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

NCT06326385 | Not Yet Recruiting

• 1 other identifier: 53244021
• Study Type: observational
• Target: 1,800
• Locations: 1 country
• Sites: 1

Brief 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

Outcome Measures

Primary Outcomes (1)

• Occurrence of sepsis

  Occurrence of sepsis

  within 30 days of admission

Study Arms (1)

intracerebral hemorrhage

Other: no intervention

Interventions

no intervention OTHER

no intervention

intracerebral hemorrhage

Eligibility Criteria

• Age: 18 Years - 89 Years
• Sex: all
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

All diagnoses in the MIMIC-IV and the eICU-CRD databases were identified based on the International Classification of Diseases, Ninth Revision (ICD-9), and ICD-10 codes. For the analysis, patients diagnosed with ICH were included. Sepsis was defined according to the Third International Consensus Definition of Sepsis and Septic Shock (Sepsis-3), which considers patients with suspected infection and a Sequential Organ Failure Assessment (SOFA) score ≥2 as septic.

You may qualify if:

• \. Diagnosed with primary intracerebral hemorrhage by ICD-9/10 coding.
• \. Aged 19-89 years old.

You may not qualify if:

• \. Patients admitted to the hospital but not to the ICU.
• \. Patients with missing follow-up data or incomplete variables.
• \. Patients with a hospital stay exceeding one month.

Sponsors & Collaborators

• Xiangya Hospital of Central South University: lead

Study Sites (1)

Le Zhang

Changsha, Hunan, 410008, China

Location

MeSH Terms

Conditions

Cerebral Hemorrhage; Sepsis

Condition Hierarchy (Ancestors)

Intracranial Hemorrhages; Cerebrovascular Disorders; Brain Diseases; Central Nervous System Diseases; Nervous System Diseases; Vascular Diseases; Cardiovascular Diseases; Hemorrhage; Pathologic Processes; Pathological Conditions, Signs and Symptoms; Infections; Systemic Inflammatory Response Syndrome; Inflammation

Central Study Contacts

Le Zhang, Doctor

CONTACT

13973187150; zlzdzlzd@csu.edu.cn

Ye Li, Doctor

CONTACT

19967131289; 17670516318@163.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: March 16, 2024
• First Posted: March 22, 2024
• Study Start: March 30, 2024
• Primary Completion: May 1, 2024
• Study Completion: May 30, 2024
• Last Updated: March 25, 2024

Record last verified: 2024-03

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)