A systematic evaluation of population pharmacokinetic models for polymyxin B in patients with liver and/or kidney dysfunction

Polymyxin B (PMB) is considered a last-line treatment for multidrug-resistant (MDR) gram-negative bacterial infections. Model-informed precision dosing with population pharmacokinetics (PopPK) models could help to individualize PMB dosing regimens and improve therapy. However, the external prediction ability of the established PopPK models has not been fully elaborated. This study aimed to systemically evaluate eleven PMB PopPK models from ten published literature based on a new independent population, which was divided into four different populations, patients with liver dysfunction, kidney dysfunction, liver and kidney dysfunction, and normal liver and kidney function. The whole data set consisted of 146 patients with 391 PMB concentrations. The prediction- and simulation-based diagnostics and Bayesian forecasting were conducted to evaluate model predictability. In the overall evaluation process, none of the models exhibited satisfactory predictive ability in both prediction- and simulation-based diagnostic simultaneously. However, the evaluation of the models in the subgroup of patients with normal liver and kidney function revealed improved predictive performance compared to those with liver and/or kidney dysfunction. Bayesian forecasting demonstrated enhanced predictability with the incorporation of two to three prior observations. The external evaluation highlighted a lack of consistency between the prediction results of published models and the external validation dataset. Nonetheless, Bayesian forecasting holds promise in improving the predictive performance of the models, and feedback from therapeutic drug monitoring is crucial in optimizing individual dosing regimens.

External evaluation of the predictive performance of published population pharmacokinetic models of linezolid in adult patients

OBJECTIVES

Several linezolid population pharmacokinetic (popPK) models have been established to facilitate optimal therapy; however, their extrapolated predictive performance to other clinical sites is unknown. This study aimed to externally evaluate the predictive performance of published pharmacokinetic models of linezolid in adult patients.

METHODS

For the evaluation dataset, 150 samples were collected from 70 adult patients (72.9% of which were critically ill) treated with linezolid at our center. Twenty-five published popPK models were identified from PubMed and Embase. Model predictability was evaluated using prediction-based, simulation-based, and Bayesian forecasting-based approaches to assess model predictability.

RESULTS

Prediction-based diagnostics found that the prediction error within ±30% (F30) was less than 40% in all models, indicating unsatisfactory predictability. The simulation-based prediction- and variability-corrected visual predictive check and normalized prediction distribution error test indicated large discrepancies between the observations and simulations in most of the models. Bayesian forecasting with one or two prior observations significantly improved the models' predictive performance.

CONCLUSION

The published linezolid popPK models showed insufficient predictive ability. Therefore, their sole use is not recommended, and incorporating therapeutic drug monitoring of linezolid in clinical applications is necessary.

External evaluation of population pharmacokinetic models for voriconazole in Chinese adult patients with hematological malignancy

OBJECTIVES

Patients with hematological malignancies are prone to invasive fungal disease due to long-term chemotherapy or radiotherapy. Voriconazole is a second-generation triazole broad-spectrum antibiotic used to prevent or treat invasive fungal infections. Many population pharmacokinetic (pop PK) models have been published for voriconazole, and various diagnostic methods are available to validate the performance of these pop PK models. However, most of the published models have not been strictly evaluated externally. The purpose of this study is to evaluate these models externally and assess their predictive capabilities.

METHODS

The external dataset consists of adults receiving voriconazole treatment at Fujian Medical University Union Hospital. We re-established the published models based on their final estimated values in the literature and used our external dataset for initial screening. Each model was evaluated based on the following outcomes: prediction-based diagnostics, prediction- and variability-corrected visual predictive check (pvcVPC), normalized prediction distribution errors (NPDE), and Bayesian simulation results with one to two prior observations.

RESULTS

A total of 237 samples from 166 patients were collected as an external dataset. After screening, six candidate models suitable for the external dataset were finally obtained for comparison. Among the models, none demonstrated excellent predictive performance. Bayesian simulation shows that all models' prediction precision and accuracy were significantly improved when one or two prior concentrations were given.

