Objectivizing issues in the diagnosis of complex rare diseases: lessons learned from testing existing diagnosis support systems on ciliopathies.

BACKGROUND: There are approximately 8,000 different rare diseases that affect roughly 400 million people worldwide. Many of them suffer from delayed diagnosis. Ciliopathies are rare monogenic disorders characterized by a significant phenotypic and genetic heterogeneity that raises an important challenge for clinical diagnosis. Diagnosis support systems (DSS) applied to electronic health record (EHR) data may help identify undiagnosed patients, which is of paramount importance to improve patients' care. Our objective was to evaluate three online-accessible rare disease DSSs using phenotypes derived from EHRs for the diagnosis of ciliopathies. METHODS: Two datasets of ciliopathy cases, either proven or suspected, and two datasets of controls were used to evaluate the DSSs. Patient phenotypes were automatically extracted from their EHRs and converted to Human Phenotype Ontology terms. We tested the ability of the DSSs to diagnose cases in contrast to controls based on Orphanet ontology. RESULTS: A total of 79 cases and 38 controls were selected. Performances of the DSSs on ciliopathy real world data (best DSS with area under the ROC curve = 0.72) were not as good as published performances on the test set used in the DSS development phase. None of these systems obtained results which could be described as "expert-level". Patients with multisystemic symptoms were generally easier to diagnose than patients with isolated symptoms. Diseases easily confused with ciliopathy generally affected multiple organs and had overlapping phenotypes. Four challenges need to be considered to improve the performances: to make the DSSs interoperable with EHR systems, to validate the performances in real-life settings, to deal with data quality, and to leverage methods and resources for rare and complex diseases. CONCLUSION: Our study provides insights into the complexities of diagnosing highly heterogenous rare diseases and offers lessons derived from evaluation existing DSSs in real-world settings. These insights are not only beneficial for ciliopathy diagnosis but also hold relevance for the enhancement of DSS for various complex rare disorders, by guiding the development of more clinically relevant rare disease DSSs, that could support early diagnosis and finally make more patients eligible for treatment.

Vancomycin: An analysis and evaluation of eight population pharmacokinetic models for clinical application in general adult population.

INTRODUCTION: Based on the recent guidelines for vancomycin therapeutic drug monitoring (TDM), the area under the curve to minimum inhibitory concentration ratio was to be employed combined with the usage of population pharmacokinetic (popPK) model for dosing adaptation. Yet, deploying these models in a clinical setting requires an external evaluation of their performance. OBJECTIVES: This study aimed to evaluate existing vancomycin popPK models from the literature for the use in TDM within the general patient population in a clinical setting. METHODS: The models under external evaluation were chosen based on a review of literature covering vancomycin popPK models developed in general adult populations. Patients' data were collected from Charles-Le Moyne Hospital (CLMH). The external evaluation was performed with NONMEM® (v7.5). Additional analyses such as evaluating the impact of number of samples on external evaluation, Bayesian forecasting, and a priori dosing regimen simulations were performed on the best performing model. RESULTS: Eight popPK models were evaluated with an independent dataset that included 40 patients and 252 samples. The model developed by Goti and colleagues demonstrated superior performance in diagnostic plots and population predictive performance, with bias and inaccuracy values of 0.251% and 22.7%, respectively, and for individual predictive performance, bias and inaccuracy were -4.90% and 12.1%, respectively. When limiting the independent dataset to one or two samples per patient, the Goti model exhibited inadequate predictive performance for inaccuracy, with values exceeding 30%. Moreover, the Goti model is suitable for Bayesian forecasting with at least two samples as prior for the prediction of the next trough concentration. Furthermore, the vancomycin dosing regimen that would maximize therapeutic targets of area under the curve to minimum inhibitory concentration ratio (AUC24/MIC) and trough concentrations (Ctrough) for the Goti model was 20 mg/kg/dose twice daily. CONCLUSION: Considering the superior predictive performance and potential for Bayesian forecasting in the Goti model, future research aims to test its applicability in clinical settings at CLMH, both in a priori and a posteriori scenario.

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 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.

Does it matter that standard preparedness indices did not predict COVID-19 outcomes?

