ECMONCT06654388
To Construct a Prognosis Prediction Model for ECMO Patients Based on Machine Learning Algorithms
An observational study of ECMO, run by Zhujiang Hospital at 1 site in China.
Trial summary
Extracorporeal membrane oxygenation (ECMO) is a critical life-support technique for patients with severe medical conditions. Various factors affect the mortality rates of patients in intensive care units, presenting a significant clinical challenge in accurately predicting outcomes based on a limited set of indicators. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- ECMO
- How many people
- 290 participants
- Where
- 1 siteChina
- Study dates
- 1 Jan 2018to 1 Oct 2024
- How long will I take part?
- 3 Months
- 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: The study involved patients who received ECMO treatment at Zhujiang Hospital of Southern Medical University from 2018 to 2024. Exclusion criteria included patients who received ECMO support at outside hospitals and those under the age of 18. Of the patients, 100 were discharged alive, while 190 died during hospitalization.
Who may be able to join
- All patients who underwent ECMO in our hospital and were registered in the CSECLS registry database (ClinicalTrials.gov Identifier:NCT04158479) from January 1, 2018 to now were retrospectively collected.
Who cannot join
- ECMO was discontinued for non-medical reasons.
- Under 18 years of age.
Trial locations and contacts
1 site in 1 country.
China1 site
Zhujiang Hospital of Southern Medical University
Guangzhou, Guangdong, 510000
About this study
Extracorporeal membrane oxygenation (ECMO) is a critical life-support technique for patients with severe medical conditions. Various factors affect the mortality rates of patients in intensive care units, presenting a significant clinical challenge in accurately predicting outcomes based on a limited set of indicators.
Read the detailed description
Extracorporeal Membrane Oxygenation (ECMO) is used to provide continuous extracorporeal respiratory and circulatory support for patients with severe cardiopulmonary failure. It is the most important life support method in critical care medicine, and also one of the most complex and expensive treatment methods in intensive care unit (ICU). It takes a lot of resources to maintain. Therefore, it is particularly important to strictly grasp the indications of patients and accurately predict the prognosis of patients to assist clinical decision-making.
Several previous published studies have used clinical scores to predict the prognosis of ECMO patients, but most of them focused on ECMO outcomes in specific patient groups, such as adult respiratory distress syndrome(ARDS), respiratory failure, lung transplantation, cardiogenic shock, and so on. In addition, most of these estimates were calculated using traditional statistical methods and have limited fitting power for data sets with more characteristic variables.
Artificial Intelligence (AI) and Machine Learning (ML) provide a more advanced alternative to traditional statistical methods, and have unparalleled advantages in dealing with data sets with high-dimensional characteristic variables and nonlinear data. And it can self-iterate to improve model performance. In addition, ML, which can process information based on causal or statistical data, may reveal hidden dependencies between clinical indicators and disease prognosis and support clinical decision making, has emerged as the best alternative The primary outcome measures discharged alive from the hospital and died during hospitalization. A total of 69 clinical characteristic indicators were identified based on relevant literature and insights from ECMO experts in critical care medicine. These indicators included demographic data such as age, height, weight, and the medical history of ECMO patients. Additionally, infection indicators were assessed within 24 hours prior to the initiation of ECMO support and within 24 hours after its discontinuation. Furthermore, indicators pertaining to cardiac, renal, and hepatic function, as well as the type of shock (distributive shock, hypovolemic shock, cardiogenic shock, obstructive shock), were included. In addition, the average daily liquid volume within three days after the initiation of ECMO support, the duration of ECMO support, and the ICU length of stay were also considered.
- Conditions
- ECMO
- Keywords
- ecmo, machine learning, predict
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Survive | Not described | |
| Death | Not described |
What is being measured
The primary outcome is the main question the study is designed to answer.
survive
Measured: Patients were recorded from 48 hours before initiation of ecmo to the date of survival to hospital discharge or death for up to three months
death
Measured: Patients were recorded from 48 hours before initiation of ecmo to the date of survival to hospital discharge or death for up to three months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted23 Oct 2024
- Study start1 Jan 2018
- Primary completion1 Oct 2024
- Study completion1 Oct 2024
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 ECMOStudies of the same condition, open and under way.
- More trials run by Zhujiang HospitalThe sponsor's other registered studies.
- 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
- Other
- Enrolment
- 290 participants (actual)
- Target duration
- 3 Months
Sponsor and investigators
- Lead sponsor
- Zhujiang Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Gao Xiaoya, Zhujiang Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06654388
- Sponsor's protocol ID
- 2022-KY-026-01
- Status last verified
- Jan 2018
- First submitted
- 9 Oct 2024
- Last updated
- 23 Oct 2024
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Ayers B, Wood K, Gosev I, Prasad S. Predicting Survival After Extracorporeal Membrane Oxygenation by Using Machine Learning. Ann Thorac Surg. 2020 Oct;110(4):1193-1200. doi: 10.1016/j.athoracsur.2020.03.128. Epub 2020 May 23.
PubMed 32454016Stephens AF, Seman M, Diehl A, Pilcher D, Barbaro RP, Brodie D, Pellegrino V, Kaye DM, Gregory SD, Hodgson C; Extracorporeal Life Support Organization Member Centres. ECMO PAL: using deep neural networks for survival prediction in venoarterial extracorporeal membrane oxygenation. Intensive Care Med. 2023 Sep;49(9):1090-1099. doi: 10.1007/s00134-023-07157-x. Epub 2023 Aug 7.
