Intracerebral HemorrhageNCT06326385
Machine Learning Predictive Models for Sepsis Risk in ICU Patients With Intracerebral Hemorrhage
An observational study of Intracerebral Hemorrhage and Sepsis, run by Xiangya Hospital of Central South University at 1 site in China.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 to 89 years
- Conditions the study is for
- Intracerebral Hemorrhage, Sepsis
- How many people
- 1,800 participants
- Where
- 1 siteChina
- Study dates
- 30 Mar 2024to 30 May 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 89 years
- All sexes
- Adult, Older adult
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.
Who may be able to join
- 1. Diagnosed with primary intracerebral hemorrhage by ICD-9/10 coding.
- 2. Aged 19-89 years old.
Who cannot join
- 1. Patients admitted to the hospital but not to the ICU.
- 2. Patients with missing follow-up data or incomplete variables.
- 3. Patients with a hospital stay exceeding one month.
Trial locations and contacts
1 site in 1 country.
China1 site
Le Zhang
Changsha, Hunan, 410008
Le Zhang, 13973187150, zlzdzlzd@csu.edu.cn
Ye Li, 19967131289, 17670516318@163.com
About this study
Official title: Development and Validation of Predictive Models for Sepsis Risk in Patients With Intracerebral Hemorrhage in Intensive Care Units Based on Machine Learning: A Retrospective Cohort Study
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.
Read more
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.
Read the detailed description
- Data Collection: This study utilized two public databases. The model leveraged clinical data obtained from the Medical Information Mart for Intensive Care (MIMIC) IV database (version 2.2) and selected corresponding patients for external validation from the eICU Collaborative Research Database (eICU-CRD). Data on ICH patients were extracted from the MIMIC IV public database, including baseline characteristics, clinical parameters, therapeutic interventions, and outcomes. The data were randomly divided into two groups, with 70% serving as the training set and 30% as the validation set.
- Model Development: Feature selection was performed using Lasso regression to construct various machine learning models (such as Random Forest, Logistic Regression, and Neural Networks).
- Model Validation: In addition to the internal validation set, external validation was also conducted on the eICU database to test the model's generalizability.
- Statistical Analysis: The predictive performance of the model was evaluated using metrics including the area under the ROC curve (AUC), sensitivity, and specificity.
- Clinical Applicability Assessment: The clinical utility of the model was assessed using Decision Curve Analysis (DCA).
- Conditions
- Intracerebral Hemorrhage, Sepsis
- Being tested
- no intervention
- Medical subject headings
- Cerebral Hemorrhage, Sepsis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
no interventionOther
no intervention
| Study group | Role | What they receive |
|---|---|---|
| intracerebral hemorrhage | Not described Receives: Other: no intervention |
What is being measured
The primary outcome is the main question the study is designed to answer.
Occurrence of sepsis
Occurrence of sepsis
Measured: within 30 days of admission
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted22 Mar 2024
- Study start30 Mar 2024 (estimated)
- Primary completion1 May 2024 (estimated)
- Study completion30 May 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Intracerebral HemorrhageStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 1,800 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Xiangya Hospital of Central South University
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06326385
- Sponsor's protocol ID
- 53244021
- Status last verified
- Mar 2024
- First submitted
- 16 Mar 2024
- Last updated
- 25 Mar 2024
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: In the paper
Study at a glance
From the registry record, last updated by the sponsor on 25 Mar 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development and Validation of Predictive Models for Sepsis Risk in Patients With Intracerebral Hemorrhage in Intensive Care Units Based on Machine Learning: A Retrospective Cohort Study
- ClinicalTrials.gov ID
- NCT06326385
- Acronym / sponsor ID
- 53244021
- Conditions
- Intracerebral Hemorrhage, Sepsis
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 1,800 participants (estimated)
- Lead sponsor
- Xiangya Hospital of Central South UniversityUniversity, hospital or other
- Study start
- 30 Mar 2024 (estimated)
- Primary completion
- 1 May 2024 (estimated)
- Study completion
- 30 May 2024 (estimated)
- First posted
- 22 Mar 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
- Data Collection: This study utilized two public databases. The model leveraged clinical data obtained from the Medical Information Mart for Intensive Care (MIMIC) IV database (version 2.2) and selected corresponding patients for external validation from the eICU Collaborative Research Database (eICU-CRD). Data on ICH patients were extracted from the MIMIC IV public database, including baseline characteristics, clinical parameters, therapeutic interventions, and outcomes. The data were randomly divided into two groups, with 70% serving as the training set and 30% as the validation set.
- Model Development: Feature selection was performed using Lasso regression to construct various machine learning models (such as Random Forest, Logistic Regression, and Neural Networks).
- Model Validation: In addition to the internal validation set, external validation was also conducted on the eICU database to test the model's generalizability.
- Statistical Analysis: The predictive performance of the model was evaluated using metrics including the area under the ROC curve (AUC), sensitivity, and specificity.
- Clinical Applicability Assessment: The clinical utility of the model was assessed using Decision Curve Analysis (DCA).
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| intracerebral hemorrhage | – | Other: no intervention |
Who may qualify
- Ages
- 18 to 89 years
- Sex
- All
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (2 listed)
- 1. Diagnosed with primary intracerebral hemorrhage by ICD-9/10 coding.
- 2. Aged 19-89 years old.
Key exclusion criteria (3 listed)
- 1. Patients admitted to the hospital but not to the ICU.
- 2. Patients with missing follow-up data or incomplete variables.
- 3. Patients with a hospital stay exceeding one month.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| no interventionno intervention | Other | intracerebral hemorrhage |
Primary outcome
Occurrence of sepsis
Time frame: within 30 days of admission
Occurrence of sepsis
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 25 Mar 2024.
- Individual participant dataSharing planned: No. In the paper
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Intracerebral Hemorrhage and Sepsis, run by Xiangya Hospital of Central South University at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 25 Mar 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Le Zhang, Doctor (13973187150, zlzdzlzd@csu.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Occurrence of sepsis (time frame: within 30 days of admission). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 25 Mar 2024.