1
|
Hughes MSA, Hughes JH, Endicott J, Langton M, Ahern JW, Keizer RJ. Developing Parametric and Nonparametric Models for Model-Informed Precision Dosing: A Quality Improvement Effort in Vancomycin for Patients With Obesity. Ther Drug Monit 2024; 46:575-583. [PMID: 38758633 PMCID: PMC11389886 DOI: 10.1097/ftd.0000000000001214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 03/02/2024] [Indexed: 05/19/2024]
Abstract
BACKGROUND Both parametric and nonparametric methods have been proposed to support model-informed precision dosing (MIPD). However, which approach leads to better models remains uncertain. Using open-source software, these 2 statistical approaches for model development were compared using the pharmacokinetics of vancomycin in a challenging subpopulation of class 3 obesity. METHODS Patients on vancomycin at the University of Vermont Medical Center from November 1, 2021, to February 14, 2023, were entered into the MIPD software. The inclusion criteria were body mass index (BMI) of at least 40 kg/m 2 and 1 or more vancomycin levels. A parametric model was created using nlmixr2/NONMEM, and a nonparametric model was created using Pmetrics. Then, a priori and a posteriori predictions were evaluated using the normalized root mean squared error (nRMSE) for precision and the mean percentage error (MPE) for bias. The parametric model was evaluated in a simulated MIPD context using an external validation dataset. RESULTS In total, 83 patients were included in the model development, with a median age of 56.6 years (range: 24-89 years), and a median BMI of 46.3 kg/m 2 (range: 40-70.3 kg/m 2 ). Both parametric and nonparametric models were 2-compartmental, with creatinine clearance and fat-free mass as covariates to clearance and volume parameters, respectively. The a priori MPE and nRMSE for the parametric versus nonparametric models were -6.3% versus 2.69% and 27.2% versus 30.7%, respectively. The a posteriori MPE and RMSE were 0.16% and 0.84%, and 13.8% and 13.1%. The parametric model matched or outperformed previously published models on an external validation dataset (n = 576 patients). CONCLUSIONS Minimal differences were found in the model structure and predictive error between the parametric and nonparametric approaches for modeling vancomycin class 3 obesity. However, the parametric model outperformed several other models, suggesting that institution-specific models may improve pharmacokinetics management.
Collapse
Affiliation(s)
| | | | | | - Meagan Langton
- University of Vermont Medical Center, Burlington, Vermont
| | - John W Ahern
- University of Vermont Medical Center, Burlington, Vermont
| | | |
Collapse
|
2
|
Alsultan A, Dasuqi SA, Almohaizeie A, Aljutayli A, Aljamaan F, Omran RA, Alolayan A, Hamad MA, Alotaibi H, Altamimi S, Alghanem SS. External Validation of Obese/Critically Ill Vancomycin Population Pharmacokinetic Models in Critically Ill Patients Who Are Obese. J Clin Pharmacol 2024; 64:353-361. [PMID: 37862131 DOI: 10.1002/jcph.2375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 10/17/2023] [Indexed: 10/22/2023]
Abstract
Obesity combined with critical illness might increase the risk of acquiring infections and hence mortality. In this patient population the pharmacokinetics of antimicrobials vary significantly, making antimicrobial dosing challenging. The objective of this study was to assess the predictive performance of published population pharmacokinetic models of vancomycin in patients who are critically ill or obese for a cohort of critically ill patients who are obese. This was a multi-center retrospective study conducted at 2 hospitals. Adult patients with a body mass index of ≥30 kg/m2 were included. PubMed was searched for published population pharmacokinetic studies in patients who were critically ill or obese. External validation was performed using Monolix software. A total of 4 models were identified in patients who were obese and 5 models were identified in patients who were critically ill. In total, 138 patients who were critically ill and obese were included, and the most accurate models for these patients were the Goti and Roberts models. In our analysis, models in patients who were critically ill outperformed models in patients who were obese. When looking at the most accurate models, both the Goti and the Roberts models had patient characteristics similar to ours in terms of age and creatinine clearance. This indicates that when selecting the proper model to apply in practice, it is important to account for all relevant variables, besides obesity.
