Ignite Creation Date:
2024-06-16 @ 11:49 AM
Last Modification Date:
2024-10-26 @ 3:30 PM
Study NCT ID:
NCT06431412
Status:
ACTIVE_NOT_RECRUITING
Last Update Posted:
2024-05-28
First Post:
2024-05-08
Brief Title:
A Model for Drug Concentration Prediction of Vancomycin
Sponsor:
Peking Union Medical College Hospital
Organization:
Peking Union Medical College Hospital
Study Overview
Official Title:
A Clinical Data-Based Model for Drug Concentration Prediction of Vancomycin in Critical Patients
Status:
ACTIVE_NOT_RECRUITING
Status Verified Date:
2024-05
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:
Objective This study aims to use machine learning methods to establish an optimal model for predicting serum vancomycin trough concentrations in critically ill patients
Methods This is a single-center retrospective study Data on serum vancomycin concentration in the Critical Care Database of Peking Union Medical College Hospital were screened and extracted to construct a prediction model using machine learning methods The MIMIC-IV Medical Information Mart for Intensive Care database will be further used for external verification of the constructed model
The study has been approved by the Medical Ethics Committee of Peking Union Medical College Hospital K24C1161
Methods This is a single-center retrospective study Data on serum vancomycin concentration in the Critical Care Database of Peking Union Medical College Hospital were screened and extracted to construct a prediction model using machine learning methods The MIMIC-IV Medical Information Mart for Intensive Care database will be further used for external verification of the constructed model
The study has been approved by the Medical Ethics Committee of Peking Union Medical College Hospital K24C1161
Detailed Description:
Background Vancomycin is a glycopeptide antibiotic primarily used to treat infections caused by methicillin-resistant Staphylococcus aureus MRSA As a time-dependent antibiotic the serum concentration of vancomycin is closely related to the clinical efficacy toxicity and emergence of drug resistance Therefore therapeutic drug monitoring TDM is considered an important component of vancomycin treatment management According to vancomycin surveillance guidelines It is recommended to maintain a serum vancomycin concentration of 15-20 mgL in patients with severe infections in order to improve clinical outcomes and prevent drug resistance However serum vancomycin concentration testing is not widely used in clinical practices especially in resource-constrained areas and medical institutions so individualized monitoring remains a challenge Currently studies on vancomycin concentration prediction generally use the population pharmacokinetic PPK model However this model is affected by many factors such as age weight and creatinine clearance rate However since critically ill patients have complex diseases accompanied by multiple organ dysfunction vancomycin pharmacokinetics may be altered In such patients the evidence for concentration prediction using PPK models is insufficient
Currently the rapidly developing machine learning methods can help capture nonlinear variable relationships while making predictions through multiple variables to achieve a high degree of accuracy in prediction results This study aims to use machine learning methods to establish an optimal model for predicting serum vancomycin trough concentrations in critically ill patients
Objective This study aims to extract the serum vancomycin concentration data from the Critical Care Database of Peking Union Medical College Hospital from January 2014 to December 2023 and use machine learning methods to establish the optimal model for predicting vancomycin concentrations in critically ill patients
Methods 1This is a single-center retrospective study Data on serum vancomycin concentration in the Critical Care Database of Peking Union Medical College Hospital were screened After meeting the eligibility criteria the clinical data of included patients are collected through the inpatient medical record system including demographic characteristics severity scores laboratory test information and treatment information 2 After extracting the available data five models of machine learning including Linear Regression Lasso Regression Ridge Regression Random Forest and LightGBM are used to build prediction models The model with the best prediction accuracy is selected based on the percent error PE the mean percentage error MPE and the mean absolute percentage error MAPE 3 The MIMIC-IV Medical Information Mart for Intensive Care database is used to conduct external validation of the model constructed by machine learning Moreover the investigators will compare the predictive performance of the PPK model with the constructed model
Quality control Patients who meet the inclusion criteria are included Patients with missing information are not enrolled in order to reduce bias The information of included patients is recorded and registered by a dedicated research person
Ethics and patient privacy protection Personal information in the study will be used only for the purposes described in the protocol for this study Medical information obtained will be kept confidential The results will also be published in academic journals without revealing any identifiable patient information The study has been approved by the Medical Ethics Committee of Peking Union Medical College Hospital K24C1161
Currently the rapidly developing machine learning methods can help capture nonlinear variable relationships while making predictions through multiple variables to achieve a high degree of accuracy in prediction results This study aims to use machine learning methods to establish an optimal model for predicting serum vancomycin trough concentrations in critically ill patients
Objective This study aims to extract the serum vancomycin concentration data from the Critical Care Database of Peking Union Medical College Hospital from January 2014 to December 2023 and use machine learning methods to establish the optimal model for predicting vancomycin concentrations in critically ill patients
Methods 1This is a single-center retrospective study Data on serum vancomycin concentration in the Critical Care Database of Peking Union Medical College Hospital were screened After meeting the eligibility criteria the clinical data of included patients are collected through the inpatient medical record system including demographic characteristics severity scores laboratory test information and treatment information 2 After extracting the available data five models of machine learning including Linear Regression Lasso Regression Ridge Regression Random Forest and LightGBM are used to build prediction models The model with the best prediction accuracy is selected based on the percent error PE the mean percentage error MPE and the mean absolute percentage error MAPE 3 The MIMIC-IV Medical Information Mart for Intensive Care database is used to conduct external validation of the model constructed by machine learning Moreover the investigators will compare the predictive performance of the PPK model with the constructed model
Quality control Patients who meet the inclusion criteria are included Patients with missing information are not enrolled in order to reduce bias The information of included patients is recorded and registered by a dedicated research person
Ethics and patient privacy protection Personal information in the study will be used only for the purposes described in the protocol for this study Medical information obtained will be kept confidential The results will also be published in academic journals without revealing any identifiable patient information The study has been approved by the Medical Ethics Committee of Peking Union Medical College Hospital K24C1161
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