A Model for Drug Concentration Prediction of Vancomycin

A Clinical Data-Based Model for Drug Concentration Prediction of Vancomycin in Critical Patients

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).

Study Overview

• Status: Active, not recruiting
• Conditions: Infections; Critical Illness; Drug Use
• Intervention / Treatment: Other: no intervention

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 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 Type: Observational
• Enrollment (Actual): 401

Contacts and Locations

Study Locations

• China
  • Beijing
    • Beijing, Beijing, China, 100730
      • Peking Union Medical College Hospita

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

Adult ICU patients who receivied intravenous vancomycin treatment

Description

Inclusion Criteria:

• Age ≥18 years;
• Patients admitted to ICUs;
• Patients were administered intravenous vancomycin;
• Vancomycin TDM was performed at least two times.

Exclusion Criteria:

• Vancomycin TDM was performed in a ward rather than in an ICU;
• Patients with missing data.

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
The predicted serum vancomycin concentration	The serum vancomycin concentration predicted by the constructed model	1 day

Collaborators and Investigators

• Sponsor: Peking Union Medical College Hospital
• Investigators: Study Chair: Li Weng, MD, Peking Union Medical College Hospital

Publications and helpful links

General Publications

• Obermeyer Z, Emanuel EJ. Predicting the Future - Big Data, Machine Learning, and Clinical Medicine. N Engl J Med. 2016 Sep 29;375(13):1216-9. doi: 10.1056/NEJMp1606181. No abstract available.
• Ye ZK, Li C, Zhai SD. Guidelines for therapeutic drug monitoring of vancomycin: a systematic review. PLoS One. 2014 Jun 16;9(6):e99044. doi: 10.1371/journal.pone.0099044. eCollection 2014.
• Ingram PR, Lye DC, Tambyah PA, Goh WP, Tam VH, Fisher DA. Risk factors for nephrotoxicity associated with continuous vancomycin infusion in outpatient parenteral antibiotic therapy. J Antimicrob Chemother. 2008 Jul;62(1):168-71. doi: 10.1093/jac/dkn080. Epub 2008 Mar 10.
• Rybak MJ, Le J, Lodise TP, Levine DP, Bradley JS, Liu C, Mueller BA, Pai MP, Wong-Beringer A, Rotschafer JC, Rodvold KA, Maples HD, Lomaestro B. Therapeutic Monitoring of Vancomycin for Serious Methicillin-resistant Staphylococcus aureus Infections: A Revised Consensus Guideline and Review by the American Society of Health-system Pharmacists, the Infectious Diseases Society of America, the Pediatric Infectious Diseases Society, and the Society of Infectious Diseases Pharmacists. Clin Infect Dis. 2020 Sep 12;71(6):1361-1364. doi: 10.1093/cid/ciaa303.
• Yasuhara M, Iga T, Zenda H, Okumura K, Oguma T, Yano Y, Hori R. Population pharmacokinetics of vancomycin in Japanese adult patients. Ther Drug Monit. 1998 Apr;20(2):139-48. doi: 10.1097/00007691-199804000-00003.
• Doupe P, Faghmous J, Basu S. Machine Learning for Health Services Researchers. Value Health. 2019 Jul;22(7):808-815. doi: 10.1016/j.jval.2019.02.012.

Study record dates

Study Major Dates

• Study Start (Actual): March 1, 2024
• Primary Completion (Estimated): August 31, 2024
• Study Completion (Estimated): August 31, 2024

Study Registration Dates

• First Submitted: May 8, 2024
• First Submitted That Met QC Criteria: May 27, 2024
• First Posted (Actual): May 28, 2024

Study Record Updates

• Last Update Posted (Actual): May 28, 2024
• Last Update Submitted That Met QC Criteria: May 27, 2024
• Last Verified: May 1, 2024

More Information

Terms related to this study

• Keywords: Machine learning; Serum vancomycin concentration
• Additional Relevant MeSH Terms: Pathologic Processes; Disease Attributes; Critical Illness
• Other Study ID Numbers: K5927

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No