Ignite Creation Date:
2025-12-24 @ 11:08 PM
Ignite Modification Date:
2026-02-18 @ 9:56 AM
Study NCT ID:
NCT05482269
Status:
RECRUITING
Last Update Posted:
2023-11-02
First Post:
2022-07-29
Is Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Adverse Outcome of Acute Pulmonary Embolism by Artificial Intelligence System Based on CT Pulmonary Angiography
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D011655', 'term': 'Pulmonary Embolism'}, {'id': 'D013927', 'term': 'Thrombosis'}, {'id': 'D000075902', 'term': 'Clinical Deterioration'}], 'ancestors': [{'id': 'D008171', 'term': 'Lung Diseases'}, {'id': 'D012140', 'term': 'Respiratory Tract Diseases'}, {'id': 'D004617', 'term': 'Embolism'}, {'id': 'D016769', 'term': 'Embolism and Thrombosis'}, {'id': 'D014652', 'term': 'Vascular Diseases'}, {'id': 'D002318', 'term': 'Cardiovascular Diseases'}, {'id': 'D018450', 'term': 'Disease Progression'}, {'id': 'D020969', 'term': 'Disease Attributes'}, {'id': 'D010335', 'term': 'Pathologic Processes'}, {'id': 'D013568', 'term': 'Pathological Conditions, Signs and Symptoms'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 2000}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'RECRUITING', 'startDateStruct': {'date': '2011-01-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2023-11', 'completionDateStruct': {'date': '2025-12-31', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2023-11-01', 'studyFirstSubmitDate': '2022-07-29', 'studyFirstSubmitQcDate': '2022-07-29', 'lastUpdatePostDateStruct': {'date': '2023-11-02', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2022-08-01', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2022-07-31', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Incidence of Treatment-Emergent Adverse Events', 'timeFrame': '30 days', 'description': 'The outcomes of interest were defined as the occurrence of adverse outcomes within 30 days after admission. Adverse outcomes were defined as deaths, the need for mechanical ventilation, the need for cardiopulmonary resuscitation, and the need for life-saving vasopressor and reperfusion treatment.'}], 'secondaryOutcomes': [{'measure': 'Incidence of Treatment-Emergent Adverse Events', 'timeFrame': '2 years', 'description': 'The outcomes of interest were defined as the occurrence of adverse outcomes within 2 years after admission. Adverse outcomes were defined as deaths, the need for mechanical ventilation, the need for cardiopulmonary resuscitation, and the need for life-saving vasopressor and reperfusion treatment.'}]}, 'oversightModule': {'oversightHasDmc': True, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Pulmonary Embolism and Thrombosis', 'Deterioration, Clinical', 'Artificial Intelligence'], 'conditions': ['Pulmonary Embolism and Thrombosis', 'Deterioration, Clinical', 'Artificial Intelligence']}, 'descriptionModule': {'briefSummary': 'The investigators aim to build a predictive tool for Adverse Outcome of Acute Pulmonary Embolism by Artificial Intelligence System Based on CT Pulmonary Angiography.', 'detailedDescription': 'This study collected clinical, laboratory, and CT parameters of acute patients with acute pulmonary embolism from admission to predict adverse outcomes within 30 days after admission into hospital. The investigators aim to build a predictive tool for Adverse Outcome of Acute Pulmonary Embolism by Artificial Intelligence System Based on CT Pulmonary Angiography.\n\nEligible patients were randomized in some ratio into derivation and validation cohorts. The derivation cohort was used to develop and evaluate a multivariable logistic regression model for predicting the outcomes of interest. The discriminatory power was evaluated by comparing the nomogram to the established risk stratification systems. The consistency of the nomogram was evaluated using the validation cohort.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'PROBABILITY_SAMPLE', 'studyPopulation': 'Chinese', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* age of ≥ 18 years and a pulmonary embolism diagnosis based on CT pulmonary angiography\n\nExclusion Criteria:\n\n* pregnancy\n* reception of reperfusion treatment before admission\n* missing data regarding CT parameters, echocardiography, cardiac troponin I (c-Tn I), and N-terminal-pro brain natriuretic peptide (NT-pro BNP) levels.'}, 'identificationModule': {'nctId': 'NCT05482269', 'acronym': 'PEAICTPA', 'briefTitle': 'Adverse Outcome of Acute Pulmonary Embolism by Artificial Intelligence System Based on CT Pulmonary Angiography', 'organization': {'class': 'OTHER', 'fullName': 'Shengjing Hospital'}, 'officialTitle': 'Prediction of Adverse Outcome of Acute Pulmonary Embolism by Artificial Intelligence System Based on CT Pulmonary Angiography', 'orgStudyIdInfo': {'id': 'PEAICTPA'}}, 'armsInterventionsModule': {'interventions': [{'name': 'no intervention', 'type': 'OTHER', 'description': 'no intervention'}]}, 'contactsLocationsModule': {'locations': [{'zip': '110004', 'city': 'Shenyang', 'state': 'Liaoning', 'status': 'RECRUITING', 'country': 'China', 'contacts': [{'name': 'YIZHUO GAO', 'role': 'CONTACT', 'email': 'gaoyizhuo.sy@hotmail.com', 'phone': '86+18940257523'}], 'facility': 'Shengjing Hospital', 'geoPoint': {'lat': 41.79222, 'lon': 123.43278}}], 'centralContacts': [{'name': 'DONG JIA', 'role': 'CONTACT', 'email': 'jiadong0101@126.com', 'phone': '+86-18940252800'}, {'name': 'YIZHUO GAO', 'role': 'CONTACT', 'email': 'gaoyizhuo.sy@hotmail.com', 'phone': '+86-18940257523'}], 'overallOfficials': [{'name': 'DONG JIA', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'Shengjing Hospital'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'UNDECIDED'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Shengjing Hospital', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Doctor', 'investigatorFullName': 'YIZHUO GAO', 'investigatorAffiliation': 'Shengjing Hospital'}}}}