Collapse
Affiliation(s)
- Abdullah Alsultan
- Department of Clinical Pharmacy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
| | - Shereen A Dasuqi
- Department of Pharmacy, King Khalid University Hospital, King Saud University Medical City, Riyadh, Saudi Arabia
| | - Abdullah Almohaizeie
- Pharmaceutical Care Division, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Abdullah Aljutayli
- Department of Pharmaceutics, Faculty of Pharmacy, Qassim University, Riyadh, Saudi Arabia
| | - Fadi Aljamaan
- College of Medicine, King Saud University, Riyadh, Saudi Arabia
- Critical Care Department, King Saud University Medical City, King Saud University, Riyadh, Saudi Arabia
| | - Rasha A Omran
- Department of Pharmaceutics and Pharmaceutical Technology, School of Pharmacy, University of Jordan, Amman, Jordan
| | - Abdulaziz Alolayan
- Pharmacy Department, Prince Sultan Military Medical City, Riyadh, Kingdom of Saudi Arabia, Riyadh, Saudi Arabia
| | - Mohammed A Hamad
- Critical Care Department, King Saud University Medical City, King Saud University, Riyadh, Saudi Arabia
- Department of Acute Medicine, Wirral University Teaching Hospital NHS Foundation Trust, Arrowe Park Hospital, Wirral, UK
| | - Haifa Alotaibi
- Pharmaceutical Care Division, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Sarah Altamimi
- Pharmaceutical Care Division, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Sarah S Alghanem
- Department of Pharmacy Practice, College of Pharmacy at Kuwait University, Safat, Kuwait
| |
Collapse
|
3
|
Verhaeghe J, Dhaese SAM, De Corte T, Vander Mijnsbrugge D, Aardema H, Zijlstra JG, Verstraete AG, Stove V, Colin P, Ongenae F, De Waele JJ, Van Hoecke S. Development and evaluation of uncertainty quantifying machine learning models to predict piperacillin plasma concentrations in critically ill patients. BMC Med Inform Decis Mak 2022; 22:224. [PMID: 36008808 PMCID: PMC9404625 DOI: 10.1186/s12911-022-01970-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Accepted: 08/10/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Beta-lactam antimicrobial concentrations are frequently suboptimal in critically ill patients. Population pharmacokinetic (PopPK) modeling is the golden standard to predict drug concentrations. However, currently available PopPK models often lack predictive accuracy, making them less suited to guide dosing regimen adaptations. Furthermore, many currently developed models for clinical applications often lack uncertainty quantification. We, therefore, aimed to develop machine learning (ML) models for the prediction of piperacillin plasma concentrations while also providing uncertainty quantification with the aim of clinical practice. METHODS Blood samples for piperacillin analysis were prospectively collected from critically ill patients receiving continuous infusion of piperacillin/tazobactam. Interpretable ML models for the prediction of piperacillin concentrations were designed using CatBoost and Gaussian processes. Distribution-based Uncertainty Quantification was added to the CatBoost model using a proposed Quantile Ensemble method, useable for any model optimizing a quantile function. These models are subsequently evaluated using the distribution coverage error, a proposed interpretable uncertainty quantification calibration metric. Development and internal evaluation of the ML models were performed on the Ghent University Hospital database (752 piperacillin concentrations from 282 patients). Ensuing, ML models were compared with a published PopPK model on a database from the University Medical Centre of Groningen where a different dosing regimen is used (46 piperacillin concentrations from 15 patients.). RESULTS The best performing model was the Catboost model with an RMSE and [Formula: see text] of 31.94-0.64 and 33.53-0.60 for internal evaluation with and without previous concentration. Furthermore, the results prove the added value of the proposed Quantile Ensemble model in providing clinically useful individualized uncertainty predictions and show the limits of homoscedastic methods like Gaussian Processes in clinical applications. CONCLUSIONS Our results show that ML models can consistently estimate piperacillin concentrations with acceptable and high predictive accuracy when identical dosing regimens as in the training data are used while providing highly relevant uncertainty predictions. However, generalization capabilities to other dosing schemes are limited. Notwithstanding, incorporating ML models in therapeutic drug monitoring programs seems definitely promising and the current work provides a basis for validating the model in clinical practice.
