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1.
J Clin Sleep Med ; 8(2): 177-85, 2012 Apr 15.
Article in English | MEDLINE | ID: mdl-22505863

ABSTRACT

STUDY OBJECTIVES: Sleep disordered breathing events conceptually separate into "obstructive" and "central" events. Esophageal manometry is the definitive but invasive means of classifying hypopneas. The purpose of this project was to identify noninvasive markers for discriminating high vs. low resistance hypopneas. METHODS: Forty subjects with obstructive or central sleep apnea underwent diagnostic polysomnography with nasal cannula airflow and esophageal manometry; 200% resistance relative to reference breaths was used to define "high" resistance. Noninvasive parameters from 292 randomly selected hypopneas in 20 subjects were analyzed and correlated to resistance. The best parameter and cutoff for predicting high relative resistance was determined and tested prospectively in 2 test sets in the 20 remaining subjects. Test Set A: 15 randomly selected hypopneas in each subject; Test Set B: all hypopneas in 7 subjects. RESULTS: In the development set, prolongation of inspiratory time during the 2 smallest breaths of a hypopnea (T(i)) relative to baseline had the best correlation to high relative resistance. In the Test Set A, relative T(i) > 110% classified obstructive events with sensitivity = 72%, specificity = 77%, PPV = 64%, NPV = 83%. Similar numbers were obtained for classification of hypopneas based on presence of flow limitation (FL) alone. When either relative T(i) or presence of FL were used to define high resistance, sensitivity = 84%, specificity = 74%, PPV = 65%, NPV = 89%. Similar results were obtained for Test Set B. CONCLUSIONS: Relative prolongation of T(i) is a good noninvasive predictor of high/low resistance in a dataset with both FL and NFL hypopneas. Combination of FL and relative T(i) improves this classification. The use of T(i) to separate obstructive and central hypopneas needs to be further tested for clinical utility (outcomes and treatment effects).


Subject(s)
Inhalation/physiology , Sleep Apnea Syndromes/diagnosis , Sleep Apnea, Central/diagnosis , Airway Resistance/physiology , Female , Humans , Male , Middle Aged , Polysomnography , Predictive Value of Tests , ROC Curve , Sensitivity and Specificity , Sleep Apnea Syndromes/classification , Sleep Apnea Syndromes/physiopathology , Sleep Apnea, Central/physiopathology , Sleep Apnea, Obstructive/diagnosis , Sleep Apnea, Obstructive/physiopathology , Time Factors
2.
J Bronchology Interv Pulmonol ; 18(1): 23-30, 2011 Jan.
Article in English | MEDLINE | ID: mdl-23169014

ABSTRACT

BACKGROUND: Fibered confocal fluorescence microscopy (FCFM) is a new imaging modality in bronchoscopy. The purpose of this study was to assess FCFM reliability, interpretation, and to make image-pathologic correlations. METHODS: Twenty-six patients underwent FCFM. A validation set was used to determine image characteristics and interobserver reliability. Each patient underwent bronchoscopy using a standardized protocol. The images were evaluated by 4 observers based on brightness, fiber thickness, and alveolar cellularity. Image characteristics showing good interobserver agreement were tested to see if they were related to smoking status. Subsequently, 18 consecutive patients underwent FCFM and biopsy to correlate images with pathology. The blinded reviewers were asked to distinguish between controls and patients with pathologically proven disease. RESULTS: Interobserver agreement for image brightness, as measured by intraclass correlation coefficients (ICCs), ranged from 0.48 to 0.92 (P<0.001) and varied by location. ICCs for image brightness were high, ranging from 0.53 to 0.99 (P<0.001). Agreement for fiber thickness was poor for respiratory bronchioles (ICC 0.12, P<0.05) and fair for alveoli (ICC range, 0.37 to 0.42, P<0.001). The intraobserver (ICC range, 0.69 to 0.91, P<0.001) and intrapatient (ICC 0.65 to 0.84, P<0.001) reliability were excellent. Computer image interpretation showed excellent agreement with humans (ICC 0.62 to 0.99, P<0.001). Smoking was inversely associated with respiratory bronchiole brightness (P<0.001). In FCFM-pathologic correlation, FCFM could distinguish normal from diseased tissue; however, specific diseases could not be distinguished from other diseases. CONCLUSION: FCFM shows a high degree of image reliability and can detect changes in the respiratory bronchioles because of smoking and other diseases, but whether it can discriminate among diseases requires additional study.

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