Ignite Creation Date:
2025-12-24 @ 4:04 PM
Ignite Modification Date:
2025-12-27 @ 11:28 AM
Study NCT ID:
NCT07030166
Status:
RECRUITING
Last Update Posted:
2025-09-04
First Post:
2025-06-07
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
A Machine Learning Prediction Model for Postoperative Acute Kidney Injury in Non-Cardiac Surgery Patients
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D058186', 'term': 'Acute Kidney Injury'}], 'ancestors': [{'id': 'D051437', 'term': 'Renal Insufficiency'}, {'id': 'D007674', 'term': 'Kidney Diseases'}, {'id': 'D014570', 'term': 'Urologic Diseases'}, {'id': 'D052776', 'term': 'Female Urogenital Diseases'}, {'id': 'D005261', 'term': 'Female Urogenital Diseases and Pregnancy Complications'}, {'id': 'D000091642', 'term': 'Urogenital Diseases'}, {'id': 'D052801', 'term': 'Male Urogenital Diseases'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'OTHER', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 2500}, 'targetDuration': '1 Year', 'patientRegistry': True}, 'statusModule': {'overallStatus': 'RECRUITING', 'startDateStruct': {'date': '2025-07-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-08', 'completionDateStruct': {'date': '2026-12-31', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-08-27', 'studyFirstSubmitDate': '2025-06-07', 'studyFirstSubmitQcDate': '2025-06-19', 'lastUpdatePostDateStruct': {'date': '2025-09-04', 'type': 'ESTIMATED'}, 'studyFirstPostDateStruct': {'date': '2025-06-22', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2026-12-31', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Acute kidney injury', 'timeFrame': 'Within 7 days after the operation'}], 'secondaryOutcomes': [{'measure': 'Postoperative complications', 'timeFrame': 'Perioperative period'}, {'measure': 'Postoperative mortality', 'timeFrame': 'Perioperative period'}, {'measure': 'Hospitalization costs', 'timeFrame': 'Perioperative period'}, {'measure': 'Hospital stays', 'timeFrame': 'Perioperative period'}]}, 'oversightModule': {'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Kidney Injury, Acute']}, 'descriptionModule': {'briefSummary': 'Primary objectives of this study is to develop and validate a predictive model for acute kidney injury after non-cardiac surgery based on machine learning. Secondary objectives of this study is to incorporate frailty assessment as a new predictor into the model and measure its incremental value was measured.', 'detailedDescription': 'The data of this study are divided into two parts: retrospective and prospective. The retrospective data were from the electronic medical records of adult patients who underwent non-cardiac surgery during hospitalization from July 2015 to June 2025. The ratio of the training set, the internal validation set and the test set was 7:1:2. The prospective data is an external (temporal) validation set. Data collection began in July 2025 and is expected to end in February 2026'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'Patients who are scheduled to undergo surgery at Zhongda Hospital Southeast University.', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* 18 years old or above\n* Undergo non-cardiac surgery\n\nExclusion Criteria:\n\n* At least one measurement of serum creatinine (SCr) was not conducted before and after the operation\n* End-stage renal disease (ESRD) that has received dialysis within the past year\n* Baseline SCr ≥ 4.5 mg/dl (because the clinical criteria for AKI based on elevated SCr may not be applicable to these patients)\n* Acute kidney injury occurred within 7 days before the operation\n* The operation time is less than 2 hours'}, 'identificationModule': {'nctId': 'NCT07030166', 'briefTitle': 'A Machine Learning Prediction Model for Postoperative Acute Kidney Injury in Non-Cardiac Surgery Patients', 'organization': {'class': 'OTHER', 'fullName': 'Zhongda Hospital'}, 'officialTitle': 'A Machine Learning Prediction Model for Postoperative Acute Kidney Injury in Non-Cardiac Surgery Patients: Development, Validation, and the Incremental Value of Frailty Assessment', 'orgStudyIdInfo': {'id': '2025ZDSYLL200-P01'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Development group', 'description': 'The development group is used for fitting the model and optimizing the model. We used preoperative demographic characteristics (gender, age, BMI, marital status, and occupation, etc.), laboratory indicators (blood and urine routine, liver and kidney function, coagulation function and other blood test indicators), preoperative comorbidities and surgical information (surgical department, surgical grade, ASA grade, operation time, anesthesia method, intraoperative position, intake and output volume, vital signs, and intraoperative medication, etc.) Variables such as logistic regression, extreme gradient boosting, decision tree, random forest and Bayesian are used for screening, and multiple methods such as machine learning are employed for modeling.', 'interventionNames': ['Other: No intervention measures were used.']}, {'label': 'Testing group', 'description': 'The testing set is used for the initial performance evaluation of the model. We use indicators such as discrimination and calibration for model comparison and optimization to select the best model.', 'interventionNames': ['Other: No intervention measures were used.']}, {'label': 'External (time) validation group', 'description': 'The external (time) validation group is used for future generalization ability assessment. We prospectively collected patient-related data. In addition to the same variables as those in the development group and the testing group, we also evaluated and collected the frailty status of patients before the operation, and recorded prognostic indicators such as the incidence of in-hospital complications, in-hospital mortality, length of hospital stay and hospitalization cost of patients. We used the data from the external (time) validation group to validate the model performance, incorporated the frailty assessment as a new predictor into the model, calculated the incremental values and evaluated the performance of the updated model.', 'interventionNames': ['Other: No intervention measures were used.']}], 'interventions': [{'name': 'No intervention measures were used.', 'type': 'OTHER', 'description': 'The exposure factors were the perioperative related operations experienced by the patients and their individual conditions', 'armGroupLabels': ['Development group', 'External (time) validation group', 'Testing group']}]}, 'contactsLocationsModule': {'locations': [{'city': 'Nanjing', 'status': 'RECRUITING', 'country': 'China', 'facility': 'Zhongda Hospital Southeast University', 'geoPoint': {'lat': 32.06167, 'lon': 118.77778}}], 'centralContacts': [{'name': 'Yue Lan Zhu', 'role': 'CONTACT', 'email': 'zhulanyue1993@126.com', 'phone': '+8618795969178'}]}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Lanyue Zhu', 'class': 'OTHER'}, 'responsibleParty': {'type': 'SPONSOR_INVESTIGATOR', 'investigatorTitle': 'Attending Physician', 'investigatorFullName': 'Lanyue Zhu', 'investigatorAffiliation': 'Zhongda Hospital'}}}}