Prospective registry of symptomatic severe aortic stenosis in octogenarians: a need for intervention.

OBJECTIVE To study the factors associated with choice of therapy and prognosis in octogenarians with severe symptomatic aortic stenosis (AS). STUDY DESIGN Prospective, observational, multicenter registry. Centralized follow-up included survival status and, if possible, mode of death and Katz index. SETTING Transnational registry in Spain. SUBJECTS We included 928 patients aged ≥80 years with severe symptomatic AS. INTERVENTIONS Aortic-valve replacement (AVR), transcatheter aortic-valve implantation (TAVI) or conservative therapy. MAIN OUTCOME MEASURES All-cause death. RESULTS Mean age was 84.2 ± 3.5 years, and only 49.0% were independent (Katz index A). The most frequent planned management was conservative therapy in 423 (46%) patients, followed by TAVI in 261 (28%) and AVR in 244 (26%). The main reason against recommending AVR in 684 patients was high surgical risk [322 (47.1%)], other medical motives [193 (28.2%)], patient refusal [134 (19.6%)] and family refusal in the case of incompetent patients [35 (5.1%)]. The mean time from treatment decision to AVR was 4.8 ± 4.6 months and to TAVI 2.1 ± 3.2 months, P < 0.001. During follow-up (11.2-38.9 months), 357 patients (38.5%) died. Survival rates at 6, 12, 18 and 24 months were 81.8%, 72.6%, 64.1% and 57.3%, respectively. Planned intervention, adjusted for multiple propensity score, was associated with lower mortality when compared with planned conservative treatment: TAVI Hazard ratio (HR) 0.68 (95% confidence interval [CI] 0.49-0.93; P = 0.016) and AVR HR 0.56 (95% CI 0.39-0.8; P = 0.002). CONCLUSION Octogenarians with symptomatic severe AS are frequently managed conservatively. Planned conservative management is associated with a poor prognosis.

Assessment of Severe Apnoea through Voice Analysis, Automatic Speech, and Speaker Recognition Techniques

This study is part of an ongoing collaborative effort between the medical and the signal processing communities to promote research on applying standard Automatic Speech Recognition (ASR) techniques for the automatic diagnosis of patients with severe obstructive sleep apnoea (OSA). Early detection of severe apnoea cases is important so that patients can receive early treatment. Effective ASR-based detection could dramatically cut medical testing time. Working with a carefully designed speech database of healthy and apnoea subjects, we describe an acoustic search for distinctive apnoea voice characteristics. We also study abnormal nasalization in OSA patients by modelling vowels in nasal and nonnasal phonetic contexts using Gaussian Mixture Model (GMM) pattern recognition on speech spectra. Finally, we present experimental findings regarding the discriminative power of GMMs applied to severe apnoea detection. We have achieved an 81% correct classification rate, which is very promising and underpins the interest in this line of inquiry.