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| Name | Class |
|---|---|
| University of Parma | OTHER |
| University of Milan | OTHER |
| University of Milano Bicocca | OTHER |
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During the current pandemic, in Italy the majority of asymptomatic or pauci-symptomatic COVID-19 cases were not identified nor diagnosed and this fact caused a decrease in the effectiveness of the various containment measures implemented. Therefore, in a future scenario where a new viral swarm is expected, the early identification of all infected cases becomes essential to plan and activate a containment strategy for the spread of the virus, given the current absence of vaccines.
The typical radiological finding of COVID-19 is an interstitial pneumonia, which can be responsible, in a significant portion of patients, of an acute respiratory distress syndrome (ARDS).
Low-dose chest CT and simple blood tests could identify sub-solid pulmonary nodules (SSNs) indicative of COVID-19 infection in asymptomatic subjects.
Objectives of this observational study are the early detection of COVID-19 markers indicative of prior exposure or persisting viral infection in asymptomatic subjects and the assessment of the frequency and outcome of COVID-19-related SSNs in asymptomatic subjects by time, domicile, and other individual risk factors.
SMILE lung CT screening program cohort has been considered, based on 960 subjects at high lung cancer risk for tobacco smoking (≥20 pack/year) and age (50-75 years), together with inflammatory and respiratory profile. SMILE utilizes a top technology dual-source CT scanner (Somatom Force) with the lowest radiation dose ever applied to lung screening. All chest CT images from screening subjects will be re-evaluated by two additional CAD programs, specifically designed for the analysis of SSNs and quantification of the total volume of lung parenchyma showing an increased density. This re-evaluation will improve the sensitivity and specificity of radiomic assessment.
This study cohort, enriched by the already established longitudinal biobank of frozen plasma samples, represent an ideal opportunity to assess the frequency of SSNs in asymptomatic subjects, due to the effect of COVID-19, particularly among subjects living in areas at high risk of viral exposure. It will also be possible to evaluate if COVID-19-related SSNs are associated with chronic co-morbidity, other individual risk factors, inflammatory (CRP) / immunomodulatory (25(OH)D) blood profile, and/or can be traced by immune markers such as IgM/IgG and other cytokines.
Clinical data will be integrated with an analysis of the IgG-IgM profile specific for covid-19, on the plasma samples taken at the time of the CT scan, or subsequently, in collaboration with University of Milan, Luigi Devoto Work Clinic.
The lasting collaboration with the Radiological Science Department of the University of Parma in lung screening also offers the opportunity to validate the results obtained in this cohort on chest CT performed at the University Parma Hospital during the last two months in symptomatic subjects for suspected covid-19 pneumonia.
In collaboration with University of Milano Bicocca, Machine Learning (ML) tools will be applied to predict the clinical relevance, severity and ultimate outcome of SSNs, based on radiomic CT features, epidemiologic risk, co-morbidity and inflammatory/immune blood biomarkers. ML analysis will generate a predictive algorithm for clinical outcome of SSNs, and specifically the risk of COV-I9 infection and unfavorable disease prognosis.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Lung cancer screening subjects | Subjects enrolled in SMILE lung cancer screening trial |
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| Measure | Description | Time Frame |
|---|---|---|
| Temporal variations in the frequency and morphology of SSNs according to the COVID-19 epidemic | Evaluate the increase of frequency and severity of SSNs according to the COVID-19 epidemic. The results of chest CT performed in different periods will be compared (December 2019 -February 2020; September 2019 - November 2019) | September 2019 - February 2020 |
| Geographical variations in the frequency and morphology of SSNs according to the COVID-19 epidemic | To this end, subjects domiciled in different geographical areas will be compared: a) high risk (≥ 200 covid cases / 100,000 inhabitants); b) medium risk (100-200 cases / 100,000); low risk (<100 cases / 100,000) | September 2019 - February 2020 |
