External Evaluation of Population Pharmacokinetic Models for High-Dose Methotrexate in Adult Patients with Hematological Tumors

Currently, numerous population pharmacokinetic (popPK) models for methotrexate (MTX) have been published for estimating PK parameters and variability. However, it is unclear whether the accuracy of these models is sufficient for clinical application. The aim of this study is to evaluate published models and assess their predictive performance according to the standards of scientific research. A total of 237 samples from 74 adult patients who underwent high-dose MTX (HDMTX) treatment at Shanghai Changzheng Hospital were collected. The software package NONMEM was used to perform an external evaluation for each model, including prediction-based diagnosis, simulation-based diagnosis, and Bayesian forecasting. The simulation-based diagnosis includes normalized prediction distribution error (NPDE) and visual predictive check (VPC). Following screening, 7 candidate models suitable for external validation were identified for comparison. However, none of these models exhibited excellent predictive performance. Bayesian simulation results indicated that the prediction precision and accuracy of all models significantly improved when incorporating prior concentration information. The published popPK models for MTX exhibit significant differences in their predictive performance, and none of the models were able to accurately predict MTX concentrations in our data set. Therefore, before adopting any model in clinical practice, extensive evaluation should be conducted.

External Evaluation of Population Pharmacokinetics Models of Lithium in the Bipolar Population

Lithium has been used in the treatment of bipolar disorder for several decades. Treatment optimization is recommended for this drug, due to its narrow therapeutic range and a large pharmacokinetics (PK) variability. In addition to therapeutic drug monitoring, attempts have been made to predict individual lithium doses using population pharmacokinetics (popPK) models. This study aims to assess the clinical applicability of published lithium popPK models by testing their predictive performance on two different external datasets. Available PopPK models were identified and their predictive performance was determined using a clinical dataset (46 patients/samples) and the literature dataset (89 patients/samples). The median prediction error (PE) and median absolute PE were used to assess bias and inaccuracy. The potential factors influencing model predictability were also investigated, and the results of both external evaluations compared. Only one model met the acceptability criteria for both datasets. Overall, there was a lack of predictability of models; median PE and median absolute PE, respectively, ranged from -6.6% to 111.2% and from 24.4% to 111.2% for the literature dataset, and from -4.5% to 137.6% and from 24.9% to 137.6% for the clinical dataset. Most models underpredicted the observed concentrations (7 out of 10 models presented a negative bias). Renal status was included as a covariate of lithium's clearance in only two models. To conclude, most of lithium's PopPK models had limited predictive performances related to the absence of covariates of interest included, such as renal status. A solution to this problem could be to improve the models with methodologies such as metamodeling. This could be useful in the perspective of model-informed precision dosing.

Modeling the protein binding non-linearity in population pharmacokinetic model of valproic acid in children with epilepsy: a systematic evaluation study

Background: Several studies have investigated the population pharmacokinetics (popPK) of valproic acid (VPA) in children with epilepsy. However, the predictive performance of these models in the extrapolation to other clinical environments has not been studied. Hence, this study evaluated the predictive abilities of pediatric popPK models of VPA and identified the potential effects of protein binding modeling strategies. Methods: A dataset of 255 trough concentrations in 202 children with epilepsy was analyzed to assess the predictive performance of qualified models, following literature review. The evaluation of external predictive ability was conducted by prediction- and simulation-based diagnostics as well as Bayesian forecasting. Furthermore, five popPK models with different protein binding modeling strategies were developed to investigate the discrepancy among the one-binding site model, Langmuir equation, dose-dependent maximum effect model, linear non-saturable binding equation and the simple exponent model on model predictive ability. Results: Ten popPK models were identified in the literature. Co-medication, body weight, daily dose, and age were the four most commonly involved covariates influencing VPA clearance. The model proposed by Serrano et al. showed the best performance with a median prediction error (MDPE) of 1.40%, median absolute prediction error (MAPE) of 17.38%, and percentages of PE within 20% (F20, 55.69%) and 30% (F30, 76.47%). However, all models performed inadequately in terms of the simulation-based normalized prediction distribution error, indicating unsatisfactory normality. Bayesian forecasting enhanced predictive performance, as prior observations were available. More prior observations are needed for model predictability to reach a stable state. The linear non-saturable binding equation had a higher predictive value than other protein binding models. Conclusion: The predictive abilities of most popPK models of VPA in children with epilepsy were unsatisfactory. The linear non-saturable binding equation is more suitable for modeling non-linearity. Moreover, Bayesian forecasting with prior observations improved model fitness.

