A pilot study evaluating mid-point vancomycin concentrations to estimate pharmacokinetics and area under the curve: a prospective study

Abstract

Background

Guidelines recommend monitoring vancomycin by estimating the area under the curve (AUC₂₄) with two levels, peak and trough, preferably using Bayesian software. However, such an approach is not feasible or cost-effective in all settings. In this study, we evaluated the accuracy and precision of estimating AUC₂₄ using a single mid-point level in critically ill cancer patients. We hypothesized that concentrations at the midpoint of the dosing interval may provide insight into the volume of distribution (Vd) and clearance (Cl), capturing pharmacokinetic parameters assessed by the peak and trough concentrations, respectively.

Methods

A prospective study that included critically ill cancer patients with stable kidney function who received vancomycin during their ICU stay. Trough, peak, and midpoint concentrations were measured at steady-state. Pharmacokinetic equations from the short infusion model was used to calculate the AUC₂₄, Cl, and Vd using a single midpoint. We also determined AUC₂₄, Cl, and Vd using Bayesian software with peak and trough concentrations. Accuracy and precision of the pharmacokinetic estimations utilizing the mid-point level were determined, with a comparison to the Bayesian approach.

Results

We included 91 patients, with a mean age of 53 years ± 17 (SD), 54% were males, and 77% had solid tumors. The mean prescribed vancomycin dose was 15 mg/kg/dose ± 3 (SD). Median AUC₂₄ was 426 mg.dl/hr (IQR 326–559) and 464 mg.dl/hr (IQR 345–664), using the midpoint and Bayesian approach, respectively. The mean Vd was 48 L (± 11) and 45 L (± 13) and Cl was 5 L/hr (± 3) and 5 L/hr (± 2) for both approaches, respectively. The accuracy and precision for the midpoint, against the Bayesian, were: AUC₂₄ (11%, 22%, respectively), Cl (17%, 24%, respectively), and Vd (6%, 9%, respectively).

Conclusions

Utilization of mid-point vancomycin levels to calculate pharmacokinetic parameters demonstrated promising findings. Further research with larger and more diverse patient populations is needed to evaluate this approach further.

Background

Therapeutic drug monitoring of vancomycin is recommended to achieve the desired efficacy and minimize nephrotoxicity. Though the area under the curve to minimum inhibitory concentration ratio (AUC₂₄/MIC) is considered the most useful pharmacodynamic parameter to predict vancomycin effectiveness, previous guidelines recommended the use of trough serum concentrations to monitor vancomycin, as it was considered a surrogate marker for AUC₂₄/MIC and a more practical approach [1].

Subsequent research demonstrated that vancomycin trough concentrations may not be the optimal surrogate for AUC₂₄ [2–8]. In addition, studies reported that the recommended trough concentrations of 15–20 mg/L for methicillin-resistant Staphylococcus aureus infection often result in AUC₂₄/MIC values exceeding the recommended target of 400–600 mg.h/L and have been associated with nephrotoxicity [9, 10]. Therefore, the guidelines were updated to recommend against the use of trough concentrations to assess vancomycin dosing [11].The most recent guidelines consider AUC₂₄-guided dosing as the most accurate and optimal way to monitor vancomycin, with a recommended target of 400 to 600 (assuming a MIC of 1 mg/L) to achieve clinical efficacy while improving patient safety [11].

To estimate AUC₂₄, the guidelines recommend one of two approaches. The preferred is the use of Bayesian software programs and two vancomycin samples (at 1–2 h after the end of the infusion (peak) and at the end of the dosing interval (trough)). Though the guidelines indicate that a trough concentration alone can be used to estimate the AUC₂₄ with the Bayesian approach in certain patients, they state that more data are needed across different patient populations to ensure the viability of a single level approach. The other method recommended by the guidelines to estimate AUC₂₄ involves two concentrations, peak, and trough, obtained near steady-state and utilizing the first-order pharmacokinetic equations [11].

Although estimating AUC₂₄ using the Bayesian approach is relatively simple, it requires specialized Bayesian software, which many institutions cannot afford. The alternative method also presents challenges, as it requires pharmacokinetic skills, which most clinicians may find difficult to apply. In addition, both recommended methods involve obtaining two vancomycin serum concentrations, which adds practical and resource-related challenges. Therefore, we aimed to explore an alternative approach for estimating vancomycin AUC₂₄ utilizing the non-Bayesian approach with a single level.

