[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100656351":3},{"organization":4,"outcomesModule":7,"designInfo":14,"detailedDescription":17,"studyPopulation":18,"armGroups":13,"interventions":19,"overallOfficials":13,"centralContacts":23,"locations":13,"responsibleParty":29,"collaborators":13,"id":33,"slug":34,"hasResults":35,"nctId":36,"briefTitle":37,"officialTitle":37,"acronym":13,"eligibilityCriteria":38,"healthyVolunteers":35,"sex":39,"minAge":40,"maxAge":13,"enrollmentInfo":41,"targetDuration":13,"studyType":44,"phases":13,"briefSummary":45,"conditions":46,"keywords":13,"overallStatus":48,"whyStopped":13,"lastUpdateSubmitDate":49,"lastUpdatePostDateStruct":50,"startDateStruct":53,"completionDateStruct":55,"leadSponsor":57,"locationsCount":13},{"fullName":5,"class":6},"Southeast University, China","OTHER",{"primaryOutcomes":8,"secondaryOutcomes":13,"otherOutcomes":13},[9],{"measure":10,"description":11,"timeFrame":12},"ICU mortality","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.",null,{"allocation":13,"interventionModel":13,"interventionModelDescription":13,"primaryPurpose":13,"observationalModel":15,"timePerspective":16,"maskingInfo":13},"COHORT","PROSPECTIVE","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.\n\nArtificial 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.\n\nFedHist 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.\n\nPrevious 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.\n\nThis 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.","All patients admitted to the participating ICU",[20],{"type":6,"name":21,"description":22,"armGroupLabels":13,"otherNames":13},"Risk of mortality","Patients admitted to the ICU with different risk of mortality",[24],{"name":25,"role":26,"phone":27,"phoneExt":13,"email":28},"hui chen","CONTACT","+86-18006138640","huichen.icu@gmail.com",{"type":30,"investigatorFullName":31,"investigatorTitle":32,"investigatorAffiliation":5,"oldNameTitle":13,"oldOrganization":13},"PRINCIPAL_INVESTIGATOR","Jianfeng Xie","Professor","100656351","external-validation-of-the-fedhist-dynamic-early-warning-model-for-mortality-risk-in-critically-ill-patients-a-prospective-multicenter-study-100656351",false,"NCT07825012","External Validation of the FedHist Dynamic Early Warning Model for Mortality Risk in Critically Ill Patients: A Prospective Multicenter Study","Inclusion Criteria:\n\n* Admission to a participating ICU\n\nExclusion Criteria:\n\n* Age younger than 18 years.\n* Expected ICU length of stay shorter than 24 hours.\n* Refusal to provide written informed consent.","ALL","18 Years",{"count":42,"type":43},23579,"ESTIMATED","OBSERVATIONAL","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.\n\nThis 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.",[47],"ICU","NOT_YET_RECRUITING","2026-09-13",{"date":51,"type":52},"2026-09-17","ACTUAL",{"date":54,"type":43},"2026-10-01",{"date":56,"type":43},"2027-12-31",{"name":5,"class":6}]