Cardio Respiratory ArrestAiCRNCT04462380
AiCR : Artificial Intelligence in Cardiac aRrest
An observational study of Cardio Respiratory Arrest, run by Centre Hospitalier Universitaire de Nice at 1 site in France.
Trial summary
The overall incidence of cardiorespiratory arrest in Europe is estimated at 350,000 to 700,000 cases per year. Survival rate is estimated at 10.7% for all rhythm disorders combined. Several examples of AI application in the medical field exist. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Cardio Respiratory Arrest
- How many people
- 500 participants
- Where
- 1 siteFrance
- Study dates
- 1 Feb 2020to 25 Jun 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
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
- No healthy volunteers
- Adult, Older adult
Study population: ACR for all causes, admitted in intensive care or intensive care, from the hospital centres of Nice, Antibes, Cannes, Grasse, etc.
Who may be able to join
- OCA recovered from: hypoxic, ischemic, pulmonary embolism, tamponade, rhythm or conduction disorder, shockable or not, intra or extra-hospital.
- CR computerized, typed in PDF format
Who cannot join
- -
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
romain LOMBARDI contact
France1 site, 1 recruiting
- Recruiting
Nice Hospital
Nice, 06200
Jean DELLAMONICA, dellamonica.j@chu-nice.fr
romain LOMBARDI, lombardi.r@chu-nice.fr
About this study
Official title: AiCR : Artificial Intelligence in Cardiac aRrest Application of an Algorithm in the Prognosis of Recovered Cardiorespiratory Arrests
The overall incidence of cardiorespiratory arrest in Europe is estimated at 350,000 to 700,000 cases per year. Survival rate is estimated at 10.7% for all rhythm disorders combined.
Several examples of AI application in the medical field exist. Ting et al have developed a computer tool capable of diagnosing the presence of diabetic retinopathy with excellent power. In resuscitation, Celi et al proposed a tool capable of predicting the need for crystalloid vascular filling during a systemic inflammatory state. In Nature in 2018, Komorowski demonstrated the efficacy of AI in the hemodynamic management of sepsis. In a study of the renal response to fluid challenge, Zhang et al. demonstrate the effectiveness of the learning machine.
Read more
Objectives: Determination of an algorithm capable of predicting the mortality of patients admitted to intensive care units (ICU) for ACR from hospitalization reports (CRH). Also use of the algorithm to predict the risk of recurrence of the arrest, the duration of mechanical ventilation, the appearance of sepsis, the development of organ failure, prediction of the CPC (Cerebral Performance Category), time to obtain catecholamine withdrawal, the appearance of acute renal failure with or without the need for extra-renal purification (EER) and duration under EER, the average length of stay.
This project is part of a larger, nationwide project with greater power, and includes all the data generated during hospitalization in intensive care.
Method: an estimated total number of patients included in this study to be between 300 and 500. The population will come from the intensive care units of Nice, Antibes, Cannes, Grasse.
Inclusion will be retrospective, on CRH, CR of CT imaging (cerebral and thoraco-abdomino-pelvic), MRI, EEG, and daily follow-up words, from 2014 to the end of 2020.
After anonymisation, application of semantisation using natural language processing (NLP) methods. The data to be extracted are entered in a document written by intensive care physicians. These data will then be stored in a database. In order to meet the main objective, we will develop a computer algorithm capable of predicting mortality in the study population. This algorithm, based on a large database, can be designed using machine learning or even deep learning techniques depending on the amount of data to be processed.
- Conditions
- Cardio Respiratory Arrest
- Medical subject headings
- Heart Arrest
What is being measured
The primary outcome is the main question the study is designed to answer.
Prediction of mortality in the intensive care unit
Definition of a semantic reporting tool, automated, transition from an anonymized report to an operational and relevant database.
Measured: 1day
Prediction of mortality in the intensive care unit
Use of the database thus created to create an intelligent mortality prediction algorithm. Use also on secondary judgment criteria in order to predict other parameters mentioned below.
