Evaluation of a vancomycin dosing nomogram in obese patients weighing at least 100 kilograms

Area-Under-Curve-Guided Versus Trough-Guided Monitoring of Vancomycin and Its Impact on Nephrotoxicity: A Systematic Review and Meta-Analysis

BACKGROUND

Conventionally, vancomycin trough levels have been used for therapeutic drug monitoring (TDM). Owing to the increasing evidence of trough levels being poor surrogates of area under the curve (AUC) and the advent of advanced pharmacokinetics software, a paradigm shift has been made toward AUC-guided dosing. This study aims to evaluate the impact of AUC-guided versus trough-guided TDM on vancomycin-associated nephrotoxicity.

METHODS

A systematic review was conducted using PubMed, Embase, Web of Science, Cumulative Index to Nursing and Allied Health Literature, Google scholar, and Cochrane library databases; articles published from January 01, 2009, to January 01, 2021, were retrieved and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist. Studies that evaluated trough-guided or AUC-guided vancomycin TDM and vancomycin-associated nephrotoxicity were included. Random-effects models were used to compare the differences in nephrotoxicity.

RESULTS

Of the 1191 retrieved studies, 57 were included. Most studies included adults and older adults (n = 47, 82.45%). The pooled prevalence of nephrotoxicity was lower in AUC-guided TDM [6.2%; 95% confidence interval (CI): 2.9%-9.5%] than in trough-guided TDM (17.0%; 95% CI: 14.7%-19.2%). Compared with the trough-guided approach, the AUC-guided approach had a lower risk of nephrotoxicity (odds ratio: 0.53; 95% CI: 0.32-0.89). The risk of nephrotoxicity was unaffected by the AUC derivation method. AUC thresholds correlated with nephrotoxicity only within the first 96 hours of therapy.

CONCLUSIONS

The AUC-guided approach had a lower risk of nephrotoxicity, supporting the updated American Society of Health-System Pharmacists guidelines. Further studies are needed to evaluate the optimal AUC-derivation methods and clinical utility of repeated measurements of the AUC and trough levels of vancomycin.

Experience of Vancomycin Therapeutic Drug Monitoring in Two Multidisciplinary Hospitals in Latvia

Background and Objectives: Management of infectious diseases is a huge burden to every healthcare system worldwide. Antimicrobial resistance, including antibacterial resistance, is an increasing problem worldwide; therefore, more new antibiotics are necessary to be discovered. Meanwhile, “old” antibacterial agents are still administered to fight infectious diseases caused by resistant bacteria. One of these antibacterial agents is vancomycin, which is effective in treating serious systemic infections caused by gram-positive bacteria. Thus, it is necessary to perform vancomycin concentration measurements in plasma due to its narrow therapeutic index. Various approaches are implemented for more precise therapy, including therapeutic drug monitoring (TDM) of vancomycin and with a supervision of a clinical pharmacist. The purpose of the study was to investigate if the TDM practice is improved with a local vancomycin TDM protocol applied in a hospital. The results of TDM in two multidisciplinary hospitals, one with a local TDM protocol implemented and applied and the other with no local TDM protocol implemented and applied, were compared. Materials and Methods: A retrospective study was performed in two multidisciplinary hospitals in Latvia. The data were collected for a time period of 4 years (2016−2020) in a hospital without a local TDM protocol and for a time period of 2 years (2018−2020) in a hospital with a local TDM protocol, starting with a period of time when the vancomycin TDM protocol was developed. The data about the patients included in the study were analyzed based on gender, age, body weight, and renal function. Vancomycin therapy was analyzed based on dosing schemes (vancomycin dose and dosing interval), data about loading and maintenance doses, vancomycin concentration, and details about vancomycin concentration (sampling time and concentration level). Results: Differences between the hospitals were found in terms of the initiation of vancomycin administration and concentration sampling. In the hospital with a TDM protocol compared with the hospital without a TDM protocol, more accurate initiation was found, alongside adaption of therapy (97.22% vs. 18.95%, p < 0.001), better performance of administration of a loading dose (22.73% vs. 1.29%, p < 0.01), and reaching of target concentration (55.56% vs. 35.29%, p < 0.01). Concentration sampling in the correct timeframe before the vancomycin dose and vancomycin administration did not show statistically better results in either of the hospitals (4.60% vs. 6.29%, p = 0.786). Conclusions: Better results of adequate adjustments of vancomycin therapy were achieved in the hospital with a TDM protocol. In the long term, sustainable results and regular medical professionals’ training is necessary.

AI Models to Assist Vancomycin Dosage Titration

Background: Effective treatment using antibiotic vancomycin requires close monitoring of serum drug levels due to its narrow therapeutic index. In the current practice, physicians use various dosing algorithms for dosage titration, but these algorithms reported low success in achieving therapeutic targets. We explored using artificial intelligent to assist vancomycin dosage titration. Methods: We used a novel method to generate the label for each record and only included records with appropriate label data to generate a clean cohort with 2,282 patients and 7,912 injection records. Among them, 64% of patients were used to train two machine learning models, one for initial dose recommendation and another for subsequent dose recommendation. The model performance was evaluated using two metrics: PAR, a pharmacology meaningful metric defined by us, and Mean Absolute Error (MAE), a commonly used regression metric. Results: In our 3-year data, only a small portion (34.1%) of current injection doses could reach the desired vancomycin trough level (14-20 mcg/ml). Both PAR and MAE of our machine learning models were better than the classical pharmacokinetic models. Our model also showed better performance than the other previously developed machine learning models in our test data. Conclusion: We developed machine learning models to recommend vancomycin dosage. Our results show that the new AI-assisted dosage titration approach has the potential to improve the traditional approaches. This is especially useful to guide decision making for inexperienced doctors in making consistent and safe dosing recommendations for high-risk medications like vancomycin.