Cardiovascular Digital Health Data Observatory

NCT05316025 | Unknown

• 1 other identifier: 38RC21.197
• Study Type: observational
• Target: 5,000
• Locations: 0 countries
• Sites: N/A

Brief Summary

The COVID-19 health crisis has led to a drastic decrease in the rate of myocardial infarction without the causes being completely identified. They are probably multiple, but this crisis has confirmed the need for massive health data from different horizons to better assess coronary disease in order to develop precision medicine. This objective is now achievable thanks to the use of tools such as big data and artificial intelligence (AI). Our team is developing algorithms to analyze medical images and identify people at risk of major cardiovascular events. These algorithms which are developed with retrospective data must be validated on prospective data, which is the objective of the Grenoble cardiovascular digital health data observatory. The algorithm that will be validated is currently being created as part of a RIPH 3 study "AIDECORO" (NCT: 04598997). It is being developed from clinical, biological and imaging data from 600 patients with ST+ infarction and 1000 "control" patients who have undergone coronary angiography (these data are exported and stored in the PREDIMED health data warehouse via the hospital information system).

Conditions

Cardiovascular Diseases; Digital; Health Data; Observatory; Coronarography; Heart Failure; Cardiovascular

Keywords

Deep learning; Observatory; Cardiovascular; Health data

Outcome Measures

Primary Outcomes (1)

• Prospectively validate cardiovascular medical image analysis algorithms capable of identifying patients with poor prognostic criteria using artificial intelligence and big data methods.

  The rate of occurrence of death or hospitalization for heart failure during follow-up.

  Through study completion, an average of 1 year

Secondary Outcomes (8)

• Evaluate the predictive performance of algorithms to identify patients with persistent anginal symptoms.

  12 months

• Evaluate the predictive performance of algorithms to identify patients with persistent dyspnea symptoms.

  12 months

• Evaluate the predictive performance of algorithms to identify patients with good disease perception.

  12 months

• Evaluate the predictive performance of algorithms to identify patients satisfied with their care.

  12 months

• Evaluate the predictive performance of the algorithms for quality of life at one year.

  12 months

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

All patients receiving coronary angiography during hospitalization for suspected or managed coronary artery disease at CHUGA.

You may qualify if:

• Adult patients who have undergone coronary angiography at CHUGA for whom images are usable.
• No opposition to participation

You may not qualify if:

• Coronary image not usable
• Persons referred to in articles L1121-5 to L-1121-8 of the CSP
• Patients living outside the Rhône Alpes region.

Sponsors & Collaborators

• University Hospital, Grenoble: lead

Related Publications (7)

• Skrede OJ, De Raedt S, Kleppe A, Hveem TS, Liestol K, Maddison J, Askautrud HA, Pradhan M, Nesheim JA, Albregtsen F, Farstad IN, Domingo E, Church DN, Nesbakken A, Shepherd NA, Tomlinson I, Kerr R, Novelli M, Kerr DJ, Danielsen HE. Deep learning for prediction of colorectal cancer outcome: a discovery and validation study. Lancet. 2020 Feb 1;395(10221):350-360. doi: 10.1016/S0140-6736(19)32998-8.

  PMID: 32007170 BACKGROUND

• Gonzalez G, Ash SY, Vegas-Sanchez-Ferrero G, Onieva Onieva J, Rahaghi FN, Ross JC, Diaz A, San Jose Estepar R, Washko GR; COPDGene and ECLIPSE Investigators. Disease Staging and Prognosis in Smokers Using Deep Learning in Chest Computed Tomography. Am J Respir Crit Care Med. 2018 Jan 15;197(2):193-203. doi: 10.1164/rccm.201705-0860OC.

  PMID: 28892454 BACKGROUND

• Lu MT, Ivanov A, Mayrhofer T, Hosny A, Aerts HJWL, Hoffmann U. Deep Learning to Assess Long-term Mortality From Chest Radiographs. JAMA Netw Open. 2019 Jul 3;2(7):e197416. doi: 10.1001/jamanetworkopen.2019.7416.

  PMID: 31322692 BACKGROUND

• Betancur J, Hu LH, Commandeur F, Sharir T, Einstein AJ, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Di Carli M, Germano G, Otaki Y, Liang JX, Tamarappoo BK, Dey D, Berman DS, Slomka PJ. Deep Learning Analysis of Upright-Supine High-Efficiency SPECT Myocardial Perfusion Imaging for Prediction of Obstructive Coronary Artery Disease: A Multicenter Study. J Nucl Med. 2019 May;60(5):664-670. doi: 10.2967/jnumed.118.213538. Epub 2018 Sep 27.

  PMID: 30262516 BACKGROUND

• Slama R, Morgenstern V, Cyrys J, Zutavern A, Herbarth O, Wichmann HE, Heinrich J; LISA Study Group. Traffic-related atmospheric pollutants levels during pregnancy and offspring's term birth weight: a study relying on a land-use regression exposure model. Environ Health Perspect. 2007 Sep;115(9):1283-92. doi: 10.1289/ehp.10047.

  PMID: 17805417 BACKGROUND

• Krittanawong C, Johnson KW, Rosenson RS, Wang Z, Aydar M, Baber U, Min JK, Tang WHW, Halperin JL, Narayan SM. Deep learning for cardiovascular medicine: a practical primer. Eur Heart J. 2019 Jul 1;40(25):2058-2073. doi: 10.1093/eurheartj/ehz056.

  PMID: 30815669 BACKGROUND

• Fearon WF, Low AF, Yong AS, McGeoch R, Berry C, Shah MG, Ho MY, Kim HS, Loh JP, Oldroyd KG. Prognostic value of the Index of Microcirculatory Resistance measured after primary percutaneous coronary intervention. Circulation. 2013 Jun 18;127(24):2436-41. doi: 10.1161/CIRCULATIONAHA.112.000298. Epub 2013 May 16.

  PMID: 23681066 BACKGROUND

MeSH Terms

Conditions

Cardiovascular Diseases; Heart Failure

Condition Hierarchy (Ancestors)

Heart Diseases

Study Officials

• Gilles Barone-Rochette

  CHU Grenoble Alpes

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Gilles Barone-Rochette

CONTACT

0476765172; gbarone@chu-grenoble.fr

Clémence Charlon

CONTACT

0476766652; ccharlon@chu-grenoble.fr

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Target Duration: 1 Year
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: February 3, 2022
• First Posted: April 7, 2022
• Study Start: May 1, 2022
• Primary Completion: January 1, 2025
• Study Completion: January 1, 2025
• Last Updated: April 7, 2022

Record last verified: 2022-01