Critical IllnessNCT06431412
A Model for Drug Concentration Prediction of Vancomycin
An observational study of Critical Illness and Infections, run by Peking Union Medical College Hospital at 1 site in China.
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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Critical Illness, Infections, Drug Use
- How many people
- 401 participants
- Where
- 1 siteChina
- Study dates
- 1 Mar 2024to 31 Aug 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Adult ICU patients who receivied intravenous vancomycin treatment
Who may be able to join
- Age ≥18 years;
- Patients admitted to ICUs;
- Patients were administered intravenous vancomycin;
- Vancomycin TDM was performed at least two times.
Who cannot join
- Vancomycin TDM was performed in a ward rather than in an ICU;
- Patients with missing data.
Trial locations and contacts
1 site in 1 country.
China1 site
Peking Union Medical College Hospita
Beijing, Beijing Municipality, 100730
About this study
Official title: 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).
Read the 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
- Being tested
- no intervention
- Keywords
- Serum vancomycin concentration, Machine learning
- Medical subject headings
- Critical Illness, Infections
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
no interventionOther
no intervention
What is being measured
The primary outcome is the main question the study is designed to answer.
The predicted serum vancomycin concentration
The serum vancomycin concentration predicted by the constructed model
Measured: 1 day
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted28 May 2024
- Study start1 Mar 2024
- Primary completion31 Aug 2024 (estimated)
- Study completion31 Aug 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (7)Papers the sponsor has linked to this study.
- Other trials for Critical IllnessStudies of the same condition, open and under way.
- More trials run by Peking Union Medical College HospitalThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case only
- Time perspective
- Retrospective
- Enrolment
- 401 participants (actual)
Sponsor and investigators
- Lead sponsor
- Peking Union Medical College Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Li Weng, MD, Study chair, Peking Union Medical College Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06431412
- Sponsor's protocol ID
- K5927
- Status last verified
- May 2024
- First submitted
- 8 May 2024
- Last updated
- 28 May 2024
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
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.
PubMed 24932495Ingram 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.
PubMed 18334494Rybak 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.
PubMed 32658968Yasuhara 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.
PubMed 9558127Obermeyer 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.
PubMed 27682033Doupe 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.
PubMed 31277828Wang JY, Hu J, Chen Y, Li S, Peng JM, Dong R, Hu XY, Jiang W, Wang CY, Weng L, Du B; China Critical Care Clinical Trials Group (CCCCTG). A Machine Learning-Based Model for Individualized Prediction of Vancomycin Concentration-Time Curves in ICU Patients. Pharmacol Res Perspect. 2026 Apr;14(2):e70235. doi: 10.1002/prp2.70235.
PubMed 41803667
Study at a glance
From the registry record, last updated by the sponsor on 28 May 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Clinical Data-Based Model for Drug Concentration Prediction of Vancomycin in Critical Patients
- ClinicalTrials.gov ID
- NCT06431412
- Acronym / sponsor ID
- K5927
- Conditions
- Critical Illness, Infections, Drug Use
- Keywords
- Serum vancomycin concentration, Machine learning
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 401 participants (actual)
- Lead sponsor
- Peking Union Medical College HospitalUniversity, hospital or other
- Study start
- 1 Mar 2024 (actual)
- Primary completion
- 31 Aug 2024 (estimated)
- Study completion
- 31 Aug 2024 (estimated)
- First posted
- 28 May 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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).
Study design
- Observational model
- Case only
- Time perspective
- Retrospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Adult ICU patients who receivied intravenous vancomycin treatment
Key inclusion criteria (4 listed)
- Age ≥18 years;
- Patients admitted to ICUs;
- Patients were administered intravenous vancomycin;
- Vancomycin TDM was performed at least two times.
Key exclusion criteria (2 listed)
- Vancomycin TDM was performed in a ward rather than in an ICU;
- Patients with missing data.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| no interventionno intervention | Other | – |
Primary outcome
The predicted serum vancomycin concentration
Time frame: 1 day
The serum vancomycin concentration predicted by the constructed model
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 28 May 2024.
- Results publication: PubMed 24932495Ye 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.
- Results publication: PubMed 18334494Ingram 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.
- Results publication: PubMed 32658968Rybak 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.
- Results publication: PubMed 9558127Yasuhara 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.
- Results publication: PubMed 27682033Obermeyer 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.
- Results publication: PubMed 31277828Doupe 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.
- Derived from this study: PubMed 41803667Wang JY, Hu J, Chen Y, Li S, Peng JM, Dong R, Hu XY, Jiang W, Wang CY, Weng L, Du B; China Critical Care Clinical Trials Group (CCCCTG). A Machine Learning-Based Model for Individualized Prediction of Vancomycin Concentration-Time Curves in ICU Patients. Pharmacol Res Perspect. 2026 Apr;14(2):e70235. doi: 10.1002/prp2.70235.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Li Weng, MD study chair
Peking Union Medical College Hospital
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Critical Illness and Infections, run by Peking Union Medical College Hospital at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 28 May 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
The predicted serum vancomycin concentration (time frame: 1 day). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06431412, which also lists 7 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 28 May 2024.