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Population pharmacokinetics and evaluation of the predictive performance of pharmacokinetic models in critically ill patients receiving continuous infusion meropenem: a comparison of eight pharmacokinetic models

Abstract Background Several population pharmacokinetic (PopPK) models for meropenem dosing in ICU patients are available. It is not known to what extent these models can predict meropenem concentrations in an independent validation dataset when meropenem is infused continuously. Patients and methods...

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Published in:Journal of antimicrobial chemotherapy 2019-02, Vol.74 (2), p.432-441
Main Authors: Dhaese, Sofie A M, Farkas, Andras, Colin, Pieter, Lipman, Jeffrey, Stove, Veronique, Verstraete, Alain G, Roberts, Jason A, De Waele, Jan J
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cited_by cdi_FETCH-LOGICAL-c353t-c0009496504b7f29727a2b6662f84741107667bdb8038e79a3fd477337a25aca3
cites cdi_FETCH-LOGICAL-c353t-c0009496504b7f29727a2b6662f84741107667bdb8038e79a3fd477337a25aca3
container_end_page 441
container_issue 2
container_start_page 432
container_title Journal of antimicrobial chemotherapy
container_volume 74
creator Dhaese, Sofie A M
Farkas, Andras
Colin, Pieter
Lipman, Jeffrey
Stove, Veronique
Verstraete, Alain G
Roberts, Jason A
De Waele, Jan J
description Abstract Background Several population pharmacokinetic (PopPK) models for meropenem dosing in ICU patients are available. It is not known to what extent these models can predict meropenem concentrations in an independent validation dataset when meropenem is infused continuously. Patients and methods A PopPK model was developed with concentration–time data collected from routine care of 21 ICU patients (38 samples) receiving continuous infusion meropenem. The predictability of this model and seven other published PopPK models was studied using an independent dataset that consisted of 47 ICU patients (161 samples) receiving continuous infusion meropenem. A statistical comparison of imprecision (mean square prediction error) and bias (mean prediction error) was conducted. Results A one-compartment model with linear elimination and creatinine clearance as a covariate of clearance best described our data. The mean ± SD parameter estimate for CL was 9.89 ± 3.71 L/h. The estimated volume of distribution was 48.1 L. The different PopPK models showed a bias in predicting serum concentrations from the validation dataset that ranged from −8.76 to 7.06 mg/L. Imprecision ranged from 9.90 to 42.1 mg/L. Conclusions Published PopPK models for meropenem vary considerably in their predictive performance when validated in an external dataset of ICU patients receiving continuous infusion meropenem. It is necessary to validate PopPK models in a target population before implementing them in a therapeutic drug monitoring program aimed at optimizing meropenem dosing.
doi_str_mv 10.1093/jac/dky434
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It is not known to what extent these models can predict meropenem concentrations in an independent validation dataset when meropenem is infused continuously. Patients and methods A PopPK model was developed with concentration–time data collected from routine care of 21 ICU patients (38 samples) receiving continuous infusion meropenem. The predictability of this model and seven other published PopPK models was studied using an independent dataset that consisted of 47 ICU patients (161 samples) receiving continuous infusion meropenem. A statistical comparison of imprecision (mean square prediction error) and bias (mean prediction error) was conducted. Results A one-compartment model with linear elimination and creatinine clearance as a covariate of clearance best described our data. The mean ± SD parameter estimate for CL was 9.89 ± 3.71 L/h. The estimated volume of distribution was 48.1 L. The different PopPK models showed a bias in predicting serum concentrations from the validation dataset that ranged from −8.76 to 7.06 mg/L. Imprecision ranged from 9.90 to 42.1 mg/L. Conclusions Published PopPK models for meropenem vary considerably in their predictive performance when validated in an external dataset of ICU patients receiving continuous infusion meropenem. It is necessary to validate PopPK models in a target population before implementing them in a therapeutic drug monitoring program aimed at optimizing meropenem dosing.