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Yusuf H, Hillman A, Stegeman JA, Cameron A, Badger S. Expanding access to veterinary clinical decision support in resource-limited settings: a scoping review of clinical decision support tools in medicine and antimicrobial stewardship. Front Vet Sci 2024; 11:1349188. [PMID: 38895711 PMCID: PMC11184142 DOI: 10.3389/fvets.2024.1349188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 05/13/2024] [Indexed: 06/21/2024] Open
Abstract
Introduction Digital clinical decision support (CDS) tools are of growing importance in supporting healthcare professionals in understanding complex clinical problems and arriving at decisions that improve patient outcomes. CDS tools are also increasingly used to improve antimicrobial stewardship (AMS) practices in healthcare settings. However, far fewer CDS tools are available in lowerand middle-income countries (LMICs) and in animal health settings, where their use in improving diagnostic and treatment decision-making is likely to have the greatest impact. The aim of this study was to evaluate digital CDS tools designed as a direct aid to support diagnosis and/or treatment decisionmaking, by reviewing their scope, functions, methodologies, and quality. Recommendations for the development of veterinary CDS tools in LMICs are then provided. Methods The review considered studies and reports published between January 2017 and October 2023 in the English language in peer-reviewed and gray literature. Results A total of 41 studies and reports detailing CDS tools were included in the final review, with 35 CDS tools designed for human healthcare settings and six tools for animal healthcare settings. Of the tools reviewed, the majority were deployed in high-income countries (80.5%). Support for AMS programs was a feature in 12 (29.3%) of the tools, with 10 tools in human healthcare settings. The capabilities of the CDS tools varied when reviewed against the GUIDES checklist. Discussion We recommend a methodological approach for the development of veterinary CDS tools in LMICs predicated on securing sufficient and sustainable funding. Employing a multidisciplinary development team is an important first step. Developing standalone CDS tools using Bayesian algorithms based on local expert knowledge will provide users with rapid and reliable access to quality guidance on diagnoses and treatments. Such tools are likely to contribute to improved disease management on farms and reduce inappropriate antimicrobial use, thus supporting AMS practices in areas of high need.
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Affiliation(s)
| | | | - Jan Arend Stegeman
- Department of Farm Animal Health, Faculty of Veterinary Medicine, Utrecht University, Utrecht, Netherlands
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2
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Rambaud J, Sajedi M, Al Omar S, Chomtom M, Sauthier M, De Montigny S, Jouvet P. Clinical Decision Support System to Detect the Occurrence of Ventilator-Associated Pneumonia in Pediatric Intensive Care. Diagnostics (Basel) 2023; 13:2983. [PMID: 37761350 PMCID: PMC10528404 DOI: 10.3390/diagnostics13182983] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 09/15/2023] [Accepted: 09/15/2023] [Indexed: 09/29/2023] Open
Abstract
OBJECTIVES Ventilator-associated pneumonia (VAP) is a severe care-related disease. The Centers for Disease Control defined the diagnosis criteria; however, the pediatric criteria are mainly subjective and retrospective. Clinical decision support systems have recently been developed in healthcare to help the physician to be more accurate for the early detection of severe pathology. We aimed at developing a predictive model to provide early diagnosis of VAP at the bedside in a pediatric intensive care unit (PICU). METHODS We performed a retrospective single-center study at a tertiary-care pediatric teaching hospital. All patients treated by invasive mechanical ventilation between September 2013 and October 2019 were included. Data were collected in the PICU electronic medical record and high-resolution research database. Development of the clinical decision support was then performed using open-access R software (Version 3.6.1®). MEASUREMENTS AND MAIN RESULTS In total, 2077 children were mechanically ventilated. We identified 827 episodes with almost 48 h of mechanical invasive ventilation and 77 patients who suffered from at least one VAP event. We split our database at the patient level in a training set of 461 patients free of VAP and 45 patients with VAP and in a testing set of 199 patients free of VAP and 20 patients with VAP. The Imbalanced Random Forest model was considered as the best fit with an area under the ROC curve from fitting the Imbalanced Random Forest model on the testing set being 0.82 (95% CI: (0.71, 0.93)). An optimal threshold of 0.41 gave a sensitivity of 79.7% and a specificity of 72.7%, with a positive predictive value (PPV) of 9% and a negative predictive value of 99%, and with an accuracy of 79.5% (95% CI: (0.77, 0.82)). CONCLUSIONS Using machine learning, we developed a clinical predictive algorithm based on clinical data stored prospectively in a database. The next step will be to implement the algorithm in PICUs to provide early, automatic detection of ventilator-associated pneumonia.
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Affiliation(s)
- Jerome Rambaud
- Pediatric Intensive Care Unit, Sainte-Justine Hospital, Montreal, QC H3T 1C5, Canada; (M.S.); (P.J.)
