Evidence map›Paper›PMID 27458224›Full record

ArticleAntimicrobial agents and chemotherapy2016

Artificial Intelligence and Amikacin Exposures Predictive of Outcomes in Multidrug-Resistant Tuberculosis Patients.

Chawangwa Modongo, Jotam G Pasipanodya, Beki T Magazi, Shashikant Srivastava, Nicola M Zetola, Scott M Williams, Giorgio Sirugo, Tawanda Gumbo

Registry-linked trialOpen access · hybridAbstract read
In one paragraph

Article in Antimicrobial agents and chemotherapy, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03827811 (Pharmacometrics to Advance Novel Regimens for Drug-resistant Tuberculosis), which is not on this map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
4.5field-weighted citation impact, top 5% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT03827811 unknown statusnot on this mapstarted 2020, after this paper: background citation

Pharmacometrics to Advance Novel Regimens for Drug-resistant Tuberculosis

TypeobservationalSponsorUniversity of Cape TownRan2020 to 2023Enrolled625ConditionsMulti-drug Resistant Tuberculosis
3 · Its place in the literature

Who cites it

23 citing papers in PubMed, 42 citations in OpenAlex.

  1. Trial
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  9. Integrating Pharmacokinetics and Pharmacodynamics in Operational Research to End Tuberculosis.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2020
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  10. Article
  11. Review
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  14. Pharmacokinetic/Pharmacodynamic Background and Methods and Scientific Evidence Base for Dosing of Second-line Tuberculosis Drugs.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2018
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 5 institutions in 4 countries.

Chawangwa ModongoDivision of Infectious Diseases, University of Pennsylvania, Philadelphia, Pennsylvania, USA Botswana-University of Pennsylvania Partnership, Gaborone, Botswana.
Jotam G PasipanodyaCenter for Infectious Diseases Research & Experimental Therapeutics, Baylor Research Institute, Baylor University Medical Center, Dallas, Texas, USA.
Beki T MagaziDepartment of Medical Microbiology, University of Pretoria, Pretoria, South Africa.
Shashikant SrivastavaCenter for Infectious Diseases Research & Experimental Therapeutics, Baylor Research Institute, Baylor University Medical Center, Dallas, Texas, USA.
Nicola M ZetolaDivision of Infectious Diseases, University of Pennsylvania, Philadelphia, Pennsylvania, USA Botswana-University of Pennsylvania Partnership, Gaborone, Botswana Department of Medicine, University of Botswana, Gaborone, Botswana.
Scott M WilliamsDepartment of Genetics, Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire, USA.
Giorgio SirugoCentro di Ricerca, Ospedale San Pietro Fatebenefratelli, Rome, Italy.
Tawanda GumboCenter for Infectious Diseases Research & Experimental Therapeutics, Baylor Research Institute, Baylor University Medical Center, Dallas, Texas, USA Department of Medicine, University of Cape Town, Observatory, Cape Town, South Africa Tawanda.Gumbo@BSWHealth.org.
Baylor University Medical Center · USUniversity of Pennsylvania · USDartmouth College · USOspedale San Pietro Fatebenefratelli · ITUniversity of Pretoria · ZA

Funding

Short course therapy for MDR-TB based on PK/PD answers for biological variabilityR56AI111985 · NIAID · UT SOUTHWESTERN MEDICAL CENTER · PI GUMBO, TAWANDA · 2014 to 2014
$900k
NIAID NIH HHS R56 AI111985
6 · The paper itself

Abstract

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 (AUC0-24). 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 (Cmax), AUC0-24, and trough concentrations. The primary node for failure had two competing variables, Cmax of <67 mg/liter and AUC0-24 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 Cmax of ≥67 mg/liter versus 3/15 (20%) in those with a Cmax 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 Cmax and AUC0-24 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.

Indexed as

Artificial IntelligenceAdolescentAdultAgedAged, 80 and overAmikacinAntitubercular AgentsBotswanaFemaleHumansMaleMicrobial Sensitivity TestsMiddle AgedModels, TheoreticalMycobacterium tuberculosisRegression AnalysisAmikacinAntitubercular Agents

Identifiers

PMID27458224
PMCPMC5038293
OpenAlexW2498027639

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.