Hospital accreditation: an umbrella review

Hospital accreditation is an established quality improvement intervention. Despite a growing body of research, the evidence of effect remains contested. This umbrella review synthesizes reviews that examine the impacts of hospital accreditation with regard to health-care quality, highlighting research trends and knowledge gaps. Terms specific to the population: 'hospital' and the intervention: 'accreditation' were used to search seven databases: CINAHL (via EBSCOhost), Embase, Medline (via EBSCOhost), PubMed, Scopus, the Cochrane Database of Systematic Reviews, and the Joanna Briggs Institute (JBI) EBP Database (via Ovid). 2545 references were exported to endnote. After completing a systematic screening process and chain-referencing, 33 reviews were included. Following quality assessment and data extraction, key findings were thematically grouped into the seven health-care quality dimensions. Hospital accreditation has a range of associations with health system and organizational outcomes. Effectiveness, efficiency, patient-centredness, and safety were the most researched quality dimensions. Access, equity, and timeliness were examined in only three reviews. Barriers to robust original studies were reported to have impeded conclusive evidence. The body of research was largely atheoretical, incapable of precisely explaining how or why hospital accreditation may actually influence quality improvement. The impact of hospital accreditation remains poorly understood. Future research should control for all possible variables. Research and accreditation program development should integrate concepts of implementation and behavioural science to investigate the mechanisms through which hospital accreditation may enable quality improvement.

External Evaluation of Population Pharmacokinetic Models to Inform Precision Dosing of Meropenem in Critically Ill Patients

Routine clinical meropenem therapeutic drug monitoring data can be applied to model-informed precision dosing. The current study aimed to evaluate the adequacy and predictive capabilities of the published models with routine meropenem data and identify the dosing adaptations using a priori and Bayesian estimation. For this, 14 meropenem models for the external evaluation carried out on an independent cohort of 134 patients with 205 meropenem concentrations were encoded in NONMEM 7.3. The performance was determined using: 1) prediction-based and simulation-based diagnostics; and 2) predicted meropenem concentrations by a priori prediction using patient covariates only; and Bayesian forecasting using previous observations. The clinical implications were assessed according to the required dose adaptations using the meropenem concentrations. All assessments were stratified based on the patients with or without continuous renal replacement therapy. Although none of the models passed all tests, the model by Muro et al. showed the least bias. Bayesian forecasting could improve the predictability over an a priori approach, with a relative bias of −11.63–68.89% and −302.96%–130.37%, and a relative root mean squared error of 34.99–110.11% and 14.78–241.81%, respectively. A dosing change was required in 40.00–68.97% of the meropenem observation results after Bayesian forecasting. In summary, the published models couldn’t adequately describe the meropenem pharmacokinetics of our center. Although the selection of an initial meropenem dose with a priori prediction is challenging, the further model-based analysis combining therapeutic drug monitoring could be utilized in the clinical practice of meropenem therapy.

Evaluation and Application of Population Pharmacokinetic Models for Identifying Delayed Methotrexate Elimination in Patients With Primary Central Nervous System Lymphoma

Objective: Several population pharmacokinetic (popPK) models have been developed to determine the sources of methotrexate (MTX) PK variability. It remains unknown if these published models are precise enough for use or if a new model needs to be built. The aims of this study were to 1) assess the predictability of published models and 2) analyze the potential risk factors for delayed MTX elimination.

Methods: A total of 1458 MTX plasma concentrations, including 377 courses (1–17 per patient), were collected from 77 patients who were receiving high-dose MTX for the treatment of primary central nervous system lymphoma in Huashan Hospital. PopPK analysis was performed using the NONMEM® software package. Previously published popPK models were selected and rebuilt. A new popPK model was then constructed to screen potential covariates using a stepwise approach. The covariates were included based on the combination of theoretical mechanisms and data properties. Goodness-of-fit plots, bootstrap, and prediction- and simulation-based diagnostics were used to determine the stability and predictive performance of both the published and newly built models. Monte Carlo simulations were conducted to qualify the influence of risk factors on the incidence of delayed elimination.

Results: Among the eight evaluated published models, none presented acceptable values of bias or inaccuracy. A two-compartment model was employed in the newly built model to describe the PK of MTX. The estimated mean clearance (CL/F) was 4.91 L h⁻¹ (relative standard error: 3.7%). Creatinine clearance, albumin, and age were identified as covariates of MTX CL/F. The median and median absolute prediction errors of the final model were -10.2 and 36.4%, respectively. Results of goodness-of-fit plots, bootstrap, and prediction-corrected visual predictive checks indicated the high predictability of the final model.

Conclusions: Current published models are not sufficiently reliable for cross-center use. The elderly patients and those with renal dysfunction, hypoalbuminemia are at higher risk of delayed elimination.

An appraisal of healthcare accreditation agencies and programs: similarities, differences, challenges and opportunities

BACKGROUND

The study, following similar reviews in 2000 and 2010, presents an update of knowledge about external evaluation agencies and accreditation programs.

OBJECTIVE

The study aim was to investigate the current profile of external evaluation agencies identifying their program features, and significant changes and challenges.

Evaluation of published population pharmacokinetic models to inform tacrolimus dosing in adult heart transplant recipients

BACKGROUND AND AIM

Identification of the most appropriate population pharmacokinetic model-based Bayesian estimation is required prior to its implementation in routine clinical practice to inform tacrolimus dosing decisions. This study aimed to determine the predictive performances of relevant population pharmacokinetic models of tacrolimus developed from various solid organ transplant recipient populations in adult heart transplant recipients, stratified based on concomitant azole antifungal use. Concomitant azole antifungal therapy alters tacrolimus pharmacokinetics substantially, necessitating dose adjustments.