| Correlation between sub-solid lesions and individual risk | Establish whether subjects with a higher level of risk and / or individual damage have a higher frequency and / or severity of sub-solid lung injury. For this purpose, the following parameters will be analyzed: a) inflammatory profile (PCR), b) respiratory capacity (FEV1), c) co-morbidity (e.g. obesity, diabetes, cardio-vascular disease, COPD), d) level of CO; e) age, f) gender. | September 2019 - February 2020 |
| Correlation between sub-solid lesions and incidence of acute events | Establish whether subjects with greater frequency and/or severity of sub-solid lung lesions, associated or not with other individual risk factors, have a higher incidence of acute pathological events, in particular of respiratory nature, in the three (six) months following the CT exam. For this purpose, information on the time of onset and duration of these events will be collected: fever, cough, dyspnoea, hospitalization, positivity for covid-19, treatment in the ICU, and eventual death. | September 2019 - August 2020 |
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Inclusion Criteria:
Exclusion Criteria:
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Subjects who enrolled in SMILE protocol and signed the SMILE protocol NCT03654105 informed consent
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| Name | Affiliation | Role |
|---|---|---|
| Ugo Pastorino, MD | Fondazione IRCCS Istituto Nazionale dei Tumori, Milano | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Fondazione IRCCS Istituto Nazionale dei Tumori | Milan | 20133 | Italy |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 32053470 | Result | Pan F, Ye T, Sun P, Gui S, Liang B, Li L, Zheng D, Wang J, Hesketh RL, Yang L, Zheng C. Time Course of Lung Changes at Chest CT during Recovery from Coronavirus Disease 2019 (COVID-19). Radiology. 2020 Jun;295(3):715-721. doi: 10.1148/radiol.2020200370. Epub 2020 Feb 13. | |
| 32167524 | Result | Wu C, Chen X, Cai Y, Xia J, Zhou X, Xu S, Huang H, Zhang L, Zhou X, Du C, Zhang Y, Song J, Wang S, Chao Y, Yang Z, Xu J, Zhou X, Chen D, Xiong W, Xu L, Zhou F, Jiang J, Bai C, Zheng J, Song Y. Risk Factors Associated With Acute Respiratory Distress Syndrome and Death in Patients With Coronavirus Disease 2019 Pneumonia in Wuhan, China. JAMA Intern Med. 2020 Jul 1;180(7):934-943. doi: 10.1001/jamainternmed.2020.0994. |
| Label | URL |
|---|---|
| Data of Italian Ministry of Health | View source |
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| ID | Term |
|---|---|
| D000086382 | COVID-19 |
| ID | Term |
|---|---|
| D011024 | Pneumonia, Viral |
| D011014 | Pneumonia |
| D012141 | Respiratory Tract Infections |
| D007239 | Infections |
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Blood samples to evaluate inflammatory markers
| 32162702 | Result | Li LQ, Huang T, Wang YQ, Wang ZP, Liang Y, Huang TB, Zhang HY, Sun W, Wang Y. COVID-19 patients' clinical characteristics, discharge rate, and fatality rate of meta-analysis. J Med Virol. 2020 Jun;92(6):577-583. doi: 10.1002/jmv.25757. Epub 2020 Mar 23. |
| 32252338 | Result | Grant WB, Lahore H, McDonnell SL, Baggerly CA, French CB, Aliano JL, Bhattoa HP. Evidence that Vitamin D Supplementation Could Reduce Risk of Influenza and COVID-19 Infections and Deaths. Nutrients. 2020 Apr 2;12(4):988. doi: 10.3390/nu12040988. |
| 31756987 | Result | Gallivanone F, Cava C, Corsi F, Bertoli G, Castiglioni I. In Silico Approach for the Definition of radiomiRNomic Signatures for Breast Cancer Differential Diagnosis. Int J Mol Sci. 2019 Nov 20;20(23):5825. doi: 10.3390/ijms20235825. |
| 31168572 | Result | Pastorino U, Silva M, Sestini S, Sabia F, Boeri M, Cantarutti A, Sverzellati N, Sozzi G, Corrao G, Marchiano A. Prolonged lung cancer screening reduced 10-year mortality in the MILD trial: new confirmation of lung cancer screening efficacy. Ann Oncol. 2019 Oct 1;30(10):1672. doi: 10.1093/annonc/mdz169. No abstract available. |
| 29543693 | Result | Silva M, Schaefer-Prokop CM, Jacobs C, Capretti G, Ciompi F, van Ginneken B, Pastorino U, Sverzellati N. Detection of Subsolid Nodules in Lung Cancer Screening: Complementary Sensitivity of Visual Reading and Computer-Aided Diagnosis. Invest Radiol. 2018 Aug;53(8):441-449. doi: 10.1097/RLI.0000000000000464. |
| 3558716 | Result | Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373-83. doi: 10.1016/0021-9681(87)90171-8. |
| Data of "Istituto Superiore di Sanità " | View source |
| D014777 |
| Virus Diseases |
| D018352 | Coronavirus Infections |
| D003333 | Coronaviridae Infections |
| D030341 | Nidovirales Infections |
| D012327 | RNA Virus Infections |
| D008171 | Lung Diseases |
| D012140 | Respiratory Tract Diseases |