- Pediatric and Neonatal Intensive Care Unit, Armand-Trousseau Hospital, Sorbonne University, 75012 Paris, France
| | - Masoumeh Sajedi
- Research Center, Sainte-Justine Hospital, Montreal, QC H3T 1C5, Canada; (M.S.); (S.A.O.); (S.D.M.)
| | - Sally Al Omar
- Research Center, Sainte-Justine Hospital, Montreal, QC H3T 1C5, Canada; (M.S.); (S.A.O.); (S.D.M.)
| | - Maryline Chomtom
- Pediatric Intensive Care Unit, Caen University Hospital, 14000 Caen, France;
| | - Michael Sauthier
- Pediatric Intensive Care Unit, Sainte-Justine Hospital, Montreal, QC H3T 1C5, Canada; (M.S.); (P.J.)
| | - Simon De Montigny
- Research Center, Sainte-Justine Hospital, Montreal, QC H3T 1C5, Canada; (M.S.); (S.A.O.); (S.D.M.)
- School of Public Health, Montréal University, Montreal, QC H2X 3E4, Canada
| | - Philippe Jouvet
- Pediatric Intensive Care Unit, Sainte-Justine Hospital, Montreal, QC H3T 1C5, Canada; (M.S.); (P.J.)
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Janssen A, De Waele JJ, Elbers PWG. Towards adequate and automated antibiotic dosing. Intensive Care Med 2023; 49:853-856. [PMID: 37079085 PMCID: PMC10353957 DOI: 10.1007/s00134-023-07047-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 03/22/2023] [Indexed: 04/21/2023]
Affiliation(s)
- Alexander Janssen
- Department of Intensive Care Medicine, Center for Critical Care, Computation Intelligence (C4i), Amsterdam Medical Data Science (AMDS), Amsterdam Public Health (APH), Amsterdam Cardiovascular Science (ACS), Amsterdam Institute for Infection and Immunity (AII), Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands.
- Department of Clinical Pharmacology, Hospital Pharmacy, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands.
| | - Jan J De Waele
- Department of Intensive Care Medicine, Ghent University Hospital, Ghent, Belgium
- Department of Internal Medicine and Paediatrics, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium
| | - Paul W G Elbers
- Department of Intensive Care Medicine, Center for Critical Care, Computation Intelligence (C4i), Amsterdam Medical Data Science (AMDS), Amsterdam Public Health (APH), Amsterdam Cardiovascular Science (ACS), Amsterdam Institute for Infection and Immunity (AII), Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
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Thakur L, Singh S, Singh R, Kumar A, Angrup A, Kumar N. The potential of 4D's approach in curbing antimicrobial resistance among bacterial pathogens. Expert Rev Anti Infect Ther 2022; 20:1401-1412. [PMID: 36098225 DOI: 10.1080/14787210.2022.2124968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
INTRODUCTION Antibiotics are life-saving drugs but irrational/inappropriate use leads to the emergence of antibiotic-resistant bacterial superbugs, making their treatment extremely challenging. Increasing antimicrobial resistance (AMR) among bacterial pathogens is becoming a serious public health concern globally. If ignorance persists, there would not be any antibiotics available to treat even a common bacterial infection in future. AREA COVERED This article intends to collate and discuss the potential of 4D's (right Drug, Dose, Duration, and De-escalation of therapy) approach to tackle the emerging problem of AMR. For this, we searched PubMed, Google Scholar, Medline, and clinicaltrials.gov databases primarily using keywords 'optimal antibiotic therapy,' 'antimicrobial resistance,' 'higher versus lower dose antibiotic treatment,' 'shorter versus longer duration antibiotic treatment,' 'de-escalation study', and 'antimicrobial stewardship measures' and based on the findings, form and expressed our opinion. EXPERT OPINION More efforts are needed for developing diagnostics for rapid, accurate, point-of-care, and cost-effective pathogen identification and antimicrobial susceptibility testing (AST) to facilitate rational use of antibiotics. Current dosing and duration of therapies also need to be redefined to maximize their impact. Furthermore, de-escalation approaches should be developed and encouraged in the clinic. This altogether will minimize selection pressure on the pathogens and reduce emergence of AMR.
