Patient-Ventilator Dyssynchrony Detection With a Machine Learning Algorithm

NCT ID: NCT06506123

Last Updated: 2024-07-17

Basic Information

• Recruitment Status: RECRUITING
• Total Enrollment: 80 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2024-05-25
• Study Completion Date: 2025-12-24

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.

Conditions

Respiratory Failure

Study Design

• Observational Model Type: OTHER
• Study Time Perspective: PROSPECTIVE

Study Groups

Artificial Intelligence Detection and Classification of Patient-Ventilator Dyssynchronies

This is a single arm study, since all subjects included will be exposed to both diagnostic methods (artificial intelligence and experts). The proposed diagnostic method is a machine learning algorithm integrated in the mechanical ventilator FlexiMag Max 700 (Magnamed, Brazil), which will continuously record data from mechanical ventilation of included subjects for a time period of up to 72 hours. The gold-standard involves esophageal pressure waveform recording and offline analysis by experts.

Artificial Intelligence Detection and Classification of Patient-Ventilator Dyssynchronies

Intervention Type: DEVICE

Machine learning algorithm to detect and classify patient-ventilator dyssynchronies, which is integrated in the mechanical ventilator (Fleximag Max, Magnamed, Brazil).

Interventions

Artificial Intelligence Detection and Classification of Patient-Ventilator Dyssynchronies

Machine learning algorithm to detect and classify patient-ventilator dyssynchronies, which is integrated in the mechanical ventilator (Fleximag Max, Magnamed, Brazil).

Intervention Type: DEVICE

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

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Magnamed Tecnologia Medica S/A

UNKNOWN

Sponsor Role: collaborator

University of Sao Paulo General Hospital

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Eduardo LV Costa, MD, PhD

Role: STUDY_DIRECTOR

University of Sao Paulo

Locations

Heart Institute, University of São Paulo

São Paulo, São Paulo, Brazil

Site Status: RECRUITING

Countries

Brazil

Central Contacts

Glauco M Plens, MD

Role: CONTACT

glaucomplens@gmail.com

+5511982213020

Facility Contacts

Glauco M Plens, MD

Role: primary

glaucomplens@gmail.com

+5511982213020

References

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Reference Type: BACKGROUND

PMID: 9449727 (View on PubMed)

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Reference Type: BACKGROUND

PMID: 10793162 (View on PubMed)

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Reference Type: BACKGROUND

PMID: 33883458 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 32032901 (View on PubMed)

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.

Reference Type: BACKGROUND

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Reference Type: BACKGROUND

PMID: 26017442 (View on PubMed)

Other Identifiers

CAAE 78855824.7.0000.0068

Identifier Type: -

Identifier Source: org_study_id