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• W2113282137 abstract "The hollow fiber system model of tuberculosis (HFS-TB), in tandem with Monte Carlo experiments, represents a drug development tool (DDT) with the potential for use to develop tuberculosis treatment regimens. However, the predictive accuracy of the HFS-TB, or any other nonclinical DDT such as an animal model, has yet to be robustly evaluated.To avoid hindsight bias, a literature search was performed to identify clinical studies published at least 6 months after HFS-TB experiments' quantitative predictions. Steps to minimize bias and for reporting systematic reviews were applied as outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. Publications were scored for quality of evidence. Accuracy was calculated using the mean absolute percentage error, then summated with weighting assigned by sample size and quality-of-evidence score. Given the lack of a gold-standard tuberculosis DDT, the forecasting accuracy of a completely unreliable tool was also calculated from 1000 simulated experiments for a random or total guesswork model.The quantitative forecasting accuracy (95% confidence interval [CI]) for the total guesswork model was 15.6% (95% CI, 8.7%-22.5%); bias was -0.1% (95% CI, -2.5% to 2.2%). Twenty clinical studies were published after HFS-TB experiments predicted optimal drug exposures and doses, susceptibility breakpoints, and optimal combination regimens. Based on these clinical studies, the predictive accuracy of the HFS-TB was 94.4% (95% CI, 84.3%-99.9%), and bias was 1.8% (95% CI, -13.7% to 6.2%).The HFS-TB model is highly accurate at forecasting optimal drug exposures, doses, and dosing schedules for use in the clinic." @default.
• W2113282137 created "2016-06-24" @default.
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• W2113282137 date "2015-07-29" @default.
• W2113282137 modified "2023-10-12" @default.
• W2113282137 title "Forecasting Accuracy of the Hollow Fiber Model of Tuberculosis for Clinical Therapeutic Outcomes" @default.
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• W2113282137 doi "https://doi.org/10.1093/cid/civ427" @default.
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