AiCR : Artificial Intelligence in Cardiac aRrest (AiCR)

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.

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 Overview

• Status: Unknown
• Conditions: Cardio Respiratory Arrest

• Study Type: Observational
• Enrollment (Anticipated): 500

Contacts and Locations

Study Locations

• France
  • Nice, France, 06200
    • Recruiting
    • Nice Hospital
    • Contact: Jean DELLAMONICA; Email: dellamonica.j@chu-nice.fr

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• 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.

Description

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:

-

Study Plan

How is the study designed?

Design Details

• Observational Models: Case-Only
• Time Perspectives: Retrospective

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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.	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.	1day

Collaborators and Investigators

• Sponsor: Centre Hospitalier Universitaire de Nice
• Collaborators: Institut National de Recherche en Informatique et en Automatique; AIINTENSE

Study record dates

Study Major Dates

• Study Start (Actual): February 1, 2020
• Primary Completion (Anticipated): December 31, 2020
• Study Completion (Anticipated): December 31, 2020

Study Registration Dates

• First Submitted: July 3, 2020
• First Submitted That Met QC Criteria: July 3, 2020
• First Posted (Actual): July 8, 2020

Study Record Updates

• Last Update Posted (Actual): July 8, 2020
• Last Update Submitted That Met QC Criteria: July 3, 2020
• Last Verified: July 1, 2020

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Heart Diseases; Cardiovascular Diseases; Heart Arrest
• Other Study ID Numbers: 20reamed01

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No