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Machine Learning Predictive Models for Sepsis Risk in ICU Patients With Intracerebral Hemorrhage

NCT06326385 · Status: NOT_YET_RECRUITING · Type: OBSERVATIONAL · Enrollment: 1800

Last updated 2024-03-25

No results posted yet for this study

Summary

Patients with intracerebral hemorrhage (ICH) in the intensive care unit (ICU) are at heightened risk of developing sepsis, significantly increasing mortality and healthcare burden. Currently, there is a lack of effective tools for the early prediction of sepsis in ICH patients within the ICU. This study aims to develop a reliable predictive model using machine learning techniques to assist clinicians in the early identification of patients at high risk and to facilitate timely intervention.

The Medical Information Mart for Intensive Care (MIMIC) IV database (version 2.2) is an international online repository for critical care expertise. This database contains patient-related information collected from the ICUs of Beth Israel Deaconess Medical Center between 2008 and 2019. It includes a vast dataset of 299,712 hospital admissions and 73,181 intensive care unit patients.

The eICU Collaborative Research Database (eICU-CRD) comprises data from over 200,000 ICU admissions for 139,367 unique patients across 208 US hospitals between 2014 and 2015, providing a valuable resource for critical care research.

This study aims to establish and validate multiple machine learning models to predict the onset of sepsis in ICU patients with ICH and to identify the model with the optimal predictive performance.

Conditions

  • Intracerebral Hemorrhage
  • Sepsis

Interventions

OTHER

no intervention

no intervention

Sponsors & Collaborators

  • Xiangya Hospital of Central South University

    lead OTHER

Eligibility

Min Age
18 Years
Max Age
89 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2024-03-30
Primary Completion
2024-05-01
Completion
2024-05-30

Countries

  • China

Study Locations

More Related Trials

Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06326385 on ClinicalTrials.gov