Pirfenidone in idiopathic pulmonary fibrosis: real-life experience from a German tertiary referral center for interstitial lung diseases.

BACKGROUND: Pirfenidone is a novel antifibrotic drug for the treatment of mild-to-moderate idiopathic pulmonary fibrosis (IPF). However, adverse events may offset treatment benefits and compliance. OBJECTIVES: To assess recent course of disease, adverse events and compliance in patients who started pirfenidone. METHODS: In an observational cohort study, 63 patients with mild-to-moderate IPF who started pirfenidone between May 2011 and June 2013 were reviewed. Pulmonary function, adverse events and treatment compliance were recorded at each clinic visit. Disease progression was defined as a reduction of vital capacity ≥10% and/or diffusion capacity (DLCO) ≥15%. RESULTS: Follow-up time on pirfenidone treatment was 11 (±7) months. Sixty-six percent of the patients continued with pirfenidone monotherapy and 34% of the patients received pirfenidone combined with corticosteroids (CCS) and/or N-acetylcysteine (NAC). There was a nonsignificant reduction in mean decline of percent predicted forced vital capacity after treatment start (0.7 ± 10.9%) compared to the pretreatment period (6.6 ± 6.7%, p = 0.098). Sixty-two percent of the patients had stable disease on pirfenidone treatment. Adverse events affected 85% of the patients, leading to discontinuation of pirfenidone in 20%. Adverse events and treatment discontinuation were seen more frequently in patients with concomitant CCS and/or NAC treatment. CONCLUSIONS: Adverse events affect the majority of patients treated with pirfenidone, but are mostly manageable with supportive measures. In this heterogeneous patient group, a nonsignificant effect of pirfenidone treatment on pulmonary function was seen, underlining the need for more data on patient selection criteria and efficacy of pirfenidone, particularly in patients with coexistent emphysema and concomitant NAC/CCS treatment.

Automated sleep stage identification system based on time-frequency analysis of a single EEG channel and random forest classifier.

In this work, an efficient automated new approach for sleep stage identification based on the new standard of the American academy of sleep medicine (AASM) is presented. The propose approach employs time-frequency analysis and entropy measures for feature extraction from a single electroencephalograph (EEG) channel. Three time-frequency techniques were deployed for the analysis of the EEG signal: Choi-Williams distribution (CWD), continuous wavelet transform (CWT), and Hilbert-Huang Transform (HHT). Polysomnographic recordings from sixteen subjects were used in this study and features were extracted from the time-frequency representation of the EEG signal using Renyi's entropy. The classification of the extracted features was done using random forest classifier. The performance of the new approach was tested by evaluating the accuracy and the kappa coefficient for the three time-frequency distributions: CWD, CWT, and HHT. The CWT time-frequency distribution outperformed the other two distributions and showed excellent performance with an accuracy of 0.83 and a kappa coefficient of 0.76.

Steps toward subject-specific classification in ECG-based detection of sleep apnea.

This study deals with ECG-based recognition of sleep apnea in epochs of 1 min duration using spectral- and correlation-based features extracted from the modulation of QRS amplitude, respiratory myogram interference and RR intervals. On a database comprising 140 simultaneous recordings of polysomnograms (PSGs) and 8-lead Holter-ECGs, it is shown that a single-parameter ROC threshold classification can achieve high detection rates up to 81.0% sensitivity and 85.6% specificity. Still, individual accuracy may be low, and the improvement employing feature combination by means of second order polynomial classifiers is only marginal. We speculate that individual differences, like co-morbidities, and even intra-individual confounding factors, like nocturnal changes in body position (BP), are major reasons for the difficulties to significantly raise the detection rate using multivariate techniques, which is evident in virtually all papers on that subject. Using the BP information in the PSG, we show a potential benefit for individualized single-feature classifiers by comparing the maximally achievable individual and global accuracy when either one optimal global threshold for the total dataset, individual threshold values for each subject or individual thresholds for each BP are applied. We developed an ECG-based BP segmentation algorithm and finally suggest a potential strategy to derive individually optimized subject-specific threshold values.

Time frequency analysis for automated sleep stage identification in fullterm and preterm neonates.

This work presents a new methodology for automated sleep stage identification in neonates based on the time frequency distribution of single electroencephalogram (EEG) recording and artificial neural networks (ANN). Wigner-Ville distribution (WVD), Hilbert-Hough spectrum (HHS) and continuous wavelet transform (CWT) time frequency distributions were used to represent the EEG signal from which features were extracted using time frequency entropy. The classification of features was done using feed forward back-propagation ANN. The system was trained and tested using data taken from neonates of post-conceptual age of 40 weeks for both preterm (14 recordings) and fullterm (15 recordings). The identification of sleep stages was successfully implemented and the classification based on the WVD outperformed the approaches based on CWT and HHS. The accuracy and kappa coefficient were found to be 0.84 and 0.65 respectively for the fullterm neonates' recordings and 0.74 and 0.50 respectively for preterm neonates' recordings.

ECG fingerprints of obstructed breathing in sleep apnea patients.

This article aims to identify the potential indicators of obstructive apnea (OA) in the ECG, based on the traces of QRS area from multiple ECG leads. We compare the difference in these traces' phase relation found during and after each OA episode to the difference between the first and second half of the OA and between split halves of epochs of normal respiration (NR).

[Microscopic polyangiitis as etiology for lung fibrosis--a known but often late-diagnosed cause]. / Mikroskopische Polyangiitis als ätiologischer Faktor der Lungenfibrose--eine bekannte, doch häufig spät diagnostizierte Ursache.

BACKGROUND AND PURPOSE: Lung fibrosis as first symptomatic manifestation of microscopic polyangiitis (MPA) is rare. It is characterized by progressive exertional dyspnea, radiologic detection of reticular shadowing, and increased titers of MPA-associated autoantibodies. This case study shall remind of the rare treatable cause of lung fibrosis. CASE REPORT: A 78-year-old male patient with long-standing exertional dyspnea and typical chest radiograph appearances of advanced lung fibrosis is presented. The finding of MPA autoantibodies in laboratory tests was decisive to confirm the diagnosis of MPA, and a therapy adapted to the disease severity could be initiated. CONCLUSION: An early diagnosis of MPA and a fast onset of therapy are important, because the influence of an already existent lung fibrosis is uncertain.