Measured: 1day
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted8 Jul 2020
- Study start1 Feb 2020
- Primary completion30 Sep 2022
- Study completion25 Jun 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Cardio Respiratory ArrestStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
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
- Case only
- Time perspective
- Retrospective
- Enrolment
- 500 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Centre Hospitalier Universitaire de Nice
- Sponsor type
- University, hospital or other
- Collaborators
- Institut National de Recherche en Informatique et en Automatique, AIINTENSE
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04462380
- Sponsor's protocol ID
- 20reamed01
- Acronym
- AiCR
- Status last verified
- Jun 2026
- First submitted
- 3 Jul 2020
- Last updated
- 29 Jun 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 29 Jun 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- AiCR : Artificial Intelligence in Cardiac aRrest Application of an Algorithm in the Prognosis of Recovered Cardiorespiratory Arrests
- ClinicalTrials.gov ID
- NCT04462380
- Acronym / sponsor ID
- AiCR / 20reamed01
- Conditions
- Cardio Respiratory Arrest
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 500 participants (estimated)
- Lead sponsor
- Centre Hospitalier Universitaire de NiceUniversity, hospital or other
- Collaborators
- Institut National de Recherche en Informatique et en Automatique, AIINTENSE
- Study start
- 1 Feb 2020 (actual)
- Primary completion
- 30 Sep 2022 (actual)
- Study completion
- 25 Jun 2027 (estimated)
- First posted
- 8 Jul 2020 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
The overall incidence of cardiorespiratory arrest in Europe is estimated at 350,000 to 700,000 cases per year. Survival rate is estimated at 10.7% for all rhythm disorders combined.
Several examples of AI application in the medical field exist. Ting et al have developed a computer tool capable of diagnosing the presence of diabetic retinopathy with excellent power. In resuscitation, Celi et al proposed a tool capable of predicting the need for crystalloid vascular filling during a systemic inflammatory state. In Nature in 2018, Komorowski demonstrated the efficacy of AI in the hemodynamic management of sepsis. In a study of the renal response to fluid challenge, Zhang et al. demonstrate the effectiveness of the learning machine.
Objectives: Determination of an algorithm capable of predicting the mortality of patients admitted to intensive care units (ICU) for ACR from hospitalization reports (CRH). Also use of the algorithm to predict the risk of recurrence of the arrest, the duration of mechanical ventilation, the appearance of sepsis, the development of organ failure, prediction of the CPC (Cerebral Performance Category), time to obtain catecholamine withdrawal, the appearance of acute renal failure with or without the need for extra-renal purification (EER) and duration under EER, the average length of stay.
This project is part of a larger, nationwide project with greater power, and includes all the data generated during hospitalization in intensive care.
Method: an estimated total number of patients included in this study to be between 300 and 500. The population will come from the intensive care units of Nice, Antibes, Cannes, Grasse.
Inclusion will be retrospective, on CRH, CR of CT imaging (cerebral and thoraco-abdomino-pelvic), MRI, EEG, and daily follow-up words, from 2014 to the end of 2020.
After anonymisation, application of semantisation using natural language processing (NLP) methods. The data to be extracted are entered in a document written by intensive care physicians. These data will then be stored in a database. In order to meet the main objective, we will develop a computer algorithm capable of predicting mortality in the study population. This algorithm, based on a large database, can be designed using machine learning or even deep learning techniques depending on the amount of data to be processed.
Study design
- Observational model
- Case only
- Time perspective
- Retrospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- ACR for all causes, admitted in intensive care or intensive care, from the hospital centres of Nice, Antibes, Cannes, Grasse, etc.
Key inclusion criteria (2 listed)
- OCA recovered from: hypoxic, ischemic, pulmonary embolism, tamponade, rhythm or conduction disorder, shockable or not, intra or extra-hospital.
- CR computerized, typed in PDF format
Key exclusion criteria (1 listed)
- -
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Prediction of mortality in the intensive care unit
Time frame: 1day
Definition of a semantic reporting tool, automated, transition from an anonymized report to an operational and relevant database.
Prediction of mortality in the intensive care unit
Time frame: 1day
Use of the database thus created to create an intelligent mortality prediction algorithm. Use also on secondary judgment criteria in order to predict other parameters mentioned below.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 29 Jun 2026.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
romain LOMBARDI contact
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Cardio Respiratory Arrest, run by Centre Hospitalier Universitaire de Nice at 1 site in France.
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 1 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: looking for participants now, as of the sponsor's last update on 29 Jun 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in France. Whether a site is taking part right now should be confirmed with that site. See every site.
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?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Jean DELLAMONICA (33 4 920 35 510, dellamonica.j@chu-nice.fr), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Prediction of mortality in the intensive care unit (time frame: 1day). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 29 Jun 2026.