While trough concentrations are commonly used in vancomycin monitoring, they mainly reflect the minimum concentration at the end of the dosing interval and are most useful for assessing drug accumulation. However, they provide information only about the elimination phase and require assumptions about drug distribution. On the other hand, mid-point concentrations reflect the post-distribution elimination phase and thus can provide insight into additional pharmacokinetic parameters. We hypothesized that the mid-point measurements could offer information on drug volume of distribution and clearance, capturing pharmacokinetic parameters assessed by the peak and trough concentrations, respectively. To evaluate this hypothesis, we conducted a study comparing vancomycin AUC₂₄ based on a single midpoint concentration to that estimated with the guideline-recommended approach involving two levels and utilizing Bayesian analysis.

Methods

This was a prospective study conducted in the adult intensive care units (ICUs) at a comprehensive cancer center in Amman, Jordan, between June 2019 and May 2022. The center is a 352-bed comprehensive cancer center that includes two medical-surgical ICUs with a total of 21 beds. These ICUs manage both cancer and non-cancer-related critical illnesses in patients receiving medical care at the center.

Eligible patients were adults (≥ 18 years old) who were prescribed vancomycin during their ICU stay, had stable renal function, and were expected to remain in the ICU for the duration of the vancomycin study samples. Stable renal function was defined as serum creatinine that increased by no more than 0.3 mg/dL and urine output that decreased by no more than half over the last 48 h before obtaining the study samples. Exclusion criteria included patients with a do-not-resuscitate or comfort care code status, those on dialysis, and patients admitted to the ICU for post-surgery observation. Informed consent was obtained from the patient or responsible caregiver.

The vancomycin dosing regimen was determined by the clinical team, based on the renal function of the patient and predicted pharmacokinetics. Patients enrolled in the study had one set of three vancomycin concentration levels measured at steady-state: midpoint, peak, and trough. Steady-state was defined as occurring after the patient received at least 3 doses of vancomycin in the ICU. The midpoint concentration was measured at half the dosing interval: for an 8-hour dosing interval, the midpoint sample was taken approximately 4 h after the end of the infusion; for a 12-hour interval, approximately 6 h after the end of the infusion; for a 24-hour dosing interval, approximately 12 h after the end of the infusion. The peak concentration was measured 1 h after the end of the vancomycin infusion, and the trough was measured 30 min before the start of the next infusion. Study samples taken were witnessed by one of the research co-investigators to record the exact timing of the sample. For cases where the exact time was not documented, the sample time for the trough was assumed to be 30 min before the start of the next dose, and that for the peak at 1 h after the end of the infusion, and we referred to the default standard administration times of the institution as a reference point. For the time of the mid-point concentration, it was assumed to be at 4, 6, or 12 h after the end of the infusion, based on the prescribed dosing interval. Patients with vancomycin doses that were given earlier or later than scheduled were not excluded.

To estimate the AUC₂₄ from a single midpoint concentration, we used the following short infusion vancomycin model equation to first estimate k:

• Maintenance dose in milligrams (MD) = [C_mid (Cl) (tin) (1 - e ^{– kτ})]/[(1 – e ^–ktin) e ^{– ktmax} [12].

• C_mid (µg/ml) was the vancomycin measured level through k estimation.

• Cl (L/hr) was the clearance which equals to (k. Vd).

• k (hr ^-) was the elimination constant that was derived using an iterative method.

• Vd (L) was the fixed volume of distribution of 0.7 L/kg, derived from the Thomson model [13].

• Tin (hr) was the infusion time which was either over 60–90 min depending on the vancomycin prescribed order.

• τ (hr) was vancomycin dosing frequency.

• tmax was the time interval between the midpoint level and the end of the previous vancomycin infusion.

This short infusion model was manually created as an equation in Excel to estimate k using an iterative approach. The iterative method is a mathematical technique used to calculate pharmacokinetic parameters [14]. We started with a random initial approximation for the value of k and then repeated by increasing or decreasing the number until the predicted vancomycin concentration closely matched the measured concentration in the patient. After estimating k, we estimated Cl using k * Vd. Then the AUC₂₄ was calculated using the following equation AUC₂₄ = Total daily dose/Cl.

