Individualization of piperacillin dosing for critically ill patients: dosing software to optimize antimicrobial therapy.
Identifieur interne : 003509 ( PubMed/Curation ); précédent : 003508; suivant : 003510Individualization of piperacillin dosing for critically ill patients: dosing software to optimize antimicrobial therapy.
Auteurs : T W Felton [Royaume-Uni] ; J A Roberts [Australie] ; T P Lodise [États-Unis] ; M. Van Guilder [États-Unis] ; E. Boselli [France] ; M N Neely [États-Unis] ; W W Hope [Royaume-Uni]Source :
- Antimicrobial agents and chemotherapy [ 1098-6596 ] ; 2014.
Descripteurs français
- KwdFr :
- Acide pénicillanique (administration et posologie), Acide pénicillanique (analogues et dérivés), Acide pénicillanique (pharmacocinétique), Acide pénicillanique (usage thérapeutique), Adolescent, Adulte, Adulte d'âge moyen, Antibactériens (administration et posologie), Antibactériens (pharmacocinétique), Antibactériens (usage thérapeutique), Calcul des posologies, Clairance métabolique, Créatinine (métabolisme), Créatinine (sang), Femelle, Humains, Infections bactériennes (microbiologie), Infections bactériennes (traitement médicamenteux), Jeune adulte, Maladie grave (), Mâle, Médecine individualisée (), Pharmacothérapie assistée par ordinateur, Pipéracilline (administration et posologie), Pipéracilline (pharmacocinétique), Pipéracilline (usage thérapeutique), Pseudomonas aeruginosa (), Sujet âgé, Sujet âgé de 80 ans ou plus, Tests de sensibilité microbienne, Théorème de Bayes.
- MESH :
- administration et posologie : Acide pénicillanique, Antibactériens, Pipéracilline.
- analogues et dérivés : Acide pénicillanique.
- microbiologie : Infections bactériennes.
- métabolisme : Créatinine.
- pharmacocinétique : Acide pénicillanique, Antibactériens, Pipéracilline.
- sang : Créatinine.
- traitement médicamenteux : Infections bactériennes.
- usage thérapeutique : Acide pénicillanique, Antibactériens, Pipéracilline.
- Adolescent, Adulte, Adulte d'âge moyen, Calcul des posologies, Clairance métabolique, Femelle, Humains, Jeune adulte, Maladie grave, Mâle, Médecine individualisée, Pharmacothérapie assistée par ordinateur, Pseudomonas aeruginosa, Sujet âgé, Sujet âgé de 80 ans ou plus, Tests de sensibilité microbienne, Théorème de Bayes.
English descriptors
- KwdEn :
- Adolescent, Adult, Aged, Aged, 80 and over, Anti-Bacterial Agents (administration & dosage), Anti-Bacterial Agents (pharmacokinetics), Anti-Bacterial Agents (therapeutic use), Bacterial Infections (drug therapy), Bacterial Infections (microbiology), Bayes Theorem, Creatinine (blood), Creatinine (metabolism), Critical Illness (therapy), Drug Dosage Calculations, Drug Therapy, Computer-Assisted, Female, Humans, Male, Metabolic Clearance Rate, Microbial Sensitivity Tests, Middle Aged, Penicillanic Acid (administration & dosage), Penicillanic Acid (analogs & derivatives), Penicillanic Acid (pharmacokinetics), Penicillanic Acid (therapeutic use), Piperacillin (administration & dosage), Piperacillin (pharmacokinetics), Piperacillin (therapeutic use), Precision Medicine (methods), Pseudomonas aeruginosa (drug effects), Young Adult.
- MESH :
- chemical , administration & dosage : Anti-Bacterial Agents, Penicillanic Acid, Piperacillin.
- chemical , analogs & derivatives : Penicillanic Acid.
- chemical , blood : Creatinine.
- chemical , metabolism : Creatinine.
- chemical , pharmacokinetics : Anti-Bacterial Agents, Penicillanic Acid, Piperacillin.
- chemical , therapeutic use : Anti-Bacterial Agents, Penicillanic Acid, Piperacillin.
- drug effects : Pseudomonas aeruginosa.
- drug therapy : Bacterial Infections.
- methods : Precision Medicine.
- microbiology : Bacterial Infections.
- therapy : Critical Illness.
- Adolescent, Adult, Aged, Aged, 80 and over, Bayes Theorem, Drug Dosage Calculations, Drug Therapy, Computer-Assisted, Female, Humans, Male, Metabolic Clearance Rate, Microbial Sensitivity Tests, Middle Aged, Young Adult.
