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In vancomycin treatment, the rates of correct blood sampling and initial trough concentrations within the target range are very low. Studies of interventions by clinical pharmacists based on population pharmacokinetics (PPK) models are limited. This study aimed to evaluate the intervention effect of clinical pharmacist-mediated optimization of the vancomycin administration regimen based on a PPK model. Retrospectively enrolled patients constituted the control group, and prospectively enrolled patients constituted the intervention group. The vancomycin administration regimen, trough concentration, pharmacokinetic parameters, and clinical outcomes of the two groups were compared. The control and intervention groups comprised 236 and 138 patients, respectively. Compared with those in the control group, the therapeutic drug monitoring (TDM) and correct TDM sampling time rates in the intervention group were significantly higher (76.92% vs. 43.59%; 63.9% vs. 39.0%, both p < 0.001). The rates of an initial trough concentration within 10-20 mg/L and an adjusted regimen were also significantly higher in the intervention group (55.80% vs. 30.51%, 71.95% vs. 39.18%, both p < 0.001). The rate of an area under the curve (AUC) within 400-650 mg·h/L was higher in the intervention group than in the control group (52.7% vs. 36.6%, p < 0.001). The eradication rates of Gram-positive bacteria were 91.4% in the intervention group and 81.3% in the control group (p = 0.049). Eight patients developed acute kidney injury (AKI) in the control group; however, no AKI occurred in the intervention group (p = 0.029). Intervention by clinical pharmacists can increase the rate of correct sampling time. Using the PPK model combined with Bayesian estimation, clinical pharmacists can greatly increase the trough concentration and AUCs within the target range, especially for adjusted regimens. Higher PK/PD target rates resulted in better Gram-positive bacterial eradication and reduced renal toxicity of vancomycin.

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BACKGROUND: There is a significant correlation between augmented renal clearance (ARC) and lower serum trough concentrations of vancomycin (VCM) during therapy. There is a need to evaluate the predictive performance of the population pharmacokinetic (PPK) model used for individual calculation of dosage regimens in ARC patients. OBJECTIVE: Our study aimed to estimate the predictive performance differences of the reported VCM PPK software JPKD-vancomycin and SmartDose in patients with varying renal function status, especially those with ARC. METHODS: Patients receiving VCM treatment from May 2014 to December 2019 were enrolled, and divided into the ARC group, the normal renal function (NRF) group, and the impaired renal function (IRF) group. VCM dosage, trough concentration, area under the curve (AUC) and pharmacokinetic parameters were compared among the three groups. The predictive performance of PPK software was expressed using absolute prediction error (APE), sensitivity, specificity, and regression coefficient (r2) of linear regression analysis between the measured VCM trough concentration and the predicted trough concentration. RESULTS: A total of 388 patients were included: 86 patients in the ARC group, 241 patients in the NRF group, and 61 patients in the IRF group. The daily dose of the adjusted regimen in the ARC group was higher than in the NRF group, but the trough concentration was significantly lower than in the NRF group (2.8±0.6 g vs 1.9±0.6 g, p<0.001; 10.5±5.1 mg/L vs 12.9±6.8 mg/L, p=0.030). The percentage of trough concentrations lower than 10 mg/L was 84.9% in the ARC group. Compared with the APE of the initial dosage regimen, the APE of the adjusted regimen calculated by JPKD was lower in the ARC group (p=0.041) and the NRF group (p＜0.001). Specificity of JPKD and SmartDose in the ARC group was higher than in the NRF group (p<0.001; p<0.001). According to the linear regression analysis, the coefficients of determination (r2) were all >0.6 for the initial regimen and adjusted regimen of VCM in the ARC and NRF groups, and the r2 of the adjusted regimen of JPKD was >0.8 in the ARC and NRF groups. In the IRF group, 31.1% of patients had a change in serum creatinine (Scr) level of >50%. The r2 increased from 0.527 to 0.7347 in SmartDose and from 0.55 to 0.7802 in JPKD when using Scr at the sampling time. The ARC group showed a significant decrease in AUC (p<0.001) and an increase in clearance rate (p<0.001) when compared to the NRF group. CONCLUSION: ARC was significantly associated with subtherapeutic serum VCM concentration. The pharmacokinetic parameters of VCM were diverse in patients with different renal function status. The PPK model JPKD and SmartDose had a good predictive performance for predicting VCM trough concentrations of the ARC and NRF patients, especially using JPKD for prediction of the adjusted regimen. The change of Scr is a main factor affecting the accuracy of software prediction.

