Ignite Creation Date:
2025-12-24 @ 11:08 PM
Ignite Modification Date:
2025-12-25 @ 8:41 PM
Study NCT ID:
NCT05176769
Status:
RECRUITING
Last Update Posted:
2025-01-29
First Post:
2021-11-24
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
Artificial Intelligence for Automated Clinical Data Exploration From Electronic Medical Records (CardioMining-AI)
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'RETROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 60000}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'RECRUITING', 'startDateStruct': {'date': '2022-01-14', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-01', 'completionDateStruct': {'date': '2026-03-01', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-01-27', 'studyFirstSubmitDate': '2021-11-24', 'studyFirstSubmitQcDate': '2021-12-15', 'lastUpdatePostDateStruct': {'date': '2025-01-29', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2022-01-04', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2025-12-01', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': "Accuracy of artificial intelligence to automatically extract clinical data from patients' medical records compared with traditional manual data extraction methods", 'timeFrame': '1 year', 'description': 'Rate of accurate extraction of clinical data (medical history, discharge diagnoses, medication, etc.) from unstructured clinical notes using automated artificial intelligence methods compared with traditional methods of manual data extraction'}], 'secondaryOutcomes': [{'measure': 'Time to all-cause mortality', 'timeFrame': 'up to 8 years (from hospital discharge until study primary completion date)', 'description': 'Length of time (months) until death from any cause during the follow-up period'}, {'measure': 'Time to incident major cardiovascular diseases', 'timeFrame': 'up to 8 years (from hospital discharge until study primary completion date)', 'description': 'Length of time (months) until development of heart failure, diabetes mellitus or coronary artery disease during the follow-up period'}, {'measure': 'Time to rehospitalization for cardiovascular reasons', 'timeFrame': 'up to 8 years (from hospital discharge until study primary completion date)', 'description': 'Length of time (months) until rehospitalization for cardiovascular reasons during the follow-up period'}, {'measure': 'Time to stroke or systemic embolism', 'timeFrame': 'up to 8 years (from hospital discharge until study primary completion date)', 'description': 'Length of time (months) until stroke or systemic embolism during the follow-up period'}, {'measure': 'Time to acute coronary syndrome', 'timeFrame': 'up to 8 years (from hospital discharge until study primary completion date)', 'description': 'Length of time (months) until acute coronary syndrome during the follow-up period'}]}, 'oversightModule': {'isUsExport': False, 'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['artificial intelligence', 'machine learning', 'medical records', 'digital health'], 'conditions': ['Artificial Intelligence', 'Machine Learning', 'Electronic Medical Records']}, 'referencesModule': {'references': [{'pmid': '29880128', 'type': 'BACKGROUND', 'citation': 'Johnson KW, Torres Soto J, Glicksberg BS, Shameer K, Miotto R, Ali M, Ashley E, Dudley JT. Artificial Intelligence in Cardiology. J Am Coll Cardiol. 2018 Jun 12;71(23):2668-2679. doi: 10.1016/j.jacc.2018.03.521.'}, {'pmid': '28545640', 'type': 'BACKGROUND', 'citation': 'Krittanawong C, Zhang H, Wang Z, Aydar M, Kitai T. Artificial Intelligence in Precision Cardiovascular Medicine. J Am Coll Cardiol. 2017 May 30;69(21):2657-2664. doi: 10.1016/j.jacc.2017.03.571.'}, {'pmid': '30828647', 'type': 'BACKGROUND', 'citation': 'Madani A, Arnaout R, Mofrad M, Arnaout R. Fast and accurate view classification of echocardiograms using deep learning. NPJ Digit Med. 2018;1:6. doi: 10.1038/s41746-017-0013-1. Epub 2018 Mar 21.'}, {'pmid': '29888035', 'type': 'BACKGROUND', 'citation': "Boag W, Doss D, Naumann T, Szolovits P. What's in a Note? Unpacking Predictive Value in Clinical Note Representations. AMIA Jt Summits Transl Sci Proc. 2018 May 18;2017:26-34. eCollection 2018."