Desired vancomycin trough concentration to achieve an AUC0-24 /MIC ≥400 in Chinese children with complicated infectious diseases

The impact of deep learning based- psychological capital with ideological and political education on entrepreneurial intentions

The aim of this study is to investigate the influence of psychological capital on college students' entrepreneurial intentions. Through a combination of relevant analysis and linear regression, the primary focus is on exploring the relationship between psychological capital and its four dimensions with entrepreneurial intentions. Firstly, the items in the psychological capital questionnaire were revised to align more closely with entrepreneurial contexts. Subsequently, the average deviations and standard deviations of each dimension of psychological capital were analyzed. Then, the correlation between psychological capital and entrepreneurial intentions was examined to explore the extent of their relationship. Finally, regression analysis was conducted on both psychological capital and entrepreneurial intentions, and utilizing a recurrent neural network model, the covariant relationship between entrepreneurial psychological capital and intentions was explored. The results indicated that the average scores for entrepreneurial self-efficacy, optimism, hope, and resilience were 3.91, 4.27, 4.19, and 4.15, respectively. The average value of psychological capital was 4.13, indicating a moderately high level. The correlation analysis between psychological capital and entrepreneurial intentions yielded a result of 0.562, indicating a moderate degree of correlation. The correlation coefficients of the four dimensions with entrepreneurial intentions were 0.390, 0.494, 0.531, and 0.467, respectively. The standardized coefficients for psychological capital and its four dimensions were 0.564, 0.382, 0.510, 0.536, and 0.468, all of which were statistically significant. Overall, psychological capital exhibited better predictive power for entrepreneurial intentions than its individual dimensions. The results from the deep learning model similarly demonstrated the positive role of psychological capital in entrepreneurial intentions, though the influence of ideological and political education (IPE) factors was relatively weaker. In conclusion, both psychological capital and IPE have a promotive effect on entrepreneurial intentions. This study provides a reference for the accurate evaluation of college students' entrepreneurial intentions.

Individualized Delivery of Vancomycin by Model-Informed Bayesian Dosing Approach to Maintain an AUC24 Target in Critically Ill Patients

INTRODUCTION

Monitoring of AUC24 was updated recommendation in the guideline for the therapeutic drug monitoring (TDM) of vancomycin in Chinese pharmacological society published in 2020. Vancomycin pharmacokinetic profiles are diverse and unique in critically ill patients because of the drastic variability of the patients' physiological parameters, while the study for population pharmacokinetic (PPK) models in Chinese critically ill patients has been rarely reported. The objectives of this study were to construct a PPK model to describe the pharmacokinetic characteristics of vancomycin in critically ill patients and to individualize vancomycin dosing by model-informed Bayesian estimation for maintenance of AUC24 target at 400-650 mg h/L recommended by the 2020 guideline.

METHODS

Vancomycin with different dosing was administered intravenously over 1 h for critically ill patients, TDM was started at 48 h or 72 h since initiation of vancomycin therapy for patients. Blood samples were collected from patients for trough concentrations or Cmax. Vancomycin concentrations were determined by high-performance liquid chromatography method with ultraviolet detection. PPK model was performed using the nonlinear mixed-effect model (NONMEM®). Individual PK parameters for critically ill patients treated with vancomycin were estimated using a post hoc empirical Bayesian method based on the final PPK model. AUC24 was calculated as the total daily dose divided by the clearance (L/h).

RESULTS

The PPK of vancomycin was determined by a one-compartment model with creatinine clearance as fixed effects. The PK estimates in the final model generally agreed with the median estimates and were contained within the 95% CI generated from the bootstrap results, indicating good precision and stability in the final model. The visual predictive check plots showed the adequate predictive performance of the final PK model and supported a good model fit. The model-informed Bayesian estimation was used to predict the AUC24 of critically ill patient by the acquired TDM results, and the dosing adjustment by maintenance of AUC24 at 400-650 mg h/L had made a great therapeutic effect for the case.

CONCLUSION

This study established a PPK model of vancomycin in Chinese critically ill patients, and individualized dosing of vancomycin by model-informed Bayesian estimation to maintain an AUC24 target at 400-650 mg h/L has been successfully applied in clinic. This result supports the continued use of model-informed Bayesian estimation to vancomycin treatment in critically ill patients.

Pharmacokinetics of Vancomycin among Patients with Chemotherapy-Associated Febrile Neutropenia: Which Would Be the Best Dosing to Obtain Appropriate Exposure?

Previous research has determined that the required doses for treating febrile neutropenia with vancomycin are higher than the doses used conventionally. These recommendations have been made considering pharmacotherapeutic goals based on minimum concentration (Cmin) between 15-20 mg/L. This study was developed to evaluate dose recommendations based on the achievement of a target consisting of ratio of area under the curve over minimum inhibitory concentration (AUC24h/MIC) ≥400 in this population of individuals. This study was conducted in a referral hospital for cancer treatment, study participants received vancomycin doses of 1g every 12 h in 2-4-h infusions. Vancomycin was described by a two-compartment pharmacokinetic model with clearance dependent on the estimated glomerular filtration rate. Simulations were performed taking into account a reduced version of the model to establish the influence of controllable and non-controllable variables on the probability of achieving several PK-PD targets. A dose of 2.5g/day in patients with estimated glomerular filtration rate (eGFR) between 80 and 122mL/min/1.73m2 was adequate to achieve the pharmacotherapeutic target. A discrepancy was found between AUC-based and Cmin-based PK/PD indices, the former being affected by the dose and creatinine clearance while the latter highly influenced by the interval between doses.

