Activation and disease control of patients on chronic hemodialysis: An observational study.

BACKGROUND AND OBJECTIVE: Patient activation is a concept that refers to the willingness to manage one's health and medical care. To assess it, a patient activation measure (PAM) has been developed and validated. Several studies report low activation in patients with chronic diseases. However, information on activation in hemodialysis patients is scarce. The aim of the present study is to describe the activation level of patients on chronic treatment in an HD unit and its relationship with disease control parameters. MATERIALS AND METHODS: Cross-sectional observational study in patients with advanced chronic kidney disease on chronic HD treatment. Ninety-six patients were included. Activation was measured with the PAM-13 questionnaire. Its relationship with descriptive variables (age, sex, comorbidity, studies, habitat) and disease control variables (vascular access, blood flow, potassaemia, phosphataemia, interdialytic gain) was studied. For this purpose, Spearman's correlation test, multiple linear regression model and logistic model were used as statistical methods. RESULTS: The mean (SD) PAM-13 score was 63.19 (15.21). Activation was significantly associated with vascular access (P = 0.003), blood flow (P = 0.024), and interdialytic gain of patients (P = 0.008). CONCLUSIONS: Activation in patients on chronic hemodialysis treatment is low. Higher activation is related having an arteriovenous fistula, higher blood flow and lower interdialytic gain. Future studies are needed to confirm and apply our results.

MRI texture-based radiomics analysis for the identification of altered functional networks in alcoholic patients and animal models.

Alcohol use disorder (AUD) is a complex condition representing a leading risk factor for death, disease and disability. Its high prevalence and severe health consequences make necessary a better understanding of the brain network alterations to improve diagnosis and treatment. The purpose of this study was to evaluate the potential of resting-state fMRI 3D texture features as a novel source of biomarkers to identify AUD brain network alterations following a radiomics approach. A longitudinal study was conducted in Marchigian Sardinian alcohol-preferring msP rats (N = 36) who underwent resting-state functional and structural MRI before and after 30 days of alcohol or water consumption. A cross-sectional human study was also conducted among 33 healthy controls and 35 AUD patients. The preprocessed functional data corresponding to control and alcohol conditions were used to perform a probabilistic independent component analysis, identifying seven independent components as resting-state networks. Forty-three radiomic features extracted from each network were compared using a Wilcoxon signed-rank test with Holm correction to identify the network most affected by alcohol consumption. Features extracted from this network were then used in the machine learning process, evaluating two feature selection methods and six predictive models within a nested cross-validation structure. The classification was evaluated by computing the area under the ROC curve. Images were quantized using different numbers of gray-levels to test their influence on the results. The influence of ageing, data preprocessing, and brain iron accumulation were also analyzed. The methodology was validated using structural scans. The striatal network in alcohol-exposed msP rats presented the most significant number of altered features. The radiomics approach supported this result achieving good classification performance in animals (AUC = 0.915 ± 0.100, with 12 features) and humans (AUC = 0.724 ± 0.117, with 9 features) using a random forest model. Using the structural scans, high accuracy was achieved with a multilayer perceptron in both species (animals: AUC > 0.95 with 2 features, humans: AUC > 0.82 with 18 features). The best results were obtained using a feature selection method based on the p-value. The proposed radiomics approach is able to identify AUD patients and alcohol-exposed rats with good accuracy, employing a subset of 3D features extracted from fMRI. Furthermore, it can help identify relevant networks in drug addiction.

Estudio de intervención para comprobar el efecto de la música clásica en directo durante hemodiálisis sobre la calidad de vida de pacientes con enfermedad renal crónica / Intervention study to verify the effect of live classic music during hemodialysis on the quality of life of patients with chronic kidney disease

Introducción y objetivo: La música ha estado estrechamente unida a la medicina desde la antigüedad, y ha aportado numerosos beneficios a la salud de los pacientes. El paciente con enfermedad renal crónica en tratamiento de hemodiálisis (HD), generalmente, presenta una calidad de vida relacionada con la salud (CVRS) inferior a los valores de referencia de la población general. El objetivo del presente estudio es verificar si la intervención de música clásica en directo e in situ’ durante el tratamiento de HD tiene efectos sobre la CVRS de los pacientes.Materiales y métodos: Se realizó un estudio de intervención, prospectivo y aleatorizado por grupos, en pacientes con enfermedad renal crónica en tratamiento con HD. Durante 4 semanas un grupo de pacientes recibía la intervención con música clásica en directo 30 o 40min durante las sesiones de HD, mientras el grupo control realizaba el tratamiento habitual. Variables descriptivas: edad, sexo, meses en tratamiento, Kt/V, hemoglobina y albúmina. Variable resultado: CVRS, se midió con el cuestionario de salud Kidney Diseasse Quality of life (KDQOL-SF) antes y después de la intervención musical. (AU)