}, {'pmid': '32592755', 'type': 'BACKGROUND', 'citation': 'Hashir M, Sawhney R. Towards unstructured mortality prediction with free-text clinical notes. J Biomed Inform. 2020 Aug;108:103489. doi: 10.1016/j.jbi.2020.103489. Epub 2020 Jun 25.'}, {'pmid': '30689812', 'type': 'BACKGROUND', 'citation': 'Diller GP, Kempny A, Babu-Narayan SV, Henrichs M, Brida M, Uebing A, Lammers AE, Baumgartner H, Li W, Wort SJ, Dimopoulos K, Gatzoulis MA. Machine learning algorithms estimating prognosis and guiding therapy in adult congenital heart disease: data from a single tertiary centre including 10 019 patients. Eur Heart J. 2019 Apr 1;40(13):1069-1077. doi: 10.1093/eurheartj/ehy915.'}, {'pmid': '37012018', 'type': 'DERIVED', 'citation': 'Samaras A, Bekiaridou A, Papazoglou AS, Moysidis DV, Tsoumakas G, Bamidis P, Tsigkas G, Lazaros G, Kassimis G, Fragakis N, Vassilikos V, Zarifis I, Tziakas DN, Tsioufis K, Davlouros P, Giannakoulas G; CardioMining Study Group. Artificial intelligence-based mining of electronic health record data to accelerate the digital transformation of the national cardiovascular ecosystem: design protocol of the CardioMining study. BMJ Open. 2023 Apr 3;13(4):e068698. doi: 10.1136/bmjopen-2022-068698.'}]}, 'descriptionModule': {'briefSummary': 'The purpose of this study is to highlight the usefulness of artificial intelligence and machine learning to develop computer algorithms that will achieve with great reliability, speed and accuracy the automatic extraction and processing of large volumes of raw and unstructured clinical data from electronic medical files.', 'detailedDescription': 'Despite the rapid development of medicine and computer science in recent years, the medical treatment in modern clinical practice is often empirical and based on retrospective data. With the growing number of patients and their concentration in large tertiary centers, it becomes attractive to systematically collect clinical data and apply them to risk stratification models. However, with the increasing volume of data, manual data collection and processing becomes a challenge, as this approach is time consuming and costly for the healthcare systems. In addition, unstructured information, such as clinical notes, are very often written as free text that is unsuitable for direct analysis. The use of artificial intelligence is very promising and is going to rapidly change the future of medicine in the upcoming years. Due to the automated processes it offers, it is possible to quickly and reliably extract data for further processing. The results from its use can be easily extended to different healthcare systems, amplifying the knowledge produced and improving diagnostic and therapeutic accuracy, and ultimately positively affecting health services. Collecting the vast amount of data from different sources without compromising patients\' personal data is a major challenge in modern science.\n\nElectronically-registered clinical notes of patients who were hospitalized in the Cardiology ward of tertiary hospitals will be retrospectively collected, as well as additional files such as the laboratory and imaging examinations related to each hospitalization. Given the size of the participating clinics and the years during which the recording of electronic hospital records in electronic form was applied, it is estimated that the sample of patient records will be about 60.000. All information that could potentially be used to identify a person, such as name, ID number, postal code, place of residence, occupation, will be deleted from these electronic files. Only the age will be recorded, not the exact date of birth of each patient. Only the days of hospitalization will be recorded and not the exact dates of admission and discharge from the hospital. Thus, the data will not be able to be assigned to a specific subject, as no additional information or identifiers will be collected for the subjects. After