Acute Hypoxemic Respiratory FailurePREMIERNCT06815523
Prediction of Duration of Mechanical Ventilation in Acute Hypoxemic Respiratoty Failure
An observational study of Acute Hypoxemic Respiratory Failure, run by Jesus Villar at 2 sites in Spain.
Trial summary
Acute hypoxemic respiratory failure (AHRF) is a common cause of admission in intensive care units (ICUs) worldwide. We will assess machine learning (ML) techniques for prediction of prolonged duration (> or = to 7 days) of mechanical ventilation (MV) in 1,241 patients enrolled in the PANDORA study in Spain. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Acute Hypoxemic Respiratory Failure
- What is being tested?
- Machine learning and logistic regression for the training/testing cohort and validation cohort
- How many people
- 1,241 participants
- Where
- 2 sitesSpain
- Study dates
- 2 Feb 2025to 1 Jun 2026
- 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
- No healthy volunteers
- Adult, Older adult
Study population: De-identified dataset inclusing 1241 ventilated patients with AHRF admitted in a network of Spainisg ICUs.
Who may be able to join
- enotracheal intubation puls mechanical ventilation
- PaO2/FiO2 ratio <or = 300 mmHg under MV with PEEP >or =5 and FiO2 >or = 0.3
Who cannot join
- Brain death patients
Trial locations and contacts
2 sites in 1 country.
Spain2 sites
Hospital Dr. Negrin
Las Palmas de Gran Canaria, Las Palmas, 35019
Hospital Universitario La Paz
Madrid
About this study
Official title: Prediction of Duration of Mechanical Venylation in Patients Wit Acute Hypoxemic Respiratory Failure Usinf Machine Learning Approaches
Acute hypoxemic respiratory failure (AHRF) is a common cause of admission in intensive care units (ICUs) worldwide. We will assess machine learning (ML) techniques for prediction of prolonged duration (> or = to 7 days) of mechanical ventilation (MV) in 1,241 patients enrolled in the PANDORA study in Spain. The study was registered with ClinalTrials.gov (NCT03145974). Our aim is to identify a model with the minimum number of variables that predict duration of prolonged ventilation in AHRF patients using data as early as from the first 48 hours with machine learning algorithms.
Read the detailed description
Acute hypoxemic respiratory failure (AHRF) is the most common cause of admission in intensive care units (ICUs) worldwide. The investigators will assess the value of machine learning (ML) techniques for prediction of prolonged duration (> or equeal to 7 days) of mechanical ventilation (MV) in 1,241 patients enrolled in the PANDORA study in Spain. Few studies have investigated the prediction of prolonged MV in patients with AHRF.
For model training and testing, the investigators will extract data from random pateints from the first 2 days after diagnosis of AHRF. The investigators had a database with 2,000,000 anonymized and dissociated demographics and clinically relevant data from 1,241 patients with AHRF from 22 hospitals in Spain. The investigators will follow the TRIPOD guidelines for prediction models. The investigators will screen relevant collected variables using a genetic algorithm variable selection to achieve parsimony. We will use 5-fold corss-validation in the data set of patients with data at T0, T24 and T48. We will use 25% of patients randomly selected for evaluation of the model.
- Conditions
- Acute Hypoxemic Respiratory Failure
- Being tested
- Machine learning and logistic regression for the training/testing cohort and validation cohort
- Keywords
- mechanical ventilation, machine learning, prediction of prolonged mechanical ventilation, outcome
- Medical subject headings
- Respiratory Insufficiency
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Machine learning and logistic regression for the training/testing cohort and validation cohortOther
Machine learning and logistic regression for the validation cohort
| Study group | Role | What they receive |
|---|---|---|
| Derivation/testing cohort | The investigators will use a chort of 75% of patients, randomly selected, with data at T0, T24 and T48 after diagnosis of acute hypoxemic respiratory failure (AHRF). We will apply machine learning approaches. Receives: Other: Machine learning and logistic regression for the training/testing cohort and validation cohort | |
| Validation hohort | we will use 25% of unseen patients, randomly selected, with data at T0, T24 and T48 after diagnosis of AHRF. Receives: Other: Machine learning and logistic regression for the training/testing cohort and validation cohort |
What is being measured
The primary outcome is the main question the study is designed to answer.
MV duration
duration of mechanical ventilation
Measured: up to 100 weeks
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted7 Feb 2025
- Study start2 Feb 2025
- Primary completion1 May 2026
- Study completion1 Jun 2026
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Acute Hypoxemic 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
- Cohort
- Time perspective
- Prospective
- Enrolment
- 1,241 participants (actual)
Sponsor and investigators
- Lead sponsor
- Jesus Villar
- Sponsor type
- University, hospital or other
- Collaborators
- Hospital Universitario de Gran Canaria Doctor Negrín, Instituto de Salud Carlos III
- Responsible party
- Sponsor-investigator, Jesus Villar, Hospital Universitario de Gran Canaria Doctor Negrín
- Investigators
- Jesus Villar, Study director, Fundacion Canaria Instituto de Investigación Sanitaria de Canarias
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06815523
- Sponsor's protocol ID
- PIFIISC24
- Acronym
- PREMIER
- Status last verified
- Jul 2026
- First submitted
- 3 Feb 2025
- Last updated
- 8 Jul 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Ethical reasons
Publications
Villar J, Gonzalez-Martin JM, Fernandez C, Soler JA, Rey-Abalo M, Mora-Ordonez JM, Ortiz-Diaz-Miguel R, Fernandez L, Murcia I, Robaglia D, Anon JM, Ferrando C, Parrilla D, Dominguez-Berrot AM, Cobeta P, Martinez D, Amaro-Harpigny A, Andaluz-Ojeda D, Fernandez MM, Gomez-Bentolila E, Steyerberg EW, Camporota L, Szakmany T. Predicting Outcome and Duration of Mechanical Ventilation in Acute Hypoxemic Respiratory Failure: The PREMIER Study. J Clin Med. 2025 Nov 7;14(22):7903. doi: 10.3390/jcm14227903.