Introduction and objective: Music has been closely linked to medicine since ancient times, and has brought numerous benefits to the health of patients. Patients with chronic kidney disease undergoing hemodialysis (HD) generally have a health-related quality of life (HRQL) lower than the reference values of the general population. The objective of the present study is to verify if the intervention of classical music live and “in situ” during the treatment of HD has effects on the HRQL of the patients.Materials and methods: A prospective, group-randomized intervention study of 4 weeks’ duration was carried out in patients with chronic kidney disease undergoing HD. Descriptive variables are included for data analysis: age, sex, months in treatment, Kt/V, hemoglobin and albumin. Result variable: HRQL, measured with the Kidney Disease health questionnaire Quality of Life (KDQOL-SF) before and after the musical intervention. (AU)

Impact of an intradialysis virtual-reality-based exercise program on healthcare resources expenditure: a micro-costing analysis.

BACKGROUND: Engagement in exercise by haemodialysis (HD) patients has been shown to generate benefits both in terms of improved functional capacity and in the health-related quality of life. The use of non-immersive virtual reality (VR) games represents a new format for the implementation of intradialysis exercise. Some studies have shown that engaging in exercise for 6 months reduces the consumption of antihypertensive drugs and decreases the time spent admitted to hospital among individuals receiving HD treatments. The objective of this study was to evaluate changes in the consumption of healthcare resources and micro-costing for patients on HD who completed a VR exercise program. MATERIALS AND METHODS: Design: This study is a secondary analysis of a clinical trial. The participants performed an intradialysis exercise program with non-immersive virtual reality for 3 months. The variables were recorded in two periods: 12 months before and 12 months after the start of the exercise program. RESULTS: The micro-costing analysis showed a significant decrease in the mean cost, in euros, for the consumption of laboratory tests - 330 (95% CI:[- 533, - 126];p = 0.003), outpatient visits - 351 ([- 566, - 135];p = 0.003), and radiology tests - 111 ([- 209, - 10];p = 0.03) in the 12 months after the implementation of the exercise program relative to the 12 months prior to its start. CONCLUSION: The implementation of intradialysis exercise programs decreased the expenditure of some healthcare resources. Future studies could help clarify if longer interventions would have a stronger impact on these cost reductions.

Glioblastomas and brain metastases differentiation following an MRI texture analysis-based radiomics approach.

PURPOSE: To evaluate the potential of 2D texture features extracted from magnetic resonance (MR) images for differentiating brain metastasis (BM) and glioblastomas (GBM) following a radiomics approach. METHODS: This retrospective study included 50 patients with BM and 50 with GBM who underwent T1-weighted MRI between December 2010 and January 2017. Eighty-eight rotation-invariant texture features were computed for each segmented lesion using six texture analysis methods. These features were also extracted from the four images obtained after applying the discrete wavelet transform (88 features × 4 images). Three feature selection methods and five predictive models were evaluated. A 5-fold cross-validation scheme was used to randomly split the study group into training (80 patients) and testing (20 patients), repeating the process ten times. Classification was evaluated computing the average area under the receiver operating characteristic curve. Sensibility, specificity and accuracy were also computed. The whole process was tested quantizing the images with different gray-level values to evaluate their influence in the final results. RESULTS: Highest classification accuracy was obtained using the original images quantized with 128 gray-levels and a feature selection method based on the p-value. The best overall performance was achieved using a support vector machine model with a subset of 32 features (AUC = 0.896 ± 0.067, sensitivity of 82% and specificity of 80%). Naïve Bayes and k-nearest neighbors models showed also valuable results (AUC ≈ 0.8) with a lower number of features (<13), thus suggesting that these models may be more generalizable when using external validations. CONCLUSION: The proposed radiomics MRI approach is able to discriminate between GBM and BM with high accuracy employing a set of 2D texture features, thus helping in the diagnosis of brain lesions in a fast and non-invasive way.

Identification of the presence of ischaemic stroke lesions by means of texture analysis on brain magnetic resonance images.