METHODS

Population pharmacokinetic models of tacrolimus were selected (n = 17) for evaluation from a recent systematic review. The models were transcribed and implemented in NONMEM version 7.4.3. Data from 85 heart transplant recipients (2387 tacrolimus concentrations) administered the oral immediate-release formulation of tacrolimus (Prograf) were obtained up to 391 days post-transplant. The performance of each model was evaluated using: (i) prediction-based assessment (bias and imprecision) of the individual predicted tacrolimus concentration of the fourth dosing occasion (MAXEVAL = 0, FOCE-I) from 1-3 prior dosing occasions; and (ii) simulation-based assessment (prediction-corrected visual predictive check). Both assessments were stratified based on concomitant azole antifungal use.

RESULTS

Regardless of the number of prior dosing occasions (1-3) and concomitant azole antifungal use, all models demonstrated unacceptable individual predicted tacrolimus concentration of the fourth dosing occasion (n = 152). The prediction-corrected visual predictive check graphics indicated that these models inadequately predicted observed tacrolimus concentrations.

CONCLUSION

All models evaluated were unable to adequately describe tacrolimus pharmacokinetics in adult heart transplant recipients included in this study. Further work is required to describe tacrolimus pharmacokinetics for our heart transplant recipient cohort.

External Evaluation of Population Pharmacokinetic Models and Bayes-Based Dosing of Infliximab

Despite the well-demonstrated efficacy of infliximab in inflammatory diseases, treatment failure remains frequent. Dose adjustment using Bayesian methods has shown in silico its interest in achieving target plasma concentrations. However, most of the published models have not been fully validated in accordance with the recommendations. This study aimed to submit these models to an external evaluation and verify their predictive capabilities. Eight models were selected for external evaluation, carried out on an independent database (409 concentrations from 157 patients). Each model was evaluated based on the following parameters: goodness-of-fit (comparison of predictions to observations), residual error model (population weighted residuals (PWRES), individual weighted residuals (IWRES), and normalized prediction distribution errors (NPDE)), and predictive performances (prediction-corrected visual predictive checks (pcVPC) and Bayesian simulations). The performances observed during this external evaluation varied greatly from one model to another. The eight evaluated models showed a significant bias in population predictions (from -7.19 to 7.38 mg/L). Individual predictions showed acceptable bias and precision for six of the eight models (mean error of -0.74 to -0.29 mg/L and mean percent error of -16.6 to -0.4%). Analysis of NPDE and pcVPC confirmed these results and revealed a problem with the inclusion of several covariates (weight, concomitant immunomodulatory treatment, presence of anti-drug antibodies). This external evaluation showed satisfactory results for some models, notably models A and B, and highlighted several prospects for improving the pharmacokinetic models of infliximab for clinical-biological application.

A large observational data study supporting the PROsPeR score classification in poor ovarian responders according to live birth outcome

STUDY QUESTION

Can the Poor Responder Outcome Prediction (PROsPeR) score identify live birth outcomes in subpopulations of patients with poor ovarian response (POR) defined according to the ESHRE Bologna criteria (female age, anti-Müllerian hormone (AMH), number of oocytes retrieved during the previous cycle (PNO) after treatment with originator recombinant human follitropin alfa?

SUMMARY ANSWER

The PROsPeR score discriminated the probability of live birth in patients with POR using observational data with fair discrimination (AUC ≅ 70%) and calibration, and the AUC losing less than 5% precision compared with a model developed using the observational data.

WHAT IS KNOWN ALREADY

Although scoring systems for the likelihood of live birth after ART have been developed, their accuracy may be insufficient, as they have generally been developed in the general population with infertility and were not validated for patients with POR. The PROsPeR score was developed using data from the follitropin alfa (GONAL-f; Merck KGaA, Darmstadt, Germany) arm of the Efficacy and Safety of Pergoveris in Assisted Reproductive Technology (ESPART) randomized controlled trial (RCT) and classifies women with POR as mild, moderate or severe, based upon three variables: female age, serum AMH level and number of oocytes retrieved during the previous cycle (PNO).

STUDY DESIGN, SIZE, DURATION

The external validation of the PROsPeR score was completed using data derived from eight different centres in France. In addition, the follitropin alfa data from the ESPART RCT, originally used to develop the PROsPeR score, were used as reference cohort. The external validation of the PROsPeR score l was assessed using AUC. A predetermined non-inferiority limit of 0.10 compared with a reference sample and calibration (Hosmer-Lemeshow test) were the two conditions required for evaluation.

PARTICIPANTS/MATERIALS, SETTING, METHODS

The observational cohort included data from 8085 ART treatment cycles performed with follitropin alfa in patients with POR defined according to the ESHRE Bologna criteria (17.6% of the initial data set). The ESPART cohort included 477 ART treatment cycles with ovarian stimulation performed with follitropin alfa in patients with POR.