A number of scientific publications and commentaries have suggested that standard preparedness indices such as the Global Health Security Index (GHSI) and Joint External Evaluation (JEE) scores did not predict COVID-19 outcomes. To some, the failure of these metrics to be predictive demonstrates the need for a fundamental reassessment which better aligns preparedness measurement with operational capacities in real-world stress situations, including the points at which coordination structures and decision-making may fail. There are, however, several reasons why these instruments should not be so easily rejected as preparedness measures.From a methodological point of view, these studies use relatively simple outcome measures, mostly based on cumulative numbers of cases and deaths at a fixed point of time. A country's "success" in dealing with the pandemic is highly multidimensional - both in the health outcomes and type and timing of interventions and policies - is too complex to represent with a single number. In addition, the comparability of mortality data over time and among jurisdictions is questionable due to highly variable completeness and representativeness. Furthermore, the analyses use a cross-sectional design, which is poorly suited for evaluating the impact of interventions, especially for COVID-19.Conceptually, a major reason that current preparedness measures fail to predict pandemic outcomes is that they do not adequately capture variations in the presence of effective political leadership needed to activate and implement existing system, instill confidence in the government's response; or background levels of interpersonal trust and trust in government institutions and country ability needed to mount fast and adaptable responses. These factors are crucial; capacity alone is insufficient if that capacity is not effectively leveraged. However, preparedness metrics are intended to identify gaps that countries must fill. As important as effective political leadership and trust in institutions, countries cannot be held accountable to one another for having good political leadership or trust in institutions. Therefore, JEE scores, the GHSI, and similar metrics can be useful tools for identifying critical gaps in capacities and capabilities that are necessary but not sufficient for an effective pandemic response.

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.

Moving from assessments to implementation: promising practices for strengthening multisectoral antimicrobial resistance containment capacity.

BACKGROUND: Antimicrobial resistance (AMR) poses a global threat to human, animal, and environmental health. AMR is a technical area in the Global Health Security Agenda initiative which uses the Joint External Evaluation tool to evaluate national AMR containment capacity. This paper describes four promising practices for strengthening national antimicrobial resistance containment capacity based on the experiences of the US Agency for International Development's Medicines, Technologies, and Pharmaceutical Services Program work with 13 countries to implement their national action plans on AMR in the areas of multisectoral coordination, infection prevention and control, and antimicrobial stewardship. METHODS: We use the World Health Organization (WHO) Benchmarks on International Health Regulations Capacities (2019) to guide national, subnational, and facility actions that advance Joint External Evaluation capacity levels from 1 (no capacity) to 5 (sustainable capacity). Our technical approach is based on scoping visits, baseline Joint External Evaluation scores, benchmarks tool guidance, and country resources and priorities. RESULTS: We gleaned four promising practices to achieve AMR containment objectives: (1) implement appropriate actions using the WHO benchmarks tool, which prioritizes actions, making it easier for countries to incrementally increase their Joint External Evaluation capacity from level 1 to 5; (2) integrate AMR into national and global agendas. Ongoing agendas and programs at international, regional, and national levels provide opportunities to mainstream and interlink AMR containment efforts; (3) improve governance through multisectoral coordination on AMR. Strengthening multisectoral bodies' and their technical working groups' governance improved functioning, which led to better engagement with animal/agricultural sectors and a more coordinated COVID-19 pandemic response; and (4) mobilize and diversify funding for AMR containment. Long-term funding from diversified funding streams is vital for advancing and sustaining countries' Joint External Evaluation capacities. CONCLUSIONS: The Global Health Security Agenda work has provided practical support to countries to frame and conduct AMR containment actions in terms of pandemic preparedness and health security. The WHO benchmarks tool that Global Health Security Agenda uses serves as a standardized organizing framework to prioritize capacity-appropriate AMR containment actions and transfer skills to help operationalize national action plans on AMR.

Building National Health Security Through a Rapid Self-Assessment and Annual Operational Plan in Uganda, May to September 2021.