CONCLUSIONS

The published pop PK models of voriconazole have significant differences in prediction performance, and none of the models could perfectly predict the concentrations of voriconazole for our data. Therefore, extensive evaluation should precede the adoption of any model in clinical practice.

Population Pharmacokinetic Evaluation with External Validation of Tacrolimus in Chinese Primary Nephrotic Syndrome Patients

PURPOSE

The generalizability of numerous tacrolimus population pharmacokinetic (popPK) models constructed to promote optimal tacrolimus dosing in patients with primary nephrotic syndrome (PNS) is unclear. This study aimed to evaluate the predictive performance of published tacrolimus popPK models for PNS patients with an external data set.

METHODS

We prospectively collected 223 concentrations from 50 Chinese adult patients with PNS who were undergoing tacrolimus treatment. Data on published tacrolimus popPK models for adults and children with PNS were extracted from the literature. Model predictability was evaluated with prediction-based and simulation-based diagnostics and Bayesian forecasting.

RESULTS

In prediction-based evaluation, none of the 11 identified published popPK models of tacrolimus had met a predefined criteria of a mean prediction error ≤  ± 20%, and the prediction error within ± 30% of the identified models didn't exceed 50%. Simulation-based diagnostics also indicated unsatisfactory predictability. Bayesian forecasting demonstrated amelioration in the model predictability with the inclusion of 2-3 prior observations. Moreover, the predictive performance of nonlinear models was not better than that of one-compartment models.

CONCLUSIONS

The prediction of tacrolimus concentrations for patients with PNS remains challenging; published models are not applicable for extrapolation to other hospitals. Bayesian forecasting significantly improved model predictability and thereby helped to individualize tacrolimus dosing.

External evaluation of published population pharmacokinetic models of polymyxin B

OBJECTIVES

Several population pharmacokinetics (popPK) models for polymyxin B have been constructed to optimize therapeutic regimens. However, their predictive performance remains unclear when extrapolated to different clinical centers. Therefore, this study aimed to evaluate the predictive ability of polymyxin B popPK models.

METHODS

A literature search was conducted, and the predictive performance was determined for each selected model using an independent dataset of 20 patients (92 concentrations) from the Third Xiangya Hospital. Prediction- and simulation-based diagnostics were used to evaluate model predictability. The influence of prior information was assessed using Bayesian forecasting.

RESULTS

Eight published studies were evaluated. In prediction-based diagnostics, the prediction error within ± 30% was over 50% in two models. In simulation-based diagnostics, the prediction- and variability-corrected visual predictive check (pvcVPC) showed satisfactory predictivity in three models, while the normalized prediction distribution error (NPDE) tests indicated model misspecification in all models. Bayesian forecasting demonstrated a substantially improvement in the model predictability even with one prior observation.

CONCLUSION

Not all published models were satisfactory in prediction- and simulation-based diagnostics; however, Bayesian forecasting improved the predictability considerably with priors, which can be applied to guide polymyxin B dosing recommendations and adjustments for clinicians.

External evaluation of population pharmacokinetic models for continuous administration of meropenem in critically ill adult patients

PURPOSE

Beta-lactams (BL), the most commonly prescribed class of antibiotics, are recommended as the first-line therapy for multiple indications in infectious disease guidelines. Meropenem (MERO) is frequently used in intensive care units (ICU) to treat bacterial infections with or without sepsis. The pharmacokinetics of MERO display a large variability in patients admitted to ICUs due to altered pathophysiology. The aim of this study was to perform an external evaluation of published population pharmacokinetic models of MERO in order to test their predictive performance in a cohort of ICU adult patients.

METHODS

A literature search in PubMed/Medline database was made following the PRISMA statement. External evaluation was performed using NONMEM software, and the bias and inaccuracy values were calculated.

