# Impact of Sampling Strategy and Population Model on Bayesian Estimates of Vancomycin AUC in Patients with BMI > 40 kg/m2: A Single-Center Retrospective Study

## TL;DR

This study shows that using a single vancomycin concentration to estimate drug exposure in very obese patients is reliable, but the choice of population model significantly affects results.

## Contribution

Demonstrates the feasibility of single-concentration Bayesian AUC estimation in morbidly obese patients and highlights the impact of population model selection.

## Key findings

- 97.5% of single-concentration AUC estimates were within ±20% of two-concentration estimates.
- Using the Hughes vs. Carreno population models led to only 59.1% agreement within ±20%.
- Measuring two concentrations did not ensure model concordance in AUC estimates.

## Abstract

Background/Objectives: Growing evidence supports the use of a single trough concentration, rather than both a peak and trough, to estimate the 24 h area under the curve (AUC24) of vancomycin using Bayesian software (InsightRx® Ver.1.71). However, patients with body mass index (BMI) ≥ 40 kg/m2 are underrepresented in validation studies. Studies in patients with obesity have produced mixed results, potentially because of different population models used. Methods: This single-center, retrospective study evaluated adult inpatients with BMI ≥ 40 kg/m2. Steady-state AUC24 estimates generated by Bayesian software using both two-concentration and one-concentration inputs were compared. Agreement was defined as a percent difference within ±20%. Subgroup analyses were conducted for patients with defined peak and trough concentrations and for comparisons between two Bayesian population models (Carreno vs. Hughes). Linear regression assessed covariates associated with percent difference. Results: Among 82 encounters, 97.5% of one-concentration estimates based on the smaller concentration were within ±20% of the two-concentration AUC24,SS (mean difference: 2.9%, 95% CI: 0.14 to 3.8%). Similar agreement was observed using the larger concentration (97.5%, mean difference: −3.1%, 95% CI: −4.7 to −0.1.5%). Subgroup analysis for encounters with true peak/trough levels (n = 22) also showed 100% agreement within ±20%. The percent difference did not correlate with BMI or other covariates. Comparison of Hughes vs. Carreno models showed larger variability (only 59.1% within ±20%). Conclusions: In patients with BMI ≥ 40 kg/m2, Bayesian AUC24,SS estimation using a single vancomycin concentration is feasible. Greater caution is warranted in the setting of acute kidney injury, poor model fit, or targeting AUC at the extremes of the therapeutic range. The population model used to generate the Bayesian AUC estimate has a much greater influence than the number of concentrations analyzed. Furthermore, measuring two concentrations does not ensure concordance between models.