Vancomycin area under the curve versus trough only guided dosing and the risk of acute kidney injury: Systematic review and meta‐analysis

Abdelmessih, Emily; Patel, Nandini; Vekaria, Janaki; Crovetto, Brynna; Filippo, Savanna San; Adams, Christopher D.; Brunetti, Luigi

doi:10.1002/phar.2722

Cited by 23 publications

(10 citation statements)

References 36 publications

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“…AUC is a widely used evaluation index. AUC is an index to measure the performance of binary classification model, which measures the probability that positive samples are ranked before negative samples in the test set, reflecting the ranking ability of the model [38,39]. is paper uses AUC as an evaluation index to measure the performance of employment recommendation, and its calculation method is as follows:…”

Section: Discussion and Analysis Of Resultsmentioning

confidence: 99%

The Establishment of College Student Employment Guidance System Integrating Artificial Intelligence and Civic Education

Huang

2022

Mathematical Problems in Engineering

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A logical and appropriate job advising system for college students is the only way to address the current dire work scenario for recent graduates in a society that is becoming more and more competitive. The process of building new career advice systems for college students is still fraught with many major issues. Existing problems are manifested in the lack of a perfect employment guidance system for college students and a lack of effective ideological and political education, which leads to the inability of college students to establish a correct employment concept. At the same time, the recommendation method used in the current college student employment guidance system is not reasonable enough and only considers students’ one-way preference for employment units. Then, the history of one user’s employment is likely to lead to unreliable negative samples, which affects the recommendation performance. In addition, the current employment recommendation system cannot fully integrate the service function, management function, education function, and research function of the employment guidance system. In this paper, we address the aforementioned issues and propose an unique career counseling system for college students by combining algorithmic recommendation with ideological and political education. The system adaptively aggregates the preferences and needs of students and employers and fully integrates several functions of the student career competence and career guidance system. The experimental results show that the establishment of a college students’ employment guidance system integrating artificial intelligence (AI) and civic education not only has better interpretability compared with the traditional employment guidance system but also has a higher area under curve (AUC). Furthermore, we also discuss the importance of ideological and political education and referral systems in the work of the career guidance system through ablation experiments. The results show that the AUC values of integrating ideological and political education and recommender systems were higher than those of integrating only ideological and political education or recommender systems. Therefore, establishing an employment guidance system for college students that integrates AI and civic education can effectively guide students’ employment. At the same time, the college student employment guidance system proposed in this paper can offer employment guidance courses in colleges and universities, help graduates to solve their doubts, and make graduates adjust their psychological state in the process of job hunting.

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Section: Discussion and Analysis Of Resultsmentioning

confidence: 99%

The Establishment of College Student Employment Guidance System Integrating Artificial Intelligence and Civic Education

Huang

2022

Mathematical Problems in Engineering

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“…Nephrotoxicity is the most significant adverse effect. Some risk factors for vancomycin-association nephrotoxicity should be warned, such as the combination of piperacillintazobactam (PTZ), everity of illness, pre-existing kidney disease, and so on (Fiorito, Luther, Dennehy, LaPlante, & Matson, 2018;Abdelmessih et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

Systematic review of efficacy, safety and pharmacokinetics of intravenous and intraventricular vancomycin for central nervous system infections

