As many drugs in paediatrics are used off-label, prescribers face a lack of evidence-based dosing guidelines. A Dutch framework was developed to provide dosing guidelines based on best available evidence from registration data, investigator-initiated research, professional guidelines, clinical experience and consensus. This has clarified the scientific grounds of drug use for children and encouraged uniformity in prescribing habits in the Netherlands. The developed framework and the current content of the Dutch Paediatric Formulary could be used as basis for similar initiatives worldwide, preferably in a concerted effort to ultimately provide children with effective and safe drug therapy.
As chloroquine (CHQ) is part of the Dutch Centre for Infectious Disease Control coronavirus disease 2019 experimental treatment guideline, pediatric dosing guidelines are needed. Recent pediatric data suggest that existing World Health Organization (WHO) dosing guidelines for children with malaria are suboptimal. The aim of our study was to establish best-evidence to inform pediatric CHQ doses for children infected with COVID-19. A previously developed physiologically-based pharmacokinetic (PBPK) model for CHQ was used to simulate exposure in adults and children and verified against published pharmacokinetic data. The COVID-19 recommended adult dosage regimen of 44 mg/kg total was tested in adults and children to evaluate the extent of variation in exposure. Based on differences in area under the concentration-time curve from zero to 70 hours (AUC 0-70h ) the optimal CHQ dose was determined in children of different ages compared with adults. Revised doses were re-introduced into the model to verify that overall CHQ exposure in each age band was within 5% of the predicted adult value. Simulations showed differences in drug exposure in children of different ages and adults when the same body-weight based dose is given. As such, we propose the following total cumulative doses: 35 mg/kg (CHQ base) for children 0-1 month, 47 mg/kg for 1-6 months, 55 mg/kg for 6 months-12 years, and 44 mg/kg for adolescents and adults, not to exceed 3,300 mg in any patient. Our study supports age-adjusted CHQ dosing in children with COVID-19 in order to avoid suboptimal or toxic doses. The knowledge-driven, model-informed dose selection paradigm can serve as a science-based alternative to recommend pediatric dosing when pediatric clinical trial data is absent.With coronavirus disease 2019 (COVID-19) spreading rapidly across the globe, effective drug treatment is desperately needed. Based on Chinese population data, < 1% of the infected cases were children below the age of 10 years and clinical symptoms in these patients were milder compared with adults. 1 However, it is unknown how such numbers will evolve when a higher percentage of the entire world population with diverse ethnic and socioeconomical backgrounds is infected.
is a the former director of Dutch Knowledge Center Pharmacotherapy for Children and current member of the Board of Trustees. Miriam Mooij and Nienke Vet are editorial board members of the Dutch Pediatric Formulary. Antje Neubert and Wolfgang Rascher are the project leaders for the German Pediatric Formulary. Christoph Male and Florian Lagler are the project leaders for Austrian Pediatric Formulary. Helene Grytli and Thomas Halvorsen are the project leaders for the Norwegian Pediatric Formulary (" KOBLE"). FundingThe BRAvO framework has been developed by Dutch Knowledge Center Pharmacotherapy for Children which is funded by a government grant by the Dutch Ministry of Health.
This study shows that the level of evidence for off-label pediatric pharmacotherapy is low: for only 14% of off-label records, high quality studies are available. Thirty-seven percent of offlabel records are not supported by any clinical studies at all. HOW MIGHT THIS CHANGE CLINICAL PHARMA COLOGY OR TRANSLATIONAL SCIENCE? Performing high quality randomized controlled trials for the > 2,000 off-label record is a very long pathway to close this information gap. Alternatively, modeling and simulation may be valuable approaches to strengthen the evidence base for offlabel use of drugs, especially in younger age groups.
Objectives The structured digital dosing guidelines of the web-based Dutch Paediatric Formulary provided the opportunity to develop an integrated paediatric dose calculator. In a simulated setting, we tested the ability of this calculator to reduce calculation errors. Methods Volunteer healthcare professionals were allocated to one of two groups, manual calculation versus the use of the dose calculator. Professionals in both groups were given access to a web-based questionnaire with 14 patient cases for which doses had to be calculated. The effect of group allocation on the probability of making a calculation error was determined using generalized estimated equations (GEE) logistic regression analysis. The causes of all the erroneous calculations were evaluated. Results Seventy-seven healthcare professionals completed the web-based questionnaire: thirty-seven were allocated to the manual group and 40 to the calculator group. Use of the dose calculator resulted in an estimated mean probability of a calculation error of 24.4% (95% CI 16.3-34.8) versus 39.0% (95% CI 32.4-46.1) with use of manual calculation. The mean difference of probability of calculation error between groups was 14.6% (95% CI 3.1-26.2; p = 0.013). In a secondary analysis where calculation error was defined as a 10% or greater deviation from the correct answer, the corresponding figures were 19.5% (95% CI 13-28.2) versus 26.5% (95% CI 21.6-32.1) with a mean difference of 7% between groups (95% CI 2.2-16.3; p = 0.137). Juxtaposition, typo/transcription errors and non-specified errors were more frequent as cause of error in the calculator group; exceeding the maximum dose and wrong correction for age were more frequent in the manual group. The percentage of tenfold errors was 3.1% in the manual group and 3.7% in the calculator group. Conclusions Our study shows that the use of a dose calculator as an add-on to a web-based paediatric formulary can reduce calculation errors. Furthermore, it shows that technologies may introduce new errors through transcription errors and wrongly selecting parameters from drop-down lists. Therefore, dosing calculators should be developed and used with special attention for selection and transcription errors.