Collapse
Affiliation(s)
- Jarne Verhaeghe
- IDLab, Department of Information Technology, Ghent University - imec, Ghent, Belgium.
| | - Sofie A M Dhaese
- Department of Internal Medicine and Pediatrics, Ghent University, Ghent, Belgium
| | - Thomas De Corte
- Department of Internal Medicine and Pediatrics, Ghent University, Ghent, Belgium
| | | | - Heleen Aardema
- Department of Critical Care, University Medical Center Groningen, Groningen, The Netherlands
| | - Jan G Zijlstra
- Department of Critical Care, University Medical Center Groningen, Groningen, The Netherlands
| | | | - Veronique Stove
- Department of Diagnostic Sciences, Ghent University, Ghent, Belgium
| | - Pieter Colin
- Department of Anesthesiology, University Medical Center Groningen, Groningen, The Netherlands
| | - Femke Ongenae
- IDLab, Department of Information Technology, Ghent University - imec, Ghent, Belgium
| | - Jan J De Waele
- Department of Critical Care Medicine, Ghent University Hospital, Ghent, Belgium
| | - Sofie Van Hoecke
- IDLab, Department of Information Technology, Ghent University - imec, Ghent, Belgium.
| |
Collapse
|
4
|
Reverchon J, Tuloup V, Garreau R, Nave V, Cohen S, Reix P, Durupt S, Nove-Josserand R, Durieu I, Reynaud Q, Bourguignon L, Charles S, Goutelle S. Implementation of Model-Based Dose Adjustment of Tobramycin in Adult Patients with Cystic Fibrosis. Pharmaceutics 2022; 14:pharmaceutics14081750. [PMID: 36015375 PMCID: PMC9415544 DOI: 10.3390/pharmaceutics14081750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 08/04/2022] [Accepted: 08/10/2022] [Indexed: 11/16/2022] Open
Abstract
Therapeutic drug monitoring (TDM) of tobramycin is widely performed in patients with cystic fibrosis (CF), but little is known about the value of model-informed precision dosing (MIPD) in this setting. We aim at reporting our experience with tobramycin MIPD in adult patients with CF. We analyzed data from adult patients with CF who received IV tobramycin and had model-guided TDM during the first year of implementation of MIPD. The predictive performance of a pharmacokinetic (PK) model was assessed. Observed maximal (Cmax) and minimal (Cmin) concentrations after initial dosing were compared with target values. We compared the initial doses and adjusted doses after model-based TDM, as well as renal function at the beginning and end of therapy. A total of 78 tobramycin courses were administered in 61 patients. After initial dosing set by physicians (mean, 9.2 ± 1.4 mg/kg), 68.8% of patients did not achieve the target Cmax ≥ 30 mg/L. The PK model fit the data very well, with a median absolute percentage error of 4.9%. MIPD was associated with a significant increase in tobramycin doses (p < 0.001) without significant change in renal function. Model-based dose suggestions were wellaccepted by the physicians and the expected target attainment for Cmax was 83%. To conclude, the implementation of MIPD was effective in changing prescribing practice and was not associated with nephrotoxic events in adult patients with CF.