Uganda established a National Action Plan for Health Security in 2019, following a Joint External Evaluation (JEE) of International Health Regulations (2005) capacities in 2017. The action plan enhanced national health security awareness, but implementation efforts were affected by limited funding, excess of activities, and challenges related to monitoring and evaluation. To improve implementation, Uganda conducted a multisectoral health security self-assessment in 2021 using the second edition of the JEE tool and developed a 1-year operational plan. From 2017 to 2021, Uganda's composite ReadyScore improved by 20%, with improvement in 13 of the 19 technical areas. Indicator scores showing limited capacity declined from 30% to 20%, and indicators with no capacity declined from 10% to 2%. More indicators had developed (47% vs 40%), demonstrated (29% vs 20%), and sustained (2% vs 0%) capacities in 2021 compared with 2017. Using the self-assessment JEE scores, 72 specific activities from the International Health Regulations (2005) benchmarks tool were selected for inclusion in a 1-year operational plan (2021-2022). In contrast to the 264 broad activities in the 5-year national action plan, the operational plan prioritized a small number of activities to enable sectors to focus limited resources on implementation. While certain capacities improved before and during implementation of the action plan, countries may benefit from using short-term operational planning to develop realistic and actionable health security plans to improve health security capacities.

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.

The role of pharmaceutical industry in building resilient health system.

Objectives: This study explores the interrelationship among the current sustainability agenda of the pharmaceutical industry, based on the United Nation sustainable development goals (SDGs), the elements of the Joint External Evaluation (JEE) tool, and the triad components of the One Health approach. Methods: A cross-walk exercise was conducted to identify commonalities among SDGs, JEE assessment tool, and One Health approach. An in-depth study of 10 global pharmaceutical firms' corporate sustainability reports and COVID-19 response plan for 2019-2020 was also conducted. Results: The result of the exercise showed the existence of a direct and indirect relationship among the SDGs, elements of JEE assessment tool, and One Health approach. For example, both no poverty (SDG 1) and zero hunger (SDG 2) are linked with food safety targets under the JEE and with human and animal health under the One Health approach. Conclusion: This study adds a new dimension emphasizing the possibility of tailoring the pharmaceutical industry's activities under the sustainability agenda to strengthen global health security while remaining consistent with the One Health approach.

Decisions on the quality of piano performance: Evaluation of self and others.

In common with other professional musicians, self-evaluation of practise and performance is an integral part of a pianist's professional life. They will also have opportunities to listen to and evaluate the performances of others based on their own criteria. These self-constructed perspectives towards to a piano performance will have an influence on both self-evaluation and external evaluation, but whether differently or similarly is not known. Consequently, this research study aimed to explore how judgements on the perceived quality of a performance are undertaken by professional standard pianists and what criteria are applied, both with regards their own performances as well as the performance of others. Participants were six professional pianists (3 men, 3 women) who were based in the United Kingdom (Mean age = 31.5 years old. SD = 5.1). They were asked to play individually six trials of a piece of R. Schumann's "Träumerei" Op. 15 No. 7 in a hired hall for recordings. Then, within 2 months, each participant was asked to come to a self-evaluation session to listen to and evaluate their own six recordings, using a Triadic method as a Repertory Grid. For the external evaluation focused session, the participants were asked to return again to evaluate a further six recordings made up of 'best' recordings as selected by each participant from their own individual self-evaluations. Analyses of the resultant data suggest that there was no significant difference between the participants in their overall ratings in the external phase, but that self-evaluation showed significant individual differences amongst several participants. The performance criteria in both self-evaluation and external evaluation predominately overlapped with each other in terms of musical factors, such as tone quality, phrasing, and pedalling. The ranking of the performances was highly correlated with perceptions of overall flow, tone quality and pedalling. It appears that pianists apply similar criteria to decide performance quality when evaluating their own performances as well as others.

External evaluation of published population pharmacokinetic models of posaconazole.

