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

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)…

Study Overview

• Status: Recruiting
• Conditions: Weaning From Mechanical Ventilation in Care Unit
• Intervention / Treatment: Other: Spontaneous ventilation test

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

Contacts and Locations

• Study Contact: Name: Jean DELLAMONICA; Email: dellamonica.j@chu-nice.fr
• Study Contact Backup: Name: Romain LOMBARDI; Phone Number: +33 0669032616; Email: lombardi.r@chu-nice.fr

Study Locations

• France
  • Nice, France, 06200
    • Recruiting
    • University Hospital of Nice
    • Contact: Jean DELLAMONICA; Email: dellamonica.j@chu-nice.fr
    • Contact: Romain LOMBARDI; Phone Number: +33 0669032616; Email: lombardi.r@chu-nice.fr

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

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

Description

Inclusion Criteria:

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

Exclusion Criteria:

• 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.

Study Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
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; 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; 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; The purpose is to mimic ventilation conditions after extubation and thus to help the clinician predict the outcome of an extubation decision.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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

Collaborators and Investigators

• Sponsor: Centre Hospitalier Universitaire de Nice

Study record dates

Study Major Dates

• Study Start (Actual): January 1, 2023
• Primary Completion (Estimated): December 12, 2024
• Study Completion (Estimated): December 12, 2025

Study Registration Dates

• First Submitted: May 24, 2023
• First Submitted That Met QC Criteria: May 24, 2023
• First Posted (Estimated): June 2, 2023

Study Record Updates

• Last Update Posted (Estimated): June 2, 2023
• Last Update Submitted That Met QC Criteria: May 24, 2023
• Last Verified: May 1, 2023

More Information

Terms related to this study

• Other Study ID Numbers: 23Rea01

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

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