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ObjectivesHyponatraemia is the most common electrolyte disturbance in hospitalized children with an incidence of 15-30%. The overall frequency of hyponatraemia and its severity are not well known. The objective of our study was to determine the incidence, severity and associated risk factors of community and hospital acquired hyponatraemia, on a general paediatric ward.Study designPseudonymised admission data was collected for 5550 children admitted to the paediatric ward from June 2012 to December 2019 on plasma sodium and discharge diagnosis with ICD codes. Clinically relevant diagnostic groups were created based on coding diagnosis. Hyponatraemia was classified as mild, moderate and severe; and community acquired (CAH) and hospital acquired (HAH) hyponatraemia. Incidence, severity and risk factors were analysed by logistic regression model.ResultsPlasma sodium levels were available in 3102 out of 5550 admitted children. The incidence of CAH was 15.8% in contrast to 1.4% for HAH. Most of the cases were mild (90.8%) to moderate (8.6%) with only two cases of severe CAH. There were no clinical complications in any of the hyponatraemic children. Age, length of stay and diagnosis at discharge were important factors significantly correlated with CAH.ConclusionsCommunity acquired hyponatraemia is more common than hospital acquired hyponatraemia in clinical practice. Severe cases of both are rare. Children from 2-11 years presenting with infections, cardiovascular disorders and gastrointestinal disorders are at higher risk of developing hyponatraemia with longer hospital stays. Further multicentre studies are needed to ascertain the true incidence, severity and clinical consequences of this electrolyte disturbance.

Electroencephalogram is a useful interface system that translates the human electrical brain activity into voltage signals. By these means, the recorded brain waves can be employed to characterize, classify, or diagnose mental disorders. A novel neural network model to classify patients with schizophrenia based on electroencephalograms is presented. The proposed model decomposes the multichannel electroencephalogram records into a group of multivariate novel radial basis functions using a fuzzy means algorithm. The decomposition permits to extract different electroencephalogram channel information and distinguish between two sort of classes i.e., schizophrenic patients and healthy controls. Results show improved accuracy compared to classical algorithms reported in the literature i.e., Support Vector Machine, Bayesian Linear Discriminant Analysis, Gaussian Naive Bayes, K-Nearest Neighbour or Adaboost. As a result, the method presented in this paper achieves the highest balanced accuracy, recall, precision and F1 score values, close to 93 % in all cases. The model presented in this paper may be integrated in real time tools involved during the diagnostic of schizophrenia.