Browsing by Author "Shenai, Shubhada"

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    Bacterial loads measured by the Xpert MTB/RIF assay as markers of culture conversion and bacteriological cure in pulmonary TB
    (Public Library of Science, 2016) Shenai, Shubhada; Ronache, Katharina; Malherbe, Stefanus; Stanley, Kim; Kriel, Magdalena; Winter, Jill; Peppard, Thomas; Barry, Charles E.; Wang, Jing; Dodd, Lori E.; Via, Laura E.; Barry, Clifton E. 3rd; Walzl, Gerhard; Alland, David
    Introduction: Biomarkers are needed to monitor tuberculosis (TB) treatment and predict treatment outcomes. We evaluated the Xpert MTB/RIF (Xpert) assay as a biomarker for TB treatment during and at the end of the 24 weeks therapy. Methods: Sputum from 108 HIV-negative, culture-positive pulmonary TB patients was analyzed using Xpert at time points before and during anti-TB therapy. Results were compared against culture. Direct Xpert cycle-threshold (Ct), a change in the Ct (delta Ct), or a novel “percent closing of baseline Ct deficit” (percent closing) were evaluated as classifiers of same-day and end-of-treatment culture and therapeutic outcomes. Results: Xpert was positive in 29/95 (30.5%) of subjects at week 24; and positive one year after treatment in 8/64 (12.5%) successfully-treated patients who remained free of tuberculosis. We identified a relationship between initial bacterial load measured by baseline Xpert Ct and time to culture conversion (hazard ratio 1.06, p = 0.0023), and to the likelihood of being among the 8 treatment failures at week 24 (AUC = 72.8%). Xpert Ct was even more strongly associated with culture conversion on the day the test was performed with AUCs 96.7%, 99.2%, 86.0% and 90.2%, at Day 7, Week 4, 8 and 24, respectively. Compared to baseline Ct measures alone, a combined measure of baseline Ct plus either Delta Ct or percent closing improved the classification of treatment failure status to a 75% sensitivity and 88.9% specificity. Conclusions: Genome loads measured by Xpert provide a potentially-useful biomarker for classifying same day culture status and predicting response to therapy.