Acute Hypoxemic Respiratory FailureMEMORIALNCT06333002
Machine Learning Model to Predict Outcome in Acute Hypoxemic Respiratory Failure
An observational study of Acute Hypoxemic Respiratory Failure, run by Hospital Universitario de Gran Canaria Doctor Negrín at 8 sites in Spain.
Trial summary
Acute hypoxemic respiratory failure (AHRF) is the most common cause of admission in the intensive care units (UCIs) worldwide. We will assess the value of machine learning (ML) techniques for early prediction of ICU death in 1,241 patients enrolled in the PANDORA (Prevalence AND Outcome of acute Respiratory fAilure)… Read more about this study
- Who is it for?
- Ages 18 to 100 years
- Conditions the study is for
- Acute Hypoxemic Respiratory Failure
- What is being tested?
- machine learning analysis
- How many people
- 1,241 participants
- Where
- 8 sitesSpain
- Study dates
- 19 Mar 2024to 30 May 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 to 100 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: De-identified dataset inclusing 1,241 mechanically ventilated patients with acute hypoxemic respiratory failure admitted consecutively in a network of Spanish ICUs.
Who may be able to join
- endotracheal intubation plus mechanical ventilation (MV)
- PaO2/FiO2 ratio ≤300 mmHg under MV with positive end-expiratory pressure (PEEP) ≥5 cmH2O and FiO2 ≥0.3.
Who cannot join
- Post-operative patients ventilated <24 h
- Brain death patients.
Trial locations and contacts
8 sites in 1 country.
8 sites in 1 country
Spain8 sites
Hospital General Universitario de Ciudad Real
Ciudad Real, 13005
Hospital Virgen de La Luz
Cuenca, 16002
Hospital Universitario La Paz
Madrid, 28046
Hospital Universitario Puerta de Hierro
Madrid, 28222
Hospital Universitario Virgen de Arrixaca
Murcia, 3012
Hospital Universitario NS de Candelaria
Santa Cruz de Tenerife, 38010
Hospital Cinico de Valencia
Valencia, 46010
Hospital Universitario Rio Hortega
Valladolid, 47012
About this study
Official title: Developing an Optimal Machine Learning Model to Predict ICU Outcome in Patients With Acute Hypoxemic Respiratory Failure
Acute hypoxemic respiratory failure (AHRF) is the most common cause of admission in the intensive care units (UCIs) worldwide. We will assess the value of machine learning (ML) techniques for early prediction of ICU death in 1,241 patients enrolled in the PANDORA (Prevalence AND Outcome of acute Respiratory fAilure) Study in Spain. The study was registered with ClinicalTrials.gov (NCT03145974). Our aim is to evaluate the minimum number of variables models using logistic regression and four supervised ML algorithms: Random Forest, Extreme Gradient Boosting, Support Vector Machine and Multilayer Perceptron.
Read the detailed description
Acute hypoxemic respiratory failure (AHRF) is the most common cause of admission in the intensive care units (UCIs) worldwide. We will assess the value of machine learning (ML) techniques for early prediction of ICU death in AHRF patients on mechanical ventilation (MV). Few studies have investigated the prediction of mortality in patients with AHRF.
For model development, the investigators will extract data for the first 2 days after diagnosis of AHRF from patients included in the de-identified database of the PANDORA cohort. We had a database with 2,000,000 anonymized and dissociated demographics and clinical, data from 1,241 patients with AHRF enrolled in our PANDORA cohort (Prevalence AND Outcome of acute Respiratory fAilure) from 22 Spanish hospitals and coordinated by the principal investigator (JV). The investigators will follow the Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) guidelines for model prediction. We will screen collected variables employing a genetic algorithm variable selection method to achieve parsimony. We evaluated the minimum number of variables models using logistic regression and 4 supervised ML algorithms: Random Forest, Extreme Gradient Boosting, Support Vector Machine and Multilayer Perceptron. We will use a 5-fold cross-validation in the dataset of 1,000 patients selected randomly in training data (80%) and testing data (20%). For external validation, we will use the remaining 241 patients.
- Conditions
- Acute Hypoxemic Respiratory Failure
- Being tested
- machine learning analysis
- Keywords
- outcome, mechanical ventilation, machine learning
- 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 analysisOther
Also called Logistic regression, cross validation, and area under the ROC curves
We will use robust machine learning approaches, such as Random Forest, Extreme Gradient Boosting, Support Vector Machine and Multilayer Perceptron.
| Study group | Role | What they receive |
|---|---|---|
| Derivation cohort | It will contain 800 patients randomly selected (1,000 patients with AHRF) Receives: Other: machine learning analysis | |
| Validation cohort | It will contain 200 patients randomly selected (20% of 1000 patients with AHRF Receives: Other: machine learning analysis | |
| Confirmatory cohort | It will contain the remaining 241 patients randomply selected (por external validation) Receives: Other: machine learning analysis |
What is being measured
The primary outcome is the main question the study is designed to answer.