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Background: Augmented renal clearance (ARC) risk factors and effects on vancomycin (VCM) of obstetric patients were possibly different from other populations based on pathophysiological characteristics. Our study was to establish a regression model for prediction of ARC and analyze the effects of ARC on VCM treatment in critically ill obstetric patients. Methods: We retrospectively included 427 patients, grouped into ARC and non-ARC patients. Logistic regression analysis was used to analyze the factors related to ARC. Receiver operating characteristic (ROC) curve was drawn to evaluate the predictive value of the model for ARC. Patients who received VCM therapy were collected. The published VCM population pharmacokinetic (PPK) model was used to calculate pharmacokinetic parameters. A linear regression analysis was made between the predicted and measured concentrations. Results: Of the 427 patients, ARC was present in 201 patients (47.1%). The independent risk factors of ARC were heavier, greater gestational age, higher albumin level, fewer caesarean section, severe preeclampsia and vasoactive drug; more infection, hypertriglyceridemia and acute pancreatitis. We established the above nine-variable prediction regression model and calculated the predicted probability. ROC curve showed that the predicted probability of combined weight, albumin and gestational age had better sensitivity (70.0%) and specificity (89.8%) as well as the maximal area under the curve (AUC, AUC = 0.863). 41 cases received VCM; 21 cases (51.2%) had ARC. The initial trough concentration in ARC patients was lower than in non-ARC patients (7.9 ± 3.2 mg/L vs 9.5 ± 3.3 mg/L; p = 0.033). Comparing the predicted trough concentration of two published VCM PPK models with the measured trough concentration, correlation coefficients (r) were all more than 0.8 in the ARC group and non-ARC group. AUC was significantly decreased in the ARC group (p = 0.003; p = 0.013), and clearance (CL) increased in the ARC group (p < 0.001; p = 0.008) when compared with the non-ARC group. Conclusion: ARC is a common state in critically ill obstetric patients. The regression model of nine variables had high predictive value for predicting ARC. The published VCM PPK models had good predictive performance for predicting trough concentrations of obstetric patients. Pharmacokinetic parameters of VCM are different in ARC obstetric patients, which results in enhanced VCM clearance and decreased trough concentration.

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OBJECTIVE: To estimate the predictive performance of the population pharmacokinetics software JPKD-vancomycin on predicting the vancomycin steady-state trough concentration, and to analyze the related factors affecting the predictive performance. METHODS: The clinical data of patients who were treated with vancomycin and received therapeutic drug monitoring (TDM) admitted to Suzhou Hospital Affiliated to Nanjing Medical University from July 2013 to December 2018 were enrolled. All patients were designed an empirical vancomycin regimen (initial regimen) according to vancomycin medication guidelines. Steady-state trough concentrations of vancomycin were determined at 48 hours after the first dose and 0.5 hour before the next dose. Dosage regimen was adjusted when steady-state trough concentration was not in 10-20 mg/L (adjustment regimen), and then the steady-state trough concentration was determined again 48 hours after adjustment. First, the JPKD-vancomycin software was used to calculate the initial regimen and predict the steady-state trough concentration according to the results calculated by classic pharmacokinetic software Vancomycin Calculator. Second, the JPKD-vancomycin software was used to adjust the vancomycin dosage regime and predict the steady-state trough concentration of adjustment regimen. The weight residual (WRES) between the predicted steady-state trough concentration (Cpre) and the measured steady-state trough concentration (Creal) was used to evaluate the ability of the JPKD-vancomycin software for predicting the vancomycin steady-state trough concentration. The TDM results of initial regimen were divided into accurate prediction group (WRES < 30%) and the inaccurate prediction group (WRES ≥ 30%) according to the WRES value. Patient and disease characteristics including gender, age, weight, height, the length of hospital stay, comorbidities, vasoactive agent, mechanical ventilation, smoking history, postoperative, obstetric patients, trauma, laboratory indicators, vancomycin therapy and TDM results were collected from electronic medical records. Univariate and multivariate Logistic regression analysis was used to screen the related factors that influence the predictive performance of JPKD-vancomycin software, and the receiver operating characteristic (ROC) curve was drawn to evaluate its predictive value. RESULTS: A total of 310 patients were enrolled, and 467 steady-state trough concentrations of vancomycin were collected, including 310 concentrations of initial regimen and 157 concentrations of adjustment regimen. Compared with the initial regimen, the WRES of adjusted regimen was significantly reduced [14.84 (6.05,22.89)% vs. 20.41 (11.06,45.76)%, P < 0.01], and the proportion of WRES < 30% increased significantly [82.80% (130/157) vs. 63.87% (198/310), P < 0.01]. These results indicated that JPKD-vancomycin software had a better accuracy prediction for steady-state trough concentration of the adjusted regimen than the initial regimen. There were 198 concentrations in the accurate prediction group and 112 in the inaccurate prediction group. Univariate Logistic regression analysis showed that women [odds ratio (OR) = 0.466, 95% confidence interval (95%CI) was 0.290-0.746, P = 0.002], low body weight (OR = 0.974, 95%CI was 0.953-0.996, P = 0.022), short height (OR = 0.963, 95%CI was 0.935-0.992, P = 0.014), low vancomycin clearance (CLVan; OR < 0.001, 95%CI was 0.000-0.231, P = 0.023) and postoperative patients (OR = 1.695, 95%CI was 1.063-2.702, P = 0.027) were related factors affecting the predictive performance of JPKD-vancomycin software. Multivariate Logistic regression analysis indicated that women (OR = 0.449, 95%CI was 0.205-0.986, P = 0.046), low CLVan (OR < 0.001, 95%CI was 0.000-0.081, P = 0.015) and postoperative patients (OR = 2.493, 95%CI was 1.455-4.272, P = 0.001) were independent risk factors for inaccurate prediction of JPKD-vancomycin software. The ROC analysis indicated that the area under ROC curve (AUC) of the CLVan for evaluating the accuracy of JPKD-vancomycin software in predicting vancomycin steady-state trough concentration was 0.571, the sensitivity was 56.3%, and the specificity was 57.1%. The predictive performance of JPKD-vancomycin software was decreased when CLVan was lower than 0.065 L×h-1×kg-1. CONCLUSIONS: JPKD-vancomycin software had a better predictive performance for the vancomycin steady-state trough concentrations of adjustment regimen than initial regimen. JPKD-vancomycin software had a poor predictive performance when the patient was female, having low CLVan, and was postoperative. The predictive performance of JPKD-vancomycin software was decreased when CLVan was lower than 0.065 L×h-1×kg-1.

