Ignite Creation Date:
2025-12-25 @ 4:26 AM
Ignite Modification Date:
2025-12-26 @ 3:29 AM
Study NCT ID:
NCT04481620
Status:
COMPLETED
Last Update Posted:
2022-04-12
First Post:
2020-07-20
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
Development and Validation of a Prediction Model for the Transition From Mild to Moderate Form of COVID-19, Using Data From Chest CT
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D000086382', 'term': 'COVID-19'}], 'ancestors': [{'id': 'D011024', 'term': 'Pneumonia, Viral'}, {'id': 'D011014', 'term': 'Pneumonia'}, {'id': 'D012141', 'term': 'Respiratory Tract Infections'}, {'id': 'D007239', 'term': 'Infections'}, {'id': 'D014777', 'term': 'Virus Diseases'}, {'id': 'D018352', 'term': 'Coronavirus Infections'}, {'id': 'D003333', 'term': 'Coronaviridae Infections'}, {'id': 'D030341', 'term': 'Nidovirales Infections'}, {'id': 'D012327', 'term': 'RNA Virus Infections'}, {'id': 'D008171', 'term': 'Lung Diseases'}, {'id': 'D012140', 'term': 'Respiratory Tract Diseases'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'RETROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 1329}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'COMPLETED', 'startDateStruct': {'date': '2020-08-31', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2022-04', 'completionDateStruct': {'date': '2021-05-04', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2022-04-11', 'studyFirstSubmitDate': '2020-07-20', 'studyFirstSubmitQcDate': '2020-07-21', 'lastUpdatePostDateStruct': {'date': '2022-04-12', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2020-07-22', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2021-05-04', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'occurrence of significant clinical degradation', 'timeFrame': 'Day 30 following the initial chest CT', 'description': 'The primary outcome is defined by the occurrence of significant clinical degradation within 30 days following the initial chest CT.\n\nSignificant clinical degradation is defined by the transition from the mild to the moderate form of COVID-19, i.e., according to the WHO criteria, the requirement of oxygen between 3 and 5 L / min to achieve saturation greater than 97% and a respiratory rate \\<25 / min without the need for invasive ventilation.'}], 'secondaryOutcomes': [{'measure': 'occurrence of a severe form', 'timeFrame': 'Day 30 following the initial chest CT', 'description': 'the occurrence of a severe form, defined by the need for oxygen therapy greater than 5L / min to obtain a percutaneous oxygen saturation greater than 97%, within 30 days following the initial chest CT'}, {'measure': 'occurrence of an orotracheal intubation', 'timeFrame': 'Day 30 following the initial chest CT', 'description': 'the occurrence of an orotracheal intubation within 30 days following the initial chest CT (binary: yes/no)'}, {'measure': 'occurrence of an Acute Respiratory Distress Syndrom', 'timeFrame': 'Day 30 following the initial chest CT', 'description': 'the occurrence of an Acute Respiratory Distress Syndrom according to the Berlin criteria (JAMA 2012) within 30 days following the initial chest CT (binary: yes/no)'}, {'measure': 'average length of stay in hospital', 'timeFrame': 'Month 1', 'description': 'the average length of stay in hospital (days)'}, {'measure': 'mortality', 'timeFrame': 'Day 30 following the initial chest CT', 'description': 'mortality within 30 days following the initial chest CT (binary: yes/no)'}, {'measure': 'evolution of the imaging parameters', 'timeFrame': 'Day 30 following the initial chest CT', 'description': 'evolution of the imaging parameters of the successive thoracic CT scans in the acute phase of COVID-19, in patients with a positive diagnosis of COVID-19 (positive RT-PCR or positive serology)'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['COVID-19', 'SARS-CoV-2', 'CT', 'prediction score', 'prognosis', 'mild-to moderate form'], 'conditions': ['COVID-19']}, 'referencesModule': {'references': [{'pmid': '37405504', 'type': 'DERIVED', 'citation': 'Zysman M, Asselineau J, Saut O, Frison E, Oranger M, Maurac A, Charriot J, Achkir R, Regueme S, Klein E, Bommart S, Bourdin A, Dournes G, Casteigt J, Blum A, Ferretti G, Degano B, Thiebaut R, Chabot F, Berger P, Laurent F, Benlala I. Development and external validation of a prediction model for the transition from mild to moderate or severe form of COVID-19. Eur Radiol. 2023 Dec;33(12):9262-9274. doi: 10.1007/s00330-023-09759-x. Epub 2023 Jul 5.'}]}, 'descriptionModule': {'briefSummary': 'Only 5% of patients infected with COVID-19 develop severe or critical Coronavirus disease 2019 (COVID-19) and there is no reliable risk stratification tool for non-severe COVID-19 patients at admission.