</description><identifier>ISSN: 0305-7453</identifier><identifier>EISSN: 1460-2091</identifier><identifier>DOI: 10.1093/jac/dky434</identifier><identifier>PMID: 30376103</identifier><language>eng</language><publisher>England: Oxford University Press</publisher><subject>Adult ; Aged ; Anti-Bacterial Agents - administration &amp; dosage ; Anti-Bacterial Agents - pharmacokinetics ; Critical Illness ; Drug Monitoring ; Female ; Humans ; Infusions, Intravenous ; Male ; Meropenem - administration &amp; dosage ; Meropenem - pharmacokinetics ; Middle Aged ; Models, Biological</subject><ispartof>Journal of antimicrobial chemotherapy, 2019-02, Vol.74 (2), p.432-441</ispartof><rights>The Author(s) 2018. Published by Oxford University Press on behalf of the British Society for Antimicrobial Chemotherapy. All rights reserved. For permissions, please email: journals.permissions@oup.com. 2018</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c353t-c0009496504b7f29727a2b6662f84741107667bdb8038e79a3fd477337a25aca3</citedby><cites>FETCH-LOGICAL-c353t-c0009496504b7f29727a2b6662f84741107667bdb8038e79a3fd477337a25aca3</cites><orcidid>0000-0002-0956-3315 ; 0000-0001-6218-435X ; 0000-0002-2252-7167</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30376103$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Dhaese, Sofie A M</creatorcontrib><creatorcontrib>Farkas, Andras</creatorcontrib><creatorcontrib>Colin, Pieter</creatorcontrib><creatorcontrib>Lipman, Jeffrey</creatorcontrib><creatorcontrib>Stove, Veronique</creatorcontrib><creatorcontrib>Verstraete, Alain G</creatorcontrib><creatorcontrib>Roberts, Jason A</creatorcontrib><creatorcontrib>De Waele, Jan J</creatorcontrib><title>Population pharmacokinetics and evaluation of the predictive performance of pharmacokinetic models in critically ill patients receiving continuous infusion meropenem: a comparison of eight pharmacokinetic models</title><title>Journal of antimicrobial chemotherapy</title><addtitle>J Antimicrob Chemother</addtitle><description>Abstract Background Several population pharmacokinetic (PopPK) models for meropenem dosing in ICU patients are available. It is not known to what extent these models can predict meropenem concentrations in an independent validation dataset when meropenem is infused continuously. Patients and methods A PopPK model was developed with concentration–time data collected from routine care of 21 ICU patients (38 samples) receiving continuous infusion meropenem. The predictability of this model and seven other published PopPK models was studied using an independent dataset that consisted of 47 ICU patients (161 samples) receiving continuous infusion meropenem. A statistical comparison of imprecision (mean square prediction error) and bias (mean prediction error) was conducted. Results A one-compartment model with linear elimination and creatinine clearance as a covariate of clearance best described our data. The mean ± SD parameter estimate for CL was 9.89 ± 3.71 L/h. The estimated volume of distribution was 48.1 L. The different PopPK models showed a bias in predicting serum concentrations from the validation dataset that ranged from −8.76 to 7.06 mg/L. Imprecision ranged from 9.90 to 42.1 mg/L. Conclusions Published PopPK models for meropenem vary considerably in their predictive performance when validated in an external dataset of ICU patients receiving continuous infusion meropenem. It is necessary to validate PopPK models in a target population before implementing them in a therapeutic drug monitoring program aimed at optimizing meropenem dosing.</description><subject>Adult</subject><subject>Aged</subject><subject>Anti-Bacterial Agents - administration &amp; dosage</subject><subject>Anti-Bacterial Agents - pharmacokinetics</subject><subject>Critical Illness</subject><subject>Drug Monitoring</subject><subject>Female</subject><subject>Humans</subject><subject>Infusions, Intravenous</subject><subject>Male</subject><subject>Meropenem - administration &amp; dosage</subject><subject>Meropenem - pharmacokinetics</subject><subject>Middle Aged</subject><subject>Models, Biological</subject><issn>0305-7453</issn><issn>1460-2091</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNp9kcFuFSEUhonRtLe1mz5Aw8bENBkLAwMz3ZmmVZMmutD1hGHO9NIygMDc5D6nLyQ3U12Y1BWE8_GdAz9C55R8oKRjV49KX41Pe874K7ShXJCqJh19jTaEkaaSvGHH6CSlR0KIaER7hI4ZYVJQwjbo1zcfFquy8Q6HrYqz0v7JOMhGJ6zciGGn7LLW_YTzFnCIMBqdza5sIU6-3HEaDtV_BHj2I9iEjcM6mnKgrN1jYy0ORQguJxxBg9kZ94C1d9m4xS8HflrSoeEM0QdwMF9jVYA5qGjSOgiYh21-oeFb9GZSNsHZ83qKftzdfr_5XN1__fTl5uN9pVnDcqXLf3S8Ew3hg5zqTtZS1YMQop5aLjmlRAohh3FoCWtBdopNI5eSsYI1Sit2it6v3hD9zwVS7meTNFirHJR39DWtJe1aIpuCXq6ojj6lCFMfoplV3PeU9IcQ-xJiv4ZY4Itn7zLMMP5F_6RWgHcr4JfwP9FvJoCrsQ</recordid><startdate>20190201</startdate><enddate>20190201</enddate><creator>Dhaese, Sofie A M</creator><creator>Farkas, Andras</creator><creator>Colin, Pieter</creator><creator>Lipman, Jeffrey</creator><creator>Stove, Veronique</creator><creator>Verstraete, Alain G</creator><creator>Roberts, Jason A</creator><creator>De Waele, Jan J</creator><general>Oxford University Press</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-0956-3315</orcidid><orcidid>https://orcid.org/0000-0001-6218-435X</orcidid><orcidid>https://orcid.org/0000-0002-2252-7167</orcidid></search><sort><creationdate>20190201</creationdate><title>Population