The AUC₂₄ derived from the midpoint concentration was compared to the AUC₂₄, utilizing the guidelines recommended approach, which was considered the gold standard AUC₂₄. The guidelines recommend obtaining two pharmacokinetic samples, a peak taken at 1–2 h post infusion and a trough, obtained at the end of the dosing interval, and utilizing Bayesian software [1, 11]. We used the study peak and trough concentrations obtained at steady-state and the Bayesian software program from InsightRx. The Thomson model [13] and a Vd of 0.7 L/kg were used in the Bayesian analysis to estimate steady-state AUC₂₄ as well as the clearance and volume of distribution. At the time of the study, there were no pharmacokinetic models that were specific for critically ill patients with cancer in the Bayesian software, InsightRx. Therefore, we considered the Thomson to be the most appropriate to use since it reflected the pharmacokinetics of a diverse group of hospitalized patients. Though others have suggested the use of a two-compartment model for vancomycin in critically ill patients [15], we used the Thomson model for the reasons above. Furthermore, although there might be a statistically significant difference between AUCs from one- and two-compartment models, it was reported that the level of difference was acceptable from the clinical perspective [16].

Patient electronic medical records were used to derive the patient demographics and characteristics as well as the laboratory results. The following data was collected: patient demographics, ICU admission diagnosis, requirement of mechanical ventilation, and need for vasopressor support, the number of concomitant nephrotoxic medications, laboratory results, and vancomycin dosing regimens.

Statistical analysis

Categorical data were reported as counts and percentages, and continuous data as means and standard deviations or medians and interquartile ranges (IQRs). Estimating AUC₂₄ using the midpoint was evaluated by determining its accuracy and precision, in reference to the gold standard, two-level Bayesian-based approach. The accuracy was defined as the median % predicted error and was calculated using the following equation: (Xestimated − Xactual)/Xactual*100. Precision was defined as median % predicted absolute error and was calculated using the following equation: (∣Xestimated − Xactual∣)/Xactual*100. The values for the Xestimated were AUC₂₄ derived from one sample mid-point non-Bayesian and the values for Xactual were AUC₂₄ derived from using two samples and Bayesian software. Given that this was a new strategy to estimate non-Bayesian derived AUC₂₄, CL, and Vd, a threshold was not established in this study for accuracy and precision. However, we utilized the boundaries for accuracy and precision that were utilized in previously published reports, which have been < 5 to 20% [17]. The analysis was planned for the entire cohort as well as for subgroups based on the dosing intervals (8-hour, 12-hour, and 24-hour).To determine the impact of the mid-point dosing method on decision making and patient management, we also compared the proportion of AUC₂₄ that are considered therapeutic (i.e., AUC₂₄ ≥ 400, where dose adjustments are not required) in the midpoint and gold standard groups. Bland-Altman plot and a clinical decision agreement table were used to present the comparison of AUC₂₄ between both groups. Python version 3.8 was used to perform the analysis.

Results

Over the study period, a total of 91 patients were enrolled. The mean age was 53 ± 17 (SD) years, and 48 (54%) patients were males. Most of the included patients (77%) had solid tumors, among whom 29 (32%) had metastatic disease. The mean APACHE II score at admission was 21 ± 7 (SD), with an average weight of 69 kg ± 15 (SD). No patients were classified as morbidly obese (BMI > 40 kg/m²), none received a vancomycin loading dose, and the mean prescribed vancomycin dose was 15 mg/kg/dose ± 3 (SD). The majority of patients received vancomycin every 12 h (n = 89, 98%), while the remaining were prescribed every 8 h (n = 2, 2%). Table 1 outlines the characteristics of the patients enrolled.

Table 1.

Patient characteristics

Patient Characteristic	All Patients (n = 91)
Age, mean ± SD, years	53 (17)
Male, n (%)	49 (54)
Weight, mean ± SD, Kg	69 (16)
Type of malignancy
Hematology, n (%)	21 (23)
Solid, n (%)	70 (77)
Metastasis, n (%)	29 (32)
Admission diagnosis
Respiratory, n (%)	39 (43)
Infectious, n (%)	29 (32)
Neurological, n (%)	15 (17)
Cardiovascular, n (%)	4 (4)
Other, n (%)	4 (4)
Body mass index, mean ± SD, kg/m2	26 (5)
Baseline creatinine, mean ± SD, mg/dL	0.7 (0.2)
Mechanical ventilation upon admission, n (%)	34 (38)
Mechanical ventilation during admission, n (%)	50 (55)
Vasopressors upon admission, n (%)	39 (43)
Vasopressors during admission, n (%)	53 (58)
Neutropenia < 500 cells/microL upon admission, n (%)	18 (20)
Thrombocytopenia < 100 × 103/microL upon admission, n (%)	31 (34)
Empiric Vancomycin Dose, mean ± SD, mg/kg/day	30 (5)
ICU vancomycin steady-state trough concentration, mean ± SD, mg/L	12 (6)

