National Jewish Health

About: National Jewish Health is a healthcare organization based out in Denver, Colorado, United States. It is known for research contribution in the topics: T cell & Asthma. The organization has 883 authors who have published 833 publications receiving 79201 citations. The organization is also known as: National Jewish Medical and Research Center.

Methods of detecting and preventing atopic allergic diseases

Abstract: The present invention is related to novel methods for identifying a population of subjects that are at risk for developing of atopic allergic diseases, and to the prevention of these allergic diseases.

Measurement and analysis of leukotrienes

Abstract: The present invention provides a new analytical method for measuring leukotrienes in a clinical sample using liquid chromatography and tandem mass spectrometry (LCMSMS). The method provides a simple, rapid and low-cost assay for the measurement of leukotriene levels in a clinical sample with high accuracy and precision over the physiological range. The present invention further provides a method to determine the susceptibility of a subject to treatment with a leukotriene modifier, as wells as methods for diagnosis of a chronic obstructive disease of the airways and for predicting the risk of exacerbation of the same.

Challenges in assessing asthma severity in clinical practice

Abstract: The National Heart, Lung, and Blood Institute’s 1997 National Asthma Education and Prevention Program (NAEPP) classifies asthma severity on the basis of lung function and symptoms, and most clinicians use either or both of these measures to assess patients. Severity-based stratification of patients provides a useful framework for therapeutic decision making, but this approach should not be adopted without an understanding of its delimitations and challenges. Accurate assessment of asthma severity is crucial in ensuring the patient’s health and well-being. The consequences of misappraisal of patients’ clinical status include unnecessary prescription of medication when severity is overestimated and failure to intervene with resulting deterioration of asthma control—possibly culminating in death— when severity is underestimated. This review reveals that applying the NAEPP’s severity-based classification of patients with asthma in clinical practice can be challenging, particularly in the care of patients at the mild end of the asthma severity continuum. The variable nature of asthma, the poor concordance among measures of asthma severity, and patients’ tendency to underreport their asthma symptoms can contribute to inaccurate severity assessments, which can lead to inappropriate therapeutic choices, such as undertreatment. Undertreatment of the patient with mild asthma can be as dangerous as undertreatment of patients with more severe disease. By keeping the challenges associated with severity-based stratification of patients top-of-mind, healthcare providers may be better able to overcome them in clinical practice. (Adv Stud Med. 2003;3(5A):S372-S378)

Quantifying Patterns Of Emphysema By Local Density Histogram In Chest CT Scans

Abstract: Rationale:A major limitation of global densitometry analysis for emphysema quantification is the lack of specificity for early stage disease and differentiation of patterns related to emphysema pathological types. Local approaches that attempt to classify different patterns of emphysema may better quantify the burden of disease and its progression. Methods:We have developed a new approach to classify five patterns of emphysema (normal lung tissue, centrilobular (mild, moderate, severe), panlobular and paraseptal). This approach uses the local density histogram as a differentiating feature among tissue classes. The local density histogram is computed over a local region of 31x31 pixels using a kernel density approach. A training set of image patches (31x31 pixels) was first labeled by an expert in 256 subjects from the COPDGene cohort to create a total of 1525 training samples. A kNN classifier was employed to classify new samples that were not included in the training set. The performance of the classifier was analyzed using the leave-one-subject-out technique. After the lung parenchyma is classified, the percentage of each tissue class is reported with respect to the total lung volume. The algorithm was tested on 342 subjects from COPDGene that were visually characterized by a group of expert radiologists and pulmonologists. The median score provided by the expert was compared with the % amount of each tissue class. Relationships between LAA% and the percentages of each emphysema patterns observed were also computed via linear regression analysis. Results: The classification success rate in the leave-one-subject-out experiment for each emphysema type was: 90.42% for normal lung, 85.37% for paraseptal, 77.03% for panlobular, 37.33% for mild centrilobular, 63.41% for moderate centrilobular and 44.94% for severe centrilobular. 42.4% of the mild centrilobular samples were labeled as normal lung tissue. Figure 1 shows the tissue classification results for a coronal slice (top row) and the agreement between the median score of the readers and the % amount for each tissue class (bottom row). Normal lung tissue and mild centrilobular were negatively associated with LAA% (r2=0.69, p<0.0001 and r2=0.1121,p<0.0001, respectively). Meanwhile, moderate centrilobular, severe centrilobular, paraseptal and panlobular emphysema were positively associated to LAA% with increasing slope for each emphysema class (r2=0.64,p<0.0001, r2=0.85,p<0.0001, r2=0.45,p<0.0001, r2=0.56,p<0.0001, respectively). Conclusions:Local density histogram is a feasible approach to quantify different types of emphysema in volumetric CT scans. The method is fully automatic and it can be run without supervision with low computational demands Patterns of Emphysema Classification