ICU mortality
death in the intensive care unit
Measured: up to 100 weeks (from inclusion to death or diascharge from intensive care unit
Secondary outcomes (1)
MV duration
duration of mechanical ventilation
Measured: up to 100 weeks (from inclusion to extubation)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted27 Mar 2024
- Study start19 Mar 2024
- Primary completion30 May 2026
- Study completion30 May 2026
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- 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
- Hospital Universitario de Gran Canaria Doctor Negrín
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Jesus Villar, Hospital Universitario de Gran Canaria Doctor Negrín
- Investigators
- Jesus Villar, MD, PhD, Principal investigator, Fundación Canaria Instituto de Investigación Sanitaria de Canarias
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06333002
- Sponsor's protocol ID
- PI24/00325
- Acronym
- MEMORIAL
- Status last verified
- Jul 2026
- First submitted
- 19 Mar 2024
- Last updated
- 9 Jul 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 9 Jul 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Developing an Optimal Machine Learning Model to Predict ICU Outcome in Patients With Acute Hypoxemic Respiratory Failure
- ClinicalTrials.gov ID
- NCT06333002
- Acronym / sponsor ID
- MEMORIAL / PI24/00325
- Conditions
- Acute Hypoxemic Respiratory Failure
- Keywords
- outcome, mechanical ventilation, machine learning
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 1,241 participants (actual)
- Lead sponsor
- Hospital Universitario de Gran Canaria Doctor NegrínUniversity, hospital or other
- Study start
- 19 Mar 2024 (actual)
- Primary completion
- 30 May 2026 (actual)
- Study completion
- 30 May 2026 (actual)
- First posted
- 27 Mar 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.
Acute hypoxemic respiratory failure (AHRF) is the most common cause of admission in the intensive care units (UCIs) worldwide. We will assess the value of machine learning (ML) techniques for early prediction of ICU death in 1,241 patients enrolled in the PANDORA (Prevalence AND Outcome of acute Respiratory fAilure) Study in Spain. The study was registered with ClinicalTrials.gov (NCT03145974). Our aim is to evaluate the minimum number of variables models using logistic regression and four supervised ML algorithms: Random Forest, Extreme Gradient Boosting, Support Vector Machine and Multilayer Perceptron.
Detailed description
Acute hypoxemic respiratory failure (AHRF) is the most common cause of admission in the intensive care units (UCIs) worldwide. We will assess the value of machine learning (ML) techniques for early prediction of ICU death in AHRF patients on mechanical ventilation (MV). Few studies have investigated the prediction of mortality in patients with AHRF.
For model development, the investigators will extract data for the first 2 days after diagnosis of AHRF from patients included in the de-identified database of the PANDORA cohort. We had a database with 2,000,000 anonymized and dissociated demographics and clinical, data from 1,241 patients with AHRF enrolled in our PANDORA cohort (Prevalence AND Outcome of acute Respiratory fAilure) from 22 Spanish hospitals and coordinated by the principal investigator (JV). The investigators will follow the Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) guidelines for model prediction. We will screen collected variables employing a genetic algorithm variable selection method to achieve parsimony. We evaluated the minimum number of variables models using logistic regression and 4 supervised ML algorithms: Random Forest, Extreme Gradient Boosting, Support Vector Machine and Multilayer Perceptron. We will use a 5-fold cross-validation in the dataset of 1,000 patients selected randomly in training data (80%) and testing data (20%). For external validation, we will use the remaining 241 patients.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Derivation cohort | – | Other: machine learning analysisIt will contain 800 patients randomly selected (1,000 patients with AHRF) |
| Validation cohort | – | Other: machine learning analysisIt will contain 200 patients randomly selected (20% of 1000 patients with AHRF |
| Confirmatory cohort | – | Other: machine learning analysisIt will contain the remaining 241 patients randomply selected (por external validation) |
Who may qualify
- Ages
- 18 to 100 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- De-identified dataset inclusing 1,241 mechanically ventilated patients with acute hypoxemic respiratory failure admitted consecutively in a network of Spanish ICUs.
Key inclusion criteria (2 listed)
- endotracheal intubation plus mechanical ventilation (MV)
- PaO2/FiO2 ratio ≤300 mmHg under MV with positive end-expiratory pressure (PEEP) ≥5 cmH2O and FiO2 ≥0.3.
Key exclusion criteria (2 listed)
- Post-operative patients ventilated <24 h
- 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 analysisAlso: Logistic regression, cross validation, and area under the ROC curvesWe will use robust machine learning approaches, such as Random Forest, Extreme Gradient Boosting, Support Vector Machine and Multilayer Perceptron. | Other | Confirmatory cohort, Derivation cohort, Validation cohort |
Primary outcome
ICU mortality
Time frame: up to 100 weeks (from inclusion to death or diascharge from intensive care unit
death in the intensive care unit
1 secondary outcome
MV duration
Time frame: up to 100 weeks (from inclusion to extubation)
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 9 Jul 2026.
- 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.
Jesus Villar, MD, PhD principal investigator
Fundación Canaria Instituto de Investigación Sanitaria de Canarias
8 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 Hospital Universitario de Gran Canaria Doctor Negrín at 8 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 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 9 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 8 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?
ICU mortality (time frame: up to 100 weeks (from inclusion to death or diascharge from intensive care unit). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 9 Jul 2026.