Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 2 de 2
Add filters

Document Type
Year range
Lancet Microbe ; 1(7): e290-e299, 2020 Nov.
Article in English | MEDLINE | ID: covidwho-1087376


Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) targets multiple organs and causes severe coagulopathy. Histopathological organ changes might not only be attributable to a direct virus-induced effect, but also the immune response. The aims of this study were to assess the duration of viral presence, identify the extent of inflammatory response, and investigate the underlying cause of coagulopathy. Methods: This prospective autopsy cohort study was done at Amsterdam University Medical Centers (UMC), the Netherlands. With informed consent from relatives, full body autopsy was done on 21 patients with COVID-19 for whom autopsy was requested between March 9 and May 18, 2020. In addition to histopathological evaluation of organ damage, the presence of SARS-CoV-2 nucleocapsid protein and the composition of the immune infiltrate and thrombi were assessed, and all were linked to disease course. Findings: Our cohort (n=21) included 16 (76%) men, and median age was 68 years (range 41-78). Median disease course (time from onset of symptoms to death) was 22 days (range 5-44 days). In 11 patients tested for SARS-CoV-2 tropism, SARS-CoV-2 infected cells were present in multiple organs, most abundantly in the lungs, but presence in the lungs became sporadic with increased disease course. Other SARS-CoV-2-positive organs included the upper respiratory tract, heart, kidneys, and gastrointestinal tract. In histological analyses of organs (sampled from nine to 21 patients per organ), an extensive inflammatory response was present in the lungs, heart, liver, kidneys, and brain. In the brain, extensive inflammation was seen in the olfactory bulbs and medulla oblongata. Thrombi and neutrophilic plugs were present in the lungs, heart, kidneys, liver, spleen, and brain and were most frequently observed late in the disease course (15 patients with thrombi, median disease course 22 days [5-44]; ten patients with neutrophilic plugs, 21 days [5-44]). Neutrophilic plugs were observed in two forms: solely composed of neutrophils with neutrophil extracellular traps (NETs), or as aggregates of NETs and platelets.. Interpretation: In patients with lethal COVID-19, an extensive systemic inflammatory response was present, with a continued presence of neutrophils and NETs. However, SARS-CoV-2-infected cells were only sporadically present at late stages of COVID-19. This suggests a maladaptive immune response and substantiates the evidence for immunomodulation as a target in the treatment of severe COVID-19. Funding: Amsterdam UMC Corona Research Fund.

Diagnostics (Basel) ; 11(1)2020 Dec 30.
Article in English | MEDLINE | ID: covidwho-1006985


The coronavirus disease 2019 (COVID-19) outbreak has reached pandemic status. Drastic measures of social distancing are enforced in society and healthcare systems are being pushed to and beyond their limits. To help in the fight against this threat on human health, a fully automated AI framework was developed to extract radiomics features from volumetric chest computed tomography (CT) exams. The detection model was developed on a dataset of 1381 patients (181 COVID-19 patients plus 1200 non COVID control patients). A second, independent dataset of 197 RT-PCR confirmed COVID-19 patients and 500 control patients was used to assess the performance of the model. Diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC). The model had an AUC of 0.882 (95% CI: 0.851-0.913) in the independent test dataset (641 patients). The optimal decision threshold, considering the cost of false negatives twice as high as the cost of false positives, resulted in an accuracy of 85.18%, a sensitivity of 69.52%, a specificity of 91.63%, a negative predictive value (NPV) of 94.46% and a positive predictive value (PPV) of 59.44%. Benchmarked against RT-PCR confirmed cases of COVID-19, our AI framework can accurately differentiate COVID-19 from routine clinical conditions in a fully automated fashion. Thus, providing rapid accurate diagnosis in patients suspected of COVID-19 infection, facilitating the timely implementation of isolation procedures and early intervention.