Acute Respiratory Distress SyndromePOSTCARDSNCT05611177
Predicting ICU Mortality in ARDS Patients
An observational study of Acute Respiratory Distress Syndrome, run by Hospital Universitario de Gran Canaria Doctor Negrín at 3 sites in Spain.
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, NCT02288949, NCT02836444, NCT03145974), aimed to characterize the best early model to predict… 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?
- machine learning analysis
- How many people
- 1,303 participants
- Where
- 3 sitesSpain
- Study dates
- 14 Nov 2022to 1 Aug 2023
- 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 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 ARDS
Who cannot join
- Postoperative patients ventilated <24h; brain death patients.
Trial locations and contacts
3 sites in 1 country.
Spain3 sites
Department of Anesthesia, Hospital Clinic
Barcelona, 08036
Hospital Universitario Dr. Negrin
Las Palmas de Gran Canaria, Las Palmas, 35019
Hospital Universitario La Paz (ICU)
Madrid, 28046
About this study
Official title: Predicting Mortality in Patients With the 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, NCT02288949, NCT02836444, NCT03145974), aimed to characterize the best early model to predict duration of mechanical ventilation and mortality in the intensive care unit (ICU) after ARDS diagnosis using machine learning approaches.
Read the detailed description
The acute respiratory distress syndrome (ARDS) is a severe form of acute hypoxemic respiratory failure in Critical Care Units worldwide. Most ARDS patients requiere mechanical ventilation (MV). Few studies have investigated the prediction of MV duration and mortality of ARDS.
For model description, the investigators will extract data from the first two ICU days after diagnosis of moderate-to-severe ARDS from patients included in the de-identified database, which includes 1,303 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 tecniques will be implemented (Random Forest, XGBoost, Logistic regression analysis, and/or neural networks) for development of the prediction model, and the accuracy will be compared to those of existing scoring systems for assessing ICU severity (APACHE II, SOFA) and the PaO2/FiO2 ratio. 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 calculating the respective confusion matrices and several statistics such as sensitivity, specificity, positive predictive value, and negative predictive value for mortality and duration of MV. Investigators will select the best probabilistic model with a minimum number of clinical variables.
- Conditions
- Acute Respiratory Distress Syndrome
- Being tested
- machine learning analysis
- Keywords
- outcome, mechanical ventilation, intensive care unit, 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.
machine learning analysisOther
Also called Logistic regression, cross-validation, are aunder the ROC curves
We will use robust machine learning approaches, such as Random Forest, XGBoost or Neural Networks.
| Study group | Role | What they receive |
|---|---|---|
| Derivation cohort | It will contain 700 patients (70% of 1000 ARDS patients) Receives: Other: machine learning analysis | |
| Validation cohort | It will contain 300 patients (30% of 1000 ARDS patients) Receives: Other: machine learning analysis | |
| Confirmatory cohort | It will contain 303 patients (for 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
mortality in the intensive care unit
Measured: up to 6 months
Secondary outcomes (1)
MV duration
Duration of mechanical ventilation
Measured: from ARDS diagnosis to extubation
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted9 Nov 2022
- Study start14 Nov 2022
- Primary completion1 Aug 2023
- Study completion1 Aug 2023
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (2)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
- Responsible party
- Principal investigator, Jesus Villar, Hospital Universitario de Gran Canaria Doctor Negrín
- Investigators
- Jesús Villar, MD, PhD, Principal investigator, Hospital Universitario D. Negrin
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05611177
- Sponsor's protocol ID
- 7/2021
- Acronym
- POSTCARDS
- Status last verified
- Aug 2023
- First submitted
- 1 Nov 2022
- Last updated
- 21 Aug 2023
- 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 30624279Huang B, Liang D, Zou R, Yu X, Dan G, Huang H, Liu H, Liu Y. Mortality prediction for patients with acute respiratory distress syndrome based on machine learning: a population-based study. Ann Transl Med. 2021 May;9(9):794. doi: 10.21037/atm-20-6624.
