Pathological Changes in the Lungs of Patients with a Lethal COVID-19 Clinical Course.
Valters ViksneIlze StrumfaMaris SpergaJanis ZiemelisJuris AbolinsPublished in: Diagnostics (Basel, Switzerland) (2022)
The novel coronavirus SARS-CoV-2 was identified in 2019 and quickly became the cause of the fifth worst pandemic in human history. Our goal for this research paper was to examine the morphology of the lungs in 88 patients that died from COVID-19 in Latvia, thus increasing the data available about the histological characteristics of SARS-CoV-2-induced disease. Lung tissue samples from 88 autopsies were visualized in hematoxylin-eosin and assessed by light microscopy. The male-to-female ratio was 56:32, and the mean age was 62 years ± 15.5 years (22-94 years). Clinically important laboratory data were assessed, including leucocyte count, CRP (C-reactive protein) and D-dimer levels. Signs of diffuse alveolar damage were found in 83/88 (94.3%; 95% CI 87.0-97.9) of patients, 38/88 (43.2%; 95% CI 33.3-53.6) in the exudative phase, and 45/88 (51.1%; 95% CI 40.8-61.3) in the proliferative phase. Vascular damage was identified in 70/88 (79.5%; 95% CI 69.9-86.7) of patients, and 83/88 (94.3%; 95% CI 87.0-97.9) had signs of thrombosis. A sparse inflammatory infiltrate of lymphocytes and macrophages was a common finding aside from cases with an identified coinfection. Eighty patients had significant co-morbidities, including coronary heart disease (49), primary arterial hypertension (41), and diabetes mellitus (34). Since our group's demographic profile and spectrum of co-morbidities were analogous to other reports, the histological findings of marked diffuse alveolar damage, widespread vascular lesions, and active thrombosis can be considered representative of severe COVID-19.
Keyphrases
- sars cov
- coronavirus disease
- end stage renal disease
- newly diagnosed
- oxidative stress
- prognostic factors
- metabolic syndrome
- emergency department
- high resolution
- patient reported outcomes
- artificial intelligence
- peripheral blood
- big data
- deep learning
- drug induced
- optical coherence tomography
- arterial hypertension
- high grade
- adipose tissue
- single cell