MAIN RESULTS AND THE ROLE OF CHANCE

The external validation of the PROsPeR score to identify subpopulations of women with POR with different live birth outcomes was shown in the observational cohort (AUC = 0.688; 95% CI: 0.662, 0.714) compared with the ESPART cohort (AUC = 0.695; 95% CI: 0.623, 0.767). The AUC difference was -0.0074 (95% CI: -0.083, 0.0689). This provided evidence, with 97.5% one-sided confidence, that there was a maximum estimated loss of 8.4% in discrimination between the observational cohort and the ESPART cohort, which was below the predetermined margin of 10%. The Hosmer-Lemeshow test did not reject the calibration when comparing observed and predicted data (Hosmer-Lemeshow test = 1.266688; P = 0.260).

LIMITATIONS, REASONS FOR CAUTION

The study was based on secondary use of data that had not been collected specifically for the analysis reported here and the number of characteristics used to classify women with POR was limited to the available data. The data were from a limited number of ART centres in a single country, which may present a bias risk; however, baseline patient data were similar to other POR studies.

WIDER IMPLICATIONS OF THE FINDINGS

This evaluation of the PROsPeR score using observational data supports the notion that the likelihood of live birth may be calculated with reasonable precision using three readily available pieces of data (female age, serum AMH and PNO). The PROsPeR score has potential to be used to discriminate expected probability of live birth according to the degree of POR (mild, moderate, severe) after treatment with follitropin alfa, enabling comparison of performance at one centre over time and the comparison between centres.

STUDY FUNDING/COMPETING INTEREST(S)

This analysis was funded by Merck KGaA, Darmstadt, Germany. P.L. received grants from Merck KGaA, outside of the submitted work. N.M. reports grants, personal fees and non-financial support from Merck KGaA outside the submitted work. T.D.H. is Vice President and Head of Global Medical Affairs Fertility, Research and Development at Merck KGaA, Darmstadt, Germany. P.A. has received personal fees from Merck KGaA, Darmstadt, Germany, outside the submitted work. C.R. has received grants and personal fees from Gedeon Richter and Merck Serono S.A.S., France, an affiliate of Merck KGaA, Darmstadt, Germany, outside the submitted work. P.S. reports congress support from Merck Serono S.A.S., France (an affiliate of Merck KGaA, Darmstadt, Germany), Gedeon Richter, TEVA and MDS outside the submitted work. C.A., J.P., G.P. and R.W. declare no conflict of interest.

TRIAL REGISTRATION NUMBER

N/A.

How citizen science could improve species distribution models and their independent assessment

Species distribution models (SDM) have been increasingly developed in recent years, but their validity is questioned. Their assessment can be improved by the use of independent data, but this can be difficult to obtain and prohibitive to collect. Standardized data from citizen science may be used to establish external evaluation datasets and to improve SDM validation and applicability.We used opportunistic presence-only data along with presence-absence data from a standardized citizen science program to establish and assess habitat suitability maps for 9 species of amphibian in western France. We assessed Generalized Additive and Random Forest Models' performance by (1) cross-validation using 30% of the opportunistic dataset used to calibrate the model or (2) external validation using different independent datasets derived from citizen science monitoring. We tested the effects of applying different combinations of filters to the citizen data and of complementing it with additional standardized fieldwork.Cross-validation with an internal evaluation dataset resulted in higher AUC (Area Under the receiver operating Curve) than external evaluation causing overestimation of model accuracy and did not select the same models; models integrating sampling effort performed better with external validation. AUC, specificity, and sensitivity of models calculated with different filtered external datasets differed for some species. However, for most species, complementary fieldwork was not necessary to obtain coherent results, as long as the citizen science data were strongly filtered.Since external validation methods using independent data are considered more robust, filtering data from citizen sciences may make a valuable contribution to the assessment of SDM. Limited complementary fieldwork with volunteer's participation to complete ecological gradients may also possibly enhance citizen involvement and lead to better use of SDM in decision processes for nature conservation.

Consecutive cycles of accreditation and quality of in-hospital care: a Danish population-based study

BACKGROUND

Little is known about whether repeated cycles of hospital accreditation are a robust method to improve quality of care continuously.

OBJECTIVE

We aimed to examine the association between compliance with consecutive cycles of accreditation and quality of in-hospital care.

METHODS

We conducted a Danish nationwide population-based study including patients aged 18 years treated for acute stroke, chronic obstructive pulmonary disease, diabetes, heart failure or hip fracture at public, non-psychiatric hospitals. From 2012 to 2015, two cycles of national hospital accreditation were completed, resulting in 12 high and 14 low compliant hospitals (Low = partially accredited in both cycles). Our outcome measure was quality of in-hospital care measured by 39 process performance measures (PPMs), reflecting recommendations from the national clinical guidelines by adherence to (i) individual PPMs and (ii) the full bundle of PPMs (all-or-none). We computed adjusted odds ratios (ORs) using logistic regression based on robust standard error estimation for cluster sampling of data at hospital level.

