A new simple score to predict mortality of COVID-19 in the emergency department

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic is one of the greatest challenges facing global medical research. The availability of a clinical score that can predict mortality risk at the time of diagnosis could be a valuable tool in the hands of emergency physicians to make clinical decisions. Our study is designed to evaluate clinical and laboratory endpoints associated with mortality and to determine a prognostic score based on clinical and laboratory variables. We retrospectively enrolled 367 patients diagnosed with coronavirus disease 19 (COVID-19) in our emergency department (ED). We evaluated their mortality 60 days after diagnosis. Symptoms, demographic data, concomitant diseases, and various laboratory parameters were obtained from all patients. Variables related to death were assessed using multiple logistic regression analysis. From these, we created a score called ANCOC (Age, blood urea Nitrogen, C-reactive protein, Oxygen saturation, Comorbidities). The area under the receiver operating characteristic (ROC) curve was calculated for the ANCOC and for the 4C score. The 4C score has been described and validated in previous works and can predict mortality in COVID-19 patients. We compared the 2 scores and analysed sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for 60-day mortality for the ANCOC score. The ANCOC and 4C scores accurately predicted death from COVID-19. There were no differences in accuracy between the scores. An ANCOC score <-1 identified patients who will recover with a PPV and sensitivity of 100%, whereas a score >3 identified patients at high risk of death. The ANCOC score has very high diagnostic accuracy in predicting the risk of death in patients with COVID-19 diagnosed at ED. The ANCOC score has similar accuracy to the 4C score but is easier to calculate. If validated by external cohorts, this score could be an additional tool in the hands of ED physicians to identify COVID-19 patients at high risk of death.Copyright © 2023 The Author(s). Published by MRE Press.

Identification of spatially constrained homogeneous clusters of COVID-19 transmission in Italy

This paper introduces an approach to identify a set of spatially constrained homogeneous areas that are maximally homogeneous in terms of epidemic trends. The proposed hierarchical algorithm is based on the dynamic time warping distances between epidemic time trends, where units are constrained by a spatial proximity graph. Two different applications of this approach to Italy are presented, based on different data (number of positive tests and number of differential deaths with respect to previous years) and different observational units observed at different spatial scales (provinces and labour market areas). The provincial analysis was mainly used to divide the national territory into macro-areas with different contagion trends, while the more detailed partition was carried out only for the macro-areas with higher risk of transmission of the infection. Both applications, above all that related to labour market areas, show the existence of well-defined areas where the dynamics of growth of the infection have been strongly differentiated. The adoption of the same lockdown policy throughout the entire national territory has been therefore sub-optimal, highlighting once again the urgent need for local data-driven policies.