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Affiliation(s)
- Lovnish Thakur
- Translational Health Science and Technology Institute, Ncr Biotech Science Cluster, Faridabad, India.,Jawaharlal Nehru University, Delhi, India
| | - Sevaram Singh
- Translational Health Science and Technology Institute, Ncr Biotech Science Cluster, Faridabad, India.,Jawaharlal Nehru University, Delhi, India
| | - Rita Singh
- Translational Health Science and Technology Institute, Ncr Biotech Science Cluster, Faridabad, India.,Jawaharlal Nehru University, Delhi, India
| | - Ashok Kumar
- Translational Health Science and Technology Institute, Ncr Biotech Science Cluster, Faridabad, India
| | - Archana Angrup
- Department of Microbiology, Postgraduate Institute of Medical Education and Research, Chandigarh, India
| | - Niraj Kumar
- Translational Health Science and Technology Institute, Ncr Biotech Science Cluster, Faridabad, India
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Roggeveen LF, Guo T, Fleuren LM, Driessen R, Thoral P, van Hest RM, Mathot RAA, Swart EL, de Grooth HJ, van den Bogaard B, Girbes ARJ, Bosman RJ, Elbers PWG. Right dose, right now: bedside, real-time, data-driven, and personalised antibiotic dosing in critically ill patients with sepsis or septic shock—a two-centre randomised clinical trial. Crit Care 2022; 26:265. [PMID: 36064438 PMCID: PMC9443636 DOI: 10.1186/s13054-022-04098-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Accepted: 07/18/2022] [Indexed: 11/17/2022] Open
Abstract
Background Adequate antibiotic dosing may improve outcomes in critically ill patients but is challenging due to altered and variable pharmacokinetics. To address this challenge, AutoKinetics was developed, a decision support system for bedside, real-time, data-driven and personalised antibiotic dosing. This study evaluates the feasibility, safety and efficacy of its clinical implementation. Methods In this two-centre randomised clinical trial, critically ill patients with sepsis or septic shock were randomised to AutoKinetics dosing or standard dosing for four antibiotics: vancomycin, ciprofloxacin, meropenem, and ceftriaxone. Adult patients with a confirmed or suspected infection and either lactate > 2 mmol/L or vasopressor requirement were eligible for inclusion. The primary outcome was pharmacokinetic target attainment in the first 24 h after randomisation. Clinical endpoints included mortality, ICU length of stay and incidence of acute kidney injury. Results After inclusion of 252 patients, the study was stopped early due to the COVID-19 pandemic. In the ciprofloxacin intervention group, the primary outcome was obtained in 69% compared to 3% in the control group (OR 62.5, CI 11.4–1173.78, p < 0.001). Furthermore, target attainment was faster (26 h, CI 18–42 h, p < 0.001) and better (65% increase, CI 49–84%, p < 0.001). For the other antibiotics, AutoKinetics dosing did not improve target attainment. Clinical endpoints were not significantly different. Importantly, higher dosing did not lead to increased mortality or renal failure. Conclusions In critically ill patients, personalised dosing was feasible, safe and significantly improved target attainment for ciprofloxacin. Trial registration: The trial was prospectively registered at Netherlands Trial Register (NTR), NL6501/NTR6689 on 25 August 2017 and at the European Clinical Trials Database (EudraCT), 2017-002478-37 on 6 November 2017. Supplementary Information The online version contains supplementary material available at 10.1186/s13054-022-04098-7.
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Yow HY, Govindaraju K, Lim AH, Abdul Rahim N. Optimizing Antimicrobial Therapy by Integrating Multi-Omics With Pharmacokinetic/Pharmacodynamic Models and Precision Dosing. Front Pharmacol 2022; 13:915355. [PMID: 35814236 PMCID: PMC9260690 DOI: 10.3389/fphar.2022.915355] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 06/01/2022] [Indexed: 12/02/2022] Open
Abstract
In the era of “Bad Bugs, No Drugs,” optimizing antibiotic therapy against multi-drug resistant (MDR) pathogens is crucial. Mathematical modelling has been employed to further optimize dosing regimens. These models include mechanism-based PK/PD models, systems-based models, quantitative systems pharmacology (QSP) and population PK models. Quantitative systems pharmacology has significant potential in precision antimicrobial chemotherapy in the clinic. Population PK models have been employed in model-informed precision dosing (MIPD). Several antibiotics require close monitoring and dose adjustments in order to ensure optimal outcomes in patients with infectious diseases. Success or failure of antibiotic therapy is dependent on the patient, antibiotic and bacterium. For some drugs, treatment responses vary greatly between individuals due to genotype and disease characteristics. Thus, for these drugs, tailored dosing is required for successful therapy. With antibiotics, inappropriate dosing such as insufficient dosing may put patients at risk of therapeutic failure which could lead to mortality. Conversely, doses that are too high could lead to toxicities. Hence, precision dosing which customizes doses to individual patients is crucial for antibiotics especially those with a narrow therapeutic index. In this review, we discuss the various strategies in optimizing antimicrobial therapy to address the challenges in the management of infectious diseases and delivering personalized therapy.
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Affiliation(s)
- Hui-Yin Yow
- Faculty of Health and Medical Sciences, School of Pharmacy, Taylor’s University, Subang Jaya, Malaysia
- Centre for Drug Discovery and Molecular Pharmacology, Faculty of Health and Medical Sciences, Taylor’s University, Subang Jaya, Malaysia
| | - Kayatri Govindaraju
- Department of Pharmaceutical Life Sciences, Faculty of Pharmacy, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Audrey Huili Lim
- Centre for Clinical Outcome Research (CCORE), Institute for Clinical Research, National Institutes of Health, Shah Alam, Malaysia
| | - Nusaibah Abdul Rahim
- Department of Clinical Pharmacy and Pharmacy Practice, Faculty of Pharmacy, Universiti Malaya, Kuala Lumpur, Malaysia
- *Correspondence: Nusaibah Abdul Rahim,
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Abstract
PURPOSE OF REVIEW Ventilator-associated pneumonia (VAP) is a common nosocomial infection in critically ill patients requiring endotracheal intubation and mechanical ventilation. Recently, the emergence of multidrug-resistant Gram-negative bacteria, including carbapenem-resistant Enterobacterales, multidrug-resistant Pseudomonas aeruginosa and Acinetobacter species, has complicated the selection of appropriate antimicrobials and contributed to treatment failure. Although novel antimicrobials are crucial to treating VAP caused by these multidrug-resistant organisms, knowledge of how to optimize their efficacy while minimizing the development of resistance should be a requirement for their use. RECENT FINDINGS Several studies have assessed the efficacy of novel antimicrobials against multidrug-resistant organisms, but high-quality studies focusing on optimal dosing, infusion time and duration of therapy in patients with VAP are still lacking. Antimicrobial and diagnostic stewardship should be combined to optimize the use of these novel agents. SUMMARY Improvements in diagnostic tests, stewardship practices and a better understanding of dosing, infusion time, duration of treatment and the effects of combining various antimicrobials should help optimize the use of novel antimicrobials for VAP and maximize clinical outcomes while minimizing the development of resistance.
