Predictive performance of multi-model approaches for model-informed precision dosing of piperacillin in critically ill patients
•Multi-model approaches potentially increase the applicability of MIPD.•No need for individual model selection if at least one TDM-sample is available.•Comparable or better predictive performance compared to best single models.•Model averaging algorithm performs better compared to model selection al...
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| Veröffentlicht in: | International journal of antimicrobial agents 2024-10, Vol.64 (4), p.107305, Article 107305 |
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| creator | Schatz, Lea Marie Greppmair, Sebastian Kunzelmann, Alexandra K. Starp, Johannes Brinkmann, Alexander Roehr, Anka Frey, Otto Hagel, Stefan Dorn, Christoph Zoller, Michael Scharf, Christina Wicha, Sebastian G. Liebchen, Uwe |
| description | •Multi-model approaches potentially increase the applicability of MIPD.•No need for individual model selection if at least one TDM-sample is available.•Comparable or better predictive performance compared to best single models.•Model averaging algorithm performs better compared to model selection algorithm.
Piperacillin (PIP)/tazobactam is a frequently prescribed antibiotic; however, over- or underdosing may contribute to toxicity, therapeutic failure, and development of antimicrobial resistance. An external evaluation of 24 published PIP-models demonstrated that model-informed precision dosing (MIPD) can enhance target attainment. Employing various candidate models, this study aimed to assess the predictive performance of different MIPD-approaches comparing (i) a single-model approach, (ii) a model selection algorithm (MSA) and (iii) a model averaging algorithm (MAA).
Precision, accuracy and expected target attainment, considering either initial (B1) or initial and secondary (B2) therapeutic drug monitoring (TDM)-samples per patient, were assessed in a multicentre dataset (561 patients, 11 German centres, 3654 TDM-samples).
The results demonstrated a slight superiority in predictive performance using MAA in B1, regardless of the candidate models, compared to MSA and the best single models (MAA, MSA, best single models: inaccuracy ±3%, ±10%, ±8%; imprecision: 73%). The inclusion of a second TDM-sample notably improved precision and target attainment for all MIPD-approaches, particularly within the context of MSA and most of the single models. The expected target attainment is maximized (up to >90%) when the TDM-sample is integrated within 24 h.
In conclusion, MAA streamlines MIPD by reducing the risk of selecting an inappropriate model for specific patients. Therefore, MIPD of PIP using MAA implicates further optimisation of antibiotic exposure in critically ill patients, by improving predictive performance with only one sample available for Bayesian forecasting, safety, and usability in clinical practice.
[Display omitted] |
| doi_str_mv | 10.1016/j.ijantimicag.2024.107305 |
| format | Article |
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Piperacillin (PIP)/tazobactam is a frequently prescribed antibiotic; however, over- or underdosing may contribute to toxicity, therapeutic failure, and development of antimicrobial resistance. An external evaluation of 24 published PIP-models demonstrated that model-informed precision dosing (MIPD) can enhance target attainment. Employing various candidate models, this study aimed to assess the predictive performance of different MIPD-approaches comparing (i) a single-model approach, (ii) a model selection algorithm (MSA) and (iii) a model averaging algorithm (MAA).
Precision, accuracy and expected target attainment, considering either initial (B1) or initial and secondary (B2) therapeutic drug monitoring (TDM)-samples per patient, were assessed in a multicentre dataset (561 patients, 11 German centres, 3654 TDM-samples).
The results demonstrated a slight superiority in predictive performance using MAA in B1, regardless of the candidate models, compared to MSA and the best single models (MAA, MSA, best single models: inaccuracy ±3%, ±10%, ±8%; imprecision: <25%, <31%, <28%; expected target attainment >77%, >71%, >73%). The inclusion of a second TDM-sample notably improved precision and target attainment for all MIPD-approaches, particularly within the context of MSA and most of the single models. The expected target attainment is maximized (up to >90%) when the TDM-sample is integrated within 24 h.
In conclusion, MAA streamlines MIPD by reducing the risk of selecting an inappropriate model for specific patients. Therefore, MIPD of PIP using MAA implicates further optimisation of antibiotic exposure in critically ill patients, by improving predictive performance with only one sample available for Bayesian forecasting, safety, and usability in clinical practice.
