Dataset Information

Evaluation of underweight status may improve identification of the highest-risk patients during outpatient evaluation for pulmonary tuberculosis.

ABSTRACT:

Background

When evaluating symptomatic patients for tuberculosis (TB) without access to same-day diagnostic test results, clinicians often make empiric decisions about starting treatment. The number of TB symptoms and/or underweight status could help identify patients at highest risk for a positive result. We sought to evaluate the usefulness of BMI assessment and a count of characteristic TB symptoms for identifying patients at highest risk for TB.

Methods

We enrolled adult patients receiving pulmonary TB diagnoses and a representative sample with negative TB evaluations at four outpatient health facilities in Kampala, Uganda. We asked patients about symptoms of chronic cough, night sweats, chest pain, fever, hemoptysis, or weight loss; measured height and weight; and collected sputum for mycobacterial culture. We evaluated the diagnostic accuracy (for culture-positive TB) of two simple scoring systems: (a) number of TB symptoms, and (b) number of TB symptoms plus one or more additional points for underweight status (body mass index [BMI] ? 18.5 kg/m2).

Results

We included 121 patients with culture-positive TB and 370 patients with negative culture results (44 of whom had been recommended for TB treatment by evaluating clinicians). Of the six symptoms assessed, the median number of symptoms that patients reported was two (interquartile range [IQR]: 1, 3). The median BMI was 20.9 kg/m2 (IQR: 18.6, 24.0), and 118 (24%) patients were underweight. Counting the number of symptoms provided an area under the Receiver Operating Characteristic curve (c-statistic) of 0.77 (95% confidence interval, CI: 0.72, 0.81) for identifying culture-positive TB; adding two points for underweight status increased the c-statistic to 0.81 (95%CI: 0.76, 0.85). A cutoff of ?3 symptoms had sensitivity and specificity of 65% and 74%, whereas a score of ?4 on the combined score (?2 symptoms if underweight, ?4 symptoms if not underweight) gave higher sensitivity and specificity of 69% and 81% respectively. A sensitivity analysis defining TB by Xpert MTB/RIF status produced similar results.

Conclusion

A count of patients' TB symptoms may be useful in clinical decision-making about TB diagnosis. Consideration of underweight status adds additional diagnostic value.

SUBMITTER: Kitonsa PJ

PROVIDER: S-EPMC7732099 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Evaluation of underweight status may improve identification of the highest-risk patients during outpatient evaluation for pulmonary tuberculosis.

Kitonsa Peter J PJ Nalutaaya Annet A Mukiibi James J Nakasolya Olga O Isooba David D Kamoga Caleb C Baik Yeonsoo Y Robsky Katherine K Dowdy David W DW Katamba Achilles A Kendall Emily A EA

PloS one 20201211 12

<h4>Background</h4>When evaluating symptomatic patients for tuberculosis (TB) without access to same-day diagnostic test results, clinicians often make empiric decisions about starting treatment. The number of TB symptoms and/or underweight status could help identify patients at highest risk for a positive result. We sought to evaluate the usefulness of BMI assessment and a count of characteristic TB symptoms for identifying patients at highest risk for TB.<h4>Methods</h4>We enrolled adult patie ...[more]

PMID: 33306710

Similar Datasets

Project description:Population monitoring must be accurate and reliable to correctly classify population status. For sea turtles, nesting beach surveys are often the only population-level surveys that are accessible. However, process and observation errors, compounded by delayed maturity, obscure the relationship between trends on the nesting beach and the population. We present a simulation-based tool, monitoring strategy evaluation (MoSE), to test the relationships between monitoring data and assessment accuracy, using green sea turtles, Chelonia mydas, as a case study. To explore this first application of MoSE, we apply different treatments of population impacts to virtual true populations, and sample the nests or nesters, with observation error, to test if the observation data can be used to diagnose population status accurately. Based on the observed data, we examine population trend and compare it to the known values from the operating model. We ran a series of scenarios including harvest impacts, cyclical breeding probability, and sampling biases, to see how these factors impact accuracy in estimating population trend. We explored the necessary duration of monitoring for accurate trend estimation and the probability of a false trend diagnosis. Our results suggest that disturbance type and severity can have important and persistent effects on the accuracy of population assessments drawn from monitoring nesting beaches. The underlying population phase, age classes disturbed, and impact severity influenced the accuracy of estimating population trend. At least 10 yr of monitoring data is necessary to estimate population trend accurately, and >20 yr if juvenile age classes were disturbed and the population is recovering. In general, there is a greater probability of making a false positive trend diagnosis than a false negative, but this depends on impact type and severity, population phase, and sampling duration. Improving detection rates to 90% does little to lower probability of a false trend diagnosis with shorter monitoring spans. Altogether, monitoring strategies for specific populations may be tailored based on the impact history, population phase, and environmental drivers. The MoSE is an important framework for analysis through simulation that can comprehensively test population assessments for accuracy and inform policy recommendations regarding the best monitoring strategies.

Dataset Information

Evaluation of underweight status may improve identification of the highest-risk patients during outpatient evaluation for pulmonary tuberculosis.

Background

Methods

Results

Conclusion

Publications

Evaluation of underweight status may improve identification of the highest-risk patients during outpatient evaluation for pulmonary tuberculosis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure