A Model for Drug Concentration Prediction of Vancomycin

NCT ID: NCT06431412

Last Updated: 2024-05-28

Basic Information

• Recruitment Status: ACTIVE_NOT_RECRUITING
• Total Enrollment: 401 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2024-03-01
• Study Completion Date: 2024-08-31

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).

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).

Conditions

Critical Illness; Infections; Drug Use

Study Design

• Observational Model Type: CASE_ONLY
• Study Time Perspective: RETROSPECTIVE

Interventions

no intervention

no intervention

Intervention Type: OTHER

Eligibility Criteria

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.

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Peking Union Medical College Hospital

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Li Weng, MD

Role: STUDY_CHAIR

Peking Union Medical College Hospital

Locations

Peking Union Medical College Hospita

Beijing, Beijing Municipality, China

Countries

China

References

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.

Reference Type: RESULT

PMID: 24932495 (View on PubMed)

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.

Reference Type: RESULT

PMID: 18334494 (View on PubMed)

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.

Reference Type: RESULT

PMID: 32658968 (View on PubMed)

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.

Reference Type: RESULT

PMID: 9558127 (View on PubMed)

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.

Reference Type: RESULT

PMID: 27682033 (View on PubMed)

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.

Reference Type: RESULT

PMID: 31277828 (View on PubMed)

Other Identifiers

K5927

Identifier Type: -

Identifier Source: org_study_id