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8
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Guo T, Abdulla A, Koch BCP, van Hasselt JGC, Endeman H, Schouten JA, Elbers PWG, Brüggemann RJM, van Hest RM. Pooled Population Pharmacokinetic Analysis for Exploring Ciprofloxacin Pharmacokinetic Variability in Intensive Care Patients. Clin Pharmacokinet 2022; 61:869-879. [PMID: 35262847 PMCID: PMC9249715 DOI: 10.1007/s40262-022-01114-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/13/2022] [Indexed: 12/02/2022]
Abstract
Background and Objective Previous pharmacokinetic (PK) studies of ciprofloxacin in intensive care (ICU) patients have shown large differences in estimated PK parameters, suggesting that further investigation is needed for this population. Hence, we performed a pooled population PK analysis of ciprofloxacin after intravenous administration using individual patient data from three studies. Additionally, we studied the PK differences between these studies through a post-hoc analysis. Methods Individual patient data from three studies (study 1, 2, and 3) were pooled. The pooled data set consisted of 1094 ciprofloxacin concentration–time data points from 140 ICU patients. Nonlinear mixed-effects modeling was used to develop a population PK model. Covariates were selected following a stepwise covariate modeling procedure. To analyze PK differences between the three original studies, random samples were drawn from the posterior distribution of individual PK parameters. These samples were used for a simulation study comparing PK exposure and the percentage of target attainment between patients of these studies. Results A two-compartment model with first-order elimination best described the data. Inter-individual variability was added to the clearance, central volume, and peripheral volume. Inter-occasion variability was added to clearance only. Body weight was added to all parameters allometrically. Estimated glomerular filtration rate on ciprofloxacin clearance was identified as the only covariate relationship resulting in a drop in inter-individual variability of clearance from 58.7 to 47.2%. In the post-hoc analysis, clearance showed the highest deviation between the three studies with a coefficient of variation of 14.3% for posterior mean and 24.1% for posterior inter-individual variability. The simulation study showed that following the same dose regimen of 400 mg three times daily, the area under the concentration–time curve of study 3 was the highest with a mean area under the concentration–time curve at 24 h of 58 mg·h/L compared with that of 47.7 mg·h/L for study 1 and 47.6 mg·h/L for study 2. Similar differences were also observed in the percentage of target attainment, defined as the ratio of area under the concentration–time curve at 24 h and the minimum inhibitory concentration. At the epidemiological cut-off minimum inhibitory concentration of Pseudomonas aeruginosa of 0.5 mg/L, percentage of target attainment was only 21%, 18%, and 38% for study 1, 2, and 3, respectively. Conclusions We developed a population PK model of ciprofloxacin in ICU patients using pooled data of individual patients from three studies. A simple ciprofloxacin dose recommendation for the entire ICU population remains challenging owing to the PK differences within ICU patients, hence dose individualization may be needed for the optimization of ciprofloxacin treatment.
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Affiliation(s)
- Tingjie Guo
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, The Netherlands.
