Non invasive ventilation during exercise in COPD patients: a systematic review with meta-analysis

COPD is a major cause of morbidity and mortality worldwide, representing a major public health problem due to the high health and economic resource consumption. Pulmonary rehabilitation is a standard care recommendation for these patients, in order to control the symptoms and optimize the functional capacity, reducing health care costs associated with exacerbations and activity limitations and participation. However, in patients with severe COPD exercise performance can be difficult, due to extreme dyspnea, decreased muscle strength and fatigue. In addition, hypoxemia and dyspnea during efforts and daily activities may occur, limiting their quality of life. Thus, NIV have been used as adjunct to exercise, in order to improve exercise capacity in these patients. However, there is no consensus for this technique recommendation. Our objective was to verify whether the use of NIV during exercise is effective than exercise without NIV in dyspnea, walked distance, blood gases and health status in COPD patients, through a systematic review and meta-analysis.

Chronic liver disease staging classification based on ultrasound, clinical and laboratorial data

In this work the identification and diagnosis of various stages of chronic liver disease is addressed. The classification results of a support vector machine, a decision tree and a k-nearest neighbor classifier are compared. Ultrasound image intensity and textural features are jointly used with clinical and laboratorial data in the staging process. The classifiers training is performed by using a population of 97 patients at six different stages of chronic liver disease and a leave-one-out cross-validation strategy. The best results are obtained using the support vector machine with a radial-basis kernel, with 73.20% of overall accuracy. The good performance of the method is a promising indicator that it can be used, in a non invasive way, to provide reliable information about the chronic liver disease staging.

Prevalência da doença pulmonar obstrutiva crónica em Lisboa, Portugal: estudo burden of obstructive lung disease

Introdução - A prevalência da doença pulmonar obstrutiva crónica (DPOC) apresenta valores muito heterogéneos em todo o mundo. A iniciativa Burden of Obstructive Lung Disease (BOLD) foi desenvolvida para que a prevalência da DPOC possa ser avaliada com metodologia uniformizada. O objetivo deste estudo foi estimar a prevalência da DPOC em adultos com 40 ou mais anos numa população alvo de 2 700 000 habitantes na região de Lisboa, de acordo com o protocolo BOLD. Métodos - A amostra foi estratificada de forma aleatória multifaseada selecionando-se 12 freguesias. O inquérito compreendia um questionário com informação sobre fatores de risco para a DPOC e doença respiratória autoreportada; adicionalmente, foi efetuada espirometria com prova de broncodilatação. Resultados - Foram incluídos 710 participantes com questionário e espirometria aceitáveis. A prevalência estimada da DPOC na população no estadio GOLD I+ foi de 14,2% (IC 95%: 11,1; 18,1) e de 7,3% no estadio ii+ (IC 95%: 4,7; 11,3). A prevalência não ajustada foi de 20,2% (IC 95%: 17,4; 23,3) no estadio i+ e de 9,5% (IC 95%: 7,6; 11,9) no estadio ii+. A prevalência da DPOC no estadio GOLD II+ aumentou com a idade, sendo mais elevada no sexo masculino. A prevalência estimada da DPOC no estadio GOLD I+ foi de 9,2% (IC 95%: 5,9; 14,0) nos não fumadores versus 27,4% (IC 95%: 18,5; 38,5) nos fumadores com carga tabágica de ≥ 20 Unidades Maço Ano. Detetou-se uma fraca concordância entre a referência a diagnóstico médico prévio e o diagnóstico espirométrico, com 86,8% de subdiagnósticos. Conclusões - O achado de uma prevalência estimada da DPOC de 14,2% sugere que esta é uma doença comum na região de Lisboa, contudo com uma elevada proporção de subdiagnósticos. Estes dados apontam para a necessidade de aumentar o grau de conhecimento dos profissionais de saúde sobre a DPOC, bem como a necessidade de maior utilização da espirometria nos cuidados de saúde primários.

Nutritional status influences generic and disease-specific quality of life measures in haemodialysis patients

Background: Poor nutritional status and worse health-related quality of life (QoL) have been reported in haemodialysis (HD) patients. The utilization of generic and disease specific QoL questionnaires in the same population may provide a better understanding of the significance of nutrition in QoL dimensions. Objective: To assess nutritional status by easy to use parameters and to evaluate the potential relationship with QoL measured by generic and disease specific questionnaires. Methods: Nutritional status was assessed by subjective global assessment adapted to renal patients (SGA), body mass index (BMI), nutritional intake and appetite. QoL was assessed by the generic EuroQoL and disease specific Kidney Disease Quality of Life-Short Form (KDQoL-SF) questionnaires. Results: The study comprised 130 patients of both genders, mean age 62.7 ± 14.7 years. The prevalence of undernutrition ranged from 3.1% by BMI ≤ 18.5 kg/m2 to 75.4% for patients below energy and protein intake recommendations. With the exception of BMI classification, undernourished patients had worse scores in nearly all QoL dimensions (EuroQoL and KDQoL-SF), a pattern which was dominantly maintained when adjusted for demographics and disease-related variables. Overweight/obese patients (BMI ≥ 25) also had worse scores in some QoL dimensions, but after adjustment the pattern was maintained only in the symptoms and problems dimension of KDQoL-SF (p = 0.011). Conclusion: Our study reveals that even in mildly undernourished HD patients, nutritional status has a significant impact in several QoL dimensions. The questionnaires used provided different, almost complementary perspectives, yet for daily practice EuroQoL is simpler. Assuring a good nutritional status, may positively influence QoL.