Collapse
Affiliation(s)
- Jérémy Reverchon
- Hospices Civils de Lyon, GH Nord, Service de Pharmacie, 69004 Lyon, France
| | - Vianney Tuloup
- Hospices Civils de Lyon, GH Nord, Service de Pharmacie, 69004 Lyon, France
- Univ Lyon, Université Claude Bernard Lyon 1, UMR CNRS 5558, LBBE—Laboratoire de Biométrie et Biologie Évolutive, 69622 Villeurbanne, France
| | - Romain Garreau
- Hospices Civils de Lyon, GH Nord, Service de Pharmacie, 69004 Lyon, France
- Univ Lyon, Université Claude Bernard Lyon 1, UMR CNRS 5558, LBBE—Laboratoire de Biométrie et Biologie Évolutive, 69622 Villeurbanne, France
| | - Viviane Nave
- Hospices Civils de Lyon, Pharmacie Centrale, 69230 St. Genis Laval, France
| | - Sabine Cohen
- Hospices Civils de Lyon, Groupement Hospitalier Sud, Laboratoire de Pharmaco-Toxicologie, 69495 Pierre-Bénite, France
| | - Philippe Reix
- Univ Lyon, Université Claude Bernard Lyon 1, UMR CNRS 5558, LBBE—Laboratoire de Biométrie et Biologie Évolutive, 69622 Villeurbanne, France
- Hospices Civils de Lyon, Centre de Ressources et de Compétences de la Mucoviscidose, 69500 Bron, France
| | - Stéphane Durupt
- Hospices Civils de Lyon, Centre de Ressources et de Compétences de la Mucoviscidose (Adulte), GH Sud, Service de Médecine Interne, 69495 Pierre-Bénite, France
| | - Raphaele Nove-Josserand
- Hospices Civils de Lyon, Centre de Ressources et de Compétences de la Mucoviscidose (Adulte), GH Sud, Service de Médecine Interne, 69495 Pierre-Bénite, France
| | - Isabelle Durieu
- Hospices Civils de Lyon, Centre de Ressources et de Compétences de la Mucoviscidose (Adulte), GH Sud, Service de Médecine Interne, 69495 Pierre-Bénite, France
- Univ Lyon, Université Claude Bernard Lyon 1, RESHAPE, INSERM U1290, 69008 Lyon, France
| | - Quitterie Reynaud
- Hospices Civils de Lyon, Centre de Ressources et de Compétences de la Mucoviscidose (Adulte), GH Sud, Service de Médecine Interne, 69495 Pierre-Bénite, France
- Univ Lyon, Université Claude Bernard Lyon 1, RESHAPE, INSERM U1290, 69008 Lyon, France
| | - Laurent Bourguignon
- Hospices Civils de Lyon, GH Nord, Service de Pharmacie, 69004 Lyon, France
- Univ Lyon, Université Claude Bernard Lyon 1, UMR CNRS 5558, LBBE—Laboratoire de Biométrie et Biologie Évolutive, 69622 Villeurbanne, France
- Univ Lyon, Université Claude Bernard Lyon 1, ISPB—Faculté de Pharmacie de Lyon, 69008 Lyon, France
| | - Sandrine Charles
- Univ Lyon, Université Claude Bernard Lyon 1, UMR CNRS 5558, LBBE—Laboratoire de Biométrie et Biologie Évolutive, 69622 Villeurbanne, France
| | - Sylvain Goutelle
- Hospices Civils de Lyon, GH Nord, Service de Pharmacie, 69004 Lyon, France
- Univ Lyon, Université Claude Bernard Lyon 1, UMR CNRS 5558, LBBE—Laboratoire de Biométrie et Biologie Évolutive, 69622 Villeurbanne, France
- Univ Lyon, Université Claude Bernard Lyon 1, ISPB—Faculté de Pharmacie de Lyon, 69008 Lyon, France
- Correspondence: ; Tel.: +33-4-7216-8099
| |
Collapse
|
5
|
Wong S, Reuter SE, Jones GR, Stocker SL. Review and evaluation of vancomycin dosing guidelines for obese individuals. Expert Opin Drug Metab Toxicol 2022; 18:323-335. [PMID: 35815356 DOI: 10.1080/17425255.2022.2098106] [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: 11/04/2022]
Abstract
INTRODUCTION Vancomycin dosing decisions are informed by factors such as body weight and renal function. It is important to understand the impact of obesity on vancomycin pharmacokinetics and how this may influence dosing decisions. Vancomycin dosing guidelines use varied descriptors of body weight and renal function. There is uncertainty whether current dosing guidelines result in attainment of therapeutic targets in obese individuals. AREAS COVERED Literature was explored using PubMed, Embase and Google Scholar for articles from January 1980 to July 2021 regarding obesity-driven physiological changes, their influence on vancomycin pharmacokinetics and body size descriptors and renal function calculations in