Ignite Creation Date:
2024-05-06 @ 8:18 PM
Last Modification Date:
2024-10-26 @ 3:24 PM
Study NCT ID:
NCT06326385
Status:
NOT_YET_RECRUITING
Last Update Posted:
2024-03-25
First Post:
2024-03-16
Brief Title:
Machine Learning Predictive Models for Sepsis Risk in ICU Patients With Intracerebral Hemorrhage
Sponsor:
Xiangya Hospital of Central South University
Organization:
Xiangya Hospital of Central South University
Study Overview
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
Status:
NOT_YET_RECRUITING
Status Verified Date:
2024-03
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
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 22 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 299712 hospital admissions and 73181 intensive care unit patients
The eICU Collaborative Research Database eICU-CRD comprises data from over 200000 ICU admissions for 139367 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
The Medical Information Mart for Intensive Care MIMIC IV database version 22 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 299712 hospital admissions and 73181 intensive care unit patients
The eICU Collaborative Research Database eICU-CRD comprises data from over 200000 ICU admissions for 139367 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 22 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 models 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
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 models 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 Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None