Prediction of the Spontaneous Breathing Test Success Using Biosignal and Biomarker in Critical Care Unit by a Machine Learning Approach

NCT05886803 | Unknown

• 1 other identifier: 23Rea01
• Study Type: observational
• Target: 500
• Locations: 1 country
• Sites: 1

Brief Summary

Context: Several authors have been interested in applying Artificial Intelligence (AI) to medicine, using various Machine Learning (ML) techniques: managing septic shock, predicting renal failure... \[1, 2\] AI has an important place in decision support for clinicians \[3\]. The weaning period is a really important time in the management of a patient on mechanical ventilation and can take up to half of the time spent in intensive care unit. The first weaning attempt is unsuccessful in 20% of patients However, mortality can be as high as 38% in patients with the most difficult weaning \[4\]. Only a few studies have looked at the application of machine learning in this area, and only one has looked at the use of biosignals (cardiac rate, ECG, ventilatory parameters…) \[5-7\]. To improve morbidity, mortality and reduce length of stay, it is essential to be able to predict the success of the spontaneous breathing test and extubation. Investigators propose to develop a predictive algorithm for the success of a ventilatory weaning test based on biosignal records and others features. Methods: It is a critical care, oligo-centric and retrospective study the investigators included biosignal variables extracted from the electronic medical record, such as respiratory (RR, minute volume...), cardiac (systolic pressure, heart rate...), ventilator parameters and other discrete variables (age, comorbidity...). Most biosignal variables are minute-by-minute records. Recording starts 48 hours before the test and stops at the start of the weaning test. The investigators extracted features from these records, combined them with other biomarkers, and applied several machine learning algorithms: Logistic Regression, Random Forest Classifier, Support Vector Classifier (SVC), XGBoost, and Light Gradient Boosting Method (LGBM)…

Conditions

Weaning From Mechanical Ventilation in Care Unit

Outcome Measures

Primary Outcomes (1)

• Prediction of the spontaneous breathing test outcome.

  Two modalities of test are performed in clinical : either a T-tube test or a spontaneous ventilation test at low level of Inspiratory Support and PEEP (7AI 4PEEP, 7Ai 0PEEP). A successful weaning test is defined by the absence of the following criteria at the end of the test: (i) increase in respiratory rate \> 35cpm, (ii) SpO2 \<90%, (iii) change of more than 20% in heart rate or blood pressure, (iv) modification of consciousness.

  2 years

Study Arms (2)

Spontaneous Breathing Test

The first group will be composed only by patients admitted in intensive care/critical care for ventilation support, and who successed the spontaneous breathing test.

Other: Spontaneous ventilation test

Non Spontaneous Breathing Test

The second group will be composed only by patients admitted in intensive care/critical care for ventilation support, and who failed the spontaneous breathing test.

Other: Spontaneous ventilation test

Interventions

Spontaneous ventilation test OTHER

The purpose is to mimic ventilation conditions after extubation and thus to help the clinician predict the outcome of an extubation decision.

Non Spontaneous Breathing Test; Spontaneous Breathing Test

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

All patient admited in Intensive Care/Critical Care who needed ventilatory support, whatever the etiology requiring it.

You may qualify if:

• Computerized health report (CHR)
• Spontaneous breathing test should have been performed

You may not qualify if:

• Spontaneous breathing test has not been performed,
• Biosignal (cardiac, respiratory) are not registered in the CHR
• Patient died before the spontaneous breathing test
• Opposition to the study has been expressed.

Sponsors & Collaborators

• Centre Hospitalier Universitaire de Nice: lead

Study Sites (1)

University Hospital of Nice

Nice, 06200, France

RECRUITING

Central Study Contacts

Romain LOMBARDI

CONTACT

0669032616; lombardi.r@chu-nice.fr

Jean DELLAMONICA

CONTACT

dellamonica.j@chu-nice.fr

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: May 24, 2023
• First Posted: June 2, 2023
• Study Start: January 1, 2023
• Primary Completion: December 12, 2024
• Study Completion: December 12, 2025
• Last Updated: June 2, 2023

Record last verified: 2023-05

Data Sharing

• IPD Sharing: Will not share

Locations

FR (1)