At steady-state, the median midpoint, trough, and peak concentrations were 18.1 mg/L (IQR 13.7–23.8), 11.5 mg/L (IQR 7.4–15.2), and 27 mg/L (19.4–32.2), respectively. The median AUC₂₄ was 426 mg.dl/hr (IQR 326–559) using the midpoint non-Bayesian approach and 464 mg.dl/hr (IQR 345–664) using the gold standard two-level Bayesian method. The mean Vd was 48 L ± 11 (SD) and 45 L ± 13 (SD), and the Cl was 5 L/hr ± 3 (SD) and 5 L/hr ± 2 (SD) for the midpoint and gold standard approaches, respectively. The accuracy and precision for the midpoint, against the gold standard, were the following: AUC₂₄ (11% and 22%, respectively), Cl (17% and 24%, respectively), and Vd (6% and 9%, respectively). Given that most of the patients were on 12-hour dosing interval, a subgroup analysis based on the dosing interval was not performed.

The AUC₂₄ was considered therapeutic (i.e., ≥ 400) in 54 out of 91 patients (60%) with the use of the single midpoint non-Bayesian method and in 64 out of 91 patients (70%) with the use of the two-level Bayesian approach. This implies that 40% will require a dose increase with the use of the single midpoint non-Bayesian method, while 30% with the use of the two-level Bayesian approach. The comparison between AUC_24, Cl, and Vd methods was further illustrated using a Bland-Altman plot (Figs. 1, 2 and 3, respectively) with a clinical decision agreement table (Table 2).

Fig. 1.

Comparison of AUC₂₄ (mg.dl/hr) Methods by Bland-Altman Plotting

Fig. 2.

Comparison of Clearance (L/hr) Methods by Bland-Altman Plotting

Fig. 3.

Comparison of Volume of Distribution (L/Kg) Methods by Bland-Altman Plotting

Table 2.

Clinical decision agreement

Calculated AUC24	Gold Standard AUC24
		< 400	400–600	> 600	Total
	< 400	20 (57%)	12 (34%)	3 (9%)	35
	400–600	7 (17%)	15 (37%)	19 (46%)	41
	> 600	2 (13%)	7 (47%)	6 (40%)	15
	Total	29	34	28	91
	Total Matched = 41/91 = 45%

Discussion

In this study, we investigated whether a single mid-point vancomycin concentration could be used to estimate AUC₂₄ in critically ill patients with cancer. To our knowledge, this is the first study to evaluate such an approach. Compared to the gold standard recommended method, which involves two levels and Bayesian software, the single mid-point concentration yielded moderate accuracy and precision, demonstrating a reasonable performance in comparison to what is reported in the literature [18, 19]. However, when assessing the proportion of patients achieving therapeutic AUC₂₄, the Bayesian method classified a higher proportion of patients in comparison to the midpoint approach (70% vs. 60%). In addition, less than half of the predicted AUCs calculated using mid-point concentrations were in agreement with the gold standard Bayesian method in regard to the anticipated clinical decision.

The primary goal of evaluating this approach was to identify a method for determining AUC₂₄ without the need for Bayesian software or obtaining two levels. However, our approach utilized a fixed volume of distribution value in both methods, which needs to be considered when implemented. Moreover, the iterative method, though not new, requires time and some level of pharmacokinetic expertise. This can be addressed by incorporating such a method into an Excel-based equation to facilitate the process of estimating the k when utilizing the midpoint concentration.

