Critical Care MedicineNCT07795450
Clinical Validation of a Pre-trained and Fine-tuned Model for Analyzing Time-Series Electronic Health Records in Critical Care
An observational study of Critical Care Medicine and Mortality Prediction, run by Peking University People's Hospital at 1 site in China.
Trial summary
The goal of this observational study is to validate the generalization capability and predictive performance of the "Time-Series Electronic Health Record Foundation Model (EHR Foundation Model)", which was pre-trained on a large-scale critical care dataset, within the real-world clinical environment of the Intensive… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Critical Care Medicine, Mortality Prediction
- How many people
- 4,800 participants
- Where
- 1 siteChina
- Study dates
- 1 Jan 2026to 31 Dec 2027
- How long will I take part?
- 28 Days
- 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: Study Setting: Department of Critical Care Medicine (ICU), Peking University People's Hospital. Data Source and Time Period: Retrospective Validation Cohort: Electronic health record data from all patients admitted to the ICU of Peking University People's Hospital between January 1, 2009 and December 31, 2025. This dataset will be used for local fine-tuning and internal validation of the model. Prospective Validation Cohort: Newly diagnosed cases admitted to the ICU of Peking University People's Hospital from March 1, 2026 to December 31, 2026 (following project initiation). This dataset will be used for external independent validation of the model.
Who may be able to join
- Age ≥ 18 years, no gender restriction;
- ICU length of stay ≥ 24 hours (to ensure sufficient time-series monitoring data for model generation);
- Complete electronic health record data, including baseline demographic characteristics and at least one complete laboratory test record after ICU admission.
Who cannot join
- Patients who are transferred out or die within 24 hours of ICU admission;
- Duplicate admission records for non-initial ICU admissions (only the initial ICU admission record is retained to ensure independence);
- Severe deficiency in core data (e.g., absence of major vital sign recordings or > 50% missing key laboratory test results);
- Patients with abandonment of treatment or discharge against medical advice, leading to inability to ascertain the definitive clinical outcome.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site, 1 recruiting
- Recruiting
Peking University People's Hospital
Beijing, Beijing Municipality, 100044
Huiying Zhao, PhD, +86 13811088270, zhaohuiying@pku.edu.cn
Huiying Zhao, PhD
About this study
The goal of this observational study is to validate the generalization capability and predictive performance of the "Time-Series Electronic Health Record Foundation Model (EHR Foundation Model)", which was pre-trained on a large-scale critical care dataset, within the real-world clinical environment of the Intensive Care Unit (ICU) at Peking University People's Hospital. The main question it aims to answer is:
- Based on the "pre-training + fine-tuning" paradigm, whether the EHR foundation model can effectively provide accurate and dynamic prognostic support information in real-world clinical scenarios.
Participants who agree to take part in the research, in addition to completing the signed informed consent form, if the patient's hospital stay is too short, will be obtained information on prognostic outcomes (e.g., 28-day mortality) through telephone follow-up.
Read the detailed description
The clinical validation design combines a retrospective cohort study with a prospective cohort study.
- Conditions
- Critical Care Medicine, Mortality Prediction
- Keywords
- Intensive Care Units, Electronic Health Records, Validation Study, Models, Theoretical, Hospital Mortality
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 |
|---|---|---|
| Non-survivor group | Patients confirmed to have died within 28 days (outcome obtained from hospital records or telephone follow-up). | |
| Survivor group | Patients confirmed to have survived beyond 28 days (outcome obtained from hospital records or telephone follow-up). |
What is being measured
The primary outcome is the main question the study is designed to answer.
In-hospital Mortality
Death events occurring during the patient's current hospitalization.
Measured: The endpoints are patient discharge or death. For prognostic outcomes (e.g., 28-day mortality), if the patient's hospital stay is insufficient, the outcome will be obtained through telephone follow-up.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted31 Aug 2026
- Study start1 Jan 2026
- Primary completion31 Dec 2026 (estimated)
- Study completion31 Dec 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (10)Papers the sponsor has linked to this study.
- Other trials for Critical Care MedicineStudies 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
- Other
- Enrolment
- 4,800 participants (estimated)
- Target duration
- 28 Days
Sponsor and investigators
- Lead sponsor
- Peking University People's Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- Peking University
- Responsible party
- Principal investigator, Zhao Huiying, Peking University People's Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07795450
- Sponsor's protocol ID
- 2026PHB018-001
- Status last verified
- Mar 2026
- First submitted
- 26 Aug 2026
- Last updated
- 31 Aug 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
MUHAMMAD G, ALSHEHRI F, KARRAY F, et al. A comprehensive survey on multimodal medical signals fusion for smart healthcare systems [J]. Information Fusion, 2021.
