Extracorporeal membrane oxygenation (ECMO) in COVID-19 patients: a pocket guide for radiologists

Gabelloni, Michela; Faggioni, Lorenzo; Cioni, Dania; Mendola, Vincenzo; Falaschi, Zeno; Coppola, Sara; Corradi, Francesco; Isirdi, Alessandro; Brandi, Nicolò; Coppola, Francesca; Granata, Vincenza; Golfieri, R; Grassi, Roberto; Neri, Emanuele

doi:10.1007/s11547-022-01473-w

Cited by 27 publications

(24 citation statements)

References 75 publications

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“…Several complications can arise during ICU stay, from both COVID-19 extensive lung damage and extra-pulmonary involvement, as well as those secondary to mechanical supporting systems [ 2 , 3 , 4 , 5 , 6 ]. Among these, bacterial and fungal co-infections and superinfections (or secondary infections) play an important role in COVID-19 disease and have been associated with increased morbidity and mortality, especially in critically ill patients [ 7 , 8 , 9 , 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Co-Infections and Superinfections in COVID-19 Critically Ill Patients Are Associated with CT Imaging Abnormalities and the Worst Outcomes

et al. 2022

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Background: Bacterial and fungal co-infections and superinfections have a critical role in the outcome of the COVID-19 patients admitted to the Intensive Care Unit (ICU). Methods: The present study is a retrospective analysis of 95 patients admitted to the ICU for COVID-19-related ARDS during the first (February–May 2020) and second waves of the pandemic (October 2020–January 2021). Demographic and clinical data, CT imaging features, and pulmonary and extra-pulmonary complications were recorded, as well as the temporal evolution of CT findings when more than one scan was available. The presence of co-infections and superinfections was registered, reporting the culprit pathogens and the specimen type for culture. A comparison between patients with and without bacterial and/or co-infections/superinfections was performed. Results: Sixty-three patients (66.3%) developed at least one confirmed co-infection/superinfection, with 52 (82.5%) developing pneumonia and 43 (68.3%) bloodstream infection. Gram-negative bacteria were the most common co-pathogens identified and Aspergillus spp. was the most frequent pulmonary microorganism. Consolidations, cavitations, and bronchiectasis were significantly associated with the presence of co-infections/superinfections (p = 0.009, p = 0.010 and p = 0.009, respectively); when considering only patients with pulmonary co-pathogens, only consolidations remained statistically significative (p = 0.004). Invasive pulmonary aspergillosis was significantly associated with the presence of cavitations and bronchiectasis (p < 0.001). Patients with co-infections/superinfections presented a significantly higher mortality rate compared to patients with COVID-19 only (52.4% vs. 25%, p = 0.016). Conclusions: Bacterial and fungal co-infections and superinfections are frequent in COVID-19 patients admitted to ICU and are associated with worse outcomes. Imaging plays an important role in monitoring critically ill COVID-19 patients and may help detect these complications, suggesting further laboratory investigations.

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Section: Introductionmentioning

confidence: 99%

Co-Infections and Superinfections in COVID-19 Critically Ill Patients Are Associated with CT Imaging Abnormalities and the Worst Outcomes

et al. 2022

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“…Over two years after the first described SARS-CoV-2 patient, the COVID-19 pandemic is still ongoing, with many countries undergoing new infection waves [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 ]. Extensive vaccination promotion is underway all over the world, although with extremely variable levels of population coverage [ 1 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 ].…”

Section: Introductionmentioning

confidence: 99%

Imaging Severity COVID-19 Assessment in Vaccinated and Unvaccinated Patients: Comparison of the Different Variants in a High Volume Italian Reference Center

