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Cardiovascular DiseasesCADHONCT05316025

Cardiovascular Digital Health Data Observatory

An observational study of Cardiovascular Diseases and Digital, run by University Hospital, Grenoble.

Unknown status

Status not confirmed. The sponsor has not verified this record for over two years, so it may be out of date.

Official record
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Trial 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… Read more about this study

Who is it for?
Ages 18 years and older
Conditions the study is for
Cardiovascular Diseases, Digital, Health Data
How many people
5,000 participants
Where
No sites listed yet
Study dates
May 2022to Jan 2025
How long will I take part?
1 Year
Phase
Observational
Are there results?
None posted in the registry

Key requirements

Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.

  • Ages 18 years and older
  • All sexes
  • Adult, Older adult

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

Who may be able to join

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

Who cannot join

  • 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.

Trial locations and contacts

No sites are listed yet.

Gilles Barone-Rochette contact

0476765172

gbarone@chu-grenoble.fr

Clémence Charlon contact

0476766652

ccharlon@chu-grenoble.fr

About this study

Official title: Grenoble Cardiovascular Digital Health Data Observatory

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).

Read the detailed description

This a type 3 study of the Jardé law, involving the human person, It is a study : observational study, prospective, descriptive, monocentric

The main objective of the study is to prospectively validate cardiovascular medical image analysis algorithms capable of identifying patients with poor prognostic criteria using artificial intelligence and big data methods.

The primary endpoint is the rate of occurrence of death or hospitalization for heart failure during follow-up.

The predictive accuracy of the algorithms will be assessed by calculating the sensitivity, specificity, positive predictive value, and negative predictive value on the prospective cohort.

Patients who are to undergo coronary angiography during a hospitalization in the cardiology department are prospectively recruited after obtaining their non opposition. The data were collected using the CARDIO Datamart developed by the PREDIMED health data host. The collection of the primary endpoint (death from any cause and hospitalization for heart failure) will be performed by telephone follow-up.

The number of subjects needed for this study is 5000 patients.

The prospective validation of the algorithm developed retrospectively in the AIDECORO project (coronary image) will make it possible to move towards the last stage of the project, which will consist of evaluating in a randomized study the superiority of precision medicine using this algorithm, allowing for therapeutic escalation or de-escalation according to the predictive risk evaluated by the algorithm in relation to usual management.

Keywords
Deep learning, Observatory, Cardiovascular, Health data
Medical subject headings
Cardiovascular Diseases, Heart Failure

What is being measured

The primary outcome is the main question the study is designed to answer.

  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.

    Measured: Through study completion, an average of 1 year

Secondary outcomes (8)

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

    Seattle Angina Questionnaire summary score to 12 months

    Measured: 12 months

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

    Rose Angina Questionnaire to 12 months

    Measured: 12 months

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

    Seattle Angina Questionnaire to 12 months

    Measured: 12 months

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

    Seattle Angina Questionnaire to 12 months

    Measured: 12 months

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

    EuroQOL (EQ-5D-5L) to 12 months

    Measured: 12 months

  6. Evaluate the predictive performance of algorithms for healthcare consumption

    Average annual cost of care to 12 months

    Measured: 12 months

  7. Assessing the prognostic value of frailty in coronary artery disease

    Dynanometry

    Measured: Day one

  8. Assessing the prognostic value of environmental influence in coronary artery disease

    Measurement of air pollutants from the SIRANE dispersion model

    Measured: Day one

Timeline

From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.

  1. First posted7 Apr 2022
  2. Study startMay 2022 (estimated)
  3. Primary completionJan 2025 (estimated)
  4. Study completionJan 2025 (estimated)

Resources

More details

For doctors and researchers: how the study is designed, who runs it, and its registry IDs.

Study design
Study type
Observational
Observational model
Cohort
Time perspective
Prospective
Enrolment
5,000 participants (estimated)
Target duration
1 Year
IDs, dates and publications
ClinicalTrials.gov ID
NCT05316025
Sponsor's protocol ID
38RC21.197
Acronym
CADHO
Status last verified
Jan 2022
First submitted
3 Feb 2022
Last updated
7 Apr 2022
Results posted
No
FDA-regulated drug
No
FDA-regulated device
No

Publications

  • 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.

    PubMed 32007170

  • 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.

    PubMed 28892454

  • 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.

    PubMed 31322692

  • 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.

    PubMed 30262516

  • 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.

    PubMed 17805417

  • 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.

    PubMed 30815669

  • 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.

    PubMed 23681066

Questions about this trial

What is this clinical trial studying?

An observational study of Cardiovascular Diseases and Digital, run by University Hospital, Grenoble.

The full registered description is in the About this study section and the official record.

Who may be eligible to take part?

The sponsor lists 2 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.

Is this clinical trial currently recruiting?

The record says: status not confirmed, as of the sponsor's last update on 7 Apr 2022. Status can change between updates, so confirm with the study contact before making plans.

What are the possible benefits and risks?

They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.

Are results available for this study?

Results are not available in the source record.

How can a doctor refer a patient or contact the research team?

Contact Gilles Barone-Rochette (0476765172, gbarone@chu-grenoble.fr), or a site near the patient. AzTrials does not enrol participants or take referrals.

What does the primary outcome measure?

Prospectively validate cardiovascular medical image analysis algorithms capable of identifying patients with poor prognostic criteria using artificial intelligence and big data methods. (time frame: Through study completion, an average of 1 year). It is what the study was designed to measure, not a finding.

Where can I read the original study information?

On the ClinicalTrials.gov record for NCT05316025, which also lists 7 linked publications.

Does a listing mean the treatment is approved or proven to work?

No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.

How often is this information updated?

AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 7 Apr 2022; AzTrials last refreshed its copy on 5 Nov 2026.

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