Portuguese Society of Intensive Care Score for Predicting SARS-CoV-2 Infection Applied to Inpatients with Pneumonia: A Reliable Tool?

Objectives: Early identification of patients with the novel coronavirus induced-disease 2019 (COVID-19) and pneumonia is currently challenging. Few data are available on validated scores predictive of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) infection. The Portuguese Society of Intensive Care (PSIC) proposed a risk score whose main goals were to predict a higher probability of COVID-19 and optimize hospital resources, adjusting patients’ intervention. This study aimed to validate the PSIC risk score applied to inpatients with pneumonia. Methods: A retrospective analysis of 207 patients with pneumonia admitted to a suspected/confirmed SARS-CoV-2 infection specialized ward (20/03 to 20/05/2020) was performed. Score variables were analyzed to determine the significance of the independent predictive variables on the probability of a positive SARS-CoV-2 rRT-PCR test. The binary logistic regression modeling approach was selected. The best cut-off value was obtained with the Receiver Operating Characteristic (ROC) curve together with the evaluation of the discriminatory power through the Area Under the Curve (AUC). Results: The validation cohort included 145 patients. Typical chest computed-tomography features (OR, 12.16;95% CI, 3.32 - 44.50) and contact with a positive SARS-CoV-2 patient (OR, 6.56;95% CI, 1.33 - 32.30) were the most significant independent predictive variables. A score ≥ 10 increased suspicion for SARS-CoV-2 pneumonia. The AUC was 0.82 (95% CI, 0.73 - 0.91) demonstrating the good discriminating power for COVID-19 probability stratification in inpatients with pneumonia. Conclusions: The application of the PSIC score to inpatients with pneumonia may be of value in predicting the risk of COVID-19. Further studies from other centers are needed to validate this score widely.

Artificial neural network with a cross-validation approach to blast-induced ground vibration propagation modeling

Given their technical and economic advantages,the application of explosive substances to rock mass excavation is widely used.However,because of serious environmental restraints,there has been an increasing need to use complex tools to control environmental effects due to blast-induced ground vibrations.In the present study,an artificial neural network(ANN)with k-fold cross-validation was applied to a dataset containing 1114 observations that was obtained from published results;furthermore,quantitative and qualitative parameters were considered for ground vibration amplitude prediction.The best ANN model obtained has a maximum coefficient of determination of 0.840 and a mean absolute error of 5.59 and it comprises 17 input parameters,12 neurons in a one-layer hidden layer,and a sigmoid transfer function.Compared with the traditional models,the model obtained using the proposed methodology demonstrated better generalization ability.Furthermore,the proposed methodology offers an ANN model with higher prediction ability.