| | - Alan Abdulla
- Department of Hospital Pharmacy, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Birgit C P Koch
- Department of Hospital Pharmacy, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Johan G C van Hasselt
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, The Netherlands
| | - Henrik Endeman
- Department of Intensive Care, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Jeroen A Schouten
- Department of Intensive Care, Radboudumc-CWZ Center of Expertise for Mycology, Radboud UMC, Nijmegen, The Netherlands
| | - Paul W G Elbers
- Department of Intensive Care Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Roger J M Brüggemann
- Department of Pharmacy, Radboud Center for Infectious Diseases, Radboud Institute for Health Sciences, Radboud UMC, Nijmegen, The Netherlands
| | - Reinier M van Hest
- Department of Pharmacy and Clinical Pharmacology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
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Schmulenson E, Zimmermann N, Mikus G, Joerger M, Jaehde U. Current status and future outlooks on therapeutic drug monitoring of fluorouracil. Expert Opin Drug Metab Toxicol 2022; 17:1407-1422. [PMID: 35029518 DOI: 10.1080/17425255.2021.2029403] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
INTRODUCTION : Therapeutic drug monitoring (TDM) of the anticancer drug fluorouracil (5FU) as a method to support dose adjustments has been researched and discussed extensively. Despite manifold evidence of the advantages of 5FU-TDM, traditional body surface area (BSA)-guided dosing is still widely applied. AREAS COVERED : This review covers the latest evidence on 5FU-TDM based on a literature search in PubMed between June and September 2021. It particularly highlights new approaches of implementing 5FU-TDM into precision medicine by combining TDM with pharmacogenetic testing and/or pharmacometric models. This review further discusses remaining obstacles in order to incorporate 5FU-TDM into clinical routine. EXPERT OPINION : New data on 5FU-TDM further strengthen the advantages compared to BSA-guided dosing as it is able to reduce pharmacokinetic variability and thereby improve treatment efficacy and safety. Interprofessional collaboration has the potential to overcome the remaining barriers for its implementation. Pre-emptive pharmacogenetic testing followed by 5FU-TDM can further improve 5FU exposure in a substantial proportion of patients. Developing a model framework integrating pharmacokinetics and pharmacodynamics of 5FU will be crucial to fully advance into the precision medicine era. Model applications can potentially support clinicians in dose finding before starting chemotherapy. Additionally, TDM provides further assistance in continuously improving model predictions.
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Affiliation(s)
- Eduard Schmulenson
- Department of Clinical Pharmacy, Institute of Pharmacy, University of Bonn, Bonn, Germany
| | - Nigina Zimmermann
- Department of Clinical Pharmacy, Institute of Pharmacy, University of Bonn, Bonn, Germany
| | - Gerd Mikus
- Department of Clinical Pharmacy, Institute of Pharmacy, University of Bonn, Bonn, Germany.,Department of Clinical Pharmacy and Biochemistry, Institute of Pharmacy, Freie Universität Berlin, Berlin, Germany.,Department of Clinical Pharmacology and Pharmacoepidemiology, University Hospital Heidelberg, Heidelberg, Germany
| | - Markus Joerger
- Department of Medical Oncology and Hematology, Cantonal Hospital St. Gallen, St. Gallen, Switzerland
| | - Ulrich Jaehde
- Department of Clinical Pharmacy, Institute of Pharmacy, University of Bonn, Bonn, Germany
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10
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Tu Q, Cotta M, Raman S, Graham N, Schlapbach L, Roberts JA. Individualized precision dosing approaches to optimize antimicrobial therapy in pediatric populations. Expert Rev Clin Pharmacol 2021; 14:1383-1399. [PMID: 34313180 DOI: 10.1080/17512433.2021.1961578] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Introduction:Severe infections continue to impose a major burden on critically ill children and mortality rates remain stagnant. Outcomes rely on accurate and timely delivery of antimicrobials achieving target concentrations in infected tissue. Yet, developmental aspects, disease-related variables, and host factors may severely alter antimicrobial pharmacokinetics in pediatrics. The emergence of antimicrobial resistance increases the need for improved treatment approaches.Areas covered:This narrative review explores why optimization of antimicrobial therapy in neonates, infants, children, and adolescents is crucial and summarizes the possible dosing approaches to achieve antimicrobial individualization. Finally, we outline a roadmap toward scientific evidence informing the development and implementation of precision antimicrobial dosing in critically ill children.The literature search was conducted on PubMed using the following keywords: neonate, infant, child, adolescent, pediatrics, antimicrobial, pharmacokinetic, pharmacodynamic target, Bayes dosing software, optimizing, individualizing, personalizing, precision dosing, drug monitoring, validation, attainment, and software implementation. Further articles were sought from the references of the above searched articles.Expert opinion:Recently, technological innovations have emerged that enabled the development of individualized antimicrobial dosing approaches in adults. More work is required in pediatrics to make individualized antimicrobial dosing approaches widely operationalized in this population.
