Rapid Assessment of COVID-19 Mortality Risk with GASS Classifiers.

Risk prediction models are fundamental to effectively triage incoming COVID-19 patients. However, current triaging methods often have poor predictive performance, are based on variables that are expensive to measure, and often lead to hard-to-interpret decisions. We introduce two new classification methods that can predict COVID-19 mortality risk from the automatic analysis of routine clinical variables with high accuracy and interpretability. SVM22-GASS and Clinical-GASS classifiers leverage machine learning methods and clinical expertise, respectively. Both were developed using a derivation cohort of 499 patients from the first wave of the pandemic and were validated with an independent validation cohort of 250 patients from the second pandemic phase. The Clinical-GASS classifier is a threshold-based classifier that leverages the General Assessment of SARS-CoV-2 Severity (GASS) score, a COVID-19-specific clinical score that recently showed its effectiveness in predicting the COVID-19 mortality risk. The SVM22-GASS model is a binary classifier that non-linearly processes clinical data using a Support Vector Machine (SVM). In this study, we show that SMV22-GASS was able to predict the mortality risk of the validation cohort with an AUC of 0.87 and an accuracy of 0.88, better than most scores previously developed. Similarly, the Clinical-GASS classifier predicted the mortality risk of the validation cohort with an AUC of 0.77 and an accuracy of 0.78, on par with other established and emerging machine-learning-based methods. Our results demonstrate the feasibility of accurate COVID-19 mortality risk prediction using only routine clinical variables, readily collected in the early stages of hospital admission.

[Unicentric Castleman disease: a blind lymph node.] / Malattia di Castleman unicentrica: un linfonodo nascosto.

The paper reports the case of a 13-year-old female adolescent presenting with persistent fever. She had no other significant symptoms or signs. Laboratory examinations showed mild anemia and elevated C-reactive protein (CRP) and erythrosedimentation rate (ERS). The abdominal ultrasonography revealed para-aortic lymphadenopathy that was confirmed by magnetic resonance imaging (MRI) and positron emission tomography-computed tomography (PET/CT) that showed no other locations. The patient underwent laparoscopic excision but complete removal was not possible due to the position of the mass. The histological exam documented unicentric Castleman's disease. After surgery a clinical improvement was assisted but with persistence of very high CPR, ERS and serum amyloid. According to guidelines, she was treated with tocilizumab achieving complete remission of indices of inflammation. In the case with symptomatic unresectable unicentric Castleman's disease treatment with anti-IL-6 agents should be considered.