the files are anonymized, each patient\'s clinical note will be linked with a specific key ("identifier"). The electronic file that contains the correlation of the "identifier" with the patient\'s clinical note will be stored in a secure hospital electronic location. The fully anonymized files will initially be manually analyzed to extract information into a database containing all of patients\' clinical information, such as discharge diagnoses, medications, treatment protocols, laboratory and diagnostic tests. At the same time, a sample (1/3) of the clinical notes will be analyzed to identify the keywords or phrases associated with each diagnosis (for example, the atrial fibrillation diagnosis will probably be recorded as "atrial fibrillation", " AF ", etc.). By using this generated dictionary of keywords and by integrating artificial intelligence methods and text mining, such as natural language processing (NLP), an automated extraction of data and diagnoses from these electronic medical notes will be attempted. The reliability and accuracy of the computational methods will be evaluated internally, comparing the data extracted automatically with those recorded manually. In addition, the reliability and accuracy of these computational methods will be evaluated externally, applying these methods to 2/3 of the clinical notes in which no association between keywords and specific diagnoses was attempted.\n\nRegarding Greece, the present study aims to be the first to analyze the usefulness of artificial intelligence for automated extraction and processing of unstructured clinical data from patients\' medical clinical notes. The results of this study will have a positive impact on:\n\n1. the automation of large-scale data analysis and processing procedures\n2. the rapid epidemiological recording and utilization of clinical data\n3. the early diagnosis of diseases\n4. the development of phenotypic patient profiles that could benefit from targeted therapies\n5. the development of clinical decision support systems that will provide information about the possible clinical course of patients after hospital discharge and assist medical decisions\n6. the development and validation of prognostic models for major cardiovascular diseases'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'All patients who were hospitalized in the Cardiology ward of tertiary hospitals and have available electronically-stored clinical notes/hospitalization documents will be included in the study .', 'healthyVolunteers': False, 'eligibilityCriteria': "Inclusion Criteria:\n\n* Hospitalised patients in Cardiology Departments in Greece\n* Patients whose medical records are electronically stored in each hospital's computer/information systems\n\nExclusion Criteria:\n\n* Patients that died during hospitalization, and thus no discharge letter was issued"}, 'identificationModule': {'nctId': 'NCT05176769', 'briefTitle': 'Artificial Intelligence for Automated Clinical Data Exploration From Electronic Medical Records (CardioMining-AI)', 'organization': {'class': 'OTHER', 'fullName': 'AHEPA University Hospital'}, 'officialTitle': 'The Usefulness of Artificial Intelligence for Automated Extraction and Processing of Clinical Data From Electronic Medical Records (CardioMining-AI)', 'orgStudyIdInfo': {'id': '545/19.11.2021'}}, 'contactsLocationsModule': {'locations': [{'city': 'Alexandroupoli', 'status': 'NOT_YET_RECRUITING', 'country': 'Greece', 'contacts': [{'name': 'George Chalikias, MD, PhD', 'role': 'CONTACT', 'email': 'gchaliki@med.duth.gr'}, {'name': 'Dimitrios Tziakas, MD, PhD', 'role': 'PRINCIPAL_INVESTIGATOR'}], 'facility': 'University Cardiology Clinic, Democritus University of Thrace', 'geoPoint': {'lat': 40.84995, 'lon': 25.87644}}, {'city': 'Athens', 'status': 'RECRUITING', 'country': 'Greece', 'contacts': [{'name': 'George Lazaros, MD, PhD', 'role': 'CONTACT', 'email': 'glaz35@hotmail.com'}, {'name': 'Konstantinos Tsioufis, MD, PhD', 'role': 'PRINCIPAL_INVESTIGATOR'}], 'facility': '1st Department of Cardiology, Hippokration