BACKGROUND: The differential quantification of brain atrophy, white matter hyperintensities (WMH) and stroke lesions is important in studies of stroke and dementia. However, the presence of stroke lesions is usually overlooked by automatic neuroimage processing methods and the-state-of-the-art deep learning schemes, which lack sufficient annotated data. We explore the use of radiomics in identifying whether a brain magnetic resonance imaging (MRI) scan belongs to an individual that had a stroke or not. MATERIALS AND METHODS: We used 1800 3D sets of MRI data from three prospective studies: one of stroke mechanisms and two of cognitive ageing, evaluated 114 textural features in WMH, cerebrospinal fluid, deep grey and normal-appearing white matter, and attempted to classify the scans using a random forest and support vector machine classifiers with and without feature selection. We evaluated the discriminatory power of each feature independently in each population and corrected the result against Type 1 errors. We also evaluated the influence of clinical parameters in the classification results. RESULTS: Subtypes of ischaemic strokes (i.e. lacunar vs. cortical) cannot be discerned using radiomics, but the presence of a stroke-type lesion can be ascertained with accuracies ranging from 0.7 < AUC < 0.83. Feature selection, tissue type, stroke subtype and MRI sequence did not seem to determine the classification results. From all clinical variables evaluated, age correlated with the proportion of images classified correctly using either different or the same descriptors (Pearson r = 0.31 and 0.39 respectively, p < 0.001). CONCLUSIONS: Texture features in conventionally automatically segmented tissues may help in the identification of the presence of previous stroke lesions on an MRI scan, and should be taken into account in transfer learning strategies of the-state-of-the-art deep learning schemes.

ALTEA: A Software Tool for the Evaluation of New Biomarkers for Alzheimer's Disease by Means of Textures Analysis on Magnetic Resonance Images.

The current criteria for diagnosing Alzheimer's disease (AD) require the presence of relevant cognitive deficits, so the underlying neuropathological damage is important by the time the diagnosis is made. Therefore, the evaluation of new biomarkers to detect AD in its early stages has become one of the main research focuses. The purpose of the present study was to evaluate a set of texture parameters as potential biomarkers of the disease. To this end, the ALTEA (ALzheimer TExture Analyzer) software tool was created to perform 2D and 3D texture analysis on magnetic resonance images. This intuitive tool was used to analyze textures of circular and spherical regions situated in the right and left hippocampi of a cohort of 105 patients: 35 AD patients, 35 patients with early mild cognitive impairment (EMCI) and 35 cognitively normal (CN) subjects. A total of 25 statistical texture parameters derived from the histogram, the Gray-Level Co-occurrence Matrix and the Gray-Level Run-Length Matrix, were extracted from each region and analyzed statistically to study their predictive capacity. Several textural parameters were statistically significant (p < 0.05) when differentiating AD subjects from CN and EMCI patients, which indicates that texture analysis could help to identify the presence of AD.

Classifying brain metastases by their primary site of origin using a radiomics approach based on texture analysis: a feasibility study.

OBJECTIVE: To examine the capability of MRI texture analysis to differentiate the primary site of origin of brain metastases following a radiomics approach. METHODS: Sixty-seven untreated brain metastases (BM) were found in 3D T1-weighted MRI of 38 patients with cancer: 27 from lung cancer, 23 from melanoma and 17 from breast cancer. These lesions were segmented in 2D and 3D to compare the discriminative power of 2D and 3D texture features. The images were quantized using different number of gray-levels to test the influence of quantization. Forty-three rotation-invariant texture features were examined. Feature selection and random forest classification were implemented within a nested cross-validation structure. Classification was evaluated with the area under receiver operating characteristic curve (AUC) considering two strategies: multiclass and one-versus-one. RESULTS: In the multiclass approach, 3D texture features were more discriminative than 2D features. The best results were achieved for images quantized with 32 gray-levels (AUC = 0.873 ± 0.064) using the top four features provided by the feature selection method based on the p-value. In the one-versus-one approach, high accuracy was obtained when differentiating lung cancer BM from breast cancer BM (four features, AUC = 0.963 ± 0.054) and melanoma BM (eight features, AUC = 0.936 ± 0.070) using the optimal dataset (3D features, 32 gray-levels). Classification of breast cancer and melanoma BM was unsatisfactory (AUC = 0.607 ± 0.180). CONCLUSION: Volumetric MRI texture features can be useful to differentiate brain metastases from different primary cancers after quantizing the images with the proper number of gray-levels. KEY POINTS: • Texture analysis is a promising source of biomarkers for classifying brain neoplasms. • MRI texture features of brain metastases could help identifying the primary cancer. • Volumetric texture features are more discriminative than traditional 2D texture features.

A radiomics evaluation of 2D and 3D MRI texture features to classify brain metastases from lung cancer and melanoma.

Brain metastases are occasionally detected before diagnosing their primary site of origin. In these cases, simple visual examination of medical images of the metastases is not enough to identify the primary cancer, so an extensive evaluation is needed. To avoid this procedure, a radiomics approach on magnetic resonance (MR) images of the metastatic lesions is proposed to classify two of the most frequent origins (lung cancer and melanoma). In this study, 50 T1-weighted MR images of brain metastases from 30 patients were analyzed: 27 of lung cancer and 23 of melanoma origin. A total of 43 statistical texture features were extracted from the segmented lesions in 2D and 3D. Five predictive models were evaluated using a nested cross-validation scheme. The best classification results were achieved using 3D texture features for all the models, obtaining an average AUC > 0.9 in all cases and an AUC = 0.947 ± 0.067 when using the best model (naïve Bayes).