vancomycin dosing. Pharmacokinetic simulations reflective of international vancomycin dosing guidelines were conducted to evaluate the ability of using total, ideal and adjusted body weight, as well as Cockcroft-Gault and CKD-EPI equations to attain an area-under-the-curve to minimum inhibitory concentration ratio (AUC24/MIC) target (400-650) in obese individuals. EXPERT OPINION Vancomycin pharmacokinetics in obese individuals remains debated. Guidelines that determine loading doses using total body weight, and maintenance doses adjusted based on renal function and adjusted body weight, may be most appropriate for obese individuals. Use of ideal body weight leads to subtherapeutic vancomycin exposure and underestimation of renal function.
Collapse
Affiliation(s)
- Sherilyn Wong
- UniSA Clinical and Health Sciences, University of South Australia, Adelaide, Australia
| | - Stephanie E Reuter
- UniSA Clinical and Health Sciences, University of South Australia, Adelaide, Australia
| | - Graham Rd Jones
- St Vincent's Clinical School, Faculty of Medicine, The University of New South Wales, Sydney, Australia.,Department of Chemical Pathology and Clinical Pharmacology, SydPath, St Vincent's Hospital, Darlinghurst, Australia
| | - Sophie L Stocker
- St Vincent's Clinical School, Faculty of Medicine, The University of New South Wales, Sydney, Australia.,Sydney School of Pharmacy, The University of Sydney, Sydney, Australia.,Department of Clinical Pharmacology & Toxicology, St Vincent's Hospital Sydney, Darlinghurst, Australia
| |
Collapse
|
6
|
Oommen T, Thommandram A, Palanica A, Fossat Y. A Free Open-Source Bayesian Vancomycin Dosing App for Adults: Design and Evaluation Study. JMIR Form Res 2022; 6:e30577. [PMID: 35353046 PMCID: PMC9008526 DOI: 10.2196/30577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 10/08/2021] [Accepted: 02/04/2022] [Indexed: 11/23/2022] Open
Abstract
Background It has been suggested that Bayesian dosing apps can assist in the therapeutic drug monitoring of patients receiving vancomycin. Unfortunately, Bayesian dosing tools are often unaffordable to resource-limited hospitals. Our aim was to improve vancomycin dosing in adults. We created a free and open-source dose adjustment app, VancoCalc, which uses Bayesian inference to aid clinicians in dosing and monitoring of vancomycin. Objective The aim of this paper is to describe the design, development, usability, and evaluation of a free open-source Bayesian vancomycin dosing app, VancoCalc. Methods The app build and model fitting process were described. Previously published pharmacokinetic models were used as priors. The ability of the app to predict vancomycin concentrations was performed using a small data set comprising of 52 patients, aged 18 years and over, who received at least 1 dose of intravenous vancomycin and had at least 2 vancomycin concentrations drawn between July 2018 and January 2021 at Lakeridge Health Corporation Ontario, Canada. With these estimated and actual concentrations, median prediction error (bias), median absolute error (accuracy), and root mean square error (precision) were calculated to evaluate the accuracy of the Bayesian estimated pharmacokinetic parameters. Results A total of 52 unique patients’ initial vancomycin concentrations were used to predict subsequent concentration; 104 total vancomycin concentrations were assessed. The median prediction error was –0.600 ug/mL (IQR –3.06, 2.95), the median absolute error was 3.05 ug/mL (IQR 1.44, 4.50), and the root mean square error was 5.34. Conclusions We described a free, open-source Bayesian vancomycin dosing calculator based on revisions of currently available calculators. Based on this small retrospective preliminary sample of patients, the app offers reasonable accuracy and bias, which may be used in everyday practice. By offering this free, open-source app, further prospective validation could be implemented in the near future.
