Respiratory FailureNCT06506123
Patient-Ventilator Dyssynchrony Detection With a Machine Learning Algorithm
An observational study of Respiratory Failure, run by University of Sao Paulo General Hospital at 1 site in Brazil.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Respiratory Failure
- What is being tested?
- Artificial Intelligence Detection and Classification of Patient-Ventilator Dyssynchronies
- Type of treatment
- Medical device
- How many people
- 80 participants
- Where
- 1 siteBrazil
- Study dates
- 25 May 2024to 24 Dec 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Adult, Older adult
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.
Who may be able to join
- Subjects under assisted or assist-controlled mechanical ventilation and monitored with esophageal pressure balloon.
Who cannot join
- Refusal from patient's family or attending physician
Trial locations and contacts
1 site in 1 country.
Brazil1 site, 1 recruiting
- Recruiting
Heart Institute, University of São Paulo
São Paulo, São Paulo, 05403900
Glauco M Plens, MD, +5511982213020, glaucomplens@gmail.com
About this study
Official title: Automated Detection and Classification of Patient-Ventilator Dyssynchrony With a Machine Learning Algorithm
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?
Read the 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
- Being tested
- Artificial Intelligence Detection and Classification of Patient-Ventilator Dyssynchronies
- Keywords
- mechanical ventilation, artificial intelligence
- Medical subject headings
- Respiratory Insufficiency
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Artificial Intelligence Detection and Classification of Patient-Ventilator DyssynchroniesDevice
Machine learning algorithm to detect and classify patient-ventilator dyssynchronies, which is integrated in the mechanical ventilator (Fleximag Max, Magnamed, Brazil).
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Device: Artificial Intelligence Detection and Classification of Patient-Ventilator Dyssynchronies |
What is being measured
The primary outcome is the main question the study is designed to answer.
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
Measured: 3 days
Secondary outcomes (1)
Pendelluft detection
Percentage of cycles with pendelluft detected with the artificial intelligence algorithm compared to the percentage of cycles with pendelluft detected with the esophageal pressure
Measured: 3 days
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted17 Jul 2024
- Study start25 May 2024
- Primary completion24 May 2025 (estimated)
- Study completion24 Dec 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (6)Papers the sponsor has linked to this study.
- Other trials for Respiratory FailureStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Prospective
- Enrolment
- 80 participants (estimated)
Sponsor and investigators
- Lead sponsor
- University of Sao Paulo General Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- Magnamed Tecnologia Medica S/A
- Responsible party
- Sponsor
- Investigators
- Eduardo LV Costa, MD, PhD, Study director, University of Sao Paulo
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06506123
- Sponsor's protocol ID
- CAAE 78855824.7.0000.0068
- Status last verified
- May 2024
- First submitted
- 24 May 2024
- Last updated
- 17 Jul 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
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.
PubMed 9449727Acute 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.
PubMed 10793162Sousa 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.
PubMed 33883458Sousa 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.
PubMed 32032901Blanch 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.
PubMed 25693449LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015 May 28;521(7553):436-44. doi: 10.1038/nature14539.
PubMed 26017442
Study at a glance
From the registry record, last updated by the sponsor on 17 Jul 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Automated Detection and Classification of Patient-Ventilator Dyssynchrony With a Machine Learning Algorithm
- ClinicalTrials.gov ID
- NCT06506123
- Acronym / sponsor ID
- CAAE 78855824.7.0000.0068
- Conditions
- Respiratory Failure
- Keywords
- mechanical ventilation, artificial intelligence
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 80 participants (estimated)
- Lead sponsor
- University of Sao Paulo General HospitalUniversity, hospital or other
- Collaborators
- Magnamed Tecnologia Medica S/A
- Study start
- 25 May 2024 (actual)
- Primary completion
- 24 May 2025 (estimated)
- Study completion
- 24 Dec 2025 (estimated)
- First posted
- 17 Jul 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Other
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Artificial Intelligence Detection and Classification of Patient-Ventilator Dyssynchronies | – | Device: Artificial Intelligence Detection and Classification of Patient-Ventilator DyssynchroniesThis 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (1 listed)
- Subjects under assisted or assist-controlled mechanical ventilation and monitored with esophageal pressure balloon.
Key exclusion criteria (1 listed)
- Refusal from patient's family or attending physician
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Artificial Intelligence Detection and Classification of Patient-Ventilator DyssynchroniesMachine learning algorithm to detect and classify patient-ventilator dyssynchronies, which is integrated in the mechanical ventilator (Fleximag Max, Magnamed, Brazil). | Device | Artificial Intelligence Detection and Classification of Patient-Ventilator Dyssynchronies |
Primary outcome
Diagnostic Accuracy of the Artificial Intelligence algorithm
Time frame: 3 days
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
1 secondary outcome
Pendelluft detection
Time frame: 3 days
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 17 Jul 2024.
- Background: PubMed 9449727Amato 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.
- Background: PubMed 10793162Acute 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.
- Background: PubMed 33883458Sousa 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.
- Background: PubMed 32032901Sousa 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.
- Background: PubMed 25693449Blanch 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.
- Background: PubMed 26017442LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015 May 28;521(7553):436-44. doi: 10.1038/nature14539.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Eduardo LV Costa, MD, PhD study director
University of Sao Paulo
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Respiratory Failure, run by University of Sao Paulo General Hospital at 1 site in Brazil.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 17 Jul 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Brazil. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Glauco M Plens, MD (+5511982213020, glaucomplens@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnostic Accuracy of the Artificial Intelligence algorithm (time frame: 3 days). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06506123, which also lists 6 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 17 Jul 2024; AzTrials last refreshed its copy on 27 Oct 2026.