José Antonio Fiz

Endobronchial ultrasonography (EBUS) has been applied as a routine procedure for the diagnostic of hiliar and mediastinal nodes. The authors assessed the relationship between the echographic appearance of mediastinal nodes, based on endobronchial ultrasound images, and the likelihood of malignancy.

Although some authors have suggested that there is some seasonal periodicity in hemoptysis, temporal patterns of hemoptysis have been poorly investigated. The aim of this study is to describe the temporal pattern of severe hemoptysis which required bronchial artery embolization (BAE).

Sleep apnea–hypopnea syndrome (SAHS) is a serious sleep disorder, and snoring is one of its earliest and most consistent symptoms. We propose a new methodology for identifying two distinct types of snores: the so-called non-regular and regular snores. Respiratory sound signals from 34 subjects with different ranges of Apnea-Hypopnea Index (AHI = 3.7–109.9 h⁻¹) were acquired. A total number of 74,439 snores were examined. The time interval between regular snores in short segments of the all night recordings was analyzed. Severe SAHS subjects show a shorter time interval between regular snores (p = 0.0036, AHI cp: 30 h⁻¹) and less dispersion on the time interval features during all sleep. Conversely, lower intra-segment variability (p = 0.006, AHI cp: 30 h⁻¹) is seen for less severe SAHS subjects. Features derived from the analysis of time interval between regular snores achieved classification accuracies of 88.2 % (with 90 % sensitivity, 75 % specificity) and 94.1 % (with 94.4 % sensitivity, 93.8 % specificity) for AHI cut-points of severity of 5 and 30 h⁻¹, respectively. The features proved to be reliable predictors of the subjects’ SAHS severity. Our proposed method, the analysis of time interval between snores, provides promising results and puts forward a valuable aid for the early screening of subjects suspected of having SAHS.

Smoking has been linked to low-grade systemic inflammation, a known risk factor for disease. This state is reflected in elevated white blood cell (WBC) count.

Objectives/Hypothesis: We used a new automatic snoring detection and analysis system to monitor snoring during full‐night polysomnography to assess whether the acoustic characteristics of snores differ in relation to the apnea‐hypopnea index (AHI) and to classify subjects according to their AHI.Study Design: Individual Case‐Control Study.Methods: Thirty‐seven snorers (12 females and 25 males; ages 40–65 years; body mass index (BMI), 29.65 ± 4.7 kg/m²) participated. Subjects were divided into three groups: G1 (AHI P < .03). Patients were classified with thresholds AHI = 5 and AHI = 15 with a sensitivity (specificity) of 87% (71%) and 80% (90%), respectively.Conclusions: A new system for automatic monitoring and analysis of snores during the night is presented. Sound intensity and several snore frequency parameters allow differentiation of snorers according to obstructive sleep apnea syndrome severity (OSAS). Automatic snore intensity and frequency monitoring and analysis could be a promising tool for screening OSAS patients, significantly improving the managing of this pathology. Laryngoscope, 2010