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Can an AI trained on health records forecast ICU survival?

NCT ID NCT07795450

Knowledge-focused Sponsor: Peking University People's Hospital Source: ClinicalTrials.gov ↗

What the study statuses mean

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Recruitment status, easiest to join first

Recruiting now This study
This trial is taking on new participants right now.
Not yet recruiting
Registered, but not yet taking participants.
By invitation only
Not open to general applications. Only people the study team invites can take part.
Paused
Paused for now. It may or may not start again.
Ongoing
Running, but no longer taking on new participants.
Completed
The trial has finished. Results may not be published yet.
Stopped early
Stopped early, before it reached the end. That can be for many reasons, including safety.
Cancelled
Cancelled before anyone took part.

Expanded access (not trials)

Expanded access
Not a trial. This treatment can be requested outside a study, case by case, for people who qualify.
Expanded access (paused)
Not a trial. The treatment can normally be requested outside a study, but is unavailable right now.
Expanded access (ended)
Not a trial. The treatment could once be requested outside a study, but no longer can.
Approved
The treatment has been approved, so it is available normally rather than through this programme.

When the status isn't known

Details not published
The full record has not been published yet, so there is little to show here.
Status unknown
This status has not been confirmed recently, so it may be out of date.

First seen Aug 31, 2026 · Last updated Sep 01, 2026 · Updated 1 time

Summary

Researchers at Peking University People's Hospital are testing whether an AI model, pre-trained on large-scale critical care data, can accurately predict outcomes like in-hospital mortality for ICU patients. The study will enroll about 4,800 adults who stay in the ICU for at least 24 hours. By comparing the model's predictions with actual patient outcomes, the team aims to see if this approach works in a real-world hospital setting.

What this could mean

Our plain-language read of the trial. This is informational only, not medical advice or a prediction.

Active substance
An AI model (EHR Foundation Model) that analyzes time-series electronic health records to predict patient outcomes in the ICU.
What this could lead to
If the model proves accurate, it could help doctors in intensive care units identify high-risk patients earlier and make more informed treatment decisions.
What could go wrong
This is an observational study, so it does not test a treatment. The model may not perform as well in real-world settings as it did in training, and its predictions are not a substitute for clinical judgment.

This is an AI summary of the original study and may miss details. Read our disclaimer.

Study facts

What this study's own registry entry says, in plain language.

Participants

About 4,800 people

The number the study aims to enrol. It can still change while the study runs.

Started

Jan 2026

Expected to finish

Dec 2027

An estimate. End dates often move.

Lead sponsor

Other sponsor

The registry's catch-all category, for sponsors it does not file as a company, a government agency, or a research network.

Who can take part

This study's own entry requirements. Only the study team can say for certain whether you qualify.

Who is studied

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.

Ages

18 years and older

Sex

Anyone

Healthy volunteers

Not accepted

This study is not open to healthy volunteers. The entry requirements below say who it is open to.

Show the full entry requirements

Copied word for word from the study's registry entry, so the wording is the study team's rather than ours.

Inclusion Criteria: * 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. Exclusion Criteria: * 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.

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As listed by the trial registrant

The condition terms exactly as the trial's registrant entered them.

Critical care medicine Mortality prediction

How to take part

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  1. The places running it

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  2. The official record

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Contacts and locations

Locations

  • Peking University People's Hospital

    RECRUITING

    Beijing, Beijing Municipality, 100044, China

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