Clinical Trials Logo

Clinical Trial 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).


Clinical Trial 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 mg/L 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: (1)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. 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 Design


Related Conditions & MeSH terms


NCT number NCT06431412
Study type Observational
Source Peking Union Medical College Hospital
Contact
Status Active, not recruiting
Phase
Start date March 1, 2024
Completion date August 31, 2024

See also
  Status Clinical Trial Phase
Completed NCT04551508 - Delirium Screening 3 Methods Study
Recruiting NCT06037928 - Plasma Sodium and Sodium Administration in the ICU
Completed NCT03671447 - Enhanced Recovery After Intensive Care (ERIC) N/A
Recruiting NCT03941002 - Continuous Evaluation of Diaphragm Function N/A
Recruiting NCT04674657 - Does Extra-Corporeal Membrane Oxygenation Alter Antiinfectives Therapy Pharmacokinetics in Critically Ill Patients
Completed NCT04239209 - Effect of Intensivist Communication on Surrogate Prognosis Interpretation N/A
Completed NCT05531305 - Longitudinal Changes in Muscle Mass After Intensive Care N/A
Terminated NCT03335124 - The Effect of Vitamin C, Thiamine and Hydrocortisone on Clinical Course and Outcome in Patients With Severe Sepsis and Septic Shock Phase 4
Completed NCT02916004 - The Use of Nociception Flexion Reflex and Pupillary Dilatation Reflex in ICU Patients. N/A
Recruiting NCT05883137 - High-flow Nasal Oxygenation for Apnoeic Oxygenation During Intubation of the Critically Ill
Completed NCT04479254 - The Impact of IC-Guided Feeding Protocol on Clinical Outcomes in Critically Ill Patients (The IC-Study) N/A
Recruiting NCT04475666 - Replacing Protein Via Enteral Nutrition in Critically Ill Patients N/A
Not yet recruiting NCT04538469 - Absent Visitors: The Wider Implications of COVID-19 on Non-COVID Cardiothoracic ICU Patients, Relatives and Staff
Not yet recruiting NCT04516395 - Optimizing Antibiotic Dosing Regimens for the Treatment of Infection Caused by Carbapenem Resistant Enterobacteriaceae N/A
Withdrawn NCT04043091 - Coronary Angiography in Critically Ill Patients With Type II Myocardial Infarction N/A
Recruiting NCT02989051 - Fluid Restriction Keeps Children Dry Phase 2/Phase 3
Recruiting NCT02922998 - CD64 and Antibiotics in Human Sepsis N/A
Completed NCT02899208 - Can an Actigraph be Used to Predict Physical Function in Intensive Care Patients? N/A
Completed NCT03048487 - Protein Consumption in Critically Ill Patients
Recruiting NCT02163109 - Oxygen Consumption in Critical Illness