Dataset Information

Tuberculosis diagnosis and treatment under uncertainty.

ABSTRACT: In 2017, 1.6 million people worldwide died from tuberculosis (TB). A new TB diagnostic test-Xpert MTB/RIF from Cepheid-was endorsed by the World Health Organization in 2010. Trials demonstrated that Xpert is faster and has greater sensitivity and specificity than smear microscopy-the most common sputum-based diagnostic test. However, subsequent trials found no impact of introducing Xpert on morbidity and mortality. We present a decision-theoretic model of how a clinician might decide whether to order Xpert or other tests for TB, and whether to treat a patient, with or without test results. Our first result characterizes the conditions under which it is optimal to perform empirical treatment; that is, treatment without diagnostic testing. We then examine the implications for decision making of partial knowledge of TB prevalence or test accuracy. This partial knowledge generates ambiguity, also known as deep uncertainty, about the best testing and treatment policy. In the presence of such ambiguity, we show the usefulness of diversification of testing and treatment.

SUBMITTER: Cassidy R

PROVIDER: S-EPMC6859331 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Publications

Tuberculosis diagnosis and treatment under uncertainty.

Cassidy Rachel R Manski Charles F CF

Proceedings of the National Academy of Sciences of the United States of America 20191029 46

In 2017, 1.6 million people worldwide died from tuberculosis (TB). A new TB diagnostic test-Xpert MTB/RIF from Cepheid-was endorsed by the World Health Organization in 2010. Trials demonstrated that Xpert is faster and has greater sensitivity and specificity than smear microscopy-the most common sputum-based diagnostic test. However, subsequent trials found no impact of introducing Xpert on morbidity and mortality. We present a decision-theoretic model of how a clinician might decide whether to ...[more]

PMID: 31662476

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Project description:BACKGROUND: An important step in strain optimization is to identify reactions whose activities should be modified to achieve the desired cellular objective. Preferably, these reactions are identified systematically, as the number of possible combinations of reaction modifications could be very large. Over the last several years, a number of computational methods have been described for identifying combinations of reaction modifications. However, none of these methods explicitly address uncertainties in implementing the reaction activity modifications. In this work, we model the uncertainties as probability distributions in the flux carrying capacities of reactions. Based on this model, we develop an optimization method that identifies reactions for flux capacity modifications to predict outcomes with high statistical likelihood. RESULTS: We compare three optimization methods that select an intervention set comprising up- or down-regulation of reaction flux capacity: CCOpt (Chance constrained optimization), DetOpt (Deterministic optimization), and MCOpt (Monte Carlo-based optimization). We evaluate the methods using a Monte Carlo simulation-based method, MCEval (Monte Carlo Evaluations). We present two case studies analyzing a CHO cell and an adipocyte model. The flux capacity distributions required for our methods were estimated from maximal reaction velocities or elementary mode analysis. The intervention set selected by CCOpt consistently outperforms the intervention set selected by DetOpt in terms of tolerance to flux capacity variations. MCEval shows that the optimal flux predicted based on the CCOpt intervention set is more likely to be obtained, in a probabilistic sense, than the flux predicted by DetOpt. The intervention sets identified by CCOpt and MCOpt were similar; however, the exhaustive sampling required by MCOpt incurred significantly greater computational cost. CONCLUSIONS: Maximizing tolerance to variable engineering outcomes (in modifying enzyme activities) can identify intervention sets that statistically improve the desired cellular objective.

Project description:INTRODUCTION:Although pancreatic tuberculosis (TB) is traditionally considered to be a rare clinical entity, in recent times, an increase in the number of reports of pancreatic TB has been noted. We conducted a systematic review in order to summarise currently available data on pancreatic TB. METHODS:A comprehensive literature search of Medline, Scopus and ISI Web of Science databases was conducted in order to identify papers reporting cases of pancreatic TB. The eligibility criteria for inclusion in the review required that the studies reported patient(s) affected by pancreatic TB and that individual data on age, sex, clinical presentation and outcome were available. RESULTS:In total, 116 studies reporting data on 166 patients were included in the analysis. The majority of patients were males (62.1%) diagnosed at a mean age of 41.61?±?13.95 years. Most cases were diagnosed in Asia (50.0%), followed by North America (22.9%), Europe (20.5%), Africa (4.2%) and South America (2.4%). Human immunodeficiency virus (HIV) infection was diagnosed in 25.3% of those affected. Pancreatic TB most frequently presented itself in the form of a pancreatic mass (79.5%) localised mainly in the head (59.0%) and less frequently in the body (18.2%) and tail (13.4%). Extrapancreatic TB involvement most frequently affected the peripancreatic lymph nodes (47.3%). More than half of patients (55.2%) were subjected to laparotomy, while 21.08% underwent endoscopic ultrasound fine-needle aspiration biopsy. The presence of TB was identified most frequently through histological analysis (59.6%), followed by culture (28.9%), staining (27.7%) and, in a smaller number, by polymerase chain reaction (9.6%) and cytology (6.6%). Almost all patients received anti-tubercular pharmacological therapy (98.2%), while 24.1% underwent surgery. Despite treatment, 8.7% of patients died. CONCLUSION:Increased awareness of pancreatic TB is needed, not only in endemic areas but especially in relation to HIV infection and other clinical conditions associated with immunoincompetence.

Dataset Information

Tuberculosis diagnosis and treatment under uncertainty.

Publications

Tuberculosis diagnosis and treatment under uncertainty.

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