RESULTS

In total, 78 387 patient pathways covering 508 816 processes were included, of which 47% had been delivered at high compliant hospitals and 53% at low compliant hospitals, respectively. Compliance with consecutive cycles was not associated with improved quality of in-hospital care (individual: OR = 0.92, 95% confidence interval (CI): 0.77-1.10; All-or-none: OR = 0.87, 95% CI: 0.66-1.15). However, in the second cycle alone, patients treated at partially accredited hospitals had a lower adherence than patients treated at fully accredited hospitals (Individual: OR = 0.84, 95% CI: 0.71-0.99; All-or-none: OR = 0.78, 95% CI: 0.59-1.03). The association was particularly strong among patients treated at partially accredited hospitals required to submit additional documentation.

CONCLUSION

Compliance with consecutive cycles of hospital accreditation in Denmark was not associated with improved quality of in-hospital care. However, compliance with the second cycle alone was associated with improved quality of in-hospital care.

Systematic external evaluation of reported population pharmacokinetic models of vancomycin in Chinese children and adolescents

WHAT IS KNOWN AND OBJECTIVES

Various population pharmacokinetic (PopPK) models for vancomycin in children and adolescents have been constructed to optimize the therapeutic regimen of vancomycin. However, little is known about their predictive performance when extrapolated to different clinical centres. Therefore, the aim of this study was to externally validate the predictability of vancomycin PopPK model when extrapolated to different clinical centres and verify its applicability in an independent data set.

METHODS

The published models were screened from the literature and evaluated using an external data set of a total of 451 blood concentrations of vancomycin measured in 220 Chinese paediatric patients. Prediction- and simulation-based diagnostics and Bayesian forecasting were performed to evaluate the predictive performance of the models.

RESULTS

Ten published PopPK models were assessed. Prediction-based diagnostics showed that none of the investigated models met all the standards (median prediction error (MDPE) ≤ ±20%, median absolute prediction error (MAPE) ≤30%, PE% within ±20% (F₂₀ ) ≥35% and PE% within ±30% (F₃₀ ) ≥50%), indicating unsatisfactory predictability. In simulation-based diagnostics, both the visual predictive checks (VPC) and the normalized prediction distribution error (NPDE) indicated misspecification in all models. Bayesian forecasting results showed that the accuracy and precision of individual predictions could be significantly improved with one or two prior observations, but frequent monitoring might not be necessary in the clinic, since Bayesian forecasting identified that greater number of samples did not significantly improve the predictability. Model 3 established by Moffett et al showed better predictability than other models.

WHAT IS NEW AND CONCLUSION

The 10 published models performed unsatisfactorily in prediction- and simulation-based diagnostics; none of the published models was suitable for designing the initial dosing regimens of vancomycin. Pharmacokinetic characteristics and covariates, such as weight, renal function, age and underlying disease should be taken into account when extrapolating the vancomycin model. Bayesian forecasting combined with therapeutic drug monitoring based on model 3 can be used to adjust vancomycin dosing regimens.

External Evaluation of Vancomycin Population Pharmacokinetic Models at Two Clinical Centers

Background: Numerous vancomycin population pharmacokinetic models in neonates have been published; however, their predictive performances remain unknown. This study aims to evaluate their external predictability and explore the factors that might affect model performance. Methods: Published population pharmacokinetic models in neonates were identified from the literature and evaluated using datasets from two clinical centers, including 171 neonates with a total of 319 measurements of vancomycin levels. Predictive performance was assessed by prediction- and simulation-based diagnostics and Bayesian forecasting. Furthermore, the effect of model structure and a number of identified covariates was also investigated. Results: Eighteen published pharmacokinetic models of vancomycin were identified after a systematic literature search. Using prediction-based diagnostics, no model had a median prediction error of ≤ ± 15%, a median absolute prediction error of ≤30%, and a percentage of prediction error that fell within ±30% of >50%. A simulation-based visual predictive check of most models showed there were large deviations between observations and simulations. After Bayesian forecasting with one or two prior observations, the predicted performance improved significantly. Weight, age, and serum creatinine were identified as the most important covariates. Moreover, employing a maturation model based on weight and age as well as nonlinear model to incorporate serum creatinine level significantly improved predictive performance. Conclusion: The predictability of the pharmacokinetic models for vancomycin is closely related to the approach used for modeling covariates. Bayesian forecasting can significantly improve the predictive performance of models.

External evaluation of the predictive performance of seven population pharmacokinetic models for phenobarbital in neonates

AIM

Several studies have reported population pharmacokinetic models for phenobarbital (PB), but the predictive performance of these models has not been well documented. This study aims to do external evaluation of the predictive performance in published pharmacokinetic models.

METHODS

Therapeutic drug monitoring data collected in neonates and young infants treated with PB for seizure control was used for external evaluation. A literature review was conducted through PubMed to identify population pharmacokinetic models. Prediction- and simulation-based diagnostics, and Bayesian forecasting were performed for external evaluation. The incorporation of allometric scaling for body size and maturation factors into the published models was also tested for prediction improvement.

