Exhaled Nitric Oxide is Not a Biomarker for Pulmonary Tuberculosis.

To reduce transmission of tuberculosis (TB) in resource-limited countries where TB remains a major cause of mortality, novel diagnostic tools are urgently needed. We evaluated the fractional concentration of exhaled nitric oxide (FeNO) as an easily measured, noninvasive potential biomarker for diagnosis and monitoring of treatment response in participants with pulmonary TB including multidrug resistant-TB in Lima, Peru. In a longitudinal study however, we found no differences in baseline median FeNO levels between 38 TB participants and 93 age-matched controls (13 parts per billion [ppb] [interquartile range (IQR) = 8-26] versus 15 ppb [IQR = 12-24]), and there was no change over 60 days of treatment (15 ppb [IQR = 10-19] at day 60). Taking this and previous evidence together, we conclude FeNO is not of value in either the diagnosis of pulmonary TB or as a marker of treatment response

Dynamics of Cough Frequency in Adults Undergoing Treatment for Pulmonary Tuberculosis.

Background: Cough is the major determinant of tuberculosis transmission. Despite this, there is a paucity of information regarding characteristics of cough frequency throughout the day and in response to tuberculosis therapy. Here we evaluate the circadian cycle of cough, cough frequency risk factors, and the impact of appropriate treatment on cough and bacillary load. Methods: We prospectively evaluated human immunodeficiency virus-negative adults (n = 64) with a new diagnosis of culture-proven, drug-susceptible pulmonary tuberculosis immediately prior to treatment and repeatedly until treatment day 62. At each time point, participant cough was recorded (n = 670) and analyzed using the Cayetano Cough Monitor. Consecutive coughs at least 2 seconds apart were counted as separate cough episodes. Sputum samples (n = 426) were tested with microscopic-observation drug susceptibility broth culture, and in culture-positive samples (n = 252), the time to culture positivity was used to estimate bacillary load. Results: The highest cough frequency occurred from 1 pm to 2 pm, and the lowest from 1 am to 2 am (2.4 vs 1.1 cough episodes/hour, respectively). Cough frequency was higher among participants who had higher sputum bacillary load (P < .01). Pretreatment median cough episodes/hour was 2.3 (interquartile range [IQR], 1.2-4.1), which at 14 treatment days decreased to 0.48 (IQR, 0.0-1.4) and at the end of the study decreased to 0.18 (IQR, 0.0-0.59) (both reductions P < .001). By 14 treatment days, the probability of culture conversion was 29% (95% confidence interval, 19%-41%). Conclusions: Coughs were most frequent during daytime. Two weeks of appropriate treatment significantly reduced cough frequency and resulted in one-third of participants achieving culture conversion. Thus, treatment by 2 weeks considerably diminishes, but does not eliminate, the potential for airborne tuberculosis transmission

Validation of an Automated Cough Detection Algorithm for Tracking Recovery of Pulmonary Tuberculosis Patients

<div><h3>Background</h3><p>A laboratory-free test for assessing recovery from pulmonary tuberculosis (TB) would be extremely beneficial in regions of the world where laboratory facilities are lacking. Our hypothesis is that analysis of cough sound recordings may provide such a test. In the current paper, we present validation of a cough analysis tool.</p> <h3>Methodology/Principal Findings</h3><p>Cough data was collected from a cohort of TB patients in Lima, Peru and 25.5 hours of recordings were manually annotated by clinical staff. Analysis software was developed and validated by comparison to manual scoring. Because many patients cough in bursts, coughing was characterized in terms of <em>cough epochs</em>. Our software correctly detects 75.5% of cough episodes with a specificity of 99.6% (comparable to past results using the same definition) and a median false positive rate of 4 false positives/hour, due to the noisy, real-world nature of our dataset. We then manually review detected coughs to eliminate false positives, in effect using the algorithm as a pre-screening tool that reduces reviewing time to roughly 5% of the recording length. This cough analysis approach provides a foundation to support larger-scale studies of coughing rates over time for TB patients undergoing treatment.</p> </div

Waveforms of a double-cough from a TB patient (upper) and of an asthma patient (lower).

<p>Waveforms of a double-cough from a TB patient (upper) and of an asthma patient (lower).</p

Bland-Altman plot comparing the number of epochs (definition <i>epoch1</i>) found by the nurses and the reviewed algorithm (i.e. semi-automated approach).

<p>The mean of the two estimates (used in place of a gold standard) is plotted vs. the difference between nurse and semi-automated results. The mean bias and limits of agreement (+/−1.96 σ) are also shown. The plot shows the bias is not statistically significant and there is no evidence of changing agreement as a function of cough epoch count.</p

Scatterplots showing the number of epochs found under ‘<i>epoch1</i> ’ and ‘<i>epoch2</i>’ definitions, for semi-automated algorithm results.

<p>Note that the ‘<i>epoch2</i>’ definition cannot be applied to nurse assignments in our dataset. The correlation coefficient between the two definitions is 0.97.</p

Boxplot comparing semi-automated estimate of cough count at day 0 and day 14.

<p>The plot shows 25^th, 50^th, and 75^th percentiles, with outliers (1.5*IQR) are shown as ‘+’. At Day 14, the box collapses as 25^th, 50^th, and 75^th percentiles are all zero.</p

Pseudo-code for event detection logic.

<p>Pseudo-code for event detection logic.</p

Bland-Altman plot comparing repeatability of semi-automated results (blue circles) to repeatability of nurse findings (red squares).

<p>In this comparison, the first and second files for each day (‘file1’ and ‘file2’) were compared. Repeatability is similar for nurse assignments and semi-automated algorithm results.</p

Pseudo-code for clustering algorithm.

<p>Pseudo-code for clustering algorithm.</p