Correlation of ldh and presence of lung injuries in patients with covid-19
International Journal of Development Research
Correlation of ldh and presence of lung injuries in patients with covid-19
Received 20th September, 2021; Received in revised form 10th October, 2021; Accepted 16th November, 2021; Published online 25th December, 2021
Copyright © 2021, Camille Esteves Chermont et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Given the importance of knowing more about the SARS-Cov-2 infection, a study was carried out using the medical records of 104 individuals during up to 14 days of COVID-19, treated at an outpatient clinic, between January and April 2021, in the Amazon region, northern Brazil. The objective was to correlate the levels of lactic dehydrogenase (LDH), a biomarker of tissue damage, with the presence and extension of lung lesions through lung CT scans. All patients were over 18 years old, of both genders, with mild or moderate clinical symptomatic form, and had the following demographics: male (42.1%), female (57.9%), with the age groups (years) of 18-25 (2.6%), 26-35 (7.0%), 36-45 (17.5%), 46-60 (40.4%) and over 60 (32.5%). CT scans showed mainly bilateral involvement (92.3%), with a typical pattern (92.3%), affecting 10-25% (42.5%) of the lung fields, with a predominance of three types of lesions: interlobular septal thickening (30.8%), nodules (16.3%) and ground-glass (11.5%), sometimes concomitant. LDH was the biomarker with more normal results (97.3%). There was no correlation between high levels of LDH and lung parenchyma changes, possibly because normality prevailed in both parameters and it is a cohort under extra-hospital care. There is a need for prospective and multicenter studies of patients with clinical forms with no severity, in order to bring contributions to outpatient care, the most prevalent in the world, through algorithms that can ensure intervention measures to reduce the possibility of unfavorable evolution and sequelae after COVID-19.