Abstract
Piperacillin-tazobactam is frequently used for empirical and targeted therapy of infections in critically ill patients. Considerable pharmacokinetic (PK) variability is observed in critically ill patients. By estimating an individual's PK, dosage optimization Bayesian estimation techniques can be used to calculate the appropriate piperacillin regimen to achieve desired drug exposure targets. The aim of this study was to establish a population PK model for piperacillin in critically ill patients and then analyze the performance of the model in the dose optimization software program BestDose. Linear, with estimated creatinine clearance and weight as covariates, Michaelis-Menten (MM) and parallel linear/MM structural models were fitted to the data from 146 critically ill patients with nosocomial infection. Piperacillin concentrations measured in the first dosing interval, from each of 8 additional individuals, combined with the population model were embedded into the dose optimization software. The impact of the number of observations was assessed. Precision was assessed by (i) the predicted piperacillin dosage and by (ii) linear regression of the observed-versus-predicted piperacillin concentrations from the second 24 h of treatment. We found that a linear clearance model with creatinine clearance and weight as covariates for drug clearance and volume of distribution, respectively, best described the observed data. When there were at least two observed piperacillin concentrations, the dose optimization software predicted a mean piperacillin dosage of 4.02 g in the 8 patients administered piperacillin doses of 4.00 g. Linear regression of the observed-versus-predicted piperacillin concentrations for 8 individuals after 24 h of piperacillin dosing demonstrated an r(2) of >0.89. In conclusion, for most critically ill patients, individualized piperacillin regimens delivering a target serum piperacillin concentration is achievable. Further validation of the dosage optimization software in a clinical trial is required.
DOI: 10.1128/AAC.02664-14
PubMed: 24798288
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Clearance Rate</term><term>Microbial Sensitivity Tests</term><term>Middle Aged</term><term>Young Adult</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Adolescent</term><term>Adulte</term><term>Adulte d'âge moyen</term><term>Calcul des posologies</term><term>Clairance métabolique</term><term>Femelle</term><term>Humains</term><term>Jeune adulte</term><term>Maladie grave</term><term>Mâle</term><term>Médecine individualisée</term><term>Pharmacothérapie assistée par ordinateur</term><term>Pseudomonas aeruginosa</term><term>Sujet âgé</term><term>Sujet âgé de 80 ans ou plus</term><term>Tests de sensibilité microbienne</term><term>Théorème de Bayes</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Piperacillin-tazobactam is frequently used for empirical and targeted therapy of infections in critically ill patients. Considerable pharmacokinetic (PK) variability is observed in critically ill patients. By estimating an individual's PK, dosage optimization Bayesian estimation techniques can be used to calculate the appropriate piperacillin regimen to achieve desired drug exposure targets. The aim of this study was to establish a population PK model for piperacillin in critically ill patients and then analyze the performance of the model in the dose optimization software program BestDose. Linear, with estimated creatinine clearance and weight as covariates, Michaelis-Menten (MM) and parallel linear/MM structural models were fitted to the data from 146 critically ill patients with nosocomial infection. Piperacillin concentrations measured in the first dosing interval, from each of 8 additional individuals, combined with the population model were embedded into the dose optimization software. The impact of the number of observations was assessed. Precision was assessed by (i) the predicted piperacillin dosage and by (ii) linear regression of the observed-versus-predicted piperacillin concentrations from the second 24 h of treatment. We found that a linear clearance model with creatinine clearance and weight as covariates for drug clearance and volume of distribution, respectively, best described the observed data. When there were at least two observed piperacillin concentrations, the dose optimization software predicted a mean piperacillin dosage of 4.02 g in the 8 patients administered piperacillin doses of 4.00 g. Linear regression of the observed-versus-predicted piperacillin concentrations for 8 individuals after 24 h of piperacillin dosing demonstrated an r(2) of >0.89. In conclusion, for most critically ill patients, individualized piperacillin regimens delivering a target serum piperacillin concentration is achievable. Further validation of the dosage optimization software in a clinical trial is required.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">24798288</PMID><DateCreated><Year>2014</Year><Month>07</Month><Day>29</Day></DateCreated><DateCompleted><Year>2015</Year><Month>09</Month><Day>10</Day></DateCompleted><DateRevised><Year>2017</Year><Month>09</Month><Day>22</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1098-6596</ISSN><JournalIssue CitedMedium="Internet"><Volume>58</Volume><Issue>7</Issue><PubDate><Year>2014</Year><Month>Jul</Month></PubDate></JournalIssue><Title>Antimicrobial agents and chemotherapy</Title><ISOAbbreviation>Antimicrob. Agents Chemother.</ISOAbbreviation></Journal><ArticleTitle>Individualization of piperacillin dosing for critically ill patients: dosing software to optimize antimicrobial therapy.