Population pharmacokinetic (PopPK) models of posaconazole have been established to promote the precision dosing. However, the performance of these models extrapolated to other centers has not been evaluated. This study aimed to conduct an external evaluation of published posaconazole PopPK models to evaluate their predictive performance. Posaconazole PopPK models screened from the PubMed and MEDLINE databases were evaluated using an external dataset of 213 trough concentration samples collected from 97 patients. Their predictive performance was evaluated by prediction-based diagnosis (prediction error), simulation-based diagnosis (visual predictive check), and Bayesian forecasting. In addition, external cohorts with and without proton pump inhibitor were used to evaluate the models respectively. Ten models suitable for the external dataset were finally included into the study. In prediction-based diagnostics, none of the models met pre-determined criteria for predictive indexes. Only M4, M6, and M10 demonstrated favorable simulations in visual predictive check. The prediction performance of M5, M7, M8, and M9 evaluated using the cohort without proton pump inhibitor showed a significant improvement compared to that evaluated using the whole cohort. Consistent with our expectations, Bayesian forecasting significantly improved the predictive per-formance of the models with two or three prior observations. In general, the applicability of these published posaconazole PopPK models extrapolated to our center was unsatisfactory. Prospective studies combined with therapeutic drug monitoring are needed to establish a PopPK model for posaconazole in the Chinese population to promote individualized dosing.

Main challenges of the detection in the context of global health security: systematic review of Joint External Evaluation (JEE) reports.

Introduction: since 2016, Joint External Evaluation (JEE) missions have been organized in various countries. This systematic review of the JEE reports is intended to identify the main challenges (MC) of detection in WHO regions. Methods: we accessed JEE reports on the WHO website. Challenge was defined as a variable of the indicators of detection where there was a need of improvement. MC was a challenge common to at least one-third of countries in each region and globally. For consistency, we assessed challenges reported under "Areas which need strengthening/challenges" in reports. Results: we analyzed 96 JEE reports. African Region (91.7%), Eastern Mediterranean Region (80.9%) and South East Asia Region (72.7%) had the highest rates of JEE completion. The MC were 24 in European Region, 26 in Mediterranean Region, 30 in Western Pacific Region, 33 in South East Asia Region and 34 in African Region. 24 MCs were identified at global level. National laboratory system and Real time surveillance had the highest number of MC. Eleven MCs were common to all WHO regions and global level. These include insufficient capacity for core test confirmation, insufficient specimen referral system, weak quality management system, issues in laboratories licensing and accreditation, weak data management, weak electronic reporting system, absence /weak mechanism of information exchange between International Health Regulation and animal health focal points, insufficient health professional specialists, the need of workforce strategy, the need of field epidemiology and insufficient workforce retention capacity. Conclusion: the MCs identified should be addressed through a global approach.

Joint external evaluation of the international health regulations (2005) capacity in South Sudan: assessing the country´s capacity for health security.

Introduction: joint external evaluation is a voluntary and collaborative process to assess a country´s capacity under International Health Regulations (2005) to prevent, detect, and respond to public health threats. The main objective is to measure a country´s status in building the necessary capacities to prevent, detect, and respond to infectious disease threats and establish a baseline measurement of capacities and capabilities. The Republic of South Sudan conducted the Joint External Evaluation from 16-20 October 2017, where its capacities were assessed to public health threats per the International Health Regulation (2005). Methods: cross-sectional descriptive study of the Joint External Evaluation process and the findings are described along with major findings and recommendations for the country. Results: South Sudan's overall mean score across 48 indicators was 1.5 (min= 1, max= 4) and 42/48 indicators (87.5%) scored < 2 on a 1 to 5 scale. Technical areas in the prevent category with the lowest score were antimicrobial resistance, biosafety and biosecurity, and National legislation, policy, and financing. In the detect category, the mean score was 2. Technical areas with the lowest mean scores were workforce development and the National Laboratory System. Preparedness, medical countermeasures, personnel deployment, linking public health, and security authorities had the lowest scores in the respond category. Chemical events, radiation emergencies, and points of entry had a score of 1 in the other IHR-related hazards and points of entry category. Conclusion: South Sudan's mean score of 1.5 can be attributed to several civil conflicts experienced, which have impacted negatively on the health system. Recommendations from the Joint External Evaluation need to be implemented and these must be aligned with the costed National Action Plan for Health Security.

Model Re-Estimation: An Alternative for Poor Predictive Performance during External Evaluations? Example of Gentamicin in Critically Ill Patients.