pharmacokinetics and evaluation of the predictive performance of pharmacokinetic models in critically ill patients receiving continuous infusion meropenem: a comparison of eight pharmacokinetic models</title><author>Dhaese, Sofie A M ; Farkas, Andras ; Colin, Pieter ; Lipman, Jeffrey ; Stove, Veronique ; Verstraete, Alain G ; Roberts, Jason A ; De Waele, Jan J</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c353t-c0009496504b7f29727a2b6662f84741107667bdb8038e79a3fd477337a25aca3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Adult</topic><topic>Aged</topic><topic>Anti-Bacterial Agents - administration &amp; dosage</topic><topic>Anti-Bacterial Agents - pharmacokinetics</topic><topic>Critical Illness</topic><topic>Drug Monitoring</topic><topic>Female</topic><topic>Humans</topic><topic>Infusions, Intravenous</topic><topic>Male</topic><topic>Meropenem - administration &amp; dosage</topic><topic>Meropenem - pharmacokinetics</topic><topic>Middle Aged</topic><topic>Models, Biological</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Dhaese, Sofie A M</creatorcontrib><creatorcontrib>Farkas, Andras</creatorcontrib><creatorcontrib>Colin, Pieter</creatorcontrib><creatorcontrib>Lipman, Jeffrey</creatorcontrib><creatorcontrib>Stove, Veronique</creatorcontrib><creatorcontrib>Verstraete, Alain G</creatorcontrib><creatorcontrib>Roberts, Jason A</creatorcontrib><creatorcontrib>De Waele, Jan J</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of antimicrobial chemotherapy</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Dhaese, Sofie A M</au><au>Farkas, Andras</au><au>Colin, Pieter</au><au>Lipman, Jeffrey</au><au>Stove, Veronique</au><au>Verstraete, Alain G</au><au>Roberts, Jason A</au><au>De Waele, Jan J</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Population pharmacokinetics and evaluation of the predictive performance of pharmacokinetic models in critically ill patients receiving continuous infusion meropenem: a comparison of eight pharmacokinetic models</atitle><jtitle>Journal of antimicrobial chemotherapy</jtitle><addtitle>J Antimicrob Chemother</addtitle><date>2019-02-01</date><risdate>2019</risdate><volume>74</volume><issue>2</issue><spage>432</spage><epage>441</epage><pages>432-441</pages><issn>0305-7453</issn><eissn>1460-2091</eissn><abstract>Abstract Background Several population pharmacokinetic (PopPK) models for meropenem dosing in ICU patients are available. It is not known to what extent these models can predict meropenem concentrations in an independent validation dataset when meropenem is infused continuously. Patients and methods A PopPK model was developed with concentration–time data collected from routine care of 21 ICU patients (38 samples) receiving continuous infusion meropenem. The predictability of this model and seven other published PopPK models was studied using an independent dataset that consisted of 47 ICU patients (161 samples) receiving continuous infusion meropenem. A statistical comparison of imprecision (mean square prediction error) and bias (mean prediction error) was conducted. Results A one-compartment model with linear elimination and creatinine clearance as a covariate of clearance best described our data. The mean ± SD parameter estimate for CL was 9.89 ± 3.71 L/h. The estimated volume of distribution was 48.1 L. The different PopPK models showed a bias in predicting serum concentrations from the validation dataset that ranged from −8.76 to 7.06 mg/L. Imprecision ranged from 9.90 to 42.1 mg/L. Conclusions Published PopPK models for meropenem vary considerably in their predictive performance when validated in an external dataset of ICU patients receiving continuous infusion meropenem. It is necessary to validate PopPK models in a target population before implementing them in a therapeutic drug monitoring program aimed at optimizing meropenem dosing.</abstract><cop>England</cop><pub>Oxford University Press</pub><pmid>30376103</pmid><doi>10.1093/jac/dky434</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0002-0956-3315</orcidid><orcidid>https://orcid.org/0000-0001-6218-435X</orcidid><orcidid>https://orcid.org/0000-0002-2252-7167</orcidid><oa>free_for_read</oa></addata></record>
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subjects Adult
Aged
Anti-Bacterial Agents - administration & dosage
Anti-Bacterial Agents - pharmacokinetics
Critical Illness
Drug Monitoring
Female
Humans
Infusions, Intravenous
Male
Meropenem - administration & dosage
Meropenem - pharmacokinetics
Middle Aged
Models, Biological
title Population pharmacokinetics and evaluation of the predictive performance of pharmacokinetic models in critically ill patients receiving continuous infusion meropenem: a comparison of eight pharmacokinetic models
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