</ArticleTitle><Pagination><MedlinePgn>4094-102</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1128/AAC.02664-14</ELocationID><Abstract><AbstractText>Piperacillin-tazobactam is frequently used for empirical and targeted therapy of infections in critically ill patients. Considerable pharmacokinetic (PK) variability is observed in critically ill patients. By estimating an individual's PK, dosage optimization Bayesian estimation techniques can be used to calculate the appropriate piperacillin regimen to achieve desired drug exposure targets. The aim of this study was to establish a population PK model for piperacillin in critically ill patients and then analyze the performance of the model in the dose optimization software program BestDose. Linear, with estimated creatinine clearance and weight as covariates, Michaelis-Menten (MM) and parallel linear/MM structural models were fitted to the data from 146 critically ill patients with nosocomial infection. Piperacillin concentrations measured in the first dosing interval, from each of 8 additional individuals, combined with the population model were embedded into the dose optimization software. The impact of the number of observations was assessed. Precision was assessed by (i) the predicted piperacillin dosage and by (ii) linear regression of the observed-versus-predicted piperacillin concentrations from the second 24 h of treatment. We found that a linear clearance model with creatinine clearance and weight as covariates for drug clearance and volume of distribution, respectively, best described the observed data. When there were at least two observed piperacillin concentrations, the dose optimization software predicted a mean piperacillin dosage of 4.02 g in the 8 patients administered piperacillin doses of 4.00 g. Linear regression of the observed-versus-predicted piperacillin concentrations for 8 individuals after 24 h of piperacillin dosing demonstrated an r(2) of >0.89. In conclusion, for most critically ill patients, individualized piperacillin regimens delivering a target serum piperacillin concentration is achievable. Further validation of the dosage optimization software in a clinical trial is required.</AbstractText><CopyrightInformation>Copyright © 2014 Felton et al.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Felton</LastName><ForeName>T W</ForeName><Initials>TW</Initials><AffiliationInfo><Affiliation>The University of Manchester, Manchester Academic Health Science Centre, NIHR Clinical Research Facility in Respiratory Medicine, University Hospital of South Manchester NHS Foundation Trust, Manchester, United Kingdom.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Roberts</LastName><ForeName>J A</ForeName><Initials>JA</Initials><AffiliationInfo><Affiliation>Burns Trauma and Critical Care Research Centre, The University of Queensland, Brisbane and Royal Brisbane and Women's Hospital Brisbane, Queensland, Australia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Lodise</LastName><ForeName>T P</ForeName><Initials>TP</Initials><AffiliationInfo><Affiliation>Albany College of Pharmacy and Health Sciences, Albany, New York, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Van Guilder</LastName><ForeName>M</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Laboratory of Applied Pharmacokinetics, University of Southern California, School of Medicine, Los Angeles, California, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Boselli</LastName><ForeName>E</ForeName><Initials>E</Initials><AffiliationInfo><Affiliation>Department of Anaesthesiology and Intensive Care, Édouard Herriot Hospital, Hospices Civils de Lyon, Lyon, France.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Neely</LastName><ForeName>M N</ForeName><Initials>MN</Initials><AffiliationInfo><Affiliation>Laboratory of Applied Pharmacokinetics, University of Southern California, School of Medicine, Los Angeles, California, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Hope</LastName><ForeName>W W</ForeName><Initials>WW</Initials><AffiliationInfo><Affiliation>Department of Molecular and Clinical Pharmacology, University of Liverpool, Liverpool, United Kingdom william.hope@liverpool.ac.uk.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>R01 HD070886</GrantID><Acronym>HD</Acronym><Agency>NICHD NIH HHS</Agency><Country>United States</Country></Grant><Grant><GrantID>CS/08/08/10</GrantID><Agency>Department of Health</Agency><Country>United Kingdom</Country></Grant><Grant><GrantID>G1000417/94909</GrantID><Agency>Medical Research Council</Agency><Country>United Kingdom</Country></Grant><Grant><GrantID>G1000417</GrantID><Agency>Medical Research Council</Agency><Country>United Kingdom</Country></Grant><Grant><GrantID>R01 GM068968</GrantID><Acronym>GM</Acronym><Agency>NIGMS NIH HHS</Agency><Country>United States</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D052061">Research Support, N.I.H., Extramural</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2014</Year><Month>05</Month><Day>05</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>Antimicrob Agents Chemother</MedlineTA><NlmUniqueID>0315061</NlmUniqueID><ISSNLinking>0066-4804</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D000900">Anti-Bacterial Agents</NameOfSubstance></Chemical><Chemical><RegistryNumber>157044-21-8</RegistryNumber><NameOfSubstance UI="C085143">piperacillin, tazobactam drug combination</NameOfSubstance></Chemical><Chemical><RegistryNumber>87-53-6</RegistryNumber><NameOfSubstance UI="D010397">Penicillanic Acid</NameOfSubstance></Chemical><Chemical><RegistryNumber>AYI8EX34EU</RegistryNumber><NameOfSubstance UI="D003404">Creatinine</NameOfSubstance></Chemical><Chemical><RegistryNumber>X00B0D5O0E</RegistryNumber><NameOfSubstance UI="D010878">Piperacillin</NameOfSubstance></Chemical></ChemicalList><CitationSubset>IM</CitationSubset><CommentsCorrectionsList><CommentsCorrections RefType="Cites"><RefSource>Intensive Care Med. 2002 Jul;28(7):936-42</RefSource><PMID Version="1">12122533</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Antimicrob Agents Chemother. 2013 Dec;57(12):5811-9</RefSource><PMID Version="1">24002098</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Nat Rev Microbiol. 2004 Apr;2(4):289-300</RefSource><PMID Version="1">15031728</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Nephron. 1976;16(1):31-41</RefSource><PMID Version="1">1244564</PMID></CommentsCorrections><CommentsCorrections 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