Amugongo LM, Mascheroni P, Brooks S, Doering S, Seidel J. Retrieval augmented generation for large language models in healthcare: A systematic review. PLOS Digit Health. 2025 Jun 11;4(6):e0000877. doi: 10.1371/journal.pdig.0000877. eCollection 2025 Jun.
PubMed 40498738MA L, GAO J, WANG Y, et al. Adacare: Explainable clinical health status representation learning via scale-adaptive feature extraction and recalibration; proceedings of the Proceedings of the AAAI Conference on Artificial Intelligence, F 2020].
MA M, REN J, ZHAO L, et al. Smil: Multimodal learning with severely missing modality; proceedings of the Proceedings of the AAAI conference on artificial intelligence, F, 2021 [C].
Chen J, Qi TD, Vu J, Wen Y. A deep learning approach for inpatient length of stay and mortality prediction. J Biomed Inform. 2023 Nov;147:104526. doi: 10.1016/j.jbi.2023.104526. Epub 2023 Oct 17.
PubMed 37852346SURESH H, GONG J J, GUTTAG J V. Learning tasks for multitask learning: Heterogenous patient populations in the icu; proceedings of the Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, F 2018].
Meyer ML, Fitzgerald BG, Paz-Ares L, Cappuzzo F, Janne PA, Peters S, Hirsch FR. New promises and challenges in the treatment of advanced non-small-cell lung cancer. Lancet. 2024 Aug 24;404(10454):803-822. doi: 10.1016/S0140-6736(24)01029-8. Epub 2024 Aug 6.
PubMed 39121882Jackson VA, Emanuel L. Navigating and Communicating about Serious Illness and End of Life. N Engl J Med. 2024 Jan 4;390(1):63-69. doi: 10.1056/NEJMcp2304436. Epub 2023 Dec 20. No abstract available.
PubMed 38118003MA L, ZHANG C, WANG Y, et al. Concare: Personalized clinical feature embedding via capturing the healthcare context; proceedings of the Proceedings of the AAAI Conference on Artificial Intelligence, F 2020].
Mao Q, Jay M, Hoffman JL, Calvert J, Barton C, Shimabukuro D, Shieh L, Chettipally U, Fletcher G, Kerem Y, Zhou Y, Das R. Multicentre validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU. BMJ Open. 2018 Jan 26;8(1):e017833. doi: 10.1136/bmjopen-2017-017833.
PubMed 29374661
Study at a glance
From the registry record, last updated by the sponsor on 31 Aug 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Clinical Validation of a Pre-trained and Fine-tuned Model for Analyzing Time-Series Electronic Health Records in Critical Care
- ClinicalTrials.gov ID
- NCT07795450
- Acronym / sponsor ID
- 2026PHB018-001
- Conditions
- Critical Care Medicine, Mortality Prediction
- Keywords
- Intensive Care Units, Electronic Health Records, Validation Study, Models, Theoretical, Hospital Mortality
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 4,800 participants (estimated)
- Lead sponsor
- Peking University People's HospitalUniversity, hospital or other
- Collaborators
- Peking University
- Study start
- 1 Jan 2026 (actual)
- Primary completion
- 31 Dec 2026 (estimated)
- Study completion
- 31 Dec 2027 (estimated)
- First posted
- 31 Aug 2026 (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 goal of this observational study is to validate the generalization capability and predictive performance of the "Time-Series Electronic Health Record Foundation Model (EHR Foundation Model)", which was pre-trained on a large-scale critical care dataset, within the real-world clinical environment of the Intensive Care Unit (ICU) at Peking University People's Hospital. The main question it aims to answer is:
- Based on the "pre-training + fine-tuning" paradigm, whether the EHR foundation model can effectively provide accurate and dynamic prognostic support information in real-world clinical scenarios.
Participants who agree to take part in the research, in addition to completing the signed informed consent form, if the patient's hospital stay is too short, will be obtained information on prognostic outcomes (e.g., 28-day mortality) through telephone follow-up.
Detailed description
The clinical validation design combines a retrospective cohort study with a prospective cohort study.
Study design
- Observational model
- Cohort
- Time perspective
- Other
- Target follow-up
- 28 Days
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Non-survivor group | – | –Patients confirmed to have died within 28 days (outcome obtained from hospital records or telephone follow-up). |
| Survivor group | – | –Patients confirmed to have survived beyond 28 days (outcome obtained from hospital records or telephone follow-up). |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Study Setting: Department of Critical Care Medicine (ICU), Peking University People's Hospital. Data Source and Time Period: Retrospective Validation Cohort: Electronic health record data from all patients admitted to the ICU of Peking University People's Hospital between January 1, 2009 and December 31, 2025. This dataset will be used for local fine-tuning and internal validation of the model. Prospective Validation Cohort: Newly diagnosed cases admitted to the ICU of Peking University People's Hospital from March 1, 2026 to December 31, 2026 (following project initiation). This dataset will be used for external independent validation of the model.
