Acute Respiratory Distress SyndromePIONEERNCT05993377
Prediction of Duration of Mechanical Ventilation in ARDS
An observational study of Acute Respiratory Distress Syndrome, run by Hospital Universitario de Gran Canaria Doctor Negrín at 20 sites in 2 countries.
Trial summary
The investigators are planning to perform a secondary analysis of an academic dataset of 1,303 patients with moderate-to-severe acute respiratory distress syndrome (ARDS) included in several published cohorts (NCT00736892, NCT022288949, NCT02836444, NCT03145974), aimed to characterize the best early scenario during… Read more about this study
- Who is it for?
- Ages 18 to 100 years
- Conditions the study is for
- Acute Respiratory Distress Syndrome
- What is being tested?
- Logistic regression Cross validation Area under the RIC curves Machine learning analysis. .
- How many people
- 1,303 participants
- Where
- 20 sitesin 2 countries
- Study dates
- 14 Aug 2023to 2 Feb 2024
- 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 to 100 years
- All sexes
- Adult, Older adult
Study population: De-identified dataset including 1,303 patients with moderate/severe ARDS admitted consecutively in a network of Spanish ICUs.
Who may be able to join
- Berlin criteria for moderate to severe acute respiratory distress syndrome
Who cannot join
- Postoperative patients ventilated <24h
- brain death patients
Trial locations and contacts
20 sites in 2 countries.
20 sites in 2 countries
Spain19 sites
Hospital Universitario de A Coruña (ICU)
A Coruña, 15006
Complejo Hospitalario Universitario de Albacete (ICU)
Albacete, 02006
Complejo Hospitalario de Albacete
Albacete
Department of Anesthesia, Hospital Clinic
Barcelona, 08036
Hospital General de Ciudad Real (ICU)
Ciudad Real, 13005
Hospital Virgen de La Luz
Cuenca
Hospital Universitario Dr. Negrin
Las Palmas de Gran Canaria, Las Palmas, 35019
Complejo Hospitalario Universitario de León
León
Hospital Universitario Ramón y Cajal (Anesthesia)
Madrid, 28034
Hospital Universitario La Paz (ICU)
Madrid, 28046
Hospital Fundación Jiménez Díaz
Madrid
Hospital Universitario Puerta de Hierro (ICU)
Majadahonda, Madrid, 28222
Hospital Universitario Regional de Malaga Carlos Haya (ICU)
Málaga, 29010
Hospital Universitario Carlos Haya
Málaga
Hospital Universitario Virgen de Arrixaca (ICU)
Murcia, 30120
Hospital Universitario NS de Candelaria
Santa Cruz de Tenerife, Tenerife
Hospital NS del Prado
Talavera de la Reina, Toledo
Hospital Universitario Río Hortega (ICU)
Valladolid, 47012
Hospital Virgen de la Concha (ICU)
Zamora, 49022
About this study
Official title: Predicting Length of Mechanical Ventilation in Moderate-to-severe Acute Respiratory Distress Syndrome Using Machine Learning
The investigators are planning to perform a secondary analysis of an academic dataset of 1,303 patients with moderate-to-severe acute respiratory distress syndrome (ARDS) included in several published cohorts (NCT00736892, NCT022288949, NCT02836444, NCT03145974), aimed to characterize the best early scenario during the first three days of diagnosis to predict duration of mechanical ventilation in the intensive care unit (ICU) using supervised machine learning (ML) approaches.
Read the detailed description
The acute respiratory distress syndrome (ARDS) is an important cause of morbidity, mortality, and costs in intensive care units (ICUs) worldwide. Most ARDS patients require mechanical ventilation (MV). Few studies have investigated the prediction of MV duration of ARDS.
For model description and testing, the investigators will extract data from he first three ICU days after diagnosis of moderate-to-severe ARDS from patients included in the de-identified database, which includes 1,000 mechanically ventilated patients enrolled in several observational cohorts in Spain, coordinated by the principal investigator (JV), and funded by the Instituto de Salud Carlos III (ISCIII). The investigators will follow the TRIPOD guidelines and machine learning techniques will be implemented [Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Logistic regression analysis) for the development and accuracy of prediction models. Disease progression will be tracked along those 3 ICU days to assess lung severity according to Berlin criteria. For external validation, the investigators will use 303 patients enrolled in a contemporary observational study (NCT03145974). The investigators will evaluate the accuracy of prediction models by calculation several statistics, such as sensitivity, specificity, positive predictive value, negative value for each model. The investigators will select the best early prediction model with data captured on the 1st, 2nd, or 3rd day.
- Conditions
- Acute Respiratory Distress Syndrome
- Being tested
- Logistic regression Cross validation Area under the RIC curves Machine learning analysis. .
- Keywords
- mechanical ventilation, intensive care unit, duration of ventilation, machine learning.
