Tricuspid regurgitation (TR) is associated with an increased mortality. Previous studies have analyzed predictors of TR progression and the clinical impact of baseline TR. However, there is a lack of ...evidence regarding the natural history of TR: the pattern of change and clinical impact of progression.
The authors sought to evaluate predictors of TR progression and assess the prognostic impact of TR progression.
A total of 1,843 patients with at least moderate TR were prospectively followed up with consecutive echocardiographic studies and/or clinical evaluation. All patients with less than a 2-year follow-up were excluded. Clinical and echocardiographic features, hospitalizations for heart failure, and cardiovascular death and interventions were recorded to assess their impact in TR progression.
At a median 2.3-year follow-up, 19% of patients experienced progression. Patients with baseline moderate TR presented a rate progression of 4.9%, 10.1%, and 24.8% 1 year, 2 years, and 3 years, respectively. Older age (HR: 1.03), lower body mass index (HR: 0.95), chronic kidney disease (HR: 1.55), worse NYHA functional class (HR: 1.52), and right ventricle dilation (HR: 1.33) were independently associated with TR progression. TR progression was associated with an increase in chamber dilation as well as a decrease in ventriculoarterial coupling and in left ventricle ejection fraction (P < 0.001). TR progression was associated with an increased cardiovascular mortality and hospitalizations for heart failure (P < 0.001).
Marked individual variability in TR progression hindered accurate follow-up. In addition, TR progression was a determinant for survival regardless of initial TR severity.
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We describe an approach to select semantically coherent specialty subsets based on the historical use of terminology by different service areas. Our approach uses rule-based and machine learning ...techniques to obtain a reduced set of 29 specialties.
ICD-10 (International Classification of Diseases 10th revision) is a classification code for diseases, signs and symptoms, abnormal findings, complaints, social circumstances, and external causes of ...injury or diseases. This paper describes an automatic information retrieval approach to map free-text disease descriptions to ICD-10 codes. We use the Hospital Italiano de Buenos Aires (HIBA) terminology data mapped to ICD-10 codes as indexed data to find an appropriate ICD-10 code using search engine similarity metrics.
El presente estudio tuvo como objetivo determinar los niveles de Inmunoglobulina G (IgG) sérica en alpacas neonatas (5-23 días de edad) muertas por enterotoxemia y en animales de edades similares, ...pero clínicamente sanos. En una primera fase se estableció una curva estándar de degradación fisiológica de la IgG sérica a partir de sueros sanguíneos de seis neonatos aparentemente sanos a partir del día 2 y hasta los 21 días de edad (n=9). En una segunda fase se determinaron las concentraciones de IgG sérica en 17 alpacas neonatas muertas con lesiones compatibles con enterotoxemia y en 26 animales, de edades similares, aparentemente sanos. Las concentraciones de IgG, determinadas por la prueba de Inmunodifusión Radial, evidenciaron que todas las crías a las 48 horas del nacimiento presentaron concentraciones adecuadas de IgG, mientras que solo tres de los animales muertos por enterotoxemia tenían niveles de IgG por debajo de la curva estándar de degradación, aunque solo una de ellas con niveles inferiores a 900 mg/dl a los 14 días, lo que podría ser considerado como falla parcial de transferencia pasiva. Las concentraciones de IgG sérica de los 26 animales clínicamente normales (2893 mg/dl) y aquellas obtenidas de animales muertos por enterotoxemia (2361 mg/dl) fueron estadísticamente similares. El análisis de riesgo mediante la prueba de Odds Ratio (OR: 5.35; IC= 0.50-57.22) indicó que no existe asociación entre niveles adecuados de IgG y la mortalidad por enterotoxemia en alpacas neonatas.
The results of recent studies suggest the usefulness of PCT for early diagnosis of neonatal sepsis, with varying results. The aim of this prospective multicenter study was to determine the behavior ...of serum PCT concentrations in both uninfected and infected neonates, and to assess the value of this marker for diagnosis of neonatal sepsis of vertical transmission.
PCT was measured in 827 blood samples collected prospectively from 317 neonates admitted to 13 acute-care teaching hospitals in Spain over one year. Serum PCT concentrations were determined by a specific immunoluminometric assay. The diagnostic efficacy of PCT at birth and within 12-24 h and 36-48 h of life was evaluated calculating the sensitivity, specificity, and likelihood ratio of positive and negative results.
169 asymptomatic newborns and 148 symptomatic newborns (confirmed vertical sepsis: 31, vertical clinical sepsis: 38, non-infectious diseases: 79) were studied. In asymptomatic neonates, PCT values at 12-24 h were significantly higher than at birth and at 36-48 h of life. Resuscitation at birth and chorioamnionitis were independently associated to PCT values. Neonates with confirmed vertical sepsis showed significantly higher PCT values than those with clinical sepsis. PCT thresholds for the diagnosis of sepsis were 0.55 ng/mL at birth (sensitivity 75.4%, specificity 72.3%); 4.7 ng/mL within 12-24 h of life (sensitivity 73.8%, specificity 80.8%); and 1.7 ng/mL within 36-48 h of life (sensitivity 77.6%, specificity 79.2%).
Serum PCT was moderately useful for the detection of sepsis of vertical transmission, and its reliability as a maker of bacterial infection requires specific cutoff values for each evaluation point over the first 48 h of life.