et al. 2022

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Objective: The decision of vancomycin dosage for central nervous system (CNS) infections is still a challenge because its bactericidal nature in cerebrospinal fluid (CSF) has not been confirmed by human studies. This study systematically reviewed the literatures on vancomycin in patients with meningitis, ventriculitis, and CNS device-associated infections, to assess efficacy, safety, and pharmacokinetics to better serve as a practical reference.Methods: Medline, Embase, and Cochrane Library were searched using terms vancomycin, Glycopeptides, meningitis, and central nervous system infections. Data were extracted including characteristics of participants, causative organism(s), administration, dosage, etc., The clinical response, microbiological response, adverse events and pharmacokinetic parameters were analyzed.Results: Nineteen articles were included. Indications for vancomycin included meningitis, ventriculitis, and intracranial device infections. No serious adverse effects of intravenous (IV) and intraventricular (IVT) vancomycin have been reported. Dosages of IV and IVT vancomycin ranged from 1000–3000 mg/day and 2–20 mg/day. Duration of IV and IVT vancomycin therapy most commonly ranged from 3–27 days and 2–21 days. Therapeutic drug monitoring was conducted in 14 studies. Vancomycin levels in CSF in patients using IV and IVT vancomycin were varied widely from 0.06 to 22.3 mg/L and 2.5–292.9 mg/L. No clear relationships were found between vancomycin CSF levels and efficacy or toxicity.Conclusion: Using vancomycin to treat CNS infections appears effective and safe based on current evidence. However, the optimal regimens are still unclear. Higher quality clinical trials are required to explore the vancomycin disposition within CNS.

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“…[4][5][6] Some observational studies and meta-analyses have suggested that AUC-guided vancomycin dosing, which may lead to lower nephrotoxicity than trough-guided dosing. [7][8][9][10] The therapeutic AUC range should be complied with on the second day. 9,11 Bayesian forecasting using a population pharmacokinetic (PopPK) model and measured concentrations is an increasingly popular approach for estimating AUC, and is a key component of model-informed precision dosing (MIP-D).…”

Section: Introductionmentioning

confidence: 99%

“…The updated therapeutic drug monitoring (TDM) guidelines for vancomycin recommend a narrow therapeutic AUC range of 400–600 μg･h/mL, based on the achievement of an AUC/MIC ratio of 400 for clinical efficacy and AUC of 600 μg･h/mL for lower nephrotoxicity 4–6 . Some observational studies and meta‐analyses have suggested that AUC‐guided vancomycin dosing, which may lead to lower nephrotoxicity than trough‐guided dosing 7–10 . The therapeutic AUC range should be complied with on the second day 9,11 …”

Section: Introductionmentioning

confidence: 99%

Model‐informed precision dosing of vancomycin for rapid achievement of target area under the concentration‐time curve: A simulation study

Oda,

Yamada,

Matsumoto

et al. 2023

Clinical Translational Sci

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In this study, we aimed to evaluate limited sampling strategies for achieving the therapeutic ranges of the area under the concentration‐time curve (AUC) of vancomycin on the first and second day (AUC0–24, AUC24–48, respectively) of therapy. A virtual population of 1,000 individuals was created using a population pharmacokinetic (popPK) model, which was validated and incorporated into our model‐informed precision dosing (MIPD) tool. The results were evaluated using six additional popPK models selected based on a study design of prospective or retrospective data collection with sufficient concentrations. Bayesian forecasting was performed to evaluate the probability of achieving the therapeutic range of AUC, defined as a ratio of estimated/reference AUC within 0.8–1.2. The Bayesian posterior probability of achieving the AUC24–48 range increased from 51.3% (a priori probability) to 77.5% after using two‐point sampling at the trough and peak on the first day. Sampling on the first day also yielded a higher Bayesian posterior probability (86.1%) of achieving the AUC0–24 range compared to the a priori probability of 60.1%. The Bayesian posterior probability of achieving the AUC at steady state (AUCSS) range by sampling on the first or second day decreased with decreased kidney function. We demonstrated that second‐day trough and peak sampling provided accurate AUC24–48, and first‐day sampling may assist in rapidly achieving therapeutic AUC24–48, although the AUCSS should be re‐estimated in patients with reduced kidney function owing to its unreliable predictive performance.

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Vancomycin area under the curve versus trough only guided dosing and the risk of acute kidney injury: Systematic review and meta‐analysis

Cited by 23 publications

References 36 publications

The Establishment of College Student Employment Guidance System Integrating Artificial Intelligence and Civic Education

The Establishment of College Student Employment Guidance System Integrating Artificial Intelligence and Civic Education

Systematic review of efficacy, safety and pharmacokinetics of intravenous and intraventricular vancomycin for central nervous system infections

Model‐informed precision dosing of vancomycin for rapid achievement of target area under the concentration‐time curve: A simulation study

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