Molecular imaging agents for detection of β-amyloid plaques in Alzheimer’s disease

The formation of amyloid structures is a neuropathological feature that characterizes several neurodegenerative disorders, such as Alzheimer´s and Parkinson´s disease. Up to now, the definitive diagnosis of these diseases can only be accomplished by immunostaining of post mortem brain tissues with dyes such Thioflavin T and congo red. Aiming at early in vivo diagnosis of Alzheimer´s disease (AD), several amyloid-avid radioprobes have been developed for b-amyloid imaging by positron emission tomography (PET) and single-photon emission computed tomography (SPECT). The aim of this paper is to present a perspective of the available amyloid imaging agents, special those that have been selected for clinical trials and are at the different stages of the US Food and Drugs Administration (FDA) approval.

An application of structural equation modeling of test dispositional optimism as mediator or moderator in quality of life in patients with chronic disease

The aim of the present study was to test a hypothetical model to examine if dispositional optimism exerts a moderating or a mediating effect between personality traits and quality of life, in Portuguese patients with chronic diseases. A sample of 540 patients was recruited from central hospitals in various districts of Portugal. All patients completed self-reported questionnaires assessing socio-demographic and clinical variables, personality, dispositional optimism, and quality of life. Structural equation modeling (SEM) was used to analyze the moderating and mediating effects. Results suggest that dispositional optimism exerts a mediator rather than a moderator role between personality traits and quality of life, suggesting that “the expectation that good things will happen” contributes to a better general well-being and better mental functioning.

Classification and staging of chronic liver disease from multimodal data

Chronic liver disease (CLD) is most of the time an asymptomatic, progressive, and ultimately potentially fatal disease. In this study, an automatic hierarchical procedure to stage CLD using ultrasound images, laboratory tests, and clinical records are described. The first stage of the proposed method, called clinical based classifier (CBC), discriminates healthy from pathologic conditions. When nonhealthy conditions are detected, the method refines the results in three exclusive pathologies in a hierarchical basis: 1) chronic hepatitis; 2) compensated cirrhosis; and 3) decompensated cirrhosis. The features used as well as the classifiers (Bayes, Parzen, support vector machine, and k-nearest neighbor) are optimally selected for each stage. A large multimodal feature database was specifically built for this study containing 30 chronic hepatitis cases, 34 compensated cirrhosis cases, and 36 decompensated cirrhosis cases, all validated after histopathologic analysis by liver biopsy. The CBC classification scheme outperformed the nonhierachical one against all scheme, achieving an overall accuracy of 98.67% for the normal detector, 87.45% for the chronic hepatitis detector, and 95.71% for the cirrhosis detector.

Global and local detection of liver steatosis from ultrasound

Liver steatosis is a common disease usually associated with social and genetic factors. Early detection and quantification is important since it can evolve to cirrhosis. Steatosis is usually a diffuse liver disease, since it is globally affected. However, steatosis can also be focal affecting only some foci difficult to discriminate. In both cases, steatosis is detected by laboratorial analysis and visual inspection of ultrasound images of the hepatic parenchyma. Liver biopsy is the most accurate diagnostic method but its invasive nature suggest the use of other non-invasive methods, while visual inspection of the ultrasound images is subjective and prone to error. In this paper a new Computer Aided Diagnosis (CAD) system for steatosis classification and analysis is presented, where the Bayes Factor, obatined from objective intensity and textural features extracted from US images of the liver, is computed in a local or global basis. The main goal is to provide the physician with an application to make it faster and accurate the diagnosis and quantification of steatosis, namely in a screening approach. The results showed an overall accuracy of 93.54% with a sensibility of 95.83% and 85.71% for normal and steatosis class, respectively. The proposed CAD system seemed suitable as a graphical display for steatosis classification and comparison with some of the most recent works in the literature is also presented.

Data mining framework for fatty liver disease classification in ultrasound: a hybrid feature extraction paradigm

PURPOSE: Fatty liver disease (FLD) is an increasing prevalent disease that can be reversed if detected early. Ultrasound is the safest and ubiquitous method for identifying FLD. Since expert sonographers are required to accurately interpret the liver ultrasound images, lack of the same will result in interobserver variability. For more objective interpretation, high accuracy, and quick second opinions, computer aided diagnostic (CAD) techniques may be exploited. The purpose of this work is to develop one such CAD technique for accurate classification of normal livers and abnormal livers affected by FLD. METHODS: In this paper, the authors present a CAD technique (called Symtosis) that uses a novel combination of significant features based on the texture, wavelet transform, and higher order spectra of the liver ultrasound images in various supervised learning-based classifiers in order to determine parameters that classify normal and FLD-affected abnormal livers. RESULTS: On evaluating the proposed technique on a database of 58 abnormal and 42 normal liver ultrasound images, the authors were able to achieve a high classification accuracy of 93.3% using the decision tree classifier. CONCLUSIONS: This high accuracy added to the completely automated classification procedure makes the authors' proposed technique highly suitable for clinical deployment and usage.

The usefulness of ultrasound in the classification of chronic liver disease

Chronic Liver Disease is a progressive, most of the time asymptomatic, and potentially fatal disease. In this paper, a semi-automatic procedure to stage this disease is proposed based on ultrasound liver images, clinical and laboratorial data. In the core of the algorithm two classifiers are used: a k nearest neighbor and a Support Vector Machine, with different kernels. The classifiers were trained with the proposed multi-modal feature set and the results obtained were compared with the laboratorial and clinical feature set. The results showed that using ultrasound based features, in association with laboratorial and clinical features, improve the classification accuracy. The support vector machine, polynomial kernel, outperformed the others classifiers in every class studied. For the Normal class we achieved 100% accuracy, for the chronic hepatitis with cirrhosis 73.08%, for compensated cirrhosis 59.26% and for decompensated cirrhosis 91.67%.