PubMed 34268407
Study at a glance
From the registry record, last updated by the sponsor on 21 Aug 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Predicting Mortality in Patients With the Acute Respiratory Distress Syndrome Using Machine Learning
- ClinicalTrials.gov ID
- NCT05611177
- Acronym / sponsor ID
- POSTCARDS / 7/2021
- Conditions
- Acute Respiratory Distress Syndrome
- Keywords
- outcome, mechanical ventilation, intensive care unit, 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
- Study start
- 14 Nov 2022 (actual)
- Primary completion
- 1 Aug 2023 (actual)
- Study completion
- 1 Aug 2023 (actual)
- First posted
- 9 Nov 2022 (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, NCT02288949, NCT02836444, NCT03145974), aimed to characterize the best early model to predict duration of mechanical ventilation and mortality in the intensive care unit (ICU) after ARDS diagnosis using machine learning approaches.
Detailed description
The acute respiratory distress syndrome (ARDS) is a severe form of acute hypoxemic respiratory failure in Critical Care Units worldwide. Most ARDS patients requiere mechanical ventilation (MV). Few studies have investigated the prediction of MV duration and mortality of ARDS.
For model description, the investigators will extract data from the first two ICU days after diagnosis of moderate-to-severe ARDS from patients included in the de-identified database, which includes 1,303 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 tecniques will be implemented (Random Forest, XGBoost, Logistic regression analysis, and/or neural networks) for development of the prediction model, and the accuracy will be compared to those of existing scoring systems for assessing ICU severity (APACHE II, SOFA) and the PaO2/FiO2 ratio. 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 calculating the respective confusion matrices and several statistics such as sensitivity, specificity, positive predictive value, and negative predictive value for mortality and duration of MV. Investigators will select the best probabilistic model with a minimum number of clinical variables.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Derivation cohort | – | Other: machine learning analysisIt will contain 700 patients (70% of 1000 ARDS patients) |
| Validation cohort | – | Other: machine learning analysisIt will contain 300 patients (30% of 1000 ARDS patients) |
| Confirmatory cohort | – | Other: machine learning analysisIt will contain 303 patients (for 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 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 ARDS
Key exclusion criteria (1 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 |
|---|---|---|
| machine learning analysisAlso: Logistic regression, cross-validation, are aunder the ROC curvesWe will use robust machine learning approaches, such as Random Forest, XGBoost or Neural Networks. | Other | Confirmatory cohort, Derivation cohort, Validation cohort |
Primary outcome
ICU mortality
Time frame: up to 6 months
mortality in the intensive care unit
1 secondary outcome
MV duration
Time frame: from ARDS diagnosis 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 21 Aug 2023.
- Background: 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.
- Background: PubMed 34268407Huang B, Liang D, Zou R, Yu X, Dan G, Huang H, Liu H, Liu Y. Mortality prediction for patients with acute respiratory distress syndrome based on machine learning: a population-based study. Ann Transl Med. 2021 May;9(9):794. doi: 10.21037/atm-20-6624.
- Individual participant dataSharing planned: No
Related trials for Acute Respiratory Distress Syndrome
Active, not recruitingObservational study
Longitudinal Recovery Trajectories After an Acute Respiratory Distress Syndrome, a New Understanding
ARDS, ARDS Disease Progression
RecruitingObservational study
Respiratory Distress and Elective Cesarean Section at Term
Respiratory Distress Syndrome
Not yet recruitingPhase 1/2
Little Lungs Study
Invasive Mechanical Ventilation, Severe Respiratory Distress Syndrome
Not yet recruitingPhase 1
Evaluating AVM0703 for Treatment of COVID-19 or Influenza-mediated ARDS
ARDS, Covid19
RecruitingObservational study
Pulse Wave vs. Saline Contrast EIT for Pulmonary Perfusion in ARDS
ARDS (Acute Respiratory Distress Syndrome), Electrical Impedance Tomography (EIT)
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, MD, PhD principal investigator
Hospital Universitario D. Negrin
3 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 Respiratory Distress Syndrome, run by Hospital Universitario de Gran Canaria Doctor Negrín at 3 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 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: finished, as of the sponsor's last update on 21 Aug 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 3 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 6 months). 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 NCT05611177, which also lists 2 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 21 Aug 2023.