Early Routine Biomarkers of SARS-CoV-2 Morbidity and Mortality: Outcomes from an Emergency Section

Background. COVID-19 is a severe acute respiratory disease caused by SARS-CoV-2, a virus belonging to the Coronaviridae family. This disease has spread rapidly around the world and soon became an international public health emergency leading to an unpredicted pressure on the hospital emergency units. Early routine blood biomarkers could be key predicting factors of COVID-19 morbidity and mortality as suggested for C-reactive protein (CRP), IL-6, prothrombin and D-dimer. This study aims to identify other early routine blood biomarkers for COVID-19 severity prediction disclosed directly into the emergency section. Methods. Our research was conducted on 156 COVID-19 patients hospitalized at the Sapienza University Hospital “Policlinico Umberto I” of Rome, Italy, between March 2020 and April 2020 during the paroxysm’s initial phase of the pandemic. In this retrospective study, patients were divided into three groups according to their outcome: (1) emergency group (patients who entered the emergency room and were discharged shortly after because they did not show severe symptoms); (2) intensive care unit (ICU) group (patients who attended the ICU after admission to the emergency unit); (3) the deceased group (patients with a fatal outcome who attended the emergency and, afterward, the ICU units). Routine laboratory tests from medical records were collected when patients were admitted to the emergency unit. We focused on Aspartate transaminase (AST), Alanine transaminase (ALT), Lactate dehydrogenase (LDH), Creatine kinase (CK), Myoglobin (MGB), Ferritin, CRP, and D-dimer. Results. As expected, ANOVA data show an age morbidity increase in both ICU and deceased groups compared with the emergency group. A main effect of morbidity was revealed by ANOVA for all the analyzed parameters with an elevation between the emergency group and the deceased group. Furthermore, a significant increase in LDH, Ferritin, CRP, and D-dimer was also observed between the ICU group and the emergency group and between the deceased group and ICU group. Receiver operating characteristic (ROC) analyses confirmed and extended these findings. Conclusions. This study suggests that the contemporaneous presence of high levels of LDH, Ferritin, and as expected, CRP, and D-dimer could be considered as potential predictors of COVID-19 severity and death.

Machine learning approaches in Covid-19 severity risk prediction in Morocco

The purpose of this study is to develop and test machine learning-based models for COVID-19 severity prediction. COVID-19 test samples from 337 COVID-19 positive patients at Cheikh Zaid Hospital were grouped according to the severity of their illness. Ours is the first study to estimate illness severity by combining biological and non-biological data from patients with COVID-19. Moreover the use of ML for therapeutic purposes in Morocco is currently restricted, and ours is the first study to investigate the severity of COVID-19. When data analysis approaches were used to uncover patterns and essential characteristics in the data, C-reactive protein, platelets, and D-dimers were determined to be the most associated to COVID-19 severity prediction. In this research, many data reduction algorithms were used, and Machine Learning models were trained to predict the severity of sickness using patient data. A new feature engineering method based on topological data analysis called Uniform Manifold Approximation and Projection (UMAP) shown that it achieves better results. It has 100% accuracy, specificity, sensitivity, and ROC curve in conducting a prognostic prediction using different machine learning classifiers such as X_GBoost, AdaBoost, Random Forest, and ExtraTrees. The proposed approach aims to assist hospitals and medical facilities in determining who should be seen first and who has a higher priority for admission to the hospital.

Using Metrics for Risk Prediction in Object-Oriented Software: A Cross-Version Validation

This work aims to investigate the potential, from different perspectives, of a risk model to support Cross-Version Fault and Severity Prediction (CVFSP) in object-oriented software. The risk of a class is addressed from the perspective of two particular factors: the number of faults it can contain and their severity. We used various object-oriented metrics to capture the two risk factors. The risk of a class is modeled using the concept of Euclidean distance. We used a dataset collected from five successive versions of an open-source Java software system (ANT). We investigated different variants of the considered risk model, based on various combinations of object-oriented metrics pairs. We used different machine learning algorithms for building the prediction models: Naive Bayes (NB), J48, Random Forest (RF), Support Vector Machines (SVM) and Multilayer Perceptron (ANN). We investigated the effectiveness of the prediction models for Cross-Version Fault and Severity Prediction (CVFSP), using data of prior versions of the considered system. We also investigated if the considered risk model can give as output the Empirical Risk (ER) of a class, a continuous value considering both the number of faults and their different levels of severity. We used different techniques for building the prediction models: Linear Regression (LR), Gaussian Process (GP), Random forest (RF) and M5P (two decision trees algorithms), SmoReg and Artificial Neural Network (ANN). The considered risk model achieves acceptable results for both cross-version binary fault prediction (a g-mean of 0.714, an AUC of 0.725) and cross-version multi-classification of levels of severity (a g-mean of 0.758, an AUC of 0.771). The model also achieves good results in the estimation of the empirical risk of a class by considering both the number of faults and their levels of severity (intra-version analysis with a correlation coefficient of 0.659, cross-version analysis with a correlation coefficient of 0.486).

