Cardio Respiratory Arrest Clinical Trial
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.
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.
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