Out of Hospital Cardiac ArrestNCT06856018
Artificial Intelligence Cerebral Gray-white Matter Ratio Module Usage in Hsinchu District Hsinchu District Using an Artificial Intelligence Cerebral Gray-white Matter Ratio Module
An observational study of Out of Hospital Cardiac Arrest, run by National Taiwan University Hospital at 1 site in Taiwan.
Trial summary
This study aims to establish an electronic medical record and imaging database for out-of-hospital cardiac arrest (OHCA) patients at NTUH Hsinchu Branch. Leveraging an AI deep learning model and an automated brain gray-white matter analysis system developed at NTUH, the research seeks to validate these tools… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Out of Hospital Cardiac Arrest
- How many people
- 350 participants
- Where
- 1 siteTaiwan
- Study dates
- 1 Dec 2024to 31 Dec 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
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: Patients with out-of-hospital cardiac arrest
Who may be able to join
- Patients at National Taiwan University Hospital Hsinchu Branch who experienced non-traumatic cardiac arrest between January 1, 2014, and December 31, 2020, and successfully achieved return of spontaneous circulation (ROSC) following resuscitation.
Who cannot join
- Under 18 years of age;
- Pregnant women;
- Individuals who did not achieve successful resuscitation
- Individuals without computed tomography (CT) imaging after resuscitation.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Extrapolative Study on the Prognosis of Out-of-hospital Cardiac Arrest in the Hsinchu District Using an Artificial Intelligence Cerebral Gray-white Matter Ratio Module
This study aims to establish an electronic medical record and imaging database for out-of-hospital cardiac arrest (OHCA) patients at NTUH Hsinchu Branch. Leveraging an AI deep learning model and an automated brain gray-white matter analysis system developed at NTUH, the research seeks to validate these tools externally. By integrating electronic medical records and brain imaging data, the project strives to enhance the accuracy of prognostic assessments, supporting physicians and families in decision-making for post-cardiac arrest care. Validation at Hsinchu Branch will assess the model's reliability across diverse medical settings and patient populations, optimizing its applicability and accuracy.
Read the detailed description
The purpose of this study is to establish an electronic medical record and imaging database for out-of-hospital cardiac arrest patients at National Taiwan University Hospital Hsinchu Branch. Our team have developed an AI deep learning model and an automated analysis system for brain gray-white matter based on data from National Taiwan University Hospital.
These developments will be externally validated using the database at Hsinchu Branch in this project. Accurate prognosis assessment is crucial for physicians and families in making decisions regarding post-cardiac arrest care period. However, the current available assessment tools have limited accuracy. This study aims to develop a multimodal prognostic evaluation model that combines electronic medical records and the automated analysis system for brain graywhite matter. This integration will enhance the accuracy and predictive capability of prognosis assessment. The research team has already developed an automated analysis system for calculating brain gray-white matter ratio from brain computed tomography images, providing important information about pathological changes in the brain.
Additionally, the team has also developed an AI-based predictive model for post-cardiac arrest prognosis, incorporating multiple indicators and variables. This system has been validated using data from National Taiwan University Hospital.
To further validate the accuracy and reliability of our models, the research team plans to collaborate with Hsinchu Branch in collecting and organizing relevant data of post-cardiac arrest patients, including electronic medical records and imaging files. The developed automated analysis system for brain gray-white matter and the AI-based predictive model will be applied for external validation. Through this research, the goal is to establish and optimize a more comprehensive and accurate prognosis assessment model, assisting physicians and families in making better decisions for post-cardiac arrest patients.
Furthermore, the collaboration with Hsinchu Branch will enable the validation of our models'applicability in different medical institutions and patient populations.
- Conditions
- Out of Hospital Cardiac Arrest
- Keywords
- Out-of-hospital cardiac arrest patients, resuscitation,, artificial intelligence deep learning model
- Medical subject headings
- Out-of-Hospital Cardiac Arrest
What is being measured
The primary outcome is the main question the study is designed to answer.
Cerebral Performance Categories (CPC) Scale
The Cerebral Performance Categories (CPC) scale is crucial for evaluating neurological outcomes in OHCA patients, providing a standardized framework to assess brain function and recovery after cardiac arrest. Ranging from CPC 1 (good recovery) to CPC 5 (brain death), it categorizes levels of neurological impairment, offering insights into the patient's prognosis. This scale is widely used in clinical and research settings to ensure consistent outcome measurement and facilitate comparison across studies. Additionally, it plays a vital role in guiding clinical decisions and discussions with families about post-resuscitation care and expectations, ultimately supporting better-informed decision-making.
