Tuberculosis diagnosis and treatment under uncertainty

Area of Science:

• * Medical Decision Making
• * Infectious Disease Diagnostics
• * Health Economics

Background:

• * Tuberculosis (TB) caused 1.6 million deaths globally in 2017.
• * The Xpert MTB/RIF assay offers improved accuracy over smear microscopy for TB diagnosis.
• * Despite improved diagnostic accuracy, Xpert introduction has not consistently reduced TB morbidity and mortality.

Purpose of the Study:

• * To develop a decision-theoretic model for guiding clinician choices in TB testing and treatment.
• * To identify conditions favoring empirical TB treatment over diagnostic testing.
• * To explore the impact of uncertainty in TB prevalence and test accuracy on optimal strategies.

Main Methods:

• * Development of a decision-theoretic model simulating clinical decision-making for TB.
• * Analysis of optimal strategies under varying levels of knowledge about TB prevalence and test performance.
• * Evaluation of the role of empirical treatment and diagnostic diversification.

Main Results:

• * Characterization of conditions under which empirical TB treatment is the optimal strategy.
• * Demonstration that partial knowledge of TB prevalence and test accuracy leads to deep uncertainty.
• * Identification of diversification in testing and treatment as a beneficial approach in ambiguous situations.

Conclusions:

• * A decision-theoretic framework can aid clinicians in navigating complex TB diagnostic and treatment choices.
• * Empirical treatment may be optimal under specific circumstances, bypassing diagnostic testing.
• * Diversifying diagnostic and treatment approaches is a robust strategy when facing uncertainty in TB management.