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Originally developed for detecting enemy airplanes and warships during the World War II, the receiver operating characteristic (ROC) has been widely used in the biomedical field since the 1970s in, for example, patient risk group classification, outcome prediction and disease diagnosis. Today, it has become the gold standard for evaluating/comparing the performance of a classifier(s).A ROC curve is a two-dimensional plot that illustrates how well a classifier system works as the discrimination cut-off value is changed over the range of the predictor variable. The x axis or independent variable is the false positive rate for the predictive test. The y axis or dependent variable is the true positive rate for the predictive test. Each point in ROC space is a true positive/false positive data pair for a discrimination cut-off value of the predictive test. If the probability distributions for the true positive and false positive are both known, a ROC curve can be plotted from the cumulative distribution function. In most real applications, a data sample will yield a single point in the ROC space for each choice of discrimination cut-off. A perfect result would be the point (0, 1) indicating 0% false positives and 100% true positives. The generation of the true positive and false positive rates requires that we have a gold standard method for identifying true positive and true negative cases. To better understand a ROC curve, we will need to review the contingency table or confusion matrix.

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We studied the pressure-flow relationships and flow distribution under steady conditions in a model of human central airways, over a range of tracheal Reynolds' numbers (350-30,000) by using air or helium. We found that the Moody diagram [log coefficient of friction CF = delta P/[1/2 rho (V2/A2)] vs. log Reynolds' number (Re)] had a slope of -1 for Re less than 500, a slope 0 for Re greater than 10,000, and slopes between -1 and 0 for 500 less than or equal to Re less than or equal to 10,000. The distribution of flow among branches was dependent on tracheal Reynolds' number so that, as tracheal Reynolds' number increased, the upper lobes received proportionally less of the total flow than the lower lobes. Because the airways in the upper lobes generally had greater branching angles than those in the lower lobes, this result was consistent with the hypothesis that the effective resistances introduced by branching angles was flow dependent, increasing proportionally more the greater the angle.

Background: COVID-19 is a highly infectious disease which usually presents with respiratory symptoms. This virus is disseminated through respiratory droplets, and, therefore, individuals residing in close quarters are at a higher risk for the acquisition of infection. The prison population is at a significantly increased risk for infection. Methods: Prisoners from the Montford Correctional facility in Lubbock, Texas, hospitalized in the medical intensive care unit at University Medical Center between March 1, 2020 and May 15, 2020 were compared to community-based patients hospitalized in the same medical intensive care unit. Clinical information, laboratory results, radiographic results, management requirements, and outcomes were compared. Results: A total of 15 community-based patients with a mean age of 67.4 ± 15.5 years were compared to 5 prisoners with a mean age of 56.0 ± 9.0 years. All prisoners were men; 10 community-based patients were men. Prisoners presented with fever, dyspnea, and GI symptoms. The mean number of comorbidities in prisoners was 2.4 compared to 1.8 in community-based patients. Prisoners had significantly lower heart rates and respiratory rates at presentation than community-based patients. The mean length of stay in prisoners was 12.6 ± 8.9 days; the mean length of stay in community-based patients was 8.6 ± 6.5. The case fatality rate was 60% in both groups. Conclusions: Prisoners were younger than community-based patients but required longer lengths of stay and had the same mortality rate. This study provides a basis for comparisons with future studies which could involve new treatment options currently under study.

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Background: The ongoing coronavirus disease (COVID-19) pandemic has a major impact on first responders. Scarce personal protective equipment (PPE) has forced them to conserve and reuse some of their PPE. The efficacy of these practices in preventing transmission of COVID-19 from patients to first responders is unclear. There are limited data on the prevalence of antibodies specific for COVID-19 exposure in these front-line workers. Aim: Our objective was to determine the prevalence of positive immunoglobulin G antibody specific to COVID-19 among first responders in Lubbock, Texas. Methods: Blood samples were collected on 683 asymptomatic first responders who work in Lubbock, Texas and the surrounding area, after informed consents were signed. IgG antibody to SARS-CoV-2 was measured using Abbott’s SARS-CoV-2 IgG Reagent Kit in combination with the SARS-CoV-2 IgG Calibrator Kit on the Abbott’s ARCHITECT i1000SR analyzer. Results: The prevalence of IgG specific antibodies to COVID-19 was 0.73%, five of the 683 participants tested positive. Four of those who tested positive had no known prior SARS-CoV-2 infection or exposure without adequate PPE. Conclusions: The prevalence of IgG specific antibodies to COVID-19 was much lower than expected in our study population despite high sensitivity and specificity of the test reagent. The most likely explanations for this finding include limited exposure, inadequate time for a IgG response, possible clearance of COVID-19 infection locally by the respiratory tract IgA defense system without eliciting a systemic IgG response, and short persistence of IgG antibodies in mild or asymptomatic cases.