- Medical subject headings
- Respiratory Distress Syndrome
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Logistic regression Cross validation Area under the RIC curves Machine learning analysis. .Other
we will use robust machine learning approaches, such as Random Forest and XGBoost.
| Study group | Role | What they receive |
|---|---|---|
| Derivation and testing cohort | It will contain 1000 ARDS patients Receives: Other: Logistic regression Cross validation Area under the RIC curves Machine learning analysis. . | |
| Confirmatory cohort | It will contain 303 patients (for external validation) Receives: Other: Logistic regression Cross validation Area under the RIC curves Machine learning analysis. . |
What is being measured
The primary outcome is the main question the study is designed to answer.
Days on mechanical ventilation
Duration of mechanical ventilation
Measured: from diagnosis to extubation
Secondary outcomes (1)
ICU mortality
mortality in the intensive care unit
Measured: up to 24 weeks
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted15 Aug 2023
- Study start14 Aug 2023
- Primary completion2 Feb 2024
- Study completion2 Feb 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (3)Papers the sponsor has linked to this study.
- Other trials for Acute Respiratory Distress SyndromeStudies 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
- Cohort
- Time perspective
- Prospective
- Enrolment
- 1,303 participants (actual)
Sponsor and investigators
- Lead sponsor
- Hospital Universitario de Gran Canaria Doctor Negrín
- Sponsor type
- University, hospital or other
- Collaborators
- Unity Health Toronto, Cardiff University, Leiden University Medical Center
- Responsible party
- Principal investigator, Jesus Villar, Hospital Universitario de Gran Canaria Doctor Negrín
- Investigators
- Jesús Villar, Principal investigator, Hospital Universitario D. Negrin
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05993377
- Sponsor's protocol ID
- PIFIISC21-36
- Acronym
- PIONEER
- Status last verified
- Feb 2024
- First submitted
- 4 Aug 2023
- Last updated
- 20 Mar 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Villar J, Ambros A, Mosteiro F, Martinez D, Fernandez L, Ferrando C, Carriedo D, Soler JA, Parrilla D, Hernandez M, Andaluz-Ojeda D, Anon JM, Vidal A, Gonzalez-Higueras E, Martin-Rodriguez C, Diaz-Lamas AM, Blanco J, Belda J, Diaz-Dominguez FJ, Rico-Feijoo J, Martin-Delgado C, Romera MA, Gonzalez-Martin JM, Fernandez RL, Kacmarek RM; Spanish Initiative for Epidemiology, Stratification and Therapies of ARDS (SIESTA) Network. A Prognostic Enrichment Strategy for Selection of Patients With Acute Respiratory Distress Syndrome in Clinical Trials. Crit Care Med. 2019 Mar;47(3):377-385. doi: 10.1097/CCM.0000000000003624.
PubMed 30624279Figueroa-Casas JB, Dwivedi AK, Connery SM, Quansah R, Ellerbrook L, Galvis J. Predictive models of prolonged mechanical ventilation yield moderate accuracy. J Crit Care. 2015 Jun;30(3):502-5. doi: 10.1016/j.jcrc.2015.01.020. Epub 2015 Jan 30.
PubMed 25682346Villar J, Gonzalez-Martin JM, Fernandez C, Soler JA, Ambros A, Pita-Garcia L, Fernandez L, Ferrando C, Arocas B, Gonzalez-Vaquero M, Anon JM, Gonzalez-Higueras E, Parrilla D, Vidal A, Fernandez MM, Rodriguez-Suarez P, Fernandez RL, Gomez-Bentolila E, Burns KEA, Szakmany T, Steyerberg EW, The PredictION Of Duration Of mEchanical vEntilation In Ards Pioneer Network. Predicting the Length of Mechanical Ventilation in Acute Respiratory Disease Syndrome Using Machine Learning: The PIONEER Study. J Clin Med. 2024 Mar 21;13(6):1811. doi: 10.3390/jcm13061811.
PubMed 38542033
Study at a glance
From the registry record, last updated by the sponsor on 20 Mar 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Predicting Length of Mechanical Ventilation in Moderate-to-severe Acute Respiratory Distress Syndrome Using Machine Learning
- ClinicalTrials.gov ID
- NCT05993377
- Acronym / sponsor ID
- PIONEER / PIFIISC21-36
- Conditions
- Acute Respiratory Distress Syndrome
- Keywords
- mechanical ventilation, intensive care unit, duration of ventilation, machine learning.
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 1,303 participants (actual)
- Lead sponsor
- Hospital Universitario de Gran Canaria Doctor NegrínUniversity, hospital or other
- Collaborators
- Unity Health Toronto, Cardiff University, Leiden University Medical Center
- Study start
- 14 Aug 2023 (actual)
- Primary completion
- 2 Feb 2024 (actual)
- Study completion
- 2 Feb 2024 (actual)
- First posted
- 15 Aug 2023 (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.
The investigators are planning to perform a secondary analysis of an academic dataset of 1,303 patients with moderate-to-severe acute respiratory distress syndrome (ARDS) included in several published cohorts (NCT00736892, NCT022288949, NCT02836444, NCT03145974), aimed to characterize the best early scenario during the first three days of diagnosis to predict duration of mechanical ventilation in the intensive care unit (ICU) using supervised machine learning (ML) approaches.