Measured: From the time of ROSC achievement until hospital discharge or death, assessed up to 700 days
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted4 Mar 2025
- Study start1 Dec 2024
- Primary completion31 Dec 2026 (estimated)
- Study completion31 Dec 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (5)Papers the sponsor has linked to this study.
- Other trials for Out of Hospital Cardiac ArrestStudies of the same condition, open and under way.
- More trials run by National Taiwan University 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
- Retrospective
- Enrolment
- 350 participants (estimated)
Sponsor and investigators
- Lead sponsor
- National Taiwan University Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06856018
- Sponsor's protocol ID
- 202404018RINA
- Status last verified
- Nov 2025
- First submitted
- 17 Nov 2024
- Last updated
- 18 Nov 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: The file contains private information and requires too much storage capacity, making it impossible to share.
Publications
Mutasa S, Sun S, Ha R. Understanding artificial intelligence based radiology studies: What is overfitting? Clin Imaging. 2020 Sep;65:96-99. doi: 10.1016/j.clinimag.2020.04.025. Epub 2020 Apr 23.
PubMed 32387803Wang CH, Huang CH, Chang WT, Tsai MS, Yu PH, Wu YW, Chen WJ. Prognostic performance of simplified out-of-hospital cardiac arrest (OHCA) and cardiac arrest hospital prognosis (CAHP) scores in an East Asian population: A prospective cohort study. Resuscitation. 2019 Apr;137:133-139. doi: 10.1016/j.resuscitation.2019.02.015. Epub 2019 Feb 20.
PubMed 30797049Adrie C, Cariou A, Mourvillier B, Laurent I, Dabbane H, Hantala F, Rhaoui A, Thuong M, Monchi M. Predicting survival with good neurological recovery at hospital admission after successful resuscitation of out-of-hospital cardiac arrest: the OHCA score. Eur Heart J. 2006 Dec;27(23):2840-5. doi: 10.1093/eurheartj/ehl335. Epub 2006 Nov 2.
PubMed 17082207Chang HC, Tsai MS, Kuo LK, Hsu HH, Huang WC, Lai CH, Shih MC, Huang CH. Factors affecting outcomes in patients with cardiac arrest who receive target temperature management: The multi-center TIMECARD registry. J Formos Med Assoc. 2022 Jan;121(1 Pt 2):294-303. doi: 10.1016/j.jfma.2021.04.006. Epub 2021 Apr 29.
PubMed 33934947Rea TD, Eisenberg MS, Sinibaldi G, White RD. Incidence of EMS-treated out-of-hospital cardiac arrest in the United States. Resuscitation. 2004 Oct;63(1):17-24. doi: 10.1016/j.resuscitation.2004.03.025.
PubMed 15451582
Study at a glance
From the registry record, last updated by the sponsor on 18 Nov 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Extrapolative Study on the Prognosis of Out-of-hospital Cardiac Arrest in the Hsinchu District Using an Artificial Intelligence Cerebral Gray-white Matter Ratio Module
- ClinicalTrials.gov ID
- NCT06856018
- Acronym / sponsor ID
- 202404018RINA
- Conditions
- Out of Hospital Cardiac Arrest
- Keywords
- Out-of-hospital cardiac arrest patients, resuscitation,, artificial intelligence deep learning model
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 350 participants (estimated)
- Lead sponsor
- National Taiwan University HospitalUniversity, hospital or other
- Study start
- 1 Dec 2024 (actual)
- Primary completion
- 31 Dec 2026 (estimated)
- Study completion
- 31 Dec 2026 (estimated)
- First posted
- 4 Mar 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.
This study aims to establish an electronic medical record and imaging database for out-of-hospital cardiac arrest (OHCA) patients at NTUH Hsinchu Branch. Leveraging an AI deep learning model and an automated brain gray-white matter analysis system developed at NTUH, the research seeks to validate these tools externally. By integrating electronic medical records and brain imaging data, the project strives to enhance the accuracy of prognostic assessments, supporting physicians and families in decision-making for post-cardiac arrest care. Validation at Hsinchu Branch will assess the model's reliability across diverse medical settings and patient populations, optimizing its applicability and accuracy.
Detailed description
The purpose of this study is to establish an electronic medical record and imaging database for out-of-hospital cardiac arrest patients at National Taiwan University Hospital Hsinchu Branch. Our team have developed an AI deep learning model and an automated analysis system for brain gray-white matter based on data from National Taiwan University Hospital.
These developments will be externally validated using the database at Hsinchu Branch in this project. Accurate prognosis assessment is crucial for physicians and families in making decisions regarding post-cardiac arrest care period. However, the current available assessment tools have limited accuracy. This study aims to develop a multimodal prognostic evaluation model that combines electronic medical records and the automated analysis system for brain graywhite matter. This integration will enhance the accuracy and predictive capability of prognosis assessment. The research team has already developed an automated analysis system for calculating brain gray-white matter ratio from brain computed tomography images, providing important information about pathological changes in the brain.
