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Validation of Betalactam ML Prediction Models - TDMAide

Validation of Uncertainty Quantifying Machine Learning Models to Predict Beta-lactam Antimicrobial Concentrations in ICU Patients

Status
Not yet recruiting
Phases
NA
Study type
Interventional
Source
ClinicalTrials.gov
Registry ID
NCT06026852
Acronym
TDMAide
Enrollment
600
Registered
2023-09-07
Start date
2024-09-26
Completion date
2025-05-31
Last updated
2024-06-06

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Infections

Keywords

Artificial Intelligence, Machine Learning, PK/PD, Piperacillin-tazobactam, Meropenem, Intensive Care Unit

Brief summary

The goal of this study is to learn about the real wold behavior of developed machine learning models that predict the plasma concentration of piperacillin-tazobactam and meropenem in critically ill patients admitted to the intensive care unit (ICU). The main aim of the study is to validate the performance of these machine learning models. To this end, daily measured plasma concentrations of the investigated antimicrobials will be compared with the predicted concentration by the machine learning algorithms. Additional goals of the study include: * To describe the total plasma concentration over time of piperacillin-tazobactam and meropenem in patients admitted to the ICU. * To quantify the correlation between plasma concentrations of piperacillin-tazobactam and meropenem and the development of side effects. * To evaluate the perceived necessity of therapeutic drug monitoring (TDM) of consultants and physicians in training working in the ICU. * To evaluate the perceived added value of daily TDM. Samples (where possible taken routinely) from participating patients will be analyzed for meropenem and piperacillin-tazobactam plasma concentration. Participating physicians will be asked to fill in a short daily questionnaire during the time a patient under their care is treated with the antimicrobial under investigation.

Interventions

DEVICEPrediction of plasma concentration of piperacillin-tazobactam or meropenem

For included patients, a prediction will be made by developed machine learning models about the expected plasma concentration of piperacillin-tazobactam or meropenem by using routinely collected health care data.

DIAGNOSTIC_TESTDetermination of plasma concentration of piperacillin-tazobactam or meropenem

For included patients, the total plasma concentration of piperacillin-tazobactam or meropenem will be determined. Were possible, this will be done using a blood sample that was collected during routine daily bloodwork which is performed in the morning. If no routine sample is available, a study specific sample will be drawn at approximately the same time as a routine sample would be drawn.

OTHERDaily short questionnaire

Physicians who care for patients included in the study will be asked to fill in a short daily questionnaire that evaluates the perceived necessity and added value of daily therapeutic drug monitoring.

Sponsors

Research Foundation Flanders
CollaboratorOTHER
Imec
CollaboratorINDUSTRY
University Hospital, Ghent
Lead SponsorOTHER

Study design

Allocation
NON_RANDOMIZED
Intervention model
PARALLEL
Primary purpose
TREATMENT
Masking
NONE

Eligibility

Sex/Gender
ALL
Age
18 Years to 100 Years
Healthy volunteers
Yes

Inclusion criteria

Patients Inclusion Criteria: * Admission to the ICU. * Age above 18 years old. * Treatment with piperacillin-tazobactam or meropenem for less than 48 hours.

Exclusion criteria

* Pregnant or lactating patients. * Limitation of therapy beyond Do not resuscitate. * Expected demise within 48 hours after inclusion. * Haemoglobin \< 7 g/dL. * Previous inclusion in this study for a treatment course with the same antimicrobial. Consultants and physicians in training Inclusion Criteria: * Consultant or physician in training working in the ICU.

Design outcomes

Primary

MeasureTime frameDescription
Difference between predicted (TDMAIde) and measured (via HPLC-MS/MS method) plasma concentrationsThrough study completion, an average of 1 yearThe difference between the predicted concentration (from the TDMAide software) and the concentration range based on the measured concentration (measured from the blood sample from the patient and analyzed using a HPLC-MS/MS method, with and without taking into account intra- and inter measurement variabilities of the HPLC-MS/MS method.

Secondary

MeasureTime frameDescription
Plasma concentration (determined via HPLC-MS/MS) trendsThrough study completion, an average of 1 yearTrends in total plasma concentration over time of piperacillin-tazobactam and meropenem in patients admitted to the ICU
Correlation between plasma concentrations (measured by HPLC-MS/MS) and side effects as percentage of patients experiencing the side effectThrough study completion, an average of 1 yearThe correlation between plasma concentrations of piperacillin-tazobactam and meropenem and the development of renal (decline in urinary output, rise in serum creatinin), gastro-intestinal (C. difficile infections, stool consistency, elevation of ALT/AST/gamma GT/Alkalic fosfatase/INR/APTT/bilirubin), neurological (delirium as measured by Intensice Care Delirium Screening Checklist - ICDSC) or hematological (Rise or fall of thrombocytes, development of leucopenia/agranulocytosis/eosinophelia/hemolytic anemia) side effects.
Perceived necessity of therapeutic drug monitoringThrough study completion, an average of 1 yearThe perceived necessity of therapeutic drug monitoring of consultants and physicians in training working in the ICU by evaluating the perceived necessity collected during the surveys with the measured plasma concentrations.
Perceived added value of therapeutic drug monitoringThrough study completion, an average of 1 yearThe perceived added value of daily therapeutic drug monitoring from the responses to the survey

Countries

Belgium

Contacts

Primary ContactThomas De Corte, MD
thomas.decorte@uzgent.be0032093324134

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026