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Affiliation(s)
- Quyen Tu
- University of Queensland Centre for Clinical Research, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia.,Department of Pharmacy, Queensland Children's Hospital, Brisbane, QLD, Australia
| | - Menino Cotta
- University of Queensland Centre for Clinical Research, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Sainath Raman
- Department of Paediatric Intensive Care Medicine, Queensland Children's Hospital, Brisbane, QLD, Australia.,Centre for Children's Health Research (CCHR), The University of Queensland, Brisbane, QLD, Australia
| | - Nicolette Graham
- Department of Pharmacy, Queensland Children's Hospital, Brisbane, QLD, Australia
| | - Luregn Schlapbach
- Department of Paediatric Intensive Care Medicine, Queensland Children's Hospital, Brisbane, QLD, Australia.,Department of Intensive Care and Neonatology, The University Children's Hospital Zurich, Switzerland
| | - Jason A Roberts
- University of Queensland Centre for Clinical Research, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia.,Departments of Pharmacy and Intensive Care Medicine, Royal Brisbane and Women's Hospital, Brisbane, Australia.,Division of Anaesthesiology Critical Care Emergency and Pain Medicine, Nîmes University Hospital, University of Montpellier, Nîmes, France
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Barrett JS, Barrett RF, Vinks AA. Status Toward the Implementation of Precision Dosing in Children. J Clin Pharmacol 2021; 61 Suppl 1:S36-S51. [PMID: 34185896 DOI: 10.1002/jcph.1830] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Accepted: 02/04/2021] [Indexed: 01/19/2023]
Abstract
Precision dosing is progressing beyond the conceptual and proof-of-concept stages toward implementation. As the availability of dosing algorithms, tools, and platforms increases, so do the investment in technology services and actual implementation of clinical services offering these solutions to patients. Nowhere is this needed more than in pediatric populations, which are still reliant on adult drug development and bridging strategies to support dosing, often in the absence of actual dose-finding studies in the target pediatric population. Still, there is more work to be done to ensure that proper governance of these services is maintained, and that sustainability of these early implementations is guided by new science as it evolves and meaningful outcome data to confirm that such services deliver on both clinical and economic return on investment. In addition, the field should ensure that all approaches beyond a therapeutic drug monitoring-driven, pharmacokinetic-centric approach should be considered as the tools and services evolve, especially when pediatric-specific pharmacokinetic/pharmacodyamic and pharmacogenetic data are available and shown to be useful to guide dosing. This review evaluates current pediatric precision dosing efforts, highlighting their utility, longevity, and sustainability and assesses the current process for implementing such approaches examining current barriers that stand in the way of broader implementation and the stakeholders that must engage to ensure its ultimate success.
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Affiliation(s)
- Jeffrey S Barrett
- Quantitative Medicine, Critical Path Institute, Tucson, Arizona, USA
| | - Ryan F Barrett
- College of Chemical and Biological Engineering, Drexel University, Philadelphia, Pennsylvania, USA
| | - Alexander A Vinks
- Division of Clinical Pharmacology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
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12
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Abdulla A, Edwina EE, Flint RB, Allegaert K, Wildschut ED, Koch BCP, de Hoog M. Model-Informed Precision Dosing of Antibiotics in Pediatric Patients: A Narrative Review. Front Pediatr 2021; 9:624639. [PMID: 33708753 PMCID: PMC7940353 DOI: 10.3389/fped.2021.624639] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2020] [Accepted: 02/03/2021] [Indexed: 12/17/2022] Open
Abstract
Optimal pharmacotherapy in pediatric patients with suspected infections requires understanding and integration of relevant data on the antibiotic, bacterial pathogen, and patient characteristics. Because of age-related physiological maturation and non-maturational covariates (e.g., disease state, inflammation, organ failure, co-morbidity, co-medication and extracorporeal systems), antibiotic pharmacokinetics is highly variable in pediatric patients and difficult to predict without using population pharmacokinetics models. The intra- and inter-individual variability can result in under- or overexposure in a significant proportion of patients. Therapeutic drug monitoring typically covers assessment of pharmacokinetics and pharmacodynamics, and concurrent dose adaptation after initial standard dosing and drug concentration analysis. Model-informed precision dosing (MIPD) captures drug, disease, and patient characteristics in modeling approaches and can be used to perform Bayesian forecasting and dose optimization. Incorporating MIPD in the electronic patient record system brings pharmacometrics to the bedside of the patient, with the aim of a consisted and optimal drug exposure. In this narrative review, we evaluated studies assessing optimization of antibiotic pharmacotherapy using MIPD in pediatric populations. Four eligible studies involving amikacin and vancomycin were identified from 418 records. Key articles, independent of year of publication, were also selected to highlight important attributes of MIPD. Although very little research has been conducted until this moment, the available data on vancomycin indicate that MIPD is superior compared to conventional dosing strategies with respect to target attainment. The utility of MIPD in pediatrics needs to be further confirmed in frequently used antibiotic classes, particularly aminoglycosides and beta-lactams.
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Affiliation(s)
- Alan Abdulla
- Department of Hospital Pharmacy, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Elma E Edwina
- Department of Hospital Pharmacy, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Robert B Flint
- Department of Hospital Pharmacy, Erasmus University Medical Center, Rotterdam, Netherlands.,Division of Neonatology, Department of Pediatrics, Sophia Children's Hospital, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Karel Allegaert
- Department of Hospital Pharmacy, Erasmus University Medical Center, Rotterdam, Netherlands.,Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Leuven, Belgium.,Department of Development and Regeneration, KU Leuven, Leuven, Belgium
| | - Enno D Wildschut
- Department of Pediatric Intensive Care, Sophia Children's Hospital, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Birgit C P Koch
- Department of Hospital Pharmacy, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Matthijs de Hoog
- Department of Pediatric Intensive Care, Sophia Children's Hospital, Erasmus University Medical Center, Rotterdam, Netherlands
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13
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Keij FM, Achten NB, Tramper-Stranders GA, Allegaert K, van Rossum AMC, Reiss IKM, Kornelisse RF. Stratified Management for Bacterial Infections in Late Preterm and Term Neonates: Current Strategies and Future Opportunities Toward Precision Medicine. Front Pediatr 2021; 9:590969. [PMID: 33869108 PMCID: PMC8049115 DOI: 10.3389/fped.2021.590969] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 03/01/2021] [Indexed: 12/20/2022] Open
Abstract
Bacterial infections remain a major cause of morbidity and mortality in the neonatal period. Therefore, many neonates, including late preterm and term neonates, are exposed to antibiotics in the first weeks of life. Data on the importance of inter-individual differences and disease signatures are accumulating. Differences that may potentially influence treatment requirement and success rate. However, currently, many neonates are treated following a "one size fits all" approach, based on general protocols and standard antibiotic treatment regimens. Precision medicine has emerged in the last years and is perceived as a new, holistic, way of stratifying patients based on large-scale data including patient characteristics and disease specific features. Specific to sepsis, differences in disease susceptibility, disease severity, immune response and pharmacokinetics and -dynamics can be used for the development of treatment algorithms helping clinicians decide when and how to treat a specific patient or a specific subpopulation. In this review, we highlight the current and future developments that could allow transition to a more precise manner of antibiotic treatment in late preterm and term neonates, and propose a research agenda toward precision medicine for neonatal bacterial infections.