Granata

Fusco

Villanacci

et al. 2022

JPM

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Purpose: To analyze the vaccine effect by comparing five groups: unvaccinated patients with Alpha variant, unvaccinated patients with Delta variant, vaccinated patients with Delta variant, unvaccinated patients with Omicron variant, and vaccinated patients with Omicron variant, assessing the “gravity” of COVID-19 pulmonary involvement, based on CT findings in critically ill patients admitted to Intensive Care Unit (ICU). Methods: Patients were selected by ICU database considering the period from December 2021 to 23 March 2022, according to the following inclusion criteria: patients with proven Omicron variant COVID-19 infection with known COVID-19 vaccination with at least two doses and with chest Computed Tomography (CT) study during ICU hospitalization. Wee also evaluated the ICU database considering the period from March 2020 to December 2021, to select unvaccinated consecutive patients with Alpha variant, subjected to CT study, consecutive unvaccinated and vaccinated patients with Delta variant, subjected to CT study, and, consecutive unvaccinated patients with Omicron variant, subjected to CT study. CT images were evaluated qualitatively using a severity score scale of 5 levels (none involvement, mild: ≤25% of involvement, moderate: 26–50% of involvement, severe: 51–75% of involvement, and critical involvement: 76–100%) and quantitatively, using the Philips IntelliSpace Portal clinical application CT COPD computer tool. For each patient the lung volumetry was performed identifying the percentage value of aerated residual lung volume. Non-parametric tests for continuous and categorical variables were performed to assess statistically significant differences among groups. Results: The patient study group was composed of 13 vaccinated patients affected by the Omicron variant (Omicron V). As control groups we identified: 20 unvaccinated patients with Alpha variant (Alpha NV); 20 unvaccinated patients with Delta variant (Delta NV); 18 vaccinated patients with Delta variant (Delta V); and 20 unvaccinated patients affected by the Omicron variant (Omicron NV). No differences between the groups under examination were found (p value > 0.05 at Chi square test) in terms of risk factors (age, cardiovascular diseases, diabetes, immunosuppression, chronic kidney, cardiac, pulmonary, neurologic, and liver disease, etc.). A different median value of aerated residual lung volume was observed in the Delta variant groups: median value of aerated residual lung volume was 46.70% in unvaccinated patients compared to 67.10% in vaccinated patients. In addition, in patients with Delta variant every other extracted volume by automatic tool showed a statistically significant difference between vaccinated and unvaccinated group. Statistically significant differences were observed for each extracted volume by automatic tool between unvaccinated patients affected by Alpha variant and vaccinated patients affected by Delta variant of COVID-19. Good statistically significant correlations among volumes extracted by automatic tool for each lung lobe and overall radiological severity score were obtained (ICC range 0.71–0.86). GGO was the main sign of COVID-19 lesions on CT images found in 87 of the 91 (95.6%) patients. No statistically significant differences were observed in CT findings (ground glass opacities (GGO), consolidation or crazy paving sign) among patient groups. Conclusion: In our study, we showed that in critically ill patients no difference were observed in terms of severity of disease or exitus, between unvaccinated and vaccinated patients. The only statistically significant differences were observed, with regard to the severity of COVID-19 pulmonary parenchymal involvement, between unvaccinated patients affected by Alpha variant and vaccinated patients affected by Delta variant, and between unvaccinated patients with Delta variant and vaccinated patients with Delta variant.

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“…A new field of interest is radiomics, which analyzes, in a mathematical manner, data obtained by medical images [289][290][291][292][293][294][295][296][297][298][299][300][301][302]. The idea that imaging studies contain a great quantity of data, in the form of grey level patterns, which are imperceptible to the human eye, has become more and more interesting [303][304][305][306][307][308].…”

Section: Artificial Intelligence Radiomics and Pancreatic Cancermentioning

confidence: 99%

Risk Assessment and Pancreatic Cancer: Diagnostic Management and Artificial Intelligence

Granata

Fusco

Setola

et al. 2023

Cancers

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Pancreatic cancer (PC) is one of the deadliest cancers, and it is responsible for a number of deaths almost equal to its incidence. The high mortality rate is correlated with several explanations; the main one is the late disease stage at which the majority of patients are diagnosed. Since surgical resection has been recognised as the only curative treatment, a PC diagnosis at the initial stage is believed the main tool to improve survival. Therefore, patient stratification according to familial and genetic risk and the creation of screening protocol by using minimally invasive diagnostic tools would be appropriate. Pancreatic cystic neoplasms (PCNs) are subsets of lesions which deserve special management to avoid overtreatment. The current PC screening programs are based on the annual employment of magnetic resonance imaging with cholangiopancreatography sequences (MR/MRCP) and/or endoscopic ultrasonography (EUS). For patients unfit for MRI, computed tomography (CT) could be proposed, although CT results in lower detection rates, compared to MRI, for small lesions. The actual major limit is the incapacity to detect and characterize the pancreatic intraepithelial neoplasia (PanIN) by EUS and MR/MRCP. The possibility of utilizing artificial intelligence models to evaluate higher-risk patients could favour the diagnosis of these entities, although more data are needed to support the real utility of these applications in the field of screening. For these motives, it would be appropriate to realize screening programs in research settings.

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Extracorporeal membrane oxygenation (ECMO) in COVID-19 patients: a pocket guide for radiologists

Cited by 27 publications

References 75 publications

Co-Infections and Superinfections in COVID-19 Critically Ill Patients Are Associated with CT Imaging Abnormalities and the Worst Outcomes

Co-Infections and Superinfections in COVID-19 Critically Ill Patients Are Associated with CT Imaging Abnormalities and the Worst Outcomes

Imaging Severity COVID-19 Assessment in Vaccinated and Unvaccinated Patients: Comparison of the Different Variants in a High Volume Italian Reference Center

Risk Assessment and Pancreatic Cancer: Diagnostic Management and Artificial Intelligence

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