Population Pharmacokinetic Models of Vancomycin in Paediatric Patients: A Systematic Review

BACKGROUND

Vancomycin is commonly used to treat gram-positive bacterial infections in the paediatric population, but dosing can be challenging. Population pharmacokinetic (popPK) modelling can improve individualization of dosing regimens. The primary objective of this study was to describe popPK models of vancomycin and factors that influence pharmacokinetic (PK) variability in paediatric patients.

METHODS

Systematic searches were conducted in the Cochrane Central Register of Controlled Trials, MEDLINE, EMBASE, International Pharmaceutical Abstracts and the grey literature without language or publication status restrictions from inception to 17 August 2020. Observational studies that described the development of popPK models of vancomycin in paediatric patients (< 18 years of age) were included. Risk of bias was assessed using the National Heart, Lung and Blood Institute Study Quality Assessment Tool for Case Series Studies.

RESULTS

Sixty-four observational studies (1 randomized controlled trial, 13 prospective studies and 50 retrospective studies of 9019 patients with at least 25,769 serum vancomycin concentrations) were included. The mean age was 2.5 years (range 1 day-18 years), serum creatinine was 47.1 ± 33.6 µmol/L, and estimated creatinine clearance was 97.4 ± 76 mL/min/1.73m². Most studies found that vancomycin PK was best described by a one-compartment model (71.9%). There was a wide range of clearance and volume of distribution (V_d) values (range 0.014-0.27 L/kg/h and 0.43-1.46 L/kg, respectively) with interindividual variability as high as 49.7% for clearance and 136% for V_d, proportional residual variability up to 37.5% and additive residual variability up to 17.5 mg/L. The most significant covariates for clearance were weight, age, and serum creatinine or creatinine clearance, and weight for V_d. Variable dosing recommendations were suggested.

CONCLUSION

Numerous popPK models of vancomycin were derived, however external validation of suggested dosing regimens and analyses in subgroup paediatric populations such as dialysis patients are still needed before a popPK model with best predictive performance can be applied for dosing recommendations. Significant intraindividual and interindividual PK variability was present, which demonstrated the need for ongoing therapeutic drug monitoring and derivation of PK models for vancomycin for certain subgroup populations, such as dialysis patients.

A Canadian perspective on the revised 2020 ASHP-IDSA-PIDS-SIDP guidelines for vancomycin AUC-based therapeutic drug monitoring for serious MRSA infections

BACKGROUND

A revised consensus guideline on therapeutic drug monitoring (TDM) of vancomycin for serious methicillin-resistant Staphylococcus aureus (MRSA) infections was recently published with endorsement of numerous American pharmacy and medical societies. Changing practice from trough TDM to area-under-the-curve-(AUC)-guided dosing was suggested.

METHODS

Recent literature was critically appraised to determine whether AUC TDM is appropriate for Canadian hospital practice.

RESULTS

Previous 2009 vancomycin consensus guidelines recommended trough levels of 15-20 mg/L for serious MRSA infections, based on relatively poor evidence for efficacy or safety. In the past decade, aggressive trough targets have led to unnecessary toxicity. Adoption of a TDM strategy using an alternative parameter (AUC) has been suggested, although the evidence for any outcome benefits is low quality. In addition, implementation would require greater resources at health care institutions in the forms of more frequent serum levels or acquisition of costly Bayesian software programs. Most studies on this subject have been observational and retrospective; therefore, relationships between TDM parameters and outcomes have not been convincingly and consistently demonstrated to be causal in nature. Despite claims to the contrary, based on few in silico experiments, available clinical data suggest correlation of trough levels and AUC is high. TDM with lower target trough levels is a simpler solution to reduce risk of toxicity.

CONCLUSIONS

There are serious concerns with adoption of AUC TDM of vancomycin into routine practice in Canada. Trough-based monitoring with modest reduction in target levels remains the most evidence-informed practice at this time.

AUCs and 123s: a critical appraisal of vancomycin therapeutic drug monitoring in paediatrics

The revised vancomycin guidelines recommend implementing AUC24-based therapeutic drug monitoring (TDM) using Bayesian methods in both adults and paediatrics. The motivation for this change was accumulating evidence showing aggressive dosing to achieve high troughs, as recommended in the first guidelines for adults and extrapolated to paediatrics, is associated with increased nephrotoxicity without improving clinical outcomes. AUC24-based TDM requires substantial resources that may need to be diverted from other valuable interventions. It can therefore be justified only after certain assumptions are shown to be true: (i) there is a clear relationship between vancomycin efficacy and/or toxicity and the proposed therapeutic range; and (ii) maintaining exposure within the target range with AUC24-based TDM improves clinical outcomes and/or decreases toxicity. In this review, we critically appraise the scientific basis for these assumptions. We find studies evaluating the relationship between vancomycin AUC24/MIC and efficacy in adults and children do not offer strong support for the recommended lower limit of the proposed therapeutic range (i.e. AUC24/MIC ≥400). Nephrotoxicity in children increases in a stepwise manner along the vancomycin exposure continuum but it is unclear if one parameter (AUC24 versus trough) is a superior predictor. Overall, evidence in children suggests good-to-excellent correlation between AUC24 and trough. Most importantly, there is no convincing evidence that the method of vancomycin TDM has a causal role in improving efficacy or reducing toxicity. These findings question the need to transition to resource-intensive AUC24-based TDM over retaining trough-based TDM with lower targets to minimize nephrotoxicity in paediatrics.