Limited studies evaluated utilizing mid-point vancomycin to estimate AUC₂₄ and pharmacokinetic parameters. Carreno et al. evaluated the predictive performance of a Bayesian model to estimate AUC₂₄ on obese patients (BMI > 40 kg/m²) and found that the midpoint method tended to overestimate AUC₂₄ [20]. In contrast, our study in critically ill cancer patients found that the midpoint approach underestimated AUC₂₄ compared to Bayesian analysis. A previous study by Rodvold et al. evaluated vancomycin midpoint concentrations, but the study included non-steady state concentrations, and the goal was to use such concentrations to estimate vancomycin concentrations, rather than AUC₂₄ [20].

Other studies investigated the use of single vancomycin concentrations to estimate AUC₂₄, though those studies used trough concentrations, rather than midpoint, did not utilize the iterative approach, and did not compare the findings to those from Bayesian analysis [21–23]. One study compared AUC₂₄ estimated from trough-only data to AUC₂₄ calculated from peak and trough concentrations for 4,343 patients from the VancoPK.com site [21]. Patients were divided into model and test groups to derive a Vd equation. The derived equation was Vd = 0.29 (age) + 0.33 (total weight in kg) + 11 and was used to estimate an AUC₂₄ with trough-only data. Though the authors concluded that vancomycin AUC₂₄ can be estimated from a single steady-state level, with pharmacokinetic equations using the estimated Vd derived equation, the root mean square error which was used as a measure of precision was about 50, which indicates that about 68% of estimated AUC₂₄were within 50 points of the actual AUC₂₄. Though this may suggest reasonable results in about two-thirds of the patients, precise results may not have been achieved in the remaining one-third of the patients.

Our study has shown that changes in kidney function, fluid shifts, and how drug levels are measured all play a role in how accurately we predict vancomycin AUC₂₄ in critically ill and cancer patients. We included critically ill cancer patients with stable renal function, yet there was notable variability in clearance, making dosing more challenging. The study also found that vancomycin spreads more widely in the body, likely due to fluid shifts common in critically ill patients. Furthermore, several factors may contribute to changes in pharmacokinetics in critically ill cancer patients such as neutropenia. This supports the need for individualized dosing strategies rather than relying on fixed pharmacokinetic models.

In this study, several limitations exist. First is being single-centered, and the relatively small sample size. Second, a special population was enrolled which included critically ill cancer patients, and the majority of the patients had good renal function, which may impact the generalizability of the findings to other patient populations. Third, variation in dosing intervals among patients may introduce heterogeneity in pharmacokinetic estimates and mid-point concentration interpretations; however, we were unable to perform a subgroup analysis given that most patients received the 12-hour dosing interval. In addition, though the Thomson model used in the Bayesian analysis is considered appropriate in critically ill patients, it has not been studied in critically ill patients with cancer [24–26]. Furthermore, it is a one-compartment model, which may not fully capture the complex distribution phase described in critically ill patients due to fluid shifts [15]. Another limitation to consider is the timing of the peak measurement, taken one hour after the end of the infusion, which may not have reliably avoided the distribution phase. However, since this time frame reflects standard clinical practice, we adopted the same approach.

Conclusions

To our knowledge, this is the first study to evaluate the use of midpoint vancomycin levels in calculating pharmacokinetic parameters in critically-ill adults with cancer. Midpoint levels at steady-state offered reasonable accuracy and precision compared to the gold standard Bayesian-based approach with two concentrations but the overall agreement based on the expected clinical decision was low. Further studies are needed to investigate this proposed approach to vancomycin monitoring using alternative validated population pharmacokinetic models and simulation studies to determine optimal sampling points. In addition, evaluating the impact of these approaches on clinical decision-making and outcomes is crucial in future research.

Abbreviations

AUC₂₄

24 h area under the curve

Cl

Clearance

Vd

Volume of distribution

MIC

Minimum inhibitory concentration

ICU

Intensive care unit

SD

Standard deviation

APACHE II

Acute Physiology and Chronic Health Evaluation II

BMI

Body Mass Index

IQR

Interquartile range

Authors’ contributions

AA, LN and JL contributed to the study conception and design. Material preparation and data collection were performed by AA, DA and WA. Data analysis was performed by AA and SS. The first draft of the manuscript was written by AA and LN and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Funding

This research received funding for vancomycin level tests through a KHCC intramural grant.

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

The study was approved by the King Hussein cancer center (KHCC) Institutional Review Board (19KHCC14). Informed consent was obtained from the patient or responsible caregiver. The study have been performed in accordance with the Declaration of Helsinki.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.