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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...

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Autores principales: Modongo, Chawangwa, Pasipanodya, Jotam G., Magazi, Beki T., Srivastava, Shashikant, Zetola, Nicola M., Williams, Scott M., Sirugo, Giorgio, Gumbo, Tawanda
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Society for Microbiology 2016
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.
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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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