Can Clinical Symptoms and Laboratory Results Predict CT Abnormality? Initial Findings Using Novel Machine Learning Techniques in Children With COVID-19 Infections

The rapid spread of coronavirus 2019 disease (COVID-19) has manifested a global public health crisis, and chest CT has been proven to be a powerful tool for screening, triage, evaluation and prognosis in COVID-19 patients. However, CT is not only costly but also associated with an increased incidence of cancer, in particular for children. This study will question whether clinical symptoms and laboratory results can predict the CT outcomes for the pediatric patients with positive RT-PCR testing results in order to determine the necessity of CT for such a vulnerable group. Clinical data were collected from 244 consecutive pediatric patients (16 years of age and under) treated at Wuhan Children's Hospital with positive RT-PCR testing, and the chest CT were performed within 3 days of clinical data collection, from January 21 to March 8, 2020. This study was approved by the local ethics committee of Wuhan Children's Hospital. Advanced decision tree based machine learning models were developed for the prediction of CT outcomes. Results have shown that age, lymphocyte, neutrophils, ferritin and C-reactive protein are the most related clinical indicators for predicting CT outcomes for pediatric patients with positive RT-PCR testing. Our decision support system has managed to achieve an AUC of 0.84 with 0.82 accuracy and 0.84 sensitivity for predicting CT outcomes. Our model can effectively predict CT outcomes, and our findings have indicated that the use of CT should be reconsidered for pediatric patients, as it may not be indispensable.

Trends in MERS-CoV, SARS-CoV, and SARS-CoV-2 (COVID-19) Diagnosis Strategies: A Patent Review

The emergence of a new coronavirus (SARS-CoV-2) outbreak represents a challenge for the diagnostic laboratories responsible for developing test kits to identify those infected with SARS-CoV-2. Methods with rapid and accurate detection are essential to control the sources of infection, to prevent the spread of the disease and to assist decision-making by public health managers. Currently, there is a wide variety of tests available with different detection methodologies, levels of specificity and sensitivity, detection time, and with an extensive range of prices. This review therefore aimed to conduct a patent search in relation to tests for the detection of SARS-CoV, MERS-CoV, and SARS-CoV-2. The greatest number of patents identified in the search were registered between 2003 and 2011, being mainly deposited by China, the Republic of Korea, and the United States. Most of the patents used the existing RT-PCR, ELISA, and isothermal amplification methods to develop simple, sensitive, precise, easy to use, low-cost tests that reduced false-negative or false-positive results. The findings of this patent search show that an increasing number of materials and diagnostic tests for the coronavirus are being produced to identify infected individuals and combat the growth of the current pandemic; however, there is still a question in relation to the reliability of the results of these tests.