RESULTS

A total of 79 serum concentrations from 28 subjects were included in the external dataset. Seven population pharmacokinetic studies of PB were identified as relevant in the literature search and included for our evaluation. The model by Voller et al showed the best performance concerning prediction-based evaluation. In simulation-based analyses, the normalized prediction distribution error of two models (those of Shellhaas et al and Marsot et al) obeyed a normal distribution. Bayesian forecasting with more than one observation improved predictive capability. Incorporation of both allometric size scaling and maturation function generally enhanced the predictive performance, with improvement as observed in the model of Vucicevic et al. CONCLUSIONS: The predictive performance of published pharmacokinetic models of PB was diverse. Bayesian forecasting and incorporation of both size and maturation factors could improve the predictability of the models for neonates.

Accreditation in 2030

With the rapid acceleration of changes being experienced throughout the world and in particular within health and health and social care, accreditation programmes must keep pace or go the way of the dinosaur. While accreditation has deep roots in some countries, in the past 30 years, it has spread to a considerably larger range of countries in a mix of mandatory and voluntary systems. Accreditation is a tool to improve the quality of healthcare and social care, and in particular, there is recent recognition of its value in low- and middle-income countries, with promotion by the World Health Organization (WHO). The challenge is that with the rapid pace of change, how does accreditation reframe and reposition itself to ensure relevance in 2030? Accreditation must adapt and be relevant in order to be sustainable. This article outlines the fundamental principles, reviews the global trends' impact on accreditation and the challenges with the existing model and, through the lens of living in 2030, outlines how accreditation programmes will be structured and applied 10 years from now.

Systematic external evaluation of published population pharmacokinetic models of mycophenolate mofetil in adult kidney transplant recipients co-administered with tacrolimus

AIMS

Various mycophenolate mofetil (MMF) population pharmacokinetic (popPK) models have been developed to describe its PK characteristics and facilitate its optimal dosing in adult kidney transplant recipients co-administered with tacrolimus. However, the external predictive performance has been unclear. Thus, this study aimed to comprehensively evaluate the external predictability of published MMF popPK models in such populations and investigate the potential influencing factors.

METHODS

The external predictability of qualified popPK models was evaluated using an independent dataset. The evaluation included prediction- and simulation-based diagnostics, and Bayesian forecasting. In addition, factors influencing model predictability, especially the impact of structural models, were investigated.

RESULTS

Fifty full PK profiles from 45 patients were included in the evaluation dataset and 11 published popPK models were identified and evaluated. In prediction-based diagnostics, the prediction error within ±30% was less than 50% in most published models. The prediction- and variability-corrected visual predictive check and posterior predictive check showed large discrepancies between the observations and simulations in most models. Moreover, the normalized prediction distribution errors of all models did not follow a normal distribution. Bayesian forecasting demonstrated an improvement in the model predictability. Furthermore, the predictive performance of two-compartment (2CMT) models incorporating the enterohepatic circulation (EHC) process was not superior to that of conventional 2CMT models.

CONCLUSIONS

The published models showed large variability and unsatisfactory predictive performance, which indicated that therapeutic drug monitoring was necessary for MMF clinical application. Further studies incorporating potential covariates need to be conducted to investigate the key factors influencing model predictability of MMF.

External Evaluation of a Gentamicin Infant Population Pharmacokinetic Model Using Data from a National Electronic Health Record Database

Gentamicin is a common antibiotic used in neonates and infants. A recently published population pharmacokinetic (PK) model was developed using data from multiple studies, and the objective of our analyses was to evaluate the feasibility of using a national electronic health record (EHR) database for further external evaluation of this model. Our results suggest that, with proper data capture procedures, EHR data can serve as a potential data source for external evaluation of PK models.

External Evaluation of Two Fluconazole Infant Population Pharmacokinetic Models

Fluconazole is an antifungal agent used for the treatment of invasive candidiasis, a leading cause of morbidity and mortality in premature infants. Population pharmacokinetic (PK) models of fluconazole in infants have been previously published by Wade et al. (Antimicrob Agents Chemother 52:4043-4049, 2008, https://doi.org/10.1128/AAC.00569-08) and Momper et al. (Antimicrob Agents Chemother 60:5539-5545, 2016, https://doi.org/10.1128/AAC.00963-16). Here we report the results of the first external evaluation of the predictive performance of both models. We used patient-level data from both studies to externally evaluate both PK models. The predictive performance of each model was evaluated using the model prediction error (PE), mean prediction error (MPE), mean absolute prediction error (MAPE), prediction-corrected visual predictive check (pcVPC), and normalized prediction distribution errors (NPDE). The values of the parameters of each model were reestimated using both the external and merged data sets. When evaluated with the external data set, the model proposed by Wade et al. showed lower median PE, MPE, and MAPE (0.429 μg/ml, 41.9%, and 57.6%, respectively) than the model proposed by Momper et al. (2.45 μg/ml, 188%, and 195%, respectively). The values of the majority of reestimated parameters were within 20% of their respective original parameter values for all model evaluations. Our analysis determined that though both models are robust, the model proposed by Wade et al. had greater accuracy and precision than the model proposed by Momper et al., likely because it was derived from a patient population with a wider age range. This study highlights the importance of the external evaluation of infant population PK models.