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Affiliation(s)
- Fleur M Keij
- Division of Neonatology, Department of Pediatrics, Erasmus Medical Center-Sophia Children's Hospital, Rotterdam, Netherlands.,Department of Pediatrics, Franciscus Gasthuis and Vlietland, Rotterdam, Netherlands
| | - Niek B Achten
- Division of Neonatology, Department of Pediatrics, Erasmus Medical Center-Sophia Children's Hospital, Rotterdam, Netherlands
| | - Gerdien A Tramper-Stranders
- Division of Neonatology, Department of Pediatrics, Erasmus Medical Center-Sophia Children's Hospital, Rotterdam, Netherlands.,Department of Pediatrics, Franciscus Gasthuis and Vlietland, Rotterdam, Netherlands
| | - Karel Allegaert
- Department of Development and Regeneration, Department of Pharmaceutical and Pharmacological Sciences, Katholieke Universiteit Leuven, Leuven, Belgium.,Department of Clinical Pharmacy, Erasmus Medical Center Rotterdam, Rotterdam, Netherlands
| | - Annemarie M C van Rossum
- Division of Infectious Diseases, Department of Pediatrics, Erasmus Medical Center-Sophia Children's Hospital, Rotterdam, Netherlands
| | - Irwin K M Reiss
- Division of Neonatology, Department of Pediatrics, Erasmus Medical Center-Sophia Children's Hospital, Rotterdam, Netherlands
| | - René F Kornelisse
- Division of Neonatology, Department of Pediatrics, Erasmus Medical Center-Sophia Children's Hospital, Rotterdam, Netherlands
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14
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Kluwe F, Michelet R, Mueller‐Schoell A, Maier C, Klopp‐Schulze L, Dyk M, Mikus G, Huisinga W, Kloft C. Perspectives on Model‐Informed Precision Dosing in the Digital Health Era: Challenges, Opportunities, and Recommendations. Clin Pharmacol Ther 2020; 109:29-36. [DOI: 10.1002/cpt.2049] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Accepted: 09/09/2020] [Indexed: 12/20/2022]
Affiliation(s)
- Franziska Kluwe
- Department of Clinical Pharmacy and Biochemistry, Institute of Pharmacy Freie Universität Berlin Berlin Germany
- Graduate Research Training Program PharMetrX Freie Universität Berlin and Universitaet Potsdam Berlin Germany
| | - Robin Michelet
- Department of Clinical Pharmacy and Biochemistry, Institute of Pharmacy Freie Universität Berlin Berlin Germany
| | - Anna Mueller‐Schoell
- Department of Clinical Pharmacy and Biochemistry, Institute of Pharmacy Freie Universität Berlin Berlin Germany
- Graduate Research Training Program PharMetrX Freie Universität Berlin and Universitaet Potsdam Berlin Germany
| | - Corinna Maier
- Graduate Research Training Program PharMetrX Freie Universität Berlin and Universitaet Potsdam Berlin Germany
- Institute of Mathematics Universität Potsdam Potsdam Germany
| | - Lena Klopp‐Schulze
- Department of Clinical Pharmacy and Biochemistry, Institute of Pharmacy Freie Universität Berlin Berlin Germany
- Graduate Research Training Program PharMetrX Freie Universität Berlin and Universitaet Potsdam Berlin Germany
| | - Madelé Dyk
- Flinders Centre for Innovation in Cancer, College of Medicine & Public Health Flinders University Adelaide Australia
| | - Gerd Mikus
- Department of Clinical Pharmacy and Biochemistry, Institute of Pharmacy Freie Universität Berlin Berlin Germany
- Department of Clinical Pharmacology and Pharmacoepidemiology University Hospital Heidelberg Heidelberg Germany
| | | | - Charlotte Kloft
- Department of Clinical Pharmacy and Biochemistry, Institute of Pharmacy Freie Universität Berlin Berlin Germany
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15
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Parker SL, Abdul-Aziz MH, Roberts JA. The role of antibiotic pharmacokinetic studies performed post-licensing. Int J Antimicrob Agents 2020; 56:106165. [PMID: 32941948 DOI: 10.1016/j.ijantimicag.2020.106165] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Revised: 07/29/2020] [Accepted: 09/10/2020] [Indexed: 12/11/2022]
Abstract
Post-licensing pharmacometric studies can provide a better understanding of the pharmacokinetic (PK) alterations in special patient populations and may lead to better clinical outcomes. Some patient populations exhibit markedly different pathophysiology to general ward patients or healthy individuals. This may be developmental (paediatric patients), a manifestation of an underlying disease pathology (patients with obesity or haematological malignancies) or due to medical interventions (critically ill patients receiving extracorporeal therapies). This paper outlines the factors that affect the PK of special patient populations and describes some novel methods of antimicrobial administration that may increase antimicrobial concentrations at the site of infection and improve treatment of severe infection.
