Vancomycin Area under the Concentration-Time Curve Estimation Using Bayesian Modeling versus First-Order Pharmacokinetic Equations: A Quasi-Experimental Study
Aim: To evaluate the efficiency of Bayesian modeling software and first-order pharmacokinetic (PK) equations to calculate vancomycin area under the concentration-time curve (AUC) estimations. Methods: Unblinded, crossover, quasi-experimental study at a tertiary care hospital for patients receiving i...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Antibiotics |
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| Online Access: | https://www.mdpi.com/2079-6382/11/9/1239 |
| _version_ | 1797491822058012672 |
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| author | Yazed Saleh Alsowaida David W. Kubiak Brandon Dionne Mary P. Kovacevic Jeffrey C. Pearson |
| author_facet | Yazed Saleh Alsowaida David W. Kubiak Brandon Dionne Mary P. Kovacevic Jeffrey C. Pearson |
| author_sort | Yazed Saleh Alsowaida |
| collection | DOAJ |
| description | Aim: To evaluate the efficiency of Bayesian modeling software and first-order pharmacokinetic (PK) equations to calculate vancomycin area under the concentration-time curve (AUC) estimations. Methods: Unblinded, crossover, quasi-experimental study at a tertiary care hospital for patients receiving intravenous vancomycin. Vancomycin AUC monitoring was compared using Bayesian modeling software or first-order PK equations. The primary endpoint was the time taken to estimate the AUC and determine regimen adjustments. Secondary endpoints included the percentage of vancomycin concentrations usable for AUC calculations and acute kidney injury (AKI). Results: Of the 124 patients screened, 34 patients had usable vancomycin concentrations that led to 44 AUC estimations. Without electronic health record (EHR) integration, the time from assessment to intervention in the Bayesian modeling platform was a median of 9.3 min (quartiles Q<sub>1</sub>–Q<sub>3</sub> 7.8–12.4) compared to 6.8 min (Q<sub>1</sub>–Q<sub>3</sub> 4.8–8.0) in the PK equations group (<i>p</i> = 0.004). With simulated Bayesian software integration into the EHR, however, the median time was 3.8 min (Q<sub>1</sub>–Q<sub>3</sub> 2.3–6.9, <i>p</i> = 0.019). Vancomycin concentrations were usable in 88.2% in the Bayesian group compared to 48.3% in the PK equation group and there were no cases of AKI. Conclusion: Without EHR integration, Bayesian software was more time-consuming to assess vancomycin dosing than PK equations. With simulated integration, however, Bayesian software was more time efficient. In addition, vancomycin concentrations were more likely to be usable for calculations in the Bayesian group. |
| first_indexed | 2024-03-10T00:54:48Z |
| format | Article |
| id | doaj.art-660a878715864616969449a1a644a07b |
| institution | Directory Open Access Journal |
| issn | 2079-6382 |
| language | English |
| last_indexed | 2024-03-10T00:54:48Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Antibiotics |
| spelling | doaj.art-660a878715864616969449a1a644a07b2023-11-23T14:45:27ZengMDPI AGAntibiotics2079-63822022-09-01119123910.3390/antibiotics11091239Vancomycin Area under the Concentration-Time Curve Estimation Using Bayesian Modeling versus First-Order Pharmacokinetic Equations: A Quasi-Experimental StudyYazed Saleh Alsowaida0David W. Kubiak1Brandon Dionne2Mary P. Kovacevic3Jeffrey C. Pearson4Department of Clinical Pharmacy, College of Pharmacy, Hail University, Hail 81442, Saudi ArabiaDepartment of Pharmacy Services, Brigham and Women’s Hospital, Boston, MA 02115, USADepartment of Pharmacy Services, Brigham and Women’s Hospital, Boston, MA 02115, USADepartment of Pharmacy Services, Brigham and Women’s Hospital, Boston, MA 02115, USADepartment of Pharmacy Services, Brigham and Women’s Hospital, Boston, MA 02115, USAAim: To evaluate the efficiency of Bayesian modeling software and first-order pharmacokinetic (PK) equations to calculate vancomycin area under the concentration-time curve (AUC) estimations. Methods: Unblinded, crossover, quasi-experimental study at a tertiary care hospital for patients receiving intravenous vancomycin. Vancomycin AUC monitoring was compared using Bayesian modeling software or first-order PK equations. The primary endpoint was the time taken to estimate the AUC and determine regimen adjustments. Secondary endpoints included the percentage of vancomycin concentrations usable for AUC calculations and acute kidney injury (AKI). Results: Of the 124 patients screened, 34 patients had usable vancomycin concentrations that led to 44 AUC estimations. Without electronic health record (EHR) integration, the time from assessment to intervention in the Bayesian modeling platform was a median of 9.3 min (quartiles Q<sub>1</sub>–Q<sub>3</sub> 7.8–12.4) compared to 6.8 min (Q<sub>1</sub>–Q<sub>3</sub> 4.8–8.0) in the PK equations group (<i>p</i> = 0.004). With simulated Bayesian software integration into the EHR, however, the median time was 3.8 min (Q<sub>1</sub>–Q<sub>3</sub> 2.3–6.9, <i>p</i> = 0.019). Vancomycin concentrations were usable in 88.2% in the Bayesian group compared to 48.3% in the PK equation group and there were no cases of AKI. Conclusion: Without EHR integration, Bayesian software was more time-consuming to assess vancomycin dosing than PK equations. With simulated integration, however, Bayesian software was more time efficient. In addition, vancomycin concentrations were more likely to be usable for calculations in the Bayesian group.https://www.mdpi.com/2079-6382/11/9/1239vancomycinAUCBayesiantherapeutic drug monitoring |
| spellingShingle | Yazed Saleh Alsowaida David W. Kubiak Brandon Dionne Mary P. Kovacevic Jeffrey C. Pearson Vancomycin Area under the Concentration-Time Curve Estimation Using Bayesian Modeling versus First-Order Pharmacokinetic Equations: A Quasi-Experimental Study Antibiotics vancomycin AUC Bayesian therapeutic drug monitoring |
| title | Vancomycin Area under the Concentration-Time Curve Estimation Using Bayesian Modeling versus First-Order Pharmacokinetic Equations: A Quasi-Experimental Study |
| title_full | Vancomycin Area under the Concentration-Time Curve Estimation Using Bayesian Modeling versus First-Order Pharmacokinetic Equations: A Quasi-Experimental Study |
| title_fullStr | Vancomycin Area under the Concentration-Time Curve Estimation Using Bayesian Modeling versus First-Order Pharmacokinetic Equations: A Quasi-Experimental Study |
| title_full_unstemmed | Vancomycin Area under the Concentration-Time Curve Estimation Using Bayesian Modeling versus First-Order Pharmacokinetic Equations: A Quasi-Experimental Study |
| title_short | Vancomycin Area under the Concentration-Time Curve Estimation Using Bayesian Modeling versus First-Order Pharmacokinetic Equations: A Quasi-Experimental Study |
| title_sort | vancomycin area under the concentration time curve estimation using bayesian modeling versus first order pharmacokinetic equations a quasi experimental study |
| topic | vancomycin AUC Bayesian therapeutic drug monitoring |
| url | https://www.mdpi.com/2079-6382/11/9/1239 |
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