External evaluation of population pharmacokinetic models for ciclosporin in adult renal transplant recipients

AIMS

Several population pharmacokinetic (popPK) models for ciclosporin (CsA) in adult renal transplant recipients have been constructed to optimize the therapeutic regimen of CsA. However, little is known about their predictabilities when extrapolated to different clinical centres. Therefore, this study aimed to externally evaluate the predictive ability of CsA popPK models and determine the potential influencing factors.

METHODS

A literature search was conducted and the predictive performance was determined for each selected model using an independent data set of 62 patients (471 predose and 500 2-h postdose concentrations) from our hospital. Prediction-based diagnostics and simulation-based normalized prediction distribution error were used to evaluate model predictability. The influence of prior information was assessed using Bayesian forecasting. Additionally, potential factors influencing model predictability were investigated.

RESULTS

Seventeen models extracted from 17 published popPK studies were assessed. Prediction-based diagnostics showed that ethnicity potentially influenced model transferability. Simulation-based normalized prediction distribution error analyses indicated misspecification in most of the models, especially regarding variance. Bayesian forecasting demonstrated that the predictive performance of the models substantially improved with 2-3 prior observations. The predictability of nonlinear Michaelis-Menten models was superior to that of linear compartmental models when evaluating the impact of structural models, indicating the underlying nonlinear kinetics of CsA. Structural model, ethnicity, covariates and prior observations potentially affected model predictability.

CONCLUSIONS

Structural model is the predominant factor influencing model predictability. Incorporation of nonlinear kinetics in CsA popPK modelling should be considered. Moreover, Bayesian forecasting substantially improved model predictability.

A comparison of 4 predictive models of calving assistance and difficulty in dairy heifers and cows

The aim of this study was to build and compare predictive models of calving difficulty in dairy heifers and cows for the purpose of decision support and simulation modeling. Models to predict 3 levels of calving difficulty (unassisted, slight assistance, and considerable or veterinary assistance) were created using 4 machine learning techniques: multinomial regression, decision trees, random forests, and neural networks. The data used were sourced from 2,076 calving records in 10 Irish dairy herds. In total, 19.9 and 5.9% of calving events required slight assistance and considerable or veterinary assistance, respectively. Variables related to parity, genetics, BCS, breed, previous calving, and reproductive events and the calf were included in the analysis. Based on a stepwise regression modeling process, the variables included in the models were the dam's direct and maternal calving difficulty predicted transmitting abilities (PTA), BCS at calving, parity; calving assistance or difficulty at the previous calving; proportion of Holstein breed; sire breed; sire direct calving difficulty PTA; twinning; and 2-way interactions between calving BCS and previous calving difficulty and the direct calving difficulty PTA of dam and sire. The models were built using bootstrapping procedures on 70% of the data set. The held-back 30% of the data was used to evaluate the predictive performance of the models in terms of discrimination and calibration. The decision tree and random forest models omitted the effect of twinning and included only subsets of sire breeds. Only multinomial regression and neural networks explicitly included the modeled interactions. Calving BCS, calving difficulty PTA, and previous calving assistance ranked as highly important variables for all 4 models. The area under the receiver operating characteristic curve (ranging from 0.64 to 0.79) indicates that all of the models had good overall discriminatory power. The neural network and multinomial regression models performed best, correctly classifying 75% of calving cases and showing superior calibration, with an average error in predicted probability of 3.7 and 4.5%, respectively. The neural network and multinomial regression models developed are both suitable for use in decision-support and simulation modeling.

Phenobarbital in intensive care unit pediatric population: predictive performances of population pharmacokinetic model

An external evaluation of phenobarbital population pharmacokinetic model described by Marsot et al. was performed in pediatric intensive care unit. Model evaluation is an important issue for dose adjustment. This external evaluation should allow confirming the proposed dosage adaptation and extending these recommendations to the entire intensive care pediatric population. External evaluation of phenobarbital published population pharmacokinetic model of Marsot et al. was realized in a new retrospective dataset of 35 patients hospitalized in a pediatric intensive care unit. The published population pharmacokinetic model was implemented in nonmem 7.3. Predictive performance was assessed by quantifying bias and inaccuracy of model prediction. Normalized prediction distribution errors (NPDE) and visual predictive check (VPC) were also evaluated. A total of 35 infants were studied with a mean age of 33.5 weeks (range: 12 days-16 years) and a mean weight of 12.6 kg (range: 2.7-70.0 kg). The model predicted the observed phenobarbital concentrations with a reasonable bias and inaccuracy. The median prediction error was 3.03% (95% CI: -8.52 to 58.12%), and the median absolute prediction error was 26.20% (95% CI: 13.07-75.59%). No trends in NPDE and VPC were observed. The model previously proposed by Marsot et al. in neonates hospitalized in intensive care unit was externally validated for IV infusion administration. The model-based dosing regimen was extended in all pediatric intensive care unit to optimize treatment. Due to inter- and intravariability in pharmacokinetic model, this dosing regimen should be combined with therapeutic drug monitoring.

