ABSTRACT
OBJECTIVE: To explore the medical evidence published until April 20, 2022, about the efficacy and safety of tocilizumab in COVID-19 patients. METHODOLOGY: Scoping review that included PubMed and Scopus, searching for clinical trials and observational studies in English and Spanish. Additionally, records of clinical trials from the International Clinical Trials Registry Platform were analyzed. RESULT(S): Fifty-four documents were included: retrospective cohort studies (n = 20), randomized clinical trials (n = 16), case control studies (n = 7), non-randomized clinical trials (n = 5) and prospective cohort studies (n = 6), with a total study population of 20,007 patients. There were 15 records of clinical trials of which 10 were registered in the US National Library of Medicine. CONCLUSION(S): Tocilizumab could be effective and safe to treat patients with moderate to critical COVID-19, in conjunction with additional immunomodulators and antivirals. A greater number of randomized clinical trials, however, are needed to explore the efficacy and safety of tocilizumab.Copyright © 2023 Comunicaciones Cientificas Mexicanas S.A. de C.V.. All rights reserved.
ABSTRACT
This work introduces a 3D deep learning methodology to stratify patients according to the severity of lung infection caused by COVID-19 disease on computerized tomography images (CT). A set of volumetric attention maps were also obtained to explain the results and support the diagnostic tasks. The validation of the approach was carried out on a dataset composed of 350 patients, diagnosed by the RT-PCR assay either as negative (control - 175) or positive (COVID-19 - 175). Additionally, the patients were graded (0-25) by two expert radiologists according to the extent of lobar involvement. These gradings were used to define 5 COVID-19 severity categories. The model yields an average 60% accuracy for the multi-severity classification task. Additionally, a set of Mann Whitney U significance tests were conducted to compare the severity groups. Results show that patients in different severity groups have significantly different severity scores (p < 0.01) for all the compared severity groups.