Patient-Ventilator Dyssynchrony Detection With a Machine Learning Algorithm

NCT06506123 | Recruiting

• 1 other identifier: CAAE 78855824.7.0000.0068
• Study Type: observational
• Target: 80
• Locations: 1 country
• Sites: 1

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?

Conditions

Respiratory Failure

Keywords

mechanical ventilation; artificial intelligence

Outcome Measures

Primary Outcomes (1)

• Diagnostic Accuracy of the Artificial Intelligence algorithm

  Sensitivity, specificity, positive predictive value, negative predictive value of the artificial intelligence algorithm to detect and classify patient-ventilator dyssynchronies. These accuracy indexes will be estimated for each kind of dyssinchrony: ineffective effort, autotriggering, double triggering, reverse triggering, reverse triggering with a double cycle

  3 days

Secondary Outcomes (1)

• Pendelluft detection

  3 days

Study Arms (1)

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.

Device: Artificial Intelligence Detection and Classification of Patient-Ventilator Dyssynchronies

Interventions

Artificial Intelligence Detection and Classification of Patient-Ventilator Dyssynchronies DEVICE

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

Artificial Intelligence Detection and Classification of Patient-Ventilator Dyssynchronies

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Age Groups: Adult (18-64), Older Adult (65+)
• 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.

You may qualify if:

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

You may not qualify if:

• Refusal from patient's family or attending physician

Sponsors & Collaborators

• University of Sao Paulo General Hospital: lead
• Magnamed Tecnologia Medica S/A: collaborator

Study Sites (1)

Heart Institute, University of São Paulo

São Paulo, São Paulo, 05403900, Brazil

RECRUITING

Related Publications (6)

• Amato MB, Barbas CS, Medeiros DM, Magaldi RB, Schettino GP, Lorenzi-Filho G, Kairalla RA, Deheinzelin D, Munoz C, Oliveira R, Takagaki TY, Carvalho CR. Effect of a protective-ventilation strategy on mortality in the acute respiratory distress syndrome. N Engl J Med. 1998 Feb 5;338(6):347-54. doi: 10.1056/NEJM199802053380602.

  PMID: 9449727 BACKGROUND

• Acute Respiratory Distress Syndrome Network; Brower RG, Matthay MA, Morris A, Schoenfeld D, Thompson BT, Wheeler A. Ventilation with lower tidal volumes as compared with traditional tidal volumes for acute lung injury and the acute respiratory distress syndrome. N Engl J Med. 2000 May 4;342(18):1301-8. doi: 10.1056/NEJM200005043421801.

  PMID: 10793162 BACKGROUND

• Sousa MLEA, Magrans R, Hayashi FK, Blanch L, Kacmarek RM, Ferreira JC. Clusters of Double Triggering Impact Clinical Outcomes: Insights From the EPIdemiology of Patient-Ventilator aSYNChrony (EPISYNC) Cohort Study. Crit Care Med. 2021 Sep 1;49(9):1460-1469. doi: 10.1097/CCM.0000000000005029.

  PMID: 33883458 BACKGROUND

• 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.

  PMID: 32032901 BACKGROUND

• 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.

  PMID: 25693449 BACKGROUND

• LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015 May 28;521(7553):436-44. doi: 10.1038/nature14539.

  PMID: 26017442 BACKGROUND

MeSH Terms

Conditions

Respiratory Insufficiency

Condition Hierarchy (Ancestors)

Respiration Disorders; Respiratory Tract Diseases

Study Officials

• Eduardo LV Costa, MD, PhD

  University of Sao Paulo

  STUDY DIRECTOR

Central Study Contacts

Glauco M Plens, MD

CONTACT

+5511982213020; glaucomplens@gmail.com

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: May 24, 2024
• First Posted: July 17, 2024
• Study Start: May 25, 2024
• Primary Completion: May 24, 2025
• Study Completion: December 24, 2025
• Last Updated: July 17, 2024

Record last verified: 2024-05

Data Sharing

• IPD Sharing: Will not share

Locations

BR (1)