RESULTS

An external validation dataset from the Timone Hospital in Marseille, France, included 84 concentration samples from 27 patients. Four models of MERO were identified according to the inclusion criteria of the study. None of the models presented acceptable values of bias and inaccuracy.

CONCLUSION

While performing external evaluations on some populations may confirm a model's suitability to diverse groups of patients, there is still some variability that cannot be explained nor solved by the procedure. This brings to light the difficulty to develop only one model for ICU patients and the need to develop one specific model to each population of critically ill patients.

The predictive performance of propofol target-controlled infusion during robotic-assisted laparoscopic prostatectomy with CO2 pneumoperitoneum in the head-down position

PURPOSE

Propofol clearance can be reduced when cardiac output (CO) is decreased. This clearance reduction may alter the pharmacokinetics of propofol and worsen the predictive performance of target-controlled infusion (TCI) of propofol. The head-down position (HDP) and CO₂ pneumoperitoneum, which are required for robotic-assisted laparoscopic prostatectomy (RALP), may cause changes in CO. We investigated the predictive performance of propofol TCI during CO₂ pneumoperitoneum in patients who underwent RALP in the HDP.

METHODS

Fifteen male patients received propofol TCI using the Diprifusor model. Propofol concentrations were measured at seven time points: (T1) 15 min after anesthesia induction; (T2) before the insufflation; (T3, T4, and T5) 15, 60, and 90 min, respectively, after insufflation in the HDP; (T6) before the release of pneumoperitoneum in the HDP; and (T7) 15 min after the release of pneumoperitoneum in the supine position. Cardiac index (CI) was assessed using an arterial pulse contour CO monitor. The predictive performance of propofol TCI was evaluated by calculating the performance errors (PE) in propofol concentrations for each data point. The relationship between CI and PE was examined. Median PE (MDPE) and median absolute PE (MDAPE) were calculated as measures of bias and accuracy, respectively.

RESULTS

A total of 104 blood samples were analyzed. There was significantly negative correlation between CI and PE. The predictive performance of propofol TCI during pneumoperitoneum in the HDP was acceptable (MDPE = - 1.5% and MDAPE = 18.8%).

CONCLUSION

The predictive performance of propofol TCI during RALP with CO₂ pneumoperitoneum in the HDP was acceptable.

Hospitals accreditation status in Indonesia: associated with hospital characteristics, market competition intensity, and hospital performance?

Background

Hospital accreditation is widely adopted as a visible measure of an organisation’s quality and safety management standards compliance. There is still inconsistent evidence regarding the influence of hospital accreditation on hospital performance, with limited studies in developing countries. This study aims to explore the association of hospital characteristics and market competition with hospital accreditation status and to investigate whether accreditation status differentiate hospital performance.

Methods

East Java Province, with a total 346 hospitals was selected for this study. Hospital characteristics (size, specialty, ownership) and performance indicator (bed occupancy rate, turnover interval, average length of stay, gross mortality rate, and net mortality rate) were retrieved from national hospital database while hospital accreditation status were recorded based on hospital accreditation report. Market density, Herfindahl-Hirschman index (HHI), and hospitals relative size as competition indicators were calculated based on the provincial statistical report data. Logistic regression, Mann-Whitney U-test, and one sample t-test were used to analyse the data.

Results

A total of 217 (62.7%) hospitals were accredited. Hospital size and ownership were significantly associated with of accreditation status. When compared to government-owned, hospital managed by ministry of defense (B = 1.705, p = 0.012) has higher probability to be accredited. Though not statistically significant, accredited hospitals had higher utility and efficiency indicators, as well as higher mortality.

Conclusions

Hospital with higher size and managed by government have higher probability to be accredited independent to its specialty and the intensity of market competition. Higher utility and mortality in accredited hospitals needs further investigation.

Electronic supplementary material

The online version of this article (10.1186/s12913-019-4187-x) contains supplementary material, which is available to authorized users.