Clinical trial NCT06506123: Patient-Ventilator Dyssynchrony Detection With a Machine Learning Algorithm
Official title: Automated Detection and Classification of Patient-Ventilator Dyssynchrony With a Machine Learning Algorithm
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Respiratory Failure
Keywords: mechanical ventilation
artificial intelligence
Lead sponsor: University of Sao Paulo General Hospital
Sponsor class: OTHER
Collaborators: name: Magnamed Tecnologia Medica S/A
First posted: 2024-07-17
Enrollment: 80
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Observational model: OTHER
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Adult subjects under mechanical ventilation, with an assisted or assist-controlled mode, who are monitored with an esophageal pressure balloon due to clinical indication are eligible.

Brief summary: This is a diagnostic study aiming to compare accuracy to detect and classify patient-ventilator dyssynchronies by a machine learning algorithm, compared to the gold-standard defined as dyssynchronies diagnosed and classified by mechanical ventilator and esophageal pressure waveforms analyzed by experts.

The main question of this study is:

• Are patient-ventilator dyssynchronies accurately detected and classified by an artificial intelligence algorithm when compared to experts analyzing esophageal pressure and mechanical ventilator waveforms?

Detailed description: This is a diagnostic, observational study, aiming to assess patient-ventilator dyssynchrony automated detection and classification by a machine learning algorithm. Accuracy of the machine learning algorithm will be compared with the gold-standard, defined as dyssynchronies detected and classified by mechanical ventilation experts.

Experts will analyzed airway pressure, flow, volume and esophageal pressure waveforms to detect and classify dyssynchronies.

Eligibility criteria: Inclusion Criteria:

* Subjects under assisted or assist-controlled mechanical ventilation and monitored with esophageal pressure balloon.

Exclusion Criteria:

* Refusal from patient's family or attending physician

Primary outcomes:
measure: Diagnostic Accuracy of the Artificial Intelligence algorithm
timeFrame: 3 days
Secondary outcomes:
measure: Pendelluft detection
timeFrame: 3 days
Other outcomes:
Locations:
city: Sao Paulo
country: Brazil
facility: Heart Institute, University of São Paulo
state: SP
Study officials:
affiliation: University of Sao Paulo
name: Eduardo LV Costa, MD, PhD
role: STUDY_DIRECTOR
References:
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citation: Sousa MLA, Magrans R, Hayashi FK, Blanch L, Kacmarek RM, Ferreira JC. Predictors of asynchronies during assisted ventilation and its impact on clinical outcomes: The EPISYNC cohort study. J Crit Care. 2020 Jun;57:30-35. doi: 10.1016/j.jcrc.2020.01.023. Epub 2020 Jan 21.
citation: Blanch L, Villagra A, Sales B, Montanya J, Lucangelo U, Lujan M, Garcia-Esquirol O, Chacon E, Estruga A, Oliva JC, Hernandez-Abadia A, Albaiceta GM, Fernandez-Mondejar E, Fernandez R, Lopez-Aguilar J, Villar J, Murias G, Kacmarek RM. Asynchronies during mechanical ventilation are associated with mortality. Intensive Care Med. 2015 Apr;41(4):633-41. doi: 10.1007/s00134-015-3692-6. Epub 2015 Feb 19.
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