PubMed 41302939
Study at a glance
From the registry record, last updated by the sponsor on 8 Jul 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Prediction of Duration of Mechanical Venylation in Patients Wit Acute Hypoxemic Respiratory Failure Usinf Machine Learning Approaches
- ClinicalTrials.gov ID
- NCT06815523
- Acronym / sponsor ID
- PREMIER / PIFIISC24
- Conditions
- Acute Hypoxemic Respiratory Failure
- Keywords
- mechanical ventilation, machine learning, prediction of prolonged mechanical ventilation, outcome
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 1,241 participants (actual)
- Lead sponsor
- Jesus VillarUniversity, hospital or other
- Collaborators
- Hospital Universitario de Gran Canaria Doctor Negrín, Instituto de Salud Carlos III
- Study start
- 2 Feb 2025 (actual)
- Primary completion
- 1 May 2026 (actual)
- Study completion
- 1 Jun 2026 (actual)
- First posted
- 7 Feb 2025 (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.
Acute hypoxemic respiratory failure (AHRF) is a common cause of admission in intensive care units (ICUs) worldwide. We will assess machine learning (ML) techniques for prediction of prolonged duration (> or = to 7 days) of mechanical ventilation (MV) in 1,241 patients enrolled in the PANDORA study in Spain. The study was registered with ClinalTrials.gov (NCT03145974). Our aim is to identify a model with the minimum number of variables that predict duration of prolonged ventilation in AHRF patients using data as early as from the first 48 hours with machine learning algorithms.
Detailed description
Acute hypoxemic respiratory failure (AHRF) is the most common cause of admission in intensive care units (ICUs) worldwide. The investigators will assess the value of machine learning (ML) techniques for prediction of prolonged duration (> or equeal to 7 days) of mechanical ventilation (MV) in 1,241 patients enrolled in the PANDORA study in Spain. Few studies have investigated the prediction of prolonged MV in patients with AHRF.
For model training and testing, the investigators will extract data from random pateints from the first 2 days after diagnosis of AHRF. The investigators had a database with 2,000,000 anonymized and dissociated demographics and clinically relevant data from 1,241 patients with AHRF from 22 hospitals in Spain. The investigators will follow the TRIPOD guidelines for prediction models. The investigators will screen relevant collected variables using a genetic algorithm variable selection to achieve parsimony. We will use 5-fold corss-validation in the data set of patients with data at T0, T24 and T48. We will use 25% of patients randomly selected for evaluation of the model.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Derivation/testing cohort | – | Other: Machine learning and logistic regression for the training/testing cohort and validation cohortThe investigators will use a chort of 75% of patients, randomly selected, with data at T0, T24 and T48 after diagnosis of acute hypoxemic respiratory failure (AHRF). We will apply machine learning approaches. |
| Validation hohort | – | Other: Machine learning and logistic regression for the training/testing cohort and validation cohortwe will use 25% of unseen patients, randomly selected, with data at T0, T24 and T48 after diagnosis of AHRF. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- De-identified dataset inclusing 1241 ventilated patients with AHRF admitted in a network of Spainisg ICUs.
Key inclusion criteria (2 listed)
- enotracheal intubation puls mechanical ventilation
- PaO2/FiO2 ratio <or = 300 mmHg under MV with PEEP >or =5 and FiO2 >or = 0.3
Key exclusion criteria (1 listed)
- 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 |
|---|---|---|
| Machine learning and logistic regression for the training/testing cohort and validation cohortMachine learning and logistic regression for the validation cohort | Other | Derivation/testing cohort, Validation hohort |
Primary outcome
MV duration
Time frame: up to 100 weeks
duration of mechanical ventilation
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 8 Jul 2026.
- Derived from this study: PubMed 41302939Villar J, Gonzalez-Martin JM, Fernandez C, Soler JA, Rey-Abalo M, Mora-Ordonez JM, Ortiz-Diaz-Miguel R, Fernandez L, Murcia I, Robaglia D, Anon JM, Ferrando C, Parrilla D, Dominguez-Berrot AM, Cobeta P, Martinez D, Amaro-Harpigny A, Andaluz-Ojeda D, Fernandez MM, Gomez-Bentolila E, Steyerberg EW, Camporota L, Szakmany T. Predicting Outcome and Duration of Mechanical Ventilation in Acute Hypoxemic Respiratory Failure: The PREMIER Study. J Clin Med. 2025 Nov 7;14(22):7903. doi: 10.3390/jcm14227903.
- Individual participant dataSharing planned: No. Ethical reasons
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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.
Jesus Villar study director
Fundacion Canaria Instituto de Investigación Sanitaria de Canarias
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Acute Hypoxemic Respiratory Failure, run by Jesus Villar at 2 sites in Spain.
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 2 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: finished, as of the sponsor's last update on 8 Jul 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in Spain. 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?
MV duration (time frame: up to 100 weeks). 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 NCT06815523, which also lists 1 linked publication.
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 8 Jul 2026.