Effective diagnosis of Alzheimer's disease by means of large margin-based methodology.

BACKGROUND Functional brain images such as Single-Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET) have been widely used to guide the clinicians in the Alzheimer's Disease (AD) diagnosis. However, the subjectivity involved in their evaluation has favoured the development of Computer Aided Diagnosis (CAD) Systems. METHODS It is proposed a novel combination of feature extraction techniques to improve the diagnosis of AD. Firstly, Regions of Interest (ROIs) are selected by means of a t-test carried out on 3D Normalised Mean Square Error (NMSE) features restricted to be located within a predefined brain activation mask. In order to address the small sample-size problem, the dimension of the feature space was further reduced by: Large Margin Nearest Neighbours using a rectangular matrix (LMNN-RECT), Principal Component Analysis (PCA) or Partial Least Squares (PLS) (the two latter also analysed with a LMNN transformation). Regarding the classifiers, kernel Support Vector Machines (SVMs) and LMNN using Euclidean, Mahalanobis and Energy-based metrics were compared. RESULTS Several experiments were conducted in order to evaluate the proposed LMNN-based feature extraction algorithms and its benefits as: i) linear transformation of the PLS or PCA reduced data, ii) feature reduction technique, and iii) classifier (with Euclidean, Mahalanobis or Energy-based methodology). The system was evaluated by means of k-fold cross-validation yielding accuracy, sensitivity and specificity values of 92.78%, 91.07% and 95.12% (for SPECT) and 90.67%, 88% and 93.33% (for PET), respectively, when a NMSE-PLS-LMNN feature extraction method was used in combination with a SVM classifier, thus outperforming recently reported baseline methods. CONCLUSIONS All the proposed methods turned out to be a valid solution for the presented problem. One of the advances is the robustness of the LMNN algorithm that not only provides higher separation rate between the classes but it also makes (in combination with NMSE and PLS) this rate variation more stable. In addition, their generalization ability is another advance since several experiments were performed on two image modalities (SPECT and PET).

Rationale and design of a randomized, double-blind, placebo controlled multicenter trial to study efficacy, security, and long term effects of intermittent repeated levosimendan administration in patients with advanced heart failure: LAICA study.

BACKGROUND Advanced heart failure (HF) is associated with high morbidity and mortality; it represents a major burden for the health system. Episodes of acute decompensation requiring frequent and prolonged hospitalizations account for most HF-related expenditure. Inotropic drugs are frequently used during hospitalization, but rarely in out-patients. The LAICA clinical trial aims to evaluate the effectiveness and safety of monthly levosimendan infusion in patients with advanced HF to reduce the incidence of hospital admissions for acute HF decompensation. METHODS The LAICA study is a multicenter, prospective, randomized, double-blind, placebo-controlled, parallel group trial. It aims to recruit 213 out-patients, randomized to receive either a 24-h infusion of levosimendan at 0.1 μg/kg/min dose, without a loading dose, every 30 days, or placebo. RESULTS The main objective is to assess the incidence of admission for acute HF worsening during 12 months. Secondarily, the trial will assess the effect of intermittent levosimendan on other variables, including the time in days from randomization to first admission for acute HF worsening, mortality and serious adverse events. CONCLUSIONS The LAICA trial results could allow confirmation of the usefulness of intermittent levosimendan infusion in reducing the rate of hospitalization for HF worsening in advanced HF outpatients.