Additionally, the team has also developed an AI-based predictive model for post-cardiac arrest prognosis, incorporating multiple indicators and variables. This system has been validated using data from National Taiwan University Hospital.
To further validate the accuracy and reliability of our models, the research team plans to collaborate with Hsinchu Branch in collecting and organizing relevant data of post-cardiac arrest patients, including electronic medical records and imaging files. The developed automated analysis system for brain gray-white matter and the AI-based predictive model will be applied for external validation. Through this research, the goal is to establish and optimize a more comprehensive and accurate prognosis assessment model, assisting physicians and families in making better decisions for post-cardiac arrest patients.
Furthermore, the collaboration with Hsinchu Branch will enable the validation of our models'applicability in different medical institutions and patient populations.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients with out-of-hospital cardiac arrest
Key inclusion criteria (1 listed)
- Patients at National Taiwan University Hospital Hsinchu Branch who experienced non-traumatic cardiac arrest between January 1, 2014, and December 31, 2020, and successfully achieved return of spontaneous circulation (ROSC) following resuscitation.
Key exclusion criteria (4 listed)
- Under 18 years of age;
- Pregnant women;
- Individuals who did not achieve successful resuscitation
- Individuals without computed tomography (CT) imaging after resuscitation.
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
Cerebral Performance Categories (CPC) Scale
Time frame: From the time of ROSC achievement until hospital discharge or death, assessed up to 700 days
The Cerebral Performance Categories (CPC) scale is crucial for evaluating neurological outcomes in OHCA patients, providing a standardized framework to assess brain function and recovery after cardiac arrest. Ranging from CPC 1 (good recovery) to CPC 5 (brain death), it categorizes levels of neurological impairment, offering insights into the patient's prognosis. This scale is widely used in clinical and research settings to ensure consistent outcome measurement and facilitate comparison across studies. Additionally, it plays a vital role in guiding clinical decisions and discussions with families about post-resuscitation care and expectations, ultimately supporting better-informed decision-making.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 18 Nov 2025.
- Background: PubMed 32387803Mutasa S, Sun S, Ha R. Understanding artificial intelligence based radiology studies: What is overfitting? Clin Imaging. 2020 Sep;65:96-99. doi: 10.1016/j.clinimag.2020.04.025. Epub 2020 Apr 23.
- Background: PubMed 30797049Wang CH, Huang CH, Chang WT, Tsai MS, Yu PH, Wu YW, Chen WJ. Prognostic performance of simplified out-of-hospital cardiac arrest (OHCA) and cardiac arrest hospital prognosis (CAHP) scores in an East Asian population: A prospective cohort study. Resuscitation. 2019 Apr;137:133-139. doi: 10.1016/j.resuscitation.2019.02.015. Epub 2019 Feb 20.
- Background: PubMed 17082207Adrie C, Cariou A, Mourvillier B, Laurent I, Dabbane H, Hantala F, Rhaoui A, Thuong M, Monchi M. Predicting survival with good neurological recovery at hospital admission after successful resuscitation of out-of-hospital cardiac arrest: the OHCA score. Eur Heart J. 2006 Dec;27(23):2840-5. doi: 10.1093/eurheartj/ehl335. Epub 2006 Nov 2.
- Background: PubMed 33934947Chang HC, Tsai MS, Kuo LK, Hsu HH, Huang WC, Lai CH, Shih MC, Huang CH. Factors affecting outcomes in patients with cardiac arrest who receive target temperature management: The multi-center TIMECARD registry. J Formos Med Assoc. 2022 Jan;121(1 Pt 2):294-303. doi: 10.1016/j.jfma.2021.04.006. Epub 2021 Apr 29.
- Background: PubMed 15451582Rea TD, Eisenberg MS, Sinibaldi G, White RD. Incidence of EMS-treated out-of-hospital cardiac arrest in the United States. Resuscitation. 2004 Oct;63(1):17-24. doi: 10.1016/j.resuscitation.2004.03.025.
- Individual participant dataSharing planned: No. The file contains private information and requires too much storage capacity, making it impossible to share.
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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 Out of Hospital Cardiac Arrest, run by National Taiwan University Hospital at 1 site in Taiwan.
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 4 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: under way, not looking for participants, as of the sponsor's last update on 18 Nov 2025. 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 Taiwan. 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?
This study is still under way. Results are not available in the source record yet.
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?
Cerebral Performance Categories (CPC) Scale (time frame: From the time of ROSC achievement until hospital discharge or death, assessed up to 700 days). 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 NCT06856018, which also lists 5 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 18 Nov 2025.