Comprehensive assessment of nutritional status and nutritional-related complications in newly diagnosed esophageal cancer patients: A cross-sectional study.

BACKGROUND & AIMS: Malnutrition is prevalent in upper gastrointestinal cancer patients. The purpose of this study was a comprehensive assessment of nutritional status in newly diagnosed patients with esophageal cancer. METHODS: Newly diagnosed esophageal cancer patients were referred to a chemo-radiation referral center in Mashhad, Iran, between February 2017 to February 2019. Anthropometric indices, a Patient-Generated Subjective Global Assessment (PG-SGA) tool, body composition, dietary intake, nutritional-related complications, and laboratory tests were assessed. RESULTS: One hundred and eighty-nine patients with a mean age of 67.1 ± 12 and a male to female ratio of 98 to 91 were included. Ninety-seven (51.3%) of patients had experienced significant weight loss and 56 (29.6%) were underweight at diagnosis. According to PG-SGA, 179 (94.7%) needed nutritional interventions. Reduced muscle mass and low handgrip strength were observed in 70 (39.4%) and 26 (14.4%) of patients, respectively. Inadequate intakes of energy (less than 24 kcal/kg/day) and protein (less than 1.2 g/kg/day) were found in 146 (77.8%) and 171 (91%) patients, respectively. The mean total daily energy and protein intakes of subjects were 943.8 ± 540 kcal/day, and 30.6 ± 21 g/day, respectively. The most common nutritional-related complications were as follows: dysphagia (84.8%), anorexia (31.6%), constipation (62.1%), esophageal pain (48.4%), and dyspepsia (41.1%). CONCLUSION: Our study demonstrated a high prevalence of malnutrition in newly diagnosed esophageal cancer patients. This fact demonstrates the importance of early screening of nutritional status via PG-SGA tool, clinical evaluation, dietary intake evaluations, and laboratory tests, based on which effective nutritional interventions and Symptoms management may be introduced in these patients.

Towards automatic detection of atrial fibrillation: A hybrid computational approach.

In this study, new methods coupling genetic programming with orthogonal least squares (GP/OLS) and simulated annealing (GP/SA) were applied to the detection of atrial fibrillation (AF) episodes. Empirical equations were obtained to classify the samples of AF and Normal episodes based on the analysis of RR interval signals. Another important contribution of this paper was to identify the effective time domain features of heart rate variability (HRV) signals via an improved forward floating selection analysis. The models were developed using the MIT-BIH arrhythmia database. A radial basis function (RBF) neural networks-based model was further developed using the same features and data sets to benchmark the GP/OLS and GP/SA models. The diagnostic performance of the GP/OLS and GP/SA classifiers was evaluated using receiver operating characteristics analysis. The results indicate a high level of efficacy of the GP/OLS model with sensitivity, specificity, positive predictivity, and accuracy rates of 99.11%, 98.91%, 98.23%, and 99.02%, respectively. These rates are equal to 99.11%, 97.83%, 98.23%, and 98.534% for the GP/SA model. The proposed GP/OLS and GP/SA models have a significantly better performance than the RBF and several models found in the literature.