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AiCR : Artificial Intelligence in Cardiac aRrest (AiCR)

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Know the risks and potential benefits of clinical studies and talk to your health care provider before participating. Read our disclaimer for details.
 
ClinicalTrials.gov Identifier: NCT04462380
Recruitment Status : Recruiting
First Posted : July 8, 2020
Last Update Posted : July 8, 2020
Sponsor:
Collaborators:
Institut National de Recherche en Informatique et en Automatique
AIINTENSE
Information provided by (Responsible Party):
Centre Hospitalier Universitaire de Nice

Brief 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. 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.


Condition or disease
Cardio Respiratory Arrest

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Study Type : Observational
Estimated Enrollment : 500 participants
Observational Model: Case-Only
Time Perspective: Retrospective
Official Title: AiCR : Artificial Intelligence in Cardiac aRrest Application of an Algorithm in the Prognosis of Recovered Cardiorespiratory Arrests
Actual Study Start Date : February 1, 2020
Estimated Primary Completion Date : December 31, 2020
Estimated Study Completion Date : December 31, 2020

Resource links provided by the National Library of Medicine

MedlinePlus related topics: Cardiac Arrest




Primary Outcome Measures :
  1. 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.

  2. 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.



Information from the National Library of Medicine

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Ages Eligible for Study:   18 Years and older   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Sampling Method:   Non-Probability Sample
Study Population
ACR for all causes, admitted in intensive care or intensive care, from the hospital centres of Nice, Antibes, Cannes, Grasse, etc.
Criteria

Inclusion Criteria:

  • 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

Exclusion Criteria:

-


Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT04462380


Contacts
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Contact: Jean DELLAMONICA 33 4 920 35 510 dellamonica.j@chu-nice.fr
Contact: romain LOMBARDI lombardi.r@chu-nice.fr

Locations
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France
Nice Hospital Recruiting
Nice, France, 06200
Contact: Jean DELLAMONICA       dellamonica.j@chu-nice.fr   
Contact: romain LOMBARDI       lombardi.r@chu-nice.fr   
Sponsors and Collaborators
Centre Hospitalier Universitaire de Nice
Institut National de Recherche en Informatique et en Automatique
AIINTENSE
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Responsible Party: Centre Hospitalier Universitaire de Nice
ClinicalTrials.gov Identifier: NCT04462380    
Other Study ID Numbers: 20reamed01
First Posted: July 8, 2020    Key Record Dates
Last Update Posted: July 8, 2020
Last Verified: July 2020

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Additional relevant MeSH terms:
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Apnea
Heart Arrest
Heart Diseases
Cardiovascular Diseases
Respiration Disorders
Respiratory Tract Diseases
Signs and Symptoms, Respiratory
Signs and Symptoms