\n\nFinding a way to predict which patients with an initial mild to moderate presentation of COVID-19 would develop severe or critical form of COVID-19 according to CT-scan data, simple clinical and biological parameters is challenging. In this multicentric study, the study aims to construct a predictive score for early identification of cases at high risk of progression to moderate, severe or critical COVID-19 combining simple clinical and biological parameters and qualitative, quantitative or artificial intelligence (AI) data from the initial CT from non-severe patients.', 'detailedDescription': 'A few numbers of patients infected with Coronavirus disease 2019 (COVID-19) rapidly develop acute respiratory distress leading to respiratory failure, with high short-term mortality rates. However, only 5% of patients infected with COVID-19 are concerned by this pejorative evolution. At present, there is no reliable risk stratification tool for non-severe COVID-19 patients at admission.\n\nChest computed tomography (CT) is widely used for the management of COVID-19 pneumonia because of its availability and quickness. The standard of reference for confirming COVID-19 relies on microbiological tests but these tests might not be available in an emergency setting and their results are not immediately available, contrary to CT. In addition to its role for early diagnosis, CT has a prognostic role through evaluating the extent of COVID-19 lung abnormalities.\n\nFinding a way to predict which patients with an initial mild to moderate presentation of COVID-19 would develop severe or critical form of COVID-19 according to CT-scan data, simple clinical and biological parameters is challenging. In this multicentric study, the study aims to construct a predictive score for early identification of cases at high risk of progression to moderate, severe or critical COVID-19 combining simple clinical and biological parameters and qualitative, quantitative or artificial intelligence (AI) data from the initial CT from non-severe patients. The final objective is to organize optimal patient management in the appropriate health structure.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'PROBABILITY_SAMPLE', 'studyPopulation': 'Patients with suspicion of Covid-19', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* First chest CT, assessed for respiratory symptoms, without injection of contrast agent for respiratory symptoms, and whose results of the CT subjective visual analysis are compatible or typical of COVID-19\n* biological diagnosis of COVID-19 (RT-PCR) or clinical suspicion (cough and / or dyspnea and / or fever and / or need to use oxygen therapy as part of routine care) at the time of the examination\n* Authorization of the patient for the processing of his personal data, except CNIL exemption\n\nExclusion Criteria:\n\n* Patient with a moderate (oxygen between 3 and 5 L / min to achieve saturation greater than 97% and a respiratory rate \\<25 / min without the need for invasive ventilation), severe form (oxygen therapy\\> 5L / min to obtain a SpO2\\> 97%) or critical form (need to resort to ventilation and / or orotracheal intubation) at the date of the first chest CT\n* Age \\< 18 years old\n* Patient deprived of liberty by judicial decision'}, 'identificationModule': {'nctId': 'NCT04481620', 'acronym': 'PREDICTCovid19', 'briefTitle': 'Development and Validation of a Prediction Model for the Transition From Mild to Moderate Form of COVID-19, Using Data From Chest CT', 'organization': {'class': 'OTHER', 'fullName': 'University Hospital, Bordeaux'}, 'officialTitle': 'Evelopment and Validation of a Prediction Model for the Transition From Mild to Moderate Form of COVID-19, Using Data From Chest CT', 'orgStudyIdInfo': {'id': 'CHUBX 2020/23'}}, 'contactsLocationsModule': {'locations': [{'city': 'Bordeaux', 'country': 'France', 'facility': 'CHU Bordeaux', 'geoPoint': {'lat': 44.84124, 'lon': -0.58046}}, {'city': 'Bordeaux', 'country': 'France', 'facility': 'Clinique Bordeaux Nord', 'geoPoint': {'lat': 44.84124, 'lon': -0.58046}}, {'city': 'Bordeaux', 'country': 'France', 'facility': 'Clinique Saint Augustin', 'geoPoint': {'lat': 44.84124, 'lon': -0.58046}}, {'city': 'Grenoble', 'country': 'France', 'facility': 'CHU de Grenoble Alpes', 'geoPoint': {'lat': 45.17869, 'lon': 5.71479}}, {'city': 'Montpellier', 'country': 'France', 'facility': 'Hôpital Arnaud-de-Villeneuve CHU de Montpellier', 'geoPoint': {'lat': 43.61093, 'lon': 3.87635}}, {'city': 'Nancy', 'country': 'France', 'facility': 'Hôpitaux de Brabois CHU de Nancy', 'geoPoint': {'lat': 48.68439, 'lon': 6.18496}}, {'city': 'Poitiers', 'country': 'France', 'facility': 'Hôpital de la Milétrie CHU de Poitiers', 'geoPoint': {'lat': 46.58261, 'lon': 0.34348}}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'University Hospital, Bordeaux', 'class': 'OTHER'}, 'collaborators': [{'name': 'Programme Hospitalier de Recherche Clinique Inter-Régionale (PHRC-I)', 'class': 'UNKNOWN'}], 'responsibleParty': {'type': 'SPONSOR'}}}}