A recently published review article illustrated the application of Bayesian estimation to clinical pharmacy in the context of vancomycin therapeutic monitoring. With the most recent IDSA consensus guideline recommending the Bayesian method for accurately estimating the area under the curve (AUC) to inform vancomycin dosing, it is worthwhile for clinicians to become acquainted with its features and benefits.
The Problem: Balancing clinical workload and patient care
In 2023, clinician burnout is expected to persist as the labor challenge soars in the United States. Nurses and pharmacists will be expected to take on more roles in the public health sectors. With antibiotic dosing, it is pressing to re-evaluate the current method and transition to an evidence-based dosing and monitoring solution. The Bayesian method is a way to reduce clinical workload and maximize patient safety, all while ensuring therapeutic efficacy and the optimal practice of antibiotic stewardship.
The Math: What is Bayesian Estimation?
Bayesian estimation dates back to the 18th century when it was used to determine the probability of a future event occurring based on how often it occurred in the past. Since then, it has maintained the essence of using predetermined information and trends to make accurate predictions and inform current decisions. In the clinical setting, the Bayesian process consists of three main elements: 1) pre-collected population data (a priori), 2) patient-specific information (e.g. creatinine clearance, weight, age, etc.), and 3) observed patient data (e.g. dosing history and serum levels) to estimate an optimal pharmacokinetic (PK) profile (a posteriori) and dose needed to achieve that.
Limitations of Vancomycin Monitoring & PrecisePK’s Remedy
The following limitations in vancomycin therapeutic monitoring that existed before the introduction of Bayesian estimation in this clinical setting were discussed in the review article :
- Assumption of steady state after three doses
- Requirement of at least two measured serum concentrations
- Assumption of a linear elimination by estimating the slope of the line between two concentrations over 24 hours
- Lack of account for concentration measurement errors such as those related to time of measurement or assay errors
Remedy: PrecisePK’s Bayesian approach starts with a population PK assumption model and integrates a non-steady-state serum graph as a prior dataset. This eliminates the need for concentrations to be obtained at steady state and can rely on one concentration instead of two, significantly reducing the workload and bandwidth of hospital staff as well as reduce potential clinical errors that occur in a traditional dosing practice. A single trough level after the first dose is often enough to reliably estimate the AUC to a degree similar to that obtained from two levels at steady states.
Review Article Conclusion Endorsed by PrecisePK
The Bayesian method aims to enhance the precision of vancomycin dosing and monitoring to support decision making in clinical pharmacy practice. It is important to choose a Bayesian PK software program that utilizes models reflective of an institution’s specific patient population.
“PrecisePK has general population models for critically ill patients and various obesity and renal function classes as well as an option to use local population data a priori.”
PrecisePK’s user-friendly interface creates a resourceful, efficient, and intuitive workflow to maximize Bayesian benefits. Our therapeutic target guide auto-creates visual graphics that follow changes made to dosage history and serum drug level, allowing an easier grasp of critical care monitoring and minimizing events of clinical errors.
See how precision Bayesian dosing can elevate your practice and facilitate the delivery of exceptional health care. Contact us today to book a demo and start your free trial of PrecisePK.