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Affiliation(s)
- Suzanne L Parker
- UQ Centre for Clinical Research, The University of Queensland, Brisbane, Australia.
| | | | - Jason A Roberts
- UQ Centre for Clinical Research, The University of Queensland, Brisbane, Australia; Department of Intensive Care Medicine, Royal Brisbane & Women's Hospital, Brisbane, Australia; Centre for Translational Anti-Infective Pharmacodynamics, School of Pharmacy, The University of Queensland, Brisbane, Australia; Division of Anaesthesiology Critical Care Emergency and Pain Medicine, Nîmes University Hospital, University of Montpellier, Nîmes, France; Department of Pharmacy, Royal Brisbane & Women's Hospital, Brisbane, Australia
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16
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Guo T, van Hest RM, Zwep LB, Roggeveen LF, Fleuren LM, Bosman RJ, van der Voort PHJ, Girbes ARJ, Mathot RAA, Elbers PWG, van Hasselt JGC. Optimizing Predictive Performance of Bayesian Forecasting for Vancomycin Concentration in Intensive Care Patients. Pharm Res 2020; 37:171. [PMID: 32830297 PMCID: PMC7443423 DOI: 10.1007/s11095-020-02908-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Accepted: 08/11/2020] [Indexed: 01/01/2023]
Abstract
Purpose Bayesian forecasting is crucial for model-based dose optimization based on therapeutic drug monitoring (TDM) data of vancomycin in intensive care (ICU) patients. We aimed to evaluate the performance of Bayesian forecasting using maximum a posteriori (MAP) estimation for model-based TDM. Methods We used a vancomycin TDM data set (n = 408 patients). We compared standard MAP-based Bayesian forecasting with two alternative approaches: (i) adaptive MAP which handles data over multiple iterations, and (ii) weighted MAP which weights the likelihood contribution of data. We evaluated the percentage error (PE) for seven scenarios including historical TDM data from the preceding day up to seven days. Results The mean of median PEs of all scenarios for the standard MAP, adaptive MAP and weighted MAP method were − 7.7%, −4.5% and − 6.7%. The adaptive MAP also showed the narrowest inter-quartile range of PE. In addition, regardless of MAP method, including historical TDM data further in the past will increase prediction errors. Conclusions The proposed adaptive MAP method outperforms standard MAP in predictive performance and may be considered for improvement of model-based dose optimization. The inclusion of historical data beyond either one day (standard MAP and weighted MAP) or two days (adaptive MAP) reduces predictive performance. Electronic supplementary material The online version of this article (10.1007/s11095-020-02908-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Tingjie Guo
- Department of Intensive Care Medicine
- Research VUmc Intensive Care (REVIVE)
- Amsterdam Cardiovascular Sciences (ACS)
- Amsterdam Medical Data Science (AMDS), Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands. .,Department of Pharmacy, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands. .,Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, The Netherlands.
| | - Reinier M van Hest
- Department of Pharmacy, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Laura B Zwep
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, The Netherlands.,Mathematical Institute, Leiden University, Leiden, The Netherlands
| | - Luca F Roggeveen
- Department of Intensive Care Medicine
- Research VUmc Intensive Care (REVIVE)
- Amsterdam Cardiovascular Sciences (ACS)
- Amsterdam Medical Data Science (AMDS), Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Lucas M Fleuren
- Department of Intensive Care Medicine
- Research VUmc Intensive Care (REVIVE)
- Amsterdam Cardiovascular Sciences (ACS)
- Amsterdam Medical Data Science (AMDS), Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Rob J Bosman
- Intensive Care Unit, OLVG Oost, Amsterdam, The Netherlands
| | | | - Armand R J Girbes
- Department of Intensive Care Medicine
- Research VUmc Intensive Care (REVIVE)
- Amsterdam Cardiovascular Sciences (ACS)
- Amsterdam Medical Data Science (AMDS), Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Ron A A Mathot
- Department of Pharmacy, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Paul W G Elbers
- Department of Intensive Care Medicine
- Research VUmc Intensive Care (REVIVE)
- Amsterdam Cardiovascular Sciences (ACS)
- Amsterdam Medical Data Science (AMDS), Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Johan G C van Hasselt
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, The Netherlands
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