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Artificial Intelligence and Amikacin Exposures Predictive of Outcomes in Multidrug-Resistant Tuberculosis Patients
Aminoglycosides such as amikacin continue to be part of the backbone of treatment of multidrug-resistant tuberculosis (MDR-TB). We measured amikacin concentrations in 28 MDR-TB patients in Botswana receiving amikacin therapy together with oral levofloxacin, ethionamide, cycloserine, and pyrazinamide...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Society for Microbiology
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5038293/ https://www.ncbi.nlm.nih.gov/pubmed/27458224 http://dx.doi.org/10.1128/AAC.00962-16 |
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author | Modongo, Chawangwa Pasipanodya, Jotam G. Magazi, Beki T. Srivastava, Shashikant Zetola, Nicola M. Williams, Scott M. Sirugo, Giorgio Gumbo, Tawanda |
author_facet | Modongo, Chawangwa Pasipanodya, Jotam G. Magazi, Beki T. Srivastava, Shashikant Zetola, Nicola M. Williams, Scott M. Sirugo, Giorgio Gumbo, Tawanda |
author_sort | Modongo, Chawangwa |
collection | PubMed |
description | Aminoglycosides such as amikacin continue to be part of the backbone of treatment of multidrug-resistant tuberculosis (MDR-TB). We measured amikacin concentrations in 28 MDR-TB patients in Botswana receiving amikacin therapy together with oral levofloxacin, ethionamide, cycloserine, and pyrazinamide and calculated areas under the concentration-time curves from 0 to 24 h (AUC(0–24)). The patients were followed monthly for sputum culture conversion based on liquid cultures. The median duration of amikacin therapy was 184 (range, 28 to 866) days, at a median dose of 17.30 (range 11.11 to 19.23) mg/kg. Only 11 (39%) patients had sputum culture conversion during treatment; the rest failed. We utilized classification and regression tree analyses (CART) to examine all potential predictors of failure, including clinical and demographic features, comorbidities, and amikacin peak concentrations (C(max)), AUC(0–24), and trough concentrations. The primary node for failure had two competing variables, C(max) of <67 mg/liter and AUC(0–24) of <568.30 mg · h/L; weight of >41 kg was a secondary node with a score of 35% relative to the primary node. The area under the receiver operating characteristic curve for the CART model was an R(2) = 0.90 on posttest. In patients weighing >41 kg, sputum conversion was 3/3 (100%) in those with an amikacin C(max) of ≥67 mg/liter versus 3/15 (20%) in those with a C(max) of <67 mg/liter (relative risk [RR] = 5.00; 95% confidence interval [CI], 1.82 to 13.76). In all patients who had both amikacin C(max) and AUC(0–24) below the threshold, 7/7 (100%) failed, compared to 7/15 (47%) of those who had these parameters above threshold (RR = 2.14; 95% CI, 1.25 to 43.68). These amikacin dose-schedule patterns and exposures are virtually the same as those identified in the hollow-fiber system model. |
format | Online Article Text |
id | pubmed-5038293 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | American Society for Microbiology |
record_format | MEDLINE/PubMed |
spelling | pubmed-50382932016-10-13 Artificial Intelligence and Amikacin Exposures Predictive of Outcomes in Multidrug-Resistant Tuberculosis Patients Modongo, Chawangwa Pasipanodya, Jotam G. Magazi, Beki T. Srivastava, Shashikant Zetola, Nicola M. Williams, Scott M. Sirugo, Giorgio Gumbo, Tawanda Antimicrob Agents Chemother Experimental Therapeutics Aminoglycosides such as amikacin continue to be part of the backbone of treatment of multidrug-resistant tuberculosis (MDR-TB). We measured amikacin concentrations in 28 MDR-TB patients in Botswana receiving amikacin therapy together with oral levofloxacin, ethionamide, cycloserine, and pyrazinamide and calculated areas under the concentration-time curves from 0 to 24 h (AUC(0–24)). The patients were followed monthly for sputum culture conversion based on liquid cultures. The median duration of amikacin therapy was 184 (range, 28 to 866) days, at a median dose of 17.30 (range 11.11 to 19.23) mg/kg. Only 11 (39%) patients had sputum culture conversion during treatment; the rest failed. We utilized classification and regression tree analyses (CART) to examine all potential predictors of failure, including clinical and demographic features, comorbidities, and amikacin peak concentrations (C(max)), AUC(0–24), and trough concentrations. The primary node for failure had two competing variables, C(max) of <67 mg/liter and AUC(0–24) of <568.30 mg · h/L; weight of >41 kg was a secondary node with a score of 35% relative to the primary node. The area under the receiver operating characteristic curve for the CART model was an R(2) = 0.90 on posttest. In patients weighing >41 kg, sputum conversion was 3/3 (100%) in those with an amikacin C(max) of ≥67 mg/liter versus 3/15 (20%) in those with a C(max) of <67 mg/liter (relative risk [RR] = 5.00; 95% confidence interval [CI], 1.82 to 13.76). In all patients who had both amikacin C(max) and AUC(0–24) below the threshold, 7/7 (100%) failed, compared to 7/15 (47%) of those who had these parameters above threshold (RR = 2.14; 95% CI, 1.25 to 43.68). These amikacin dose-schedule patterns and exposures are virtually the same as those identified in the hollow-fiber system model. American Society for Microbiology 2016-09-23 /pmc/articles/PMC5038293/ /pubmed/27458224 http://dx.doi.org/10.1128/AAC.00962-16 Text en Copyright © 2016 Modongo et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license (http://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Experimental Therapeutics Modongo, Chawangwa Pasipanodya, Jotam G. Magazi, Beki T. Srivastava, Shashikant Zetola, Nicola M. Williams, Scott M. Sirugo, Giorgio Gumbo, Tawanda Artificial Intelligence and Amikacin Exposures Predictive of Outcomes in Multidrug-Resistant Tuberculosis Patients |
title | Artificial Intelligence and Amikacin Exposures Predictive of Outcomes in Multidrug-Resistant Tuberculosis Patients |
title_full | Artificial Intelligence and Amikacin Exposures Predictive of Outcomes in Multidrug-Resistant Tuberculosis Patients |
title_fullStr | Artificial Intelligence and Amikacin Exposures Predictive of Outcomes in Multidrug-Resistant Tuberculosis Patients |
title_full_unstemmed | Artificial Intelligence and Amikacin Exposures Predictive of Outcomes in Multidrug-Resistant Tuberculosis Patients |
title_short | Artificial Intelligence and Amikacin Exposures Predictive of Outcomes in Multidrug-Resistant Tuberculosis Patients |
title_sort | artificial intelligence and amikacin exposures predictive of outcomes in multidrug-resistant tuberculosis patients |
topic | Experimental Therapeutics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5038293/ https://www.ncbi.nlm.nih.gov/pubmed/27458224 http://dx.doi.org/10.1128/AAC.00962-16 |
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