PubMed 37548758
Study at a glance
From the registry record, last updated by the sponsor on 23 Oct 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- To Construct a Prognosis Prediction Model for ECMO Patients Based on Machine Learning Algorithms
- ClinicalTrials.gov ID
- NCT06654388
- Acronym / sponsor ID
- 2022-KY-026-01
- Conditions
- ECMO
- Keywords
- ecmo, machine learning, predict
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 290 participants (actual)
- Lead sponsor
- Zhujiang HospitalUniversity, hospital or other
- Study start
- 1 Jan 2018 (actual)
- Primary completion
- 1 Oct 2024 (actual)
- Study completion
- 1 Oct 2024 (actual)
- First posted
- 23 Oct 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.
Extracorporeal membrane oxygenation (ECMO) is a critical life-support technique for patients with severe medical conditions. Various factors affect the mortality rates of patients in intensive care units, presenting a significant clinical challenge in accurately predicting outcomes based on a limited set of indicators.
Detailed description
Extracorporeal Membrane Oxygenation (ECMO) is used to provide continuous extracorporeal respiratory and circulatory support for patients with severe cardiopulmonary failure. It is the most important life support method in critical care medicine, and also one of the most complex and expensive treatment methods in intensive care unit (ICU). It takes a lot of resources to maintain. Therefore, it is particularly important to strictly grasp the indications of patients and accurately predict the prognosis of patients to assist clinical decision-making.
Several previous published studies have used clinical scores to predict the prognosis of ECMO patients, but most of them focused on ECMO outcomes in specific patient groups, such as adult respiratory distress syndrome(ARDS), respiratory failure, lung transplantation, cardiogenic shock, and so on. In addition, most of these estimates were calculated using traditional statistical methods and have limited fitting power for data sets with more characteristic variables.
Artificial Intelligence (AI) and Machine Learning (ML) provide a more advanced alternative to traditional statistical methods, and have unparalleled advantages in dealing with data sets with high-dimensional characteristic variables and nonlinear data. And it can self-iterate to improve model performance. In addition, ML, which can process information based on causal or statistical data, may reveal hidden dependencies between clinical indicators and disease prognosis and support clinical decision making, has emerged as the best alternative The primary outcome measures discharged alive from the hospital and died during hospitalization. A total of 69 clinical characteristic indicators were identified based on relevant literature and insights from ECMO experts in critical care medicine. These indicators included demographic data such as age, height, weight, and the medical history of ECMO patients. Additionally, infection indicators were assessed within 24 hours prior to the initiation of ECMO support and within 24 hours after its discontinuation. Furthermore, indicators pertaining to cardiac, renal, and hepatic function, as well as the type of shock (distributive shock, hypovolemic shock, cardiogenic shock, obstructive shock), were included. In addition, the average daily liquid volume within three days after the initiation of ECMO support, the duration of ECMO support, and the ICU length of stay were also considered.
Study design
- Observational model
- Cohort
- Time perspective
- Other
- Target follow-up
- 3 Months
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Survive | – | – |
| Death | – | – |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- The study involved patients who received ECMO treatment at Zhujiang Hospital of Southern Medical University from 2018 to 2024. Exclusion criteria included patients who received ECMO support at outside hospitals and those under the age of 18. Of the patients, 100 were discharged alive, while 190 died during hospitalization.
Key inclusion criteria (1 listed)
- All patients who underwent ECMO in our hospital and were registered in the CSECLS registry database (ClinicalTrials.gov Identifier:NCT04158479) from January 1, 2018 to now were retrospectively collected.
Key exclusion criteria (2 listed)
- ECMO was discontinued for non-medical reasons.
- Under 18 years of age.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
survive
Time frame: Patients were recorded from 48 hours before initiation of ecmo to the date of survival to hospital discharge or death for up to three months
death
Time frame: Patients were recorded from 48 hours before initiation of ecmo to the date of survival to hospital discharge or death for up to three months
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 23 Oct 2024.
- Results publication: PubMed 32454016Ayers B, Wood K, Gosev I, Prasad S. Predicting Survival After Extracorporeal Membrane Oxygenation by Using Machine Learning. Ann Thorac Surg. 2020 Oct;110(4):1193-1200. doi: 10.1016/j.athoracsur.2020.03.128. Epub 2020 May 23.
- Results publication: PubMed 37548758Stephens AF, Seman M, Diehl A, Pilcher D, Barbaro RP, Brodie D, Pellegrino V, Kaye DM, Gregory SD, Hodgson C; Extracorporeal Life Support Organization Member Centres. ECMO PAL: using deep neural networks for survival prediction in venoarterial extracorporeal membrane oxygenation. Intensive Care Med. 2023 Sep;49(9):1090-1099. doi: 10.1007/s00134-023-07157-x. Epub 2023 Aug 7.
- 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.
The record lists no contact. Use the official record to reach the sponsor.
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 ECMO, run by Zhujiang Hospital at 1 site in China.
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 23 Oct 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 China. 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?
survive (time frame: Patients were recorded from 48 hours before initiation of ecmo to the date of survival to hospital discharge or death for up to three months). The study lists 2 primary outcomes in all. 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 NCT06654388, 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 23 Oct 2024.