Key inclusion criteria (3 listed)
- Age ≥ 18 years, no gender restriction;
- ICU length of stay ≥ 24 hours (to ensure sufficient time-series monitoring data for model generation);
- Complete electronic health record data, including baseline demographic characteristics and at least one complete laboratory test record after ICU admission.
Key exclusion criteria (4 listed)
- Patients who are transferred out or die within 24 hours of ICU admission;
- Duplicate admission records for non-initial ICU admissions (only the initial ICU admission record is retained to ensure independence);
- Severe deficiency in core data (e.g., absence of major vital sign recordings or > 50% missing key laboratory test results);
- Patients with abandonment of treatment or discharge against medical advice, leading to inability to ascertain the definitive clinical outcome.
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
In-hospital Mortality
Time frame: The endpoints are patient discharge or death. For prognostic outcomes (e.g., 28-day mortality), if the patient's hospital stay is insufficient, the outcome will be obtained through telephone follow-up.
Death events occurring during the patient's current hospitalization.
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 31 Aug 2026.
- BackgroundMUHAMMAD G, ALSHEHRI F, KARRAY F, et al. A comprehensive survey on multimodal medical signals fusion for smart healthcare systems [J]. Information Fusion, 2021.
- Background: PubMed 40498738Amugongo LM, Mascheroni P, Brooks S, Doering S, Seidel J. Retrieval augmented generation for large language models in healthcare: A systematic review. PLOS Digit Health. 2025 Jun 11;4(6):e0000877. doi: 10.1371/journal.pdig.0000877. eCollection 2025 Jun.
- BackgroundMA L, GAO J, WANG Y, et al. Adacare: Explainable clinical health status representation learning via scale-adaptive feature extraction and recalibration; proceedings of the Proceedings of the AAAI Conference on Artificial Intelligence, F 2020].
- BackgroundMA M, REN J, ZHAO L, et al. Smil: Multimodal learning with severely missing modality; proceedings of the Proceedings of the AAAI conference on artificial intelligence, F, 2021 [C].
- Background: PubMed 37852346Chen J, Qi TD, Vu J, Wen Y. A deep learning approach for inpatient length of stay and mortality prediction. J Biomed Inform. 2023 Nov;147:104526. doi: 10.1016/j.jbi.2023.104526. Epub 2023 Oct 17.
- BackgroundSURESH H, GONG J J, GUTTAG J V. Learning tasks for multitask learning: Heterogenous patient populations in the icu; proceedings of the Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, F 2018].
- Background: PubMed 39121882Meyer ML, Fitzgerald BG, Paz-Ares L, Cappuzzo F, Janne PA, Peters S, Hirsch FR. New promises and challenges in the treatment of advanced non-small-cell lung cancer. Lancet. 2024 Aug 24;404(10454):803-822. doi: 10.1016/S0140-6736(24)01029-8. Epub 2024 Aug 6.
- Background: PubMed 38118003Jackson VA, Emanuel L. Navigating and Communicating about Serious Illness and End of Life. N Engl J Med. 2024 Jan 4;390(1):63-69. doi: 10.1056/NEJMcp2304436. Epub 2023 Dec 20. No abstract available.
- BackgroundMA L, ZHANG C, WANG Y, et al. Concare: Personalized clinical feature embedding via capturing the healthcare context; proceedings of the Proceedings of the AAAI Conference on Artificial Intelligence, F 2020].
- Background: PubMed 29374661Mao Q, Jay M, Hoffman JL, Calvert J, Barton C, Shimabukuro D, Shieh L, Chettipally U, Fletcher G, Kerem Y, Zhou Y, Das R. Multicentre validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU. BMJ Open. 2018 Jan 26;8(1):e017833. doi: 10.1136/bmjopen-2017-017833.
- 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.
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 Critical Care Medicine and Mortality Prediction, run by Peking University People's 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 3 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: looking for participants now, as of the sponsor's last update on 31 Aug 2026. 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?
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?
Contact Huiying Zhao, PhD (+86 13811088270, zhaohuiying@pku.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
In-hospital Mortality (time frame: The endpoints are patient discharge or death. For prognostic outcomes (e.g., 28-day mortality), if the patient's hospital stay is insufficient, the outcome will be obtained through telephone follow-up.). 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 NCT07795450, which also lists 10 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 31 Aug 2026.