.", 'detailedDescription': '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.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': "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:\n\nAge 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.", 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Patients who have undergone hip replacement surgery\n* Age 18 years or older\n* Missing values in medical records less than 10%\n* Logically consistent medical records\n* Availability of both preoperative and postoperative creatinine values\n\nExclusion Criteria:\n\n* Non-hip replacement surgery patients (patients who did not undergo hip replacement surgery)\n* Age less than 18 years\n* Missing values greater than 10% in medical records\n* Logical inconsistencies in the medical record\n* No available preoperative or postoperative creatinine values'}, 'identificationModule': {'nctId': 'NCT06689059', 'briefTitle': 'Research on Multimodal Multi-objective Integrated Machine Algorithm for Hip Replacement Surgery', 'organization': {'class': 'OTHER', 'fullName': "Xi'an Honghui Hospital"}, 'officialTitle': 'HoPreM Platform: Efficient Multimodal Multi-Task Prediction of Perioperative Events Following Hip Replacement Surgery', 'orgStudyIdInfo': {'id': "Xi'anHongHuiH"}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Hip Replacement Cohort', 'description': '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).', 'interventionNames': ['Other: Multimodal Data Integration and Multi-Task Learning']}], 'interventions': [{'name': 'Multimodal Data Integration and Multi-Task Learning', 'type': 'OTHER', 'description': "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.", 'armGroupLabels': ['Hip Replacement Cohort']}]}, 'contactsLocationsModule': {'overallOfficials': [{'name': 'Jingkun Liu', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': "Honghui hospital, Xi'an Jiaotong University"}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Jingkun Liu', 'class': 'OTHER'}, 'responsibleParty': {'type': 'SPONSOR_INVESTIGATOR', 'investigatorTitle': 'Director', 'investigatorFullName': 'Jingkun Liu', 'investigatorAffiliation': "Xi'an Honghui Hospital"}}}}