Los estudiantes universitarios son susceptibles a presentar bajos niveles de bienestar psicológico (BP), lo que se asocia con altos niveles de ansiedad y depresión, hábitos poco saludables y baja ...aceptación de la imagen corporal (AIC), fundamentalmente en mujeres, y puede derivar en conductas alimentarias de riesgo (CAR). Objetivo: (1) Describir los niveles de BP, AIC y propensión a CAR; (2) determinar perfiles que integren la posesión diferenciada de BP con la AIC, la propensión a CAR y variables sociodemográficas; (3) identificar predictores de bajo nivel de BP, en estudiantes universitarias. Método: Estudio descriptivo, transversal y ex post facto con 781 mujeres universitarias que respondieron un cuestionario sociodemográfico, el Multidimensional Body Self Relations Questionnaire, la Escala de BP para Adultos y el Cuestionario Breve de CAR. Resultados: El 47% de las participantes presentan niveles de BP bajo, 41.4% medio y 10.8% alto; 34.8% presentó baja AIC, 50.3% media y 14.9% alta; 41.7% presentó CAR. Se detectaron cuatro perfiles de estudiantes: (1) consumen alcohol, tabaco y tienen vida sexualmente activa; (2) de áreas administrativas y ciencias sociales con una AIC alta, promedio alto, de escuelas privadas, BP de medio a alto y sin CAR; (3) áreas de la salud, ingenierías y otras, con AIC baja, promedio bajo, de escuelas públicas, BP bajo, con CAR y necesidad de atención psicológica; (4) sin consumo de alcohol y tabaco, sin vida sexual activa, con AIC media y promedio medio. Los predictores de bajo nivel de BP fueron presentar baja AIC y CAR, y reportar requerir atención psicológica.
University students are susceptible to low levels of psychological well-being (PWB), which is associated with high levels of anxiety and depression, unhealthy habits and low body image acceptance (BIA), mainly in women, and derives from risk eating behaviors (REB). Objective: (1) Describe the level of PWB, BIA and REB propensity; (2) determine profiles that integrate differentiated possession of PWB with BIA, propensity for REB, and sociodemographic variables (3) identify predictors of low PWB in women university students. Method: Descriptive, cross-sectional and ex post facto study with 781 university women answered a sociodemographic questionnaire, the Multidimensional Body-Self Relations Questionnaire, the Scale of Psychological Well-being for Adults and the Brief Questionnaire for Risk Eating Behaviors. Results: 47.9% showed a low PWB, 41.4% medium and 10.8% high; 34.8% showed low BIA, 50.3% medium and 14.9% high. 41.7% showed REB. Four student profiles were detected: (1) they consume alcohol, tobacco and are sexually active; (2) from administrative areas and social sciences with a high BIA, high average, from a private school, medium to high PWB and without REB; (3) from areas of health, engineering and others, with low BIA, low average, from public school, low PWB, with REB and in need of psychological care; (4) without alcohol and tobacco consumption, without active sexual life, with medium and medium average BIA. Low or medium BIA, presence of REB and need for psychological care were predictors of low level of PWB.
•Robust system to detect people only using depth information from a ToF camera.•System outperforms state-of-the-art methods in different datasets without fine-tuning.•Proposal runs in real time using ...conventional GPUs.•Computational demands are independent of the number of people in the scene.•Generated database is available to the research community.
This paper proposes a deep learning-based method to detect multiple people from a single overhead depth image with high precision. Our neural network, called DPDnet, is composed by two fully-convolutional encoder-decoder blocks built with residual layers. The main block takes a depth image as input and generates a pixel-wise confidence map, where each detected person in the image is represented by a Gaussian-like distribution, The refinement block combines the depth image and the output from the main block, to refine the confidence map. Both blocks are simultaneously trained end-to-end using depth images and ground truth head position labels. The paper provides a rigorous experimental comparison with some of the best methods of the state-of-the-art, being exhaustively evaluated in different publicly available datasets. DPDnet proves to outperform all the evaluated methods with statistically significant differences, and with accuracies that exceed 99%. The system was trained on one of the datasets (generated by the authors and available to the scientific community) and evaluated in the others without retraining, proving also to achieve high accuracy with varying datasets and experimental conditions. Additionally, we made a comparison of our proposal with other CNN-based alternatives that have been very recently proposed in the literature, obtaining again very high performance. Finally, the computational complexity of our proposal is shown to be independent of the number of users in the scene and runs in real time using conventional GPUs.
This paper describes a novel DNN-based system, named PD3net, that detects multiple people from a single depth image, in real time. The proposed neural network processes a depth image and outputs a ...likelihood map in image coordinates, where each detection corresponds to a Gaussian-shaped local distribution, centered at each person’s head. This likelihood map encodes both the number of detected people as well as their position in the image, from which the 3D position can be computed. The proposed DNN includes spatially separated convolutions to increase performance, and runs in real-time with low budget GPUs. We use synthetic data for initially training the network, followed by fine tuning with a small amount of real data. This allows adapting the network to different scenarios without needing large and manually labeled image datasets. Due to that, the people detection system presented in this paper has numerous potential applications in different fields, such as capacity control, automatic video-surveillance, people or groups behavior analysis, healthcare or monitoring and assistance of elderly people in ambient assisted living environments. In addition, the use of depth information does not allow recognizing the identity of people in the scene, thus enabling their detection while preserving their privacy. The proposed DNN has been experimentally evaluated and compared with other state-of-the-art approaches, including both classical and DNN-based solutions, under a wide range of experimental conditions. The achieved results allows concluding that the proposed architecture and the training strategy are effective, and the network generalize to work with scenes different from those used during training. We also demonstrate that our proposal outperforms existing methods and can accurately detect people in scenes with significant occlusions.
•Robust system to detect people only using depth information from a depth camera.•System outperforms state-of-the-art methods in different datasets without fine-tuning.•Proposal runs in real time using conventional GPUs.•Computational demands are independent of the number of people in the scene.•Generated database is available to the research community.
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