External evaluation of published population pharmacokinetic models of tacrolimus in adult renal transplant recipients

AIM

Several tacrolimus population pharmacokinetic models in adult renal transplant recipients have been established to facilitate dose individualization. However, their applicability when extrapolated to other clinical centres is not clear. This study aimed to (1) evaluate model external predictability and (2) analyze potential influencing factors.

METHODS

Published models were screened from the literature and were evaluated using an external dataset with 52 patients (609 trough samples) collected by postoperative day 90 via methods that included (1) prediction-based prediction error (PE%), (2) simulation-based prediction- and variability-corrected visual predictive check (pvcVPC) and normalized prediction distribution error (NPDE) tests and (3) Bayesian forecasting to assess the influence of prior observations on model predictability. The factors influencing model predictability, particularly the impact of structural models, were evaluated.

RESULTS

Sixteen published models were evaluated. In prediction-based diagnostics, the PE% within ±30% was less than 50% in all models, indicating unsatisfactory predictability. In simulation-based diagnostics, both the pvcVPC and the NPDE indicated model misspecification. Bayesian forecasting improved model predictability significantly with prior 2-3 observations. The various factors influencing model extrapolation included bioassays, the covariates involved (CYP3A5*3 polymorphism, postoperative time and haematocrit) and whether non-linear kinetics were used.

CONCLUSIONS

The published models were unsatisfactory in prediction- and simulation-based diagnostics, thus inappropriate for direct extrapolation correspondingly. However Bayesian forecasting could improve the predictability considerably with priors. The incorporation of non-linear pharmacokinetics in modelling might be a promising approach to improving model predictability.

Accreditation and improvement in process quality of care: a nationwide study

OBJECTIVE

To examine whether performance measures improve more in accredited hospitals than in non-accredited hospital.

DESIGN AND SETTING

A historical follow-up study was performed using process of care data from all public Danish hospitals in order to examine the development over time in performance measures according to participation in accreditation programs.

PARTICIPANTS

All patients admitted for acute stroke, heart failure or ulcer at Danish hospitals.

INTERVENTION

Hospital accreditation by either The Joint Commission International or The Health Quality Service.

MEASUREMENTS

The primary outcome was a change in opportunity-based composite score and the secondary outcome was a change in all-or-none scores, both measures were based on the individual processes of care. These processes included seven processes related to stroke, six processes to heart failure, four to bleeding ulcer and four to perforated ulcer.

RESULTS

A total of 27 273 patients were included. The overall opportunity-based composite score improved for both non-accredited and accredited hospitals (13.7% [95% CI 10.6; 16.8] and 9.9% [95% 5.4; 14.4], respectively), but the improvements were significantly higher for non-accredited hospitals (absolute difference: 3.8% [95% 0.8; 8.3]). No significant differences were found at disease level. The overall all-or-none score increased significantly for non-accredited hospitals, but not for accredited hospitals. The absolute difference between improvements in the all-or-none score at non-accredited and accredited hospitals was not significant (3.2% [95% -3.6:9.9]).

CONCLUSIONS

Participating in accreditation was not associated with larger improvement in performance measures for acute stroke, heart failure or ulcer.

Predictive performance of reported population pharmacokinetic models of vancomycin in Chinese adult patients

WHAT IS KNOWN AND OBJECTIVE

There are numerous studies on population pharmacokinetics of vancomycin in adult patients. However, there is no such research for Chinese adult patients. This study was conducted to evaluate the predictive performance of reported population pharmacokinetic models of vancomycin in Chinese adult patients and to identify some models appropriate for our population.

METHODS

A literature search was conducted in PubMed to obtain the population pharmacokinetic models of vancomycin published between December 2010 and September 2012. The models were assessed using concentration data collected from Chinese patients for external validation. Models with relatively poor predictability were excluded from further analysis. The performance of the remaining models was evaluated in patients with different levels of creatinine clearance, age, body weight and sex by Bayesian method. This method was also used to compare the predictive performance based on peak concentration and trough concentration and the predictability based on different number of observed concentrations.

RESULTS

One hundred and sixty-five blood concentrations from 72 Chinese adult patients were collected retrospectively to serve as the test data set. The evaluated models included all those reported in the seven publications reviewed by Marsot et al. and three other studies published after December 2010. Three models with poor performance on external validation were excluded from the next Bayesian analysis. The distribution of covariates in the model building data set had an important effect on prediction. The predictability based on peak/trough concentration was similar among the evaluated models, and no significant difference was found using our data set except for Roberts' model. As expected, an increased number of samples improved the performance of the Bayesian prediction.

WHAT IS NEW AND CONCLUSION

With our data set, the performance of the evaluated models varied. The characteristics of the patient population and distribution of covariates should be given more consideration when choosing a model to predict blood concentrations. The model developed by Purwonugroho et al. using a data set from patients similar to ours is appropriate for Bayesian dose predictions for vancomycin concentrations in our population of Chinese adult patients.