External Validation of the FedHist Dynamic Early Warning Model for Mortality Risk in Critically Ill Patients: A Prospective Multicenter Study
The purpose of this study is to assess how accurately FedHist, an artificial intelligence model, predicts the risk of death in critically ill patients. Patients in intensive care units (ICUs) can become worse quickly. Updating risk estimates as new clinical information becomes available may help identify patients at higher risk. FedHist uses routinely collected clinical information to estimate a patient's risk of dying in the ICU within the next 24 hours. These estimates are updated every 6 hours.
This study will evaluate FedHist prospectively across several hospitals. The model will be integrated into hospital clinical information systems, and its predictions will be compared with observed patient outcomes. The study aims to determine whether FedHist provides accurate predictions across hospitals with different patient populations and clinical practices.
Study Overview
Detailed Description
Critically ill patients can deteriorate rapidly, creating a need for timely and repeated assessment of mortality risk. Conventional severity assessments, including the Acute Physiology and Chronic Health Evaluation II (APACHE II) and Sequential Organ Failure Assessment (SOFA) scores, provide clinically useful information. However, risk assessments based on data from defined assessment periods may not fully capture evolving clinical trajectories. Models that incorporate longitudinal clinical data may support more frequent assessment of short-term mortality risk.
Artificial intelligence offers opportunities to integrate clinical information collected over time for dynamic risk prediction. However, models developed using data from a single center or database may perform differently in other settings. Differences in patient characteristics, disease severity, measurement frequency, and clinical workflows can affect predictive performance. Privacy requirements, data governance policies, and institutional control over data also limit the pooling of patient records for conventional centralized model development.
FedHist was developed using a federated learning framework incorporating electronic health records from more than 250,000 critically ill patients across five countries and regions. This framework enabled collaborative model development while raw patient data remained at the contributing institutions. FedHist generates an updated estimate of the risk of ICU death within the next 24 hours at 6-hour intervals.
Previous evaluations showed a macro-averaged area under the receiver operating characteristic curve (AUROC) of 0.893 in internal validation. AUROCs were 0.881 in the NWICU external validation cohort and 0.961 in the prospective CDIC cohort. FedHist outperformed the corresponding locally trained models. When only 10% of the development data at each site were used, its performance approached or exceeded that of local models trained on the full development datasets. These findings support further prospective evaluation following integration into routine clinical information systems across multiple hospitals.
This prospective multicenter study will externally validate the previously developed FedHist model in participating ICUs. The model will be integrated into local clinical information systems to generate risk estimates using prospectively collected clinical data. Predictions will be compared with observed ICU outcomes to assess performance in predicting death within the subsequent 24 hours. The study will evaluate predictive performance across participating hospitals and assess the generalizability of FedHist under routine clinical conditions.
Study Type
Enrollment (Estimated)
Contacts and Locations
Study Contact
- Name: hui chen
- Phone Number: +86-18006138640
- Email: huichen.icu@gmail.com
Participation Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
Inclusion Criteria:
- Admission to a participating ICU
Exclusion Criteria:
- Age younger than 18 years.
- Expected ICU length of stay shorter than 24 hours.
- Refusal to provide written informed consent.
Study Plan
How is the study designed?
Design Details
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
ICU mortality
Time Frame: From ICU admission to ICU discharge or 90 days after ICU admission, whichever occurs first.
|
The proportion of patients who die from any cause during the ICU stay, with follow-up capped at 90 days after ICU admission.
For patients whose ICU stay exceeds 90 days, vital status at day 90 will be used to determine the mortality outcome.
|
From ICU admission to ICU discharge or 90 days after ICU admission, whichever occurs first.
|
Collaborators and Investigators
Sponsor
Study record dates
Study Major Dates
Study Start (Estimated)
Primary Completion (Estimated)
Study Completion (Estimated)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Actual)
Study Record Updates
Last Update Posted (Actual)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Other Study ID Numbers
- 2026ZDSYLL335-P01
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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