Detailed description
The acute respiratory distress syndrome (ARDS) is an important cause of morbidity, mortality, and costs in intensive care units (ICUs) worldwide. Most ARDS patients require mechanical ventilation (MV). Few studies have investigated the prediction of MV duration of ARDS.
For model description and testing, the investigators will extract data from he first three ICU days after diagnosis of moderate-to-severe ARDS from patients included in the de-identified database, which includes 1,000 mechanically ventilated patients enrolled in several observational cohorts in Spain, coordinated by the principal investigator (JV), and funded by the Instituto de Salud Carlos III (ISCIII). The investigators will follow the TRIPOD guidelines and machine learning techniques will be implemented [Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Logistic regression analysis) for the development and accuracy of prediction models. Disease progression will be tracked along those 3 ICU days to assess lung severity according to Berlin criteria. For external validation, the investigators will use 303 patients enrolled in a contemporary observational study (NCT03145974). The investigators will evaluate the accuracy of prediction models by calculation several statistics, such as sensitivity, specificity, positive predictive value, negative value for each model. The investigators will select the best early prediction model with data captured on the 1st, 2nd, or 3rd day.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Derivation and testing cohort | – | Other: Logistic regression Cross validation Area under the RIC curves Machine learning analysis. .It will contain 1000 ARDS patients |
| Confirmatory cohort | – | Other: Logistic regression Cross validation Area under the RIC curves Machine learning analysis. .It will contain 303 patients (for external validation) |
Who may qualify
- Ages
- 18 to 100 years
- Sex
- All
- Age groups
- Adult, Older adult
- Study population
- De-identified dataset including 1,303 patients with moderate/severe ARDS admitted consecutively in a network of Spanish ICUs.
Key inclusion criteria (1 listed)
- Berlin criteria for moderate to severe acute respiratory distress syndrome
Key exclusion criteria (2 listed)
- Postoperative patients ventilated <24h
- brain death patients
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 |
|---|---|---|
| Logistic regression Cross validation Area under the RIC curves Machine learning analysis. .we will use robust machine learning approaches, such as Random Forest and XGBoost. | Other | Confirmatory cohort, Derivation and testing cohort |
Primary outcome
Days on mechanical ventilation
Time frame: from diagnosis to extubation
Duration of mechanical ventilation
1 secondary outcome
ICU mortality
Time frame: up to 24 weeks
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 20 Mar 2024.
- Results publication: PubMed 30624279Villar J, Ambros A, Mosteiro F, Martinez D, Fernandez L, Ferrando C, Carriedo D, Soler JA, Parrilla D, Hernandez M, Andaluz-Ojeda D, Anon JM, Vidal A, Gonzalez-Higueras E, Martin-Rodriguez C, Diaz-Lamas AM, Blanco J, Belda J, Diaz-Dominguez FJ, Rico-Feijoo J, Martin-Delgado C, Romera MA, Gonzalez-Martin JM, Fernandez RL, Kacmarek RM; Spanish Initiative for Epidemiology, Stratification and Therapies of ARDS (SIESTA) Network. A Prognostic Enrichment Strategy for Selection of Patients With Acute Respiratory Distress Syndrome in Clinical Trials. Crit Care Med. 2019 Mar;47(3):377-385. doi: 10.1097/CCM.0000000000003624.
- Results publication: PubMed 25682346Figueroa-Casas JB, Dwivedi AK, Connery SM, Quansah R, Ellerbrook L, Galvis J. Predictive models of prolonged mechanical ventilation yield moderate accuracy. J Crit Care. 2015 Jun;30(3):502-5. doi: 10.1016/j.jcrc.2015.01.020. Epub 2015 Jan 30.
- Derived from this study: PubMed 38542033Villar J, Gonzalez-Martin JM, Fernandez C, Soler JA, Ambros A, Pita-Garcia L, Fernandez L, Ferrando C, Arocas B, Gonzalez-Vaquero M, Anon JM, Gonzalez-Higueras E, Parrilla D, Vidal A, Fernandez MM, Rodriguez-Suarez P, Fernandez RL, Gomez-Bentolila E, Burns KEA, Szakmany T, Steyerberg EW, The PredictION Of Duration Of mEchanical vEntilation In Ards Pioneer Network. Predicting the Length of Mechanical Ventilation in Acute Respiratory Disease Syndrome Using Machine Learning: The PIONEER Study. J Clin Med. 2024 Mar 21;13(6):1811. doi: 10.3390/jcm13061811.
- 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.
Jesús Villar principal investigator
Hospital Universitario D. Negrin
20 sites in 2 countries: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Acute Respiratory Distress Syndrome, run by Hospital Universitario de Gran Canaria Doctor Negrín at 20 sites in 2 countries.
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 2 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: finished, as of the sponsor's last update on 20 Mar 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 20 sites in Spain, United Kingdom. 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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Days on mechanical ventilation (time frame: from diagnosis to extubation). 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 NCT05993377, which also lists 3 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 20 Mar 2024.