Collapse
Affiliation(s)
| | | | - Adam Palanica
- Klick Applied Sciences, Klick Health, Klick Inc, Toronto, ON, Canada
| | - Yan Fossat
- Klick Applied Sciences, Klick Health, Klick Inc, Toronto, ON, Canada
| |
Collapse
|
7
|
Clinical Practice Guidelines for Therapeutic Drug Monitoring of Vancomycin in the Framework of Model-Informed Precision Dosing: A Consensus Review by the Japanese Society of Chemotherapy and the Japanese Society of Therapeutic Drug Monitoring. Pharmaceutics 2022; 14:pharmaceutics14030489. [PMID: 35335866 PMCID: PMC8955715 DOI: 10.3390/pharmaceutics14030489] [Citation(s) in RCA: 47] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Revised: 02/16/2022] [Accepted: 02/17/2022] [Indexed: 01/08/2023] Open
Abstract
Background: To promote model-informed precision dosing (MIPD) for vancomycin (VCM), we developed statements for therapeutic drug monitoring (TDM). Methods: Ten clinical questions were selected. The committee conducted a systematic review and meta-analysis as well as clinical studies to establish recommendations for area under the concentration-time curve (AUC)-guided dosing. Results: AUC-guided dosing tended to more strongly decrease the risk of acute kidney injury (AKI) than trough-guided dosing, and a lower risk of treatment failure was demonstrated for higher AUC/minimum inhibitory concentration (MIC) ratios (cut-off of 400). Higher AUCs (cut-off of 600 μg·h/mL) significantly increased the risk of AKI. Although Bayesian estimation with two-point measurement was recommended, the trough concentration alone may be used in patients with mild infections in whom VCM was administered with q12h. To increase the concentration on days 1–2, the routine use of a loading dose is required. TDM on day 2 before steady state is reached should be considered to optimize the dose in patients with serious infections and a high risk of AKI. Conclusions: These VCM TDM guidelines provide recommendations based on MIPD to increase treatment response while preventing adverse effects.
Collapse
|
8
|
Vellinga R, Hannivoort LN, Koomen JV, Colin P, Absalom AR, Struys MMRF, Eleveld DJ. Clinical validation of pharmacokinetic/pharmacodynamic models for propofol infusion. Response to Br J Anaesth 2021: 126: e172-4. Br J Anaesth 2021; 127:e3-e5. [PMID: 33934890 DOI: 10.1016/j.bja.2021.03.021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Accepted: 03/26/2021] [Indexed: 11/29/2022] Open
Affiliation(s)
- Remco Vellinga
- Department of Anesthesiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
| | - Laura N Hannivoort
- Department of Anesthesiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Jeroen V Koomen
- Department of Anesthesiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands; Department of Pharmacology, Toxicology and Pharmacokinetics, Medicines Evaluation Board, Utrecht, the Netherlands
| | - Pieter Colin
- Department of Anesthesiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Anthony R Absalom
- Department of Anesthesiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Michel M R F Struys
- Department of Anesthesiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands; Department of Basic and Applied Medical Sciences, Ghent University, Ghent, Belgium
| | - Douglas J Eleveld
- Department of Anesthesiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| |
Collapse
|