Comprehensive analysis of clinical data for COVID-19 outcome estimation with machine learning models

COVID-19 is a global threat for the healthcare systems due to the rapid spread of the pathogen that causes it. In such situation, the clinicians must take important decisions, in an environment where medical resources can be insufficient. In this task, the computer-aided diagnosis systems can be very useful not only in the task of supporting the clinical decisions but also to perform relevant analyses, allowing them to understand better the disease and the factors that can identify the high risk patients. For those purposes, in this work, we use several machine learning algorithms to estimate the outcome of COVID-19 patients given their clinical information. Particularly, we perform 2 different studies: the first one estimates whether the patient is at low or at high risk of death whereas the second estimates if the patient needs hospitalization or not. The results of the analyses of this work show the most relevant features for each studied scenario, as well as the classification performance of the considered machine learning models. In particular, the XGBoost algorithm is able to estimate the need for hospitalization of a patient with an AUC-ROC of Graphical abstract

Comprehensive analysis of clinical data for COVID-19 outcome estimation with machine learning models.

COVID-19 is a global threat for the healthcare systems due to the rapid spread of the pathogen that causes it. In such situation, the clinicians must take important decisions, in an environment where medical resources can be insufficient. In this task, the computer-aided diagnosis systems can be very useful not only in the task of supporting the clinical decisions but also to perform relevant analyses, allowing them to understand better the disease and the factors that can identify the high risk patients. For those purposes, in this work, we use several machine learning algorithms to estimate the outcome of COVID-19 patients given their clinical information. Particularly, we perform 2 different studies: the first one estimates whether the patient is at low or at high risk of death whereas the second estimates if the patient needs hospitalization or not. The results of the analyses of this work show the most relevant features for each studied scenario, as well as the classification performance of the considered machine learning models. In particular, the XGBoost algorithm is able to estimate the need for hospitalization of a patient with an AUC-ROC of 0 . 8415 ± 0 . 0217 while it can also estimate the risk of death with an AUC-ROC of 0 . 7992 ± 0 . 0104 . Results have demonstrated the great potential of the proposal to determine those patients that need a greater amount of medical resources for being at a higher risk. This provides the healthcare services with a tool to better manage their resources.

Monoclonal antibody therapies in the management of SARS-CoV-2 infection.

INTRODUCTION: Neutralizing antibodies (NAbs) that target key domains of the spike protein in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may have therapeutic value because of their specificity. Depending on the targeted epitope, single agents may be effective, but combined treatment involving multiple NAbs may be necessary to prevent the emergence of resistant variants. AREAS COVERED: This article highlights the accelerated regulatory processes established to facilitate the review and approval of potential therapies. An overview of treatment approaches for SARS-CoV-2 infection, with detailed examination of the preclinical and clinical evidence supporting the use of NAbs, is provided. Finally, insights are offered into the potential benefits and challenges associated with the use of these agents. EXPERT OPINION: NAbs offer an effective, evidence-based therapeutic intervention during the early stages of SARS-CoV-2 infection when viral replication is the primary factor driving disease progression. As the pandemic progresses, appropriate use of NAbs will be important to minimize the risk of escape variants. Ultimately, the availability of effective treatments for COVID-19 will allow the establishment of treatment algorithms for minimizing the substantial rates of hospitalization, morbidity (including long COVID) and mortality currently associated with the disease.

2019 novel coronavirus disease (COVID-19) in patients with inflammatory bowel diseases.

BACKGROUND: Data on patients with inflammatory bowel diseases (IBD) who have had 2019 novel coronavirus (SARS-CoV-2) disease (COVID-19) are needed. AIMS: To report the clinical characteristics, including gastrointestinal symptoms, of COVID-19 in IBD patients, and to assess the risk of COVID-19 in IBD. METHODS: This case series included consecutive IBD patients with laboratory-confirmed COVID-19. Age-adjusted cumulative incidences were compared with the general population in the Madrid region. RESULTS: Through April 8, 12 of 1918 IBD patients were diagnosed with COVID-19. The average age was 52 years, 75% of the patients were female and 58.3% had Crohn's disease. Seven patients (58%) were on maintenance treatment with immunomodulators/biologics, of these four with combined therapy (33%). Eight patients (66%) required hospitalisation (one intensive care unit admission, and two deaths), and four patients were isolated at home. Nine patients had diarrhoea ranging between 4 and 10 loose stools per day (mean 5.4, SD 1.6). In five patients (42%) diarrhoea was a presenting symptom. In two patients, diarrhoea was the only symptom at debut. Cumulative incidence of COVID-19 was 6.2 per 1000 IBD patients. IBD patients had a lower adjusted incidence ratio of COVID-19 (OR 0.74, 95% CI 0.70-0.77; P < 0.001), and a similar associated mortality ratio (OR 0.95, 95% CI: 0.84-1.06; P = 0.36), compared with the general population. CONCLUSIONS: IBD patients do not have an increased risk of COVID-19 and associated mortality compared with the general population. In many IBD patients, diarrhoea was a presenting symptom, and sometimes, was the only symptom at onset of COVID-19.