BACKGROUND: An external evaluation is crucial before clinical applications; however, only a few gentamicin population pharmacokinetic (PopPK) models for critically ill patients included it in the model development. In this study, we aimed to evaluate gentamicin PopPK models developed for critically ill patients. METHODS: The evaluated models were selected following a literature review on aminoglycoside PopPK models for critically ill patients. The data of patients were retrospectively collected from two Quebec hospitals, the external evaluation and model re-estimation were performed with NONMEM® (v7.5) and the population bias and imprecisions were estimated. Dosing regimens were simulated using the best performing model. RESULTS: From the datasets of 39 and 48 patients from the two Quebec hospitals, none of the evaluated models presented acceptable values for bias and imprecision. Following model re-estimations, all models showed an acceptable predictive performance. An a priori dosing nomogram was developed with the best performing re-estimated model and was consistent based on recommended dosing regimens. CONCLUSION: Due to the poor predictive performance during the external evaluations, the latter must be prioritized during model development. Model re-estimation may be an alternative to developing a new model, especially when most known models display similar covariates.

External Evaluation of Population Pharmacokinetic Models of Busulfan in Chinese Adult Hematopoietic Stem Cell Transplantation Recipients.

Objective: Busulfan (BU) is a bi-functional DNA-alkylating agent used in patients undergoing hematopoietic stem cell transplantation (HSCT). Over the last decades, several population pharmacokinetic (pop PK) models of BU have been established, but external evaluation has not been performed for almost all models. The purpose of the study was to evaluate the predictive performance of published pop PK models of intravenous BU in adults using an independent dataset from Chinese HSCT patients, and to identify the best model to guide personalized dosing. Methods: The external evaluation methods included prediction-based diagnostics, simulation-based diagnostics, and Bayesian forecasting. In prediction-based diagnostics, the relative prediction error (PE%) was calculated by comparing the population predicted concentration (PRED) with the observations. Simulation-based diagnostics included the prediction- and variability-corrected visual predictive check (pvcVPC) and the normalized prediction distribution error (NPDE). Bayesian forecasting was executed by giving prior one to four observations. The factors influencing the model predictability, including the impact of structural models, were assessed. Results: A total of 440 concentrations (110 patients) were obtained for analysis. Based on prediction-based diagnostics and Bayesian forecasting, preferable predictive performance was observed in the model developed by Huang et al. The median PE% was -1.44% which was closest to 0, and the maximum F20 of 57.27% and F30 of 72.73% were achieved. Bayesian forecasting demonstrated that prior concentrations remarkably improved the prediction precision and accuracy of all models, even with only one prior concentration. Conclusion: This is the first study to comprehensively evaluate published pop PK models of BU. The model built by Huang et al. had satisfactory predictive performance, which can be used to guide individualized dosage adjustment of BU in Chinese patients.

Lack of Alignment Between WHO Joint External Evaluation and State Party Self-Assessment Scores Undermines Utility as Evaluation Tools for the Department of Defense.

The International Health Regulations 2005 (IHR) set standards for countries to detect and respond to public health threats such as COVID-19. The US Department of Defense engages with partner nations to build IHR-related health security capacities. In this article, we compare 2 elements of the IHR Monitoring and Evaluation Framework to determine if they align in a useful way. The version of the State Party Self-Assessment Annual Reporting (SPAR) tool used for this study is a self-assessment of 13 capacities, while the Joint External Evaluation (JEE) requires collaboration with international subject matter experts to evaluate 19 capacities. The SPAR indicators are scored separately from 0% to 100%, whereas the JEE uses a rank-ordered scale from 1 to 5 for variable numbers of indicators in each capacity. Using 2018-2019 data from the World Health Organization, we quantitatively and qualitatively evaluated the alignment of the SPAR and JEE scoring systems, using paired t tests for related capacities and 3 approaches to matching the scales. Whether using a simple, evenly divided scale for the SPAR or downscaling the SPAR scores to match with lower JEE scores, the paired t tests indicate that the JEE and SPAR scoring systems are not aligned. Many of the capacities in the JEE and SPAR are defined differently, pointing to one of the reasons for the discordance. We discuss implications for revision of the JEE and SPAR assessment tools along with ways in which the scores might be used for planning global health engagement capacity-building activities.

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.