Validation of Betalactam ML Prediction Models - TDMAide

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

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.

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.

Exclusion Criteria:

• None