The ordered probit model allowed a quantitative comparison of all currently licensed biologics, providing estimates on comparative effectiveness and a suggested ranking of treatments that is of potential use to decision-makers. However, the analysis is based on indirect comparisons of the primary endpoint reported from short-term randomized trials.

Objective To assess the cost-effectiveness of transcatheter aortic valve implantation (TAVI) compared with medical management in patients with severe aortic stenosis who are ineligible for conventional aortic valve replacement (SAVR) from the perspective of the UK National Health Service. Design Probabilistic decision analytical model. Methods A decision analytical model was developed to assess the costs and benefits associated with both interventions over a 10-year time horizon. A literature review was performed to identify relevant clinical evidence. Health-related quality of life and mortality were included using data from the PARTNER clinical trial (cohort B). Unit costs were taken from national databases. Costs and benefits were discounted at 3.5% per year, and extensive sensitivity analyses (probabilistic and deterministic) were performed to explore the impact of uncertainty on the cost-effectiveness estimates.

Abstract-Reverberation Time (T60) and Direct-to-Reverberant Ratio (DRR) are important parameters which together can characterize sound captured by microphones in non-anechoic rooms. These parameters are important in speech processing applications such as speech recognition and dereverberation. The values of T60 and DRR can be estimated directly from the Acoustic Impulse Response (AIR) of the room. In practice, the AIR is not normally available, in which case these parameters must be estimated blindly from the observed speech in the microphone signal. The Acoustic Characterization of Environments (ACE) Challenge aimed to determine the state-of-the-art in blind acoustic parameter estimation and also to stimulate research in this area. A summary of the ACE Challenge, and the corpus used in the challenge is presented together with an analysis of the results. Existing algorithms were submitted alongside novel contributions, the comparative results for which are presented in this paper. The challenge showed that T60 estimation is a mature field where analytical approaches dominate whilst DRR estimation is a less mature field where machine learning approaches are currently more successful.

Knowledge of the Direct-to-Reverberant Ratio (DRR) and Reverberation Time (T60) can be used to better perform speech and audio processing such as dereverberation. Established methods compute these parameters from measured Acoustic Impulse Responses (AIRs). However, in many practical situations the AIR is not available and the parameters must be estimated non-intrusively directly from noisy speech or audio signals. The Acoustic Characterization of Environments (ACE) Challenge is a competition to identify the most promising non-intrusive DRR and T60 estimation methods using real noisy reverberant speech. We describe the ACE corpus comprising multi-channel AIRs, and multi-channel noise including ambient, fan and babble noise recorded in the same environment as the measured AIRs, along with the corresponding DRR and T60 measurements. The evaluation methodology is discussed and comparative results are shown.

Cardiac resynchronization therapy is a cost-effective intervention for patients with mildly symptomatic HF and for asymptomatic patients with left ventricular dysfunction and previous HF symptoms.

Reverberation Time (T60) is an important measure of the acoustic properties of a room. It can provide information about the acoustic environment, the intelligibility, and quality of speech recorded in the room, and help improve the performance of speech processing algorithms with reverberant speech. Where the acoustic impulse response of the room is not available, the T60 must be estimated nonintrusively from reverberant speech. State-of-the-art non-intrusive T60 estimators have been shown to be strongly biased in the presence of noise. We describe a novel T60 estimation algorithm based on spectral decay distributions that provides robustness to additive noise for a range of realistic noise types for signal-to-noise ratios in the range 0 to 35 dB and T60s between 200 and 950 ms. The proposed method also has much reduced computational cost.

ObjectiveTo use patient-level data from the ADVANCE study to evaluate the cost-effectiveness of transcatheter aortic valve implantation (TAVI) compared to medical management (MM) in patients with severe aortic stenosis from the perspective of the UK NHS.MethodsA published decision-analytic model was adapted to include information on TAVI from the ADVANCE study. Patient-level data informed the choice as well as the form of mathematical functions that were used to model all-cause mortality, health-related quality of life and hospitalisations. TAVI-related resource use protocols were based on the ADVANCE study. MM was modelled on publicly available information from the PARTNER-B study. The outcome measures were incremental cost-effectiveness ratios (ICERs) estimated at a range of time horizons with benefits expressed as quality-adjusted life-years (QALY). Extensive sensitivity/subgroup analyses were undertaken to explore the impact of uncertainty in key clinical areas.ResultsUsing a 5-year time horizon, the ICER for the comparison of all ADVANCE to all PARTNER-B patients was £13 943 per QALY gained. For the subset of ADVANCE patients classified as high risk (Logistic EuroSCORE >20%) the ICER was £17 718 per QALY gained). The ICER was below £30 000 per QALY gained in all sensitivity analyses relating to choice of MM data source and alternative modelling approaches for key parameters. When the time horizon was extended to 10 years, all ICERs generated in all analyses were below £20 000 per QALY gained.ConclusionTAVI is highly likely to be a cost-effective treatment for patients with severe aortic stenosis.

Traditional training teaches specific skills and concepts often in a series of discrete and ultimately disjointed processes. Coaching, on the other hand, is an open‐ended process that analyses the present situation, defines the performance goal, combines personal, organizational and external resources and then implements a plan for achieving that goal. Following this definition, the article considers the coaching process in detail, and the role of the coach, particularly how members of the team make use of him or her. It develops two models for coaching to deal with different circumstances and illustrates their use with case studies. A “coaching culture” within the organization can also help generate a forum for creativity, planning and problem solving.