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OBJECTIVE: To evaluate the predictive value and to verify the clinical effect of JPKD-vancomycin for the trough concentration of vancomycin in patients with augmented renal clearance (ARC), and to provide a reference for clinical individualized drug therapy. METHODS: A retrospective analysis was conducted. The clinical data of 48 adult patients with ARC using vancomycin and monitoring steady-state trough concentration of vancomycin admitted to Suzhou Hospital Affiliated to Nanjing Medical University from July 2013 to July 2017 were collected. A combination of classical Vancomycin Calculator software and JPKD-vancomycin software was used. Based on the individual conditions of patients [gender, age, height, weight, serum creatinine (SCr), disease status], Vancomycin Calculator software was used to obtain the recommended regimen and its steady-state trough concentration, and then JPKD-vancomycin software was used to predict the steady-state trough concentration of initial regimen. If the regimen was adjusted during the treatment, JPKD-vancomycin software was used to predict the steady-state trough concentration of the adjusted regimen. The measured values of steady-state trough concentration were recorded. The weight deviation between predicted concentration and measured concentration (WRES) was calculated. WRES < 30% was considered as good prediction, and the predictive value of JPKD-vancomycin software was evaluated for vancomycin trough concentration. RESULTS: Forty-eight patients with ARC were enrolled, of whom 24 patients had adjusted the dosing regimen during the treatment. The initial concentration of blood samples was 48, after adjusting the dosage regimen, 24 blood samples were collected. The initial and adjusted daily dose of vancomycin was (2 000±500) mg/d and (2 500±600) mg/d, respectively, and the initial trough concentrations and adjusted trough concentrations was (8.4±7.3) mg/L and (9.1±4.3) mg/L, respectively. Only 14.6% and 25.0% of initial and adjusted trough concentrations reached the target range (10-20 mg/L) without significant difference (P > 0.05). The WRES value of adjusted trough concentrations predicted by JPKD-vancomycin software was significantly lower than that of initial regimen [10.6% (3.0%, 16.4%) vs. 14.3% (10.5%, 38.2%), P < 0.05], and the percentage of WRES < 30% also tended to increase [95.8% (23/24) vs. 70.8% (34/48), P < 0.05]. The well predictive rate of JPKD-vancomycin software for vancomycin trough concentration was 79.2% (57/72), but there were 15 patients with WRES > 30%. CONCLUSIONS: JPKD-vancomycin software has good predictive value for the vancomycin trough concentration of ARC patients, especially for the trough concentration after adjusting the treatment regimen. JPKD-vancomycin can provide a reference for the design of clinical individualized application of vancomycin.

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