Prevalence of frailty among chronic respiratory disease patients.

BACKGROUND: Although frailty is one of the aging syndromes, it can occur at a younger age and in individuals with organ diseases. Identifying frailty and pre-frailty in patients with chronic respiratory diseases (CRDs) is an emerging assessment in the field of pulmonary medicine and rehabilitation. The aim of this study was to find out the prevalence of frailty among chronic respiratory disease patients. METHODS: A single centre cross-sectional survey study with a total population of 381 patients, adults aged 18-90 years presenting to the pulmonology OPD was included based on the inclusion and exclusion criteria. Primary data collected were demographics, medical history, including comorbidities, use of long-term oxygen therapy (LTOT), BiPAP (Bilevel Positive Airway Pressure), previous hospital admissions, medication history and frailty assessment were done using the Fried frailty index. RESULTS: Univariate analysis showed that sex, ECHO abnormality, patients using LTOT, BiPAP, home nebulizers and patients who did not attend the pulmonary rehabilitation programme had a significant association with frailty. Multivariate analysis showed that female sex, LTOT use and older age were significantly associated with frailty. CONCLUSION: Frailty is frequent in CRD patients regardless of age. The prevalence of frailty has an association with female sex, patients using LTOT, and home BiPAP.

Neural classification of lung sounds using wavelet coefficients.

Electronic auscultation is an efficient technique to evaluate the condition of respiratory system using lung sounds. As lung sound signals are non-stationary, the conventional method of frequency analysis is not highly successful in diagnostic classification. This paper deals with a novel method of analysis of lung sound signals using wavelet transform, and classification using artificial neural network (ANN). Lung sound signals were decomposed into the frequency subbands using wavelet transform and a set of statistical features was extracted from the subbands to represent the distribution of wavelet coefficients. An ANN based system, trained using the resilient back propagation algorithm, was implemented to classify the lung sounds to one of the six categories: normal, wheeze, crackle, squawk, stridor, or rhonchus.