Analysis of time-to-positivity data in tuberculosis treatment studies: Identifying a new limit of quantification

Suzanne M Dufault; Geraint R Davies; Elin M Svensson; Derek J Sloan; Andrew D McCallum; Anu Patel; Pieter Van Brantegem; Paolo Denti; Patrick P J Phillips

doi:10.1016/j.ijantimicag.2024.107404

Analysis of time-to-positivity data in tuberculosis treatment studies: Identifying a new limit of quantification

Int J Antimicrob Agents. 2024 Dec 7:107404. doi: 10.1016/j.ijantimicag.2024.107404. Online ahead of print.

Authors

Suzanne M Dufault¹, Geraint R Davies², Elin M Svensson³, Derek J Sloan⁴, Andrew D McCallum⁵, Anu Patel⁶, Pieter Van Brantegem⁷, Paolo Denti⁸, Patrick P J Phillips⁹

Affiliations

¹ Division of Biostatistics, University of California, San Francisco, San Francisco, California, USA; UCSF Center for Tuberculosis, University of California, San Francisco, San Francisco, California, USA. Electronic address: [email protected].
² Institute of Infection and Global Health, University of Liverpool, Liverpool, UK.
³ Department of Pharmacy, Radboud University Medical Center, Nijmegen, The Netherlands; Department of Pharmacy, Uppsala University, Uppsala, Sweden.
⁴ School of Medicine, University of St Andrews, St Andrews, United Kingdom.
⁵ Department of Infectious Diseases, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
⁶ Division of Pulmonary and Critical Care Medicine, University of California, San Francisco, California, USA.
⁷ Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California, USA.
⁸ Division of Clinical Pharmacology, Department of Medicine, University of Cape Town, South Africa.
⁹ UCSF Center for Tuberculosis, University of California, San Francisco, San Francisco, California, USA; Division of Pulmonary and Critical Care Medicine, University of California, San Francisco, California, USA.

PMID: 39653087
DOI: 10.1016/j.ijantimicag.2024.107404

Abstract

Background: The BACTEC Mycobacteria Growth Indicator Tube (MGIT) machine is the standard globally for detecting viable mycobacteria in patients' sputum. Samples are observed for no longer than 42 days, at which point the sample is declared "negative" for tuberculosis (TB). This time to detection of bacterial growth, referred to as time-to-positivity (TTP), is increasingly of interest not solely as a diagnostic tool, but as a continuous biomarker wherein change in TTP can be used for comparing the bactericidal activity of different TB treatments. However, as a continuous measure, there are oddities in the distribution of TTP values observed, particularly at higher values.

Methods: We explored whether there is evidence to suggest setting an upper limit of quantification for modeling purposes (ULOQ_M) lower than the diagnostic limit of detection (LOD) using data from several TB-PACTS randomized clinical trials and PanACEA MAMS-TB.

Results: Across all trials, less than 7.1% of weekly samples returned TTP measurements between 25 and 42 days. Further, the relative absolute prediction error (%) was highest in this range. When modeling with ULOQ_Ms of 25 and 30 days, estimator precision improved for 23 of 25 regimen-level slopes compared to models using the LOD. Discrimination between regimens based on Bayesian posteriors also improved.

Conclusions: While TTP measurements between 25 days and the diagnostic LOD may be important for diagnostic purposes, TTP values in this range may not contribute meaningfully to its use as a quantitative measure, particularly when assessing treatment response, and may lead to under-powered clinical trials.

Keywords: Mycobacteria Growth indicator Tube (MGIT); Word count: 3,931; biomarkers; clinical trials; early bactericidal activity; limit of quantification; time-to-positivity; tuberculosis.