General Hospital', 'geoPoint': {'lat': 37.98376, 'lon': 23.72784}}, {'city': 'Heraklion', 'status': 'NOT_YET_RECRUITING', 'country': 'Greece', 'contacts': [{'name': 'Alexandros Patrianakos, MD, PhD', 'role': 'CONTACT', 'email': 'apatrianakos@yahoo.gr'}, {'name': 'Alexandros Patrianakos, MD, PhD', 'role': 'PRINCIPAL_INVESTIGATOR'}], 'facility': 'Department of Cardiology, Heraklion University Hospital', 'geoPoint': {'lat': 35.32787, 'lon': 25.14341}}, {'city': 'Larissa', 'status': 'RECRUITING', 'country': 'Greece', 'contacts': [{'name': 'Gregory Giamouzis, MD, PhD', 'role': 'CONTACT', 'email': 'grgiamouzis@gmail.com'}, {'name': 'Gregory Giamouzis, MD, PhD', 'role': 'PRINCIPAL_INVESTIGATOR'}], 'facility': 'University General Hospital of Larissa, University of Thessaly', 'geoPoint': {'lat': 39.62847, 'lon': 22.42112}}, {'city': 'Pátrai', 'status': 'RECRUITING', 'country': 'Greece', 'contacts': [{'name': 'Periklis Davlouros, MD, PhD', 'role': 'CONTACT', 'email': 'pdav@upatras.gr'}, {'name': 'Periklis Davlouros, MD, PhD', 'role': 'PRINCIPAL_INVESTIGATOR'}], 'facility': 'Department of Cardiology, University of Patras Medical School', 'geoPoint': {'lat': 38.2462, 'lon': 21.73508}}, {'zip': '54636', 'city': 'Thessaloniki', 'status': 'RECRUITING', 'country': 'Greece', 'contacts': [{'name': 'Athanasios Samaras, MD, PhD', 'role': 'CONTACT', 'email': 'ath.samaras.as@gmail.com'}, {'name': 'George Giannakoulas, MD, PhD', 'role': 'PRINCIPAL_INVESTIGATOR'}], 'facility': '1st Cardiology Department, AHEPA University Hospital', 'geoPoint': {'lat': 40.64072, 'lon': 22.93493}}, {'city': 'Thessaloniki', 'status': 'NOT_YET_RECRUITING', 'country': 'Greece', 'contacts': [{'name': 'Vassilios Vassilikos, MD, PhD', 'role': 'CONTACT', 'email': 'vvassil@auth.gr'}, {'name': 'Vassilios Vassilikos, MD, PhD', 'role': 'PRINCIPAL_INVESTIGATOR'}], 'facility': '3rd Cardiology Department, Hippokration Hospital', 'geoPoint': {'lat': 40.64072, 'lon': 22.93493}}, {'city': 'Thessaloniki', 'status': 'RECRUITING', 'country': 'Greece', 'contacts': [{'name': 'John Zarifis, MD, PhD', 'role': 'CONTACT', 'email': 'zarifis.john@gmail.com'}, {'name': 'John Zarifis, MD, PhD', 'role': 'PRINCIPAL_INVESTIGATOR'}], 'facility': 'Cardiology Department, George Papanikolaou General Hospital', 'geoPoint': {'lat': 40.64072, 'lon': 22.93493}}, {'city': 'Thessaloniki', 'status': 'RECRUITING', 'country': 'Greece', 'contacts': [{'name': 'Panagiotis Bamidis, Prof', 'role': 'CONTACT', 'email': 'pdbamidis@gmail.com'}, {'name': 'Panagiotis Bamidis, Prof', 'role': 'PRINCIPAL_INVESTIGATOR'}], 'facility': 'Laboratory of Medical Physics, Aristotle University of Thessaloniki', 'geoPoint': {'lat': 40.64072, 'lon': 22.93493}}], 'centralContacts': [{'name': 'George Giannakoulas, MD, PhD', 'role': 'CONTACT', 'email': 'ggiannakoulas@auth.gr', 'phone': '2310994830', 'phoneExt': '+30'}, {'name': 'Athanasios Samaras, MD', 'role': 'CONTACT', 'email': 'ath.samaras.as@gmail.com', 'phone': '2310994830', 'phoneExt': '+30'}]}, 'ipdSharingStatementModule': {'infoTypes': ['STUDY_PROTOCOL', 'SAP', 'ANALYTIC_CODE'], 'ipdSharing': 'YES', 'description': 'Study protocol, statistical analysis plan and results will become available through publications. The analytic code will become available in open source communities/repositories'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'AHEPA University Hospital', 'class': 'OTHER'}, 'collaborators': [{'name': 'Hippokration Hospital Athens', 'class': 'UNKNOWN'}, {'name': 'General Hospital of Larissa', 'class': 'OTHER'}, {'name': 'University Hospital, Alexandroupolis', 'class': 'OTHER'}, {'name': 'University General Hospital of Patras', 'class': 'OTHER'}, {'name': 'University General Hospital of Heraklion', 'class': 'OTHER'}, {'name': 'George Papanicolaou Hospital', 'class': 'OTHER'}, {'name': 'Ippokrateio General Hospital of Thessaloniki', 'class': 'OTHER'}], 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Associate Professor in Cardiology', 'investigatorFullName': 'George Giannakoulas', 'investigatorAffiliation': 'AHEPA University Hospital'}}}}