COVID-19 severity prediction based on patient risk factors and number of vaccines received

Background Vaccines are highly effective in preventing severe disease and death from COVID-19, and new medications that can reduce severity of disease have been approved. However, many countries are facing limited supply of vaccine doses and medications. A model estimating the probabilities for hospitalization and mortality according to individual risk factors and vaccine doses received could help prioritize vaccination and yet scarce medications to maximize lives saved and reduce the burden on hospitalization facilities. Methods Electronic health records from 101,034 individuals infected with SARS-CoV-2, since the beginning of the pandemic and until November 30, 2021 were extracted from a national healthcare organization in Israel. Logistic regression models were built to estimate the risk for subsequent hospitalization and death based on the number of BNT162b2 mRNA vaccine doses received and few major risk factors (age, sex, body mass index, hemoglobin A1C, kidney function, and presence of hypertension, pulmonary disease or malignancy). Results The models built predict the outcome of newly infected individuals with remarkable accuracy: area under the curve was 0.889 for predicting hospitalization, and 0.967 for predicting mortality. Even when a breakthrough infection occurs, having received three vaccination doses significantly reduces the risk of hospitalization by 66% (OR=0.336) and of death by 78% (OR=0.220). Conclusions The models enable rapid identification of individuals at high risk for hospitalization and death when infected. These patients can be prioritized to receive booster vaccination and the yet scarce medications. A calculator based on these models is made publicly available on http://covidest.web.app

C-REACTIVE PROTEIN/ALBUMIN RATIO AND NEUTROPHIL-LYMPHOCYTE RATIO AS PREDICTORS OF COVID-19 SEVERITY

Coronavirus disease 2019 (COVID-19) was announced in early December 2019. By genome sequencing, the virus was recognised. From Wuhan City, the virus spread globally. The pandemic situation was declared by the World Health Organization.The first case of COVID-19 in Indiawas reported in Kerala on January 27, 2020.The clinical features varied with disease severity. Most COVID-19 patients have non-severe manifestations and show a good prognosis. However, patients with severe disease may progress to pulmonary dysfunction, multiple organ dysfunction, and death. COVID-19 related to a considerable mortality rate in older patients and cases had other morbidities. Studies suggested that the inflammatory storm is a common finding in other coronaviruses.Similarly, increases in the inflammatory markers like C-reactive protein (CRP),ferritin,interleukin-6 (IL-6) and were described in COVID-19 (1). Albumin levels decreased in the inflammatory conditions reduced levels were confirmed in severe COVID-19 patients. Hypoalbuminemia and high CRP/albumin ratio were previously linked to the mortality of various clinical conditions as critically ill patients.To avoid the unnecessary or inappropriate utilisation of the healthcare resources, early prediction of the severity of COVID-19 will be helpful. Severity prediction will also improve the prognosis by reducing the mortality rate.Thus, this study aimed to evaluate the role of inflammatory markers in estimating the severity and predicting the prognosis of COVID-19. This study hypothesised that elevated values of CRP/ albumin ratio and the neutrophil-lymphocyte ratio at the time of COVID-19 diagnosis are associated with COVID-19 severity and mortality.

A Bibliometric Analysis and Benchmark of Machine Learning and AutoML in Crash Severity Prediction: The Case Study of Three Colombian Cities

Traffic accidents are of worldwide concern, as they are one of the leading causes of death globally. One policy designed to cope with them is the design and deployment of road safety systems. These aim to predict crashes based on historical records, provided by new Internet of Things (IoT) technologies, to enhance traffic flow management and promote safer roads. Increasing data availability has helped machine learning (ML) to address the prediction of crashes and their severity. The literature reports numerous contributions regarding survey papers, experimental comparisons of various techniques, and the design of new methods at the point where crash severity prediction (CSP) and ML converge. Despite such progress, and as far as we know, there are no comprehensive research articles that theoretically and practically approach the model selection problem (MSP) in CSP. Thus, this paper introduces a bibliometric analysis and experimental benchmark of ML and automated machine learning (AutoML) as a suitable approach to automatically address the MSP in CSP. Firstly, 2318 bibliographic references were consulted to identify relevant authors, trending topics, keywords evolution, and the most common ML methods used in related-case studies, which revealed an opportunity for the use AutoML in the transportation field. Then, we compared AutoML (AutoGluon, Auto-sklearn, TPOT) and ML (CatBoost, Decision Tree, Extra Trees, Gradient Boosting, Gaussian Naive Bayes, Light Gradient Boosting Machine, Random Forest) methods in three case studies using open data portals belonging to the cities of Medellín, Bogotá, and Bucaramanga in Colombia. Our experimentation reveals that AutoGluon and CatBoost are competitive and robust ML approaches to deal with various CSP problems. In addition, we concluded that general-purpose AutoML effectively supports the MSP in CSP without developing domain-focused AutoML methods for this supervised learning problem. Finally, based on the results obtained, we introduce challenges and research opportunities that the community should explore to enhance the contributions that ML and AutoML can bring to CSP and other transportation areas.