[Display omitted]</description><identifier>ISSN: 0924-8579</identifier><identifier>ISSN: 1872-7913</identifier><identifier>EISSN: 1872-7913</identifier><identifier>DOI: 10.1016/j.ijantimicag.2024.107305</identifier><identifier>PMID: 39146997</identifier><language>eng</language><publisher>Netherlands: Elsevier Ltd</publisher><subject>Aged ; Algorithms ; Anti-Bacterial Agents - administration & dosage ; Anti-Bacterial Agents - therapeutic use ; Critical Illness ; Critically ill ; Drug Monitoring - methods ; Female ; Germany ; Humans ; Intensive care unit ; Male ; Middle Aged ; Model-informed precision dosing ; Piperacillin - administration & dosage ; Piperacillin - pharmacokinetics ; Piperacillin - therapeutic use ; Piperacillin, Tazobactam Drug Combination - administration & dosage ; Population pharmacokinetics ; Sepsis ; Therapeutic drug monitoring</subject><ispartof>International journal of antimicrobial agents, 2024-10, Vol.64 (4), p.107305, Article 107305</ispartof><rights>2024 The Author(s)</rights><rights>Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c302t-39e789eee46ba1e407f0ea3ee46fd4a303afc38c19362011424af7d6b4072c963</cites><orcidid>0000-0001-9482-5094 ; 0009-0002-9076-3943 ; 0000-0002-2560-9598 ; 0000-0002-8773-4845 ; 0000-0002-2954-5130 ; 0000-0002-4375-0923 ; 0009-0007-9849-9901</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0924857924002218$$EHTML$$P50$$Gelsevier$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65306</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/39146997$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Schatz, Lea Marie</creatorcontrib><creatorcontrib>Greppmair, Sebastian</creatorcontrib><creatorcontrib>Kunzelmann, Alexandra K.</creatorcontrib><creatorcontrib>Starp, Johannes</creatorcontrib><creatorcontrib>Brinkmann, Alexander</creatorcontrib><creatorcontrib>Roehr, Anka</creatorcontrib><creatorcontrib>Frey, Otto</creatorcontrib><creatorcontrib>Hagel, Stefan</creatorcontrib><creatorcontrib>Dorn, Christoph</creatorcontrib><creatorcontrib>Zoller, Michael</creatorcontrib><creatorcontrib>Scharf, Christina</creatorcontrib><creatorcontrib>Wicha, Sebastian G.</creatorcontrib><creatorcontrib>Liebchen, Uwe</creatorcontrib><title>Predictive performance of multi-model approaches for model-informed precision dosing of piperacillin in critically ill patients</title><title>International journal of antimicrobial agents</title><addtitle>Int J Antimicrob Agents</addtitle><description>•Multi-model approaches potentially increase the applicability of MIPD.•No need for individual model selection if at least one TDM-sample is available.•Comparable or better predictive performance compared to best single models.•Model averaging algorithm performs better compared to model selection algorithm.
Piperacillin (PIP)/tazobactam is a frequently prescribed antibiotic; however, over- or underdosing may contribute to toxicity, therapeutic failure, and development of antimicrobial resistance. An external evaluation of 24 published PIP-models demonstrated that model-informed precision dosing (MIPD) can enhance target attainment. Employing various candidate models, this study aimed to assess the predictive performance of different MIPD-approaches comparing (i) a single-model approach, (ii) a model selection algorithm (MSA) and (iii) a model averaging algorithm (MAA).
Precision, accuracy and expected target attainment, considering either initial (B1) or initial and secondary (B2) therapeutic drug monitoring (TDM)-samples per patient, were assessed in a multicentre dataset (561 patients, 11 German centres, 3654 TDM-samples).
The results demonstrated a slight superiority in predictive performance using MAA in B1, regardless of the candidate models, compared to MSA and the best single models (MAA, MSA, best single models: inaccuracy ±3%, ±10%, ±8%; imprecision: <25%, <31%, <28%; expected target attainment >77%, >71%, >73%). The inclusion of a second TDM-sample notably improved precision and target attainment for all MIPD-approaches, particularly within the context of MSA and most of the single models. The expected target attainment is maximized (up to >90%) when the TDM-sample is integrated within 24 h.
In conclusion, MAA streamlines MIPD by reducing the risk of selecting an inappropriate model for specific patients. Therefore, MIPD of PIP using MAA implicates further optimisation of antibiotic exposure in critically ill patients, by improving predictive performance with only one sample available for Bayesian forecasting, safety, and usability in clinical practice.
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Piperacillin (PIP)/tazobactam is a frequently prescribed antibiotic; however, over- or underdosing may contribute to toxicity, therapeutic failure, and development of antimicrobial resistance. An external evaluation of 24 published PIP-models demonstrated that model-informed precision dosing (MIPD) can enhance target attainment. Employing various candidate models, this study aimed to assess the predictive performance of different MIPD-approaches comparing (i) a single-model approach, (ii) a model selection algorithm (MSA) and (iii) a model averaging algorithm (MAA).
Precision, accuracy and expected target attainment, considering either initial (B1) or initial and secondary (B2) therapeutic drug monitoring (TDM)-samples per patient, were assessed in a multicentre dataset (561 patients, 11 German centres, 3654 TDM-samples).
The results demonstrated a slight superiority in predictive performance using MAA in B1, regardless of the candidate models, compared to MSA and the best single models (MAA, MSA, best single models: inaccuracy ±3%, ±10%, ±8%; imprecision: <25%, <31%, <28%; expected target attainment >77%, >71%, >73%). The inclusion of a second TDM-sample notably improved precision and target attainment for all MIPD-approaches, particularly within the context of MSA and most of the single models. The expected target attainment is maximized (up to >90%) when the TDM-sample is integrated within 24 h.
In conclusion, MAA streamlines MIPD by reducing the risk of selecting an inappropriate model for specific patients. Therefore, MIPD of PIP using MAA implicates further optimisation of antibiotic exposure in critically ill patients, by improving predictive performance with only one sample available for Bayesian forecasting, safety, and usability in clinical practice.
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| ispartof | International journal of antimicrobial agents, 2024-10, Vol.64 (4), p.107305, Article 107305 |
| issn | 0924-8579 1872-7913 1872-7913 |
| language | eng |
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| subjects | Aged Algorithms Anti-Bacterial Agents - administration & dosage Anti-Bacterial Agents - therapeutic use Critical Illness Critically ill Drug Monitoring - methods Female Germany Humans Intensive care unit Male Middle Aged Model-informed precision dosing Piperacillin - administration & dosage Piperacillin - pharmacokinetics Piperacillin - therapeutic use Piperacillin, Tazobactam Drug Combination - administration & dosage Population pharmacokinetics Sepsis Therapeutic drug monitoring |
| title | Predictive performance of multi-model approaches for model-informed precision dosing of piperacillin in critically ill patients |
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