RESUMO
It is important to determine the risk for admission to the intensive care unit (ICU) in patients with COVID-19 presenting at the emergency department. Using artificial neural networks, we propose a new Data Ensemble Refinement Greedy Algorithm (DERGA) based on 15 easily accessible hematological indices. A database of 1596 patients with COVID-19 was used; it was divided into 1257 training datasets (80 % of the database) for training the algorithms and 339 testing datasets (20 % of the database) to check the reliability of the algorithms. The optimal combination of hematological indicators that gives the best prediction consists of only four hematological indicators as follows: neutrophil-to-lymphocyte ratio (NLR), lactate dehydrogenase, ferritin, and albumin. The best prediction corresponds to a particularly high accuracy of 97.12 %. In conclusion, our novel approach provides a robust model based only on basic hematological parameters for predicting the risk for ICU admission and optimize COVID-19 patient management in the clinical practice.
RESUMO
Extraintestinal manifestations of celiac disease (CD) are an integral part of the disease's clinical profile and, frequently, appear as the presenting feature. Given that anemia in CD may be multifactorial, increased awareness is needed on the part of treating physicians, and especially hematologists, to screen for CD. In this study, we highlight anemia as the presenting feature of CD which has remained undiagnosed for several years. In patients with a positive antibody testing or high suspicion of CD, endoscopy with a biopsy of the small intestine is performed, as it is considered the "gold standard" for diagnosing CD. Since most of the manifestations of CD are preventable or treatable with a gluten-free diet, an early diagnosis is vital for the prevention of serious and potentially lethal complications.