Background: Modeling and simulation is increasingly used to study pediatric pharmacokinetics, but clinical implementation of age-appropriate doses lags behind. Therefore, we aimed to develop model-informed doses using published pharmacokinetic data and a decision framework to adjust dosing guidelines based on these doses, using piperacillin and amikacin in critically ill children as proof of concept.Methods: Piperacillin and amikacin pharmacokinetic models in critically ill children were extracted from literature. Concentration-time profiles were simulated for various dosing regimens for a virtual PICU patient dataset, including the current DPF dose and doses proposed in the studied publications. Probability of target attainment (PTA) was compared between the different dosing regimens. Next, updated dosing recommendations for the DPF were proposed, and evaluated using a new framework based on PK study quality and benefit-risk analysis of clinical implementation.Results: Three studies for piperacillin (critically ill children) and one for amikacin (critically ill pediatric burn patients) were included. Simulated concentration-time profiles were performed for a virtual dataset of 307 critically ill pediatric patients, age range 0.1–17.9 y. PTA for unbound piperacillin trough concentrations >16 mg/L was >90% only for continuous infusion regimens of 400 mg/kg/day vs. 9.7% for the current DPF dose (80 mg/kg/6 h, 30 min infusion). Amikacin PTA was >90% with 20 mg/kg/d, higher than the PTA of the DPF dose of 15 mg/kg/d (63.5%). Using our new decision framework, altered DPF doses were proposed for piperacillin (better PTA with loading dose plus continuous infusion), but not for amikacin (studied and target population were not comparable and risk for toxicity with higher dose).Conclusions: We show the feasibility to develop model-informed dosing guidelines for clinical implementation using existing pharmacokinetic data. This approach could complement literature and consensus-based dosing guidelines for off-label drugs in the absence of stronger evidence to support pediatricians in daily practice.
Background Dexmedetomidine is currently off-label for use in pediatric clinical care worldwide. Nevertheless, it is frequently prescribed to pediatric patients as premedication prior to induction of anesthesia or for procedural sedation. There is ample literature on the pharmacokinetics, efficacy and safety of dexmedetomidine in this vulnerable patient population, but there is a general lack of consensus on dosing. In this project, we aimed to use the standardized workflow of the Dutch Pediatric Formulary to establish best evidence-based pediatric dosing guidelines for dexmedetomidine as premedication and for procedural sedation. Method The available literature on dexmedetomidine in pediatrics was reviewed in order to address the following three questions: (1) What is the right dose? (2) What is known about efficacy? (3) What is known about safety? Relevant literature was compiled into a risk-benefit analysis document. A team of clinical experts critically appraised the analysis and the proposed dosing recommendations. Results Dexmedetomidine is most commonly administered via the intravenous or intranasal route. Clearance is age dependent, warranting higher doses in infants to reach similar exposure as in adults. Dexmedetomidine use results in satisfactory sedation at parent separation, adequate sedation and a favorable recovery profile. The safety profile is good and comparable to adults, with dose-related hemodynamic effects. Conclusion Following the structured approach of the Dutch Pediatric Formulary, best evidence-based dosing recommendations were proposed for dexmedetomidine, used as premedication prior to induction of anesthesia (intranasal dose) and for procedural sedation (intranasal and intravenous dose) in pediatric patients.
Calculation errors are the most common dosing errors in paediatrics. While health information technology has been advocated to improve patient safety, the broad implementation of computer-calculated dosing in the paediatric setting is delayed for the lack of computable, explicit and unambiguous paediatric dosing guidelines. The structured digital dosing guidelines of the Dutch Paediatric Formulary permitted the development of a paediatric dosing calculator that combines drug dosing recommendations of the formulary with patient variables. We took into account the European Union Medical Devices Directive (EEC 93/43) and international standards describing the requirements for the development and maintenance of medical software, the risk management in relation to medical devices and the application of usability engineering to medical devices. As key steps in the development and risk management of software, we first specified the intended use and primary operating functions, then identified potential failures and resolved failures by corrective measures. The paediatric dosing calculator calculates an individual dose based on specified patient characteristics and the dose recommendations provided in the formulary. This is a step-by-step process where a user first enters patient variables (date of birth, weight) and then selects drug variables (indication, route of administration). Based on the selected patient and drug variables, an individual dose is calculated. In addition, the calculated dose in weight units (grams, milligrams) can be converted to a volume-based dose (millilitres) of a fluid product. We successfully developed and implemented a dosing calculator in our national web-based Dutch Paediatric Formulary, which so far has been used more than 65,000 times monthly.Electronic supplementary material The online version of this article (https ://doi.org/10.1007/s4026 7-020-00724 -y) contains supplementary material, which is available to authorized users.* Tjitske M. van der Zanden
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