Imaging Severity COVID-19 Assessment in Vaccinated and Unvaccinated Patients: Comparison of the Different Variants in a High Volume Italian Reference Center
Abstract
:1. Introduction
2. Materials and Methods
2.1. Patient Characteristics
2.2. CT Technique
2.3. CT Post Processing
2.4. Radiologists’ Analysis
2.5. Statistical Analysis
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Available online: https://covid19.who.int (accessed on 21 May 2022).
- Stramare, R.; Carretta, G.; Capizzi, A.; Boemo, D.G.; Contessa, C.; Motta, R.; De Conti, G.; Causin, F.; Giraudo, C.; Donato, D. Radiological management of COVID-19: Structure your diagnostic path to guarantee a safe path. Radiol. Med. 2020, 125, 691–694. [Google Scholar] [CrossRef] [PubMed]
- Granata, V.; Fusco, R.; Izzo, F.; Setola, S.V.; Coppola, M.; Grassi, R.; Reginelli, A.; Cappabianca, S.; Petrillo, A. COVID-19 infection in cancer patients: The management in a diagnostic unit. Radiol. Oncol. 2021, 55, 121–129. [Google Scholar] [CrossRef] [PubMed]
- Ashtari, S.; Vahedian-Azimi, A.; Shojaee, S.; Pourhoseingholi, M.A.; Jafari, R.; Bashar, F.R.; Zali, M.R. Características en tomografía computarizada de la neumonía por coronavirus-2019 (COVID-19) en tres grupos de pacientes iraníes: Estudio de un solo centro [Computed tomographic features of coronavirus disease-2019 (COVID-19) pneumonia in three groups of Iranian patients: A single center study]. Radiologia 2021, 63, 314–323. (In Spanish) [Google Scholar] [CrossRef] [PubMed]
- Gabelloni, M.; Faggioni, L.; Cioni, D.; Mendola, V.; Falaschi, Z.; Coppola, S.; Corradi, F.; Isirdi, A.; Brandi, N.; Coppola, F.; et al. Extracorporeal membrane oxygenation (ECMO) in COVID-19 patients: A pocket guide for radiologists. Radiol. Med. 2022, 13, 369–382. [Google Scholar] [CrossRef] [PubMed]
- Giovagnoni, A. Facing the COVID-19 emergency: We can, and we do. Radiol. Med. 2020, 125, 337–338. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Montesi, G.; Di Biase, S.; Chierchini, S.; Pavanato, G.; Virdis, G.E.; Contato, E.; Mandoliti, G. Radiotherapy during COVID-19 pandemic. How to create a No fly zone: A Northern Italy experience. Radiol. Med. 2020, 125, 600–603. [Google Scholar] [CrossRef]
- Ierardi, A.M.; Wood, B.J.; Arrichiello, A.; Bottino, N.; Bracchi, L.; Forzenigo, L.; Andrisani, M.C.; Vespro, V.; Bonelli, C.; Amalou, A.; et al. Preparation of a radiology department in an Italian hospital dedicated to COVID-19 patients. Radiol. Med. 2020, 125, 894–901. [Google Scholar] [CrossRef]
- Grassi, R.; Cappabianca, S.; Urraro, F.; Feragalli, B.; Montanelli, A.; Patelli, G.; Granata, V.; Giacobbe, G.; Russo, G.M.; Grillo, A.; et al. Chest CT Computerized Aided Quantification of PNEUMONIA Lesions in COVID-19 Infection: A Comparison among Three Commercial Software. Int. J. Environ. Res. Public Health 2020, 17, 6914. [Google Scholar] [CrossRef]
- Pediconi, F.; Galati, F.; Bernardi, D.; Belli, P.; Brancato, B.; Calabrese, M.; Camera, L.; Carbonaro, L.A.; Caumo, F.; Clauser, P.; et al. Breast imaging and cancer diagnosis during the COVID-19 pandemic: Recommendations from the Italian College of Breast Radiologists by SIRM. Radiol. Med. 2020, 125, 926–930. [Google Scholar] [CrossRef]
- Koç, A.; Sezgin, S.; Kayıpmaz, S. Comparing different planimetric methods on volumetric estimations by using cone beam computed tomography. Radiol. Med. 2020, 125, 398–405. [Google Scholar] [CrossRef]
- Fusco, R.; Simonetti, I.; Ianniello, S.; Villanacci, A.; Grassi, F.; Dell’Aversana, F.; Grassi, R.; Cozzi, D.; Bicci, E.; Palumbo, P.; et al. Pulmonary Lymphangitis Poses a Major Challenge for Radiologists in an Oncological Setting during the COVID-19 Pandemic. J. Pers. Med. 2022, 12, 624. [Google Scholar] [CrossRef] [PubMed]
- de Souza, A.S.; de Freitas Amorim, V.M.; Guardia, G.D.A.; Dos Santos, F.F.; Ulrich, H.; Galante, P.A.F.; de Souza, R.F.; Guzzo, C.R. Severe Acute Respiratory Syndrome Coronavirus 2 Variants of Concern: A Perspective for Emerging More Transmissible and Vaccine-Resistant Strains. Viruses 2022, 14, 827. [Google Scholar] [CrossRef] [PubMed]
- Agostini, A.; Floridi, C.; Borgheresi, A.; Badaloni, M.; Pirani, P.E.; Terilli, F.; Ottaviani, L.; Giovagnoni, A. Proposal of a low-dose, long-pitch, dual-source chest CT protocol on third-generation dual-source CT using a tin filter for spectral shaping at 100 kVp for CoronaVirus Disease 2019 (COVID-19) patients: A feasibility study. Radiol. Med. 2020, 125, 365–373. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Borghesi, A.; Maroldi, R. COVID-19 outbreak in Italy: Experimental chest X-ray scoring system for quantifying and monitoring disease progression. Radiol. Med. 2020, 125, 509–513. [Google Scholar] [CrossRef] [PubMed]
- Neri, E.; Miele, V.; Coppola, F.; Grassi, R. Use of CT and artificial intelligence in suspected or COVID-19 positive patients: Statement of the Italian Society of Medical and Interventional Radiology. Radiol. Med. 2020, 125, 505–508. [Google Scholar] [CrossRef]
- Carotti, M.; Salaffi, F.; Sarzi-Puttini, P.; Agostini, A.; Borgheresi, A.; Minorati, D.; Galli, M.; Marotto, D.; Giovagnoni, A. Chest CT features of coronavirus disease 2019 (COVID-19) pneumonia: Key points for radiologists. Radiol. Med. 2020, 125, 636–646. [Google Scholar] [CrossRef]
- Alyasseri, Z.A.A.; Al-Betar, M.A.; Doush, I.A.; Awadallah, M.A.; Abasi, A.K.; Makhadmeh, S.N.; Alomari, O.A.; Abdulkareem, K.H.; Adam, A.; Damasevicius, R.; et al. Review on COVID-19 diagnosis models based on machine learning and deep learning approaches. Expert Syst. 2021, 28, e12759. [Google Scholar] [CrossRef]
- Borghesi, A.; Zigliani, A.; Masciullo, R.; Golemi, S.; Maculotti, P.; Farina, D.; Maroldi, R. Radiographic severity index in COVID-19 pneumonia: Relationship to age and sex in 783 Italian patients. Radiol. Med. 2020, 125, 461–464. [Google Scholar] [CrossRef]
- Cozzi, D.; Albanesi, M.; Cavigli, E.; Moroni, C.; Bindi, A.; Luvarà, S.; Lucarini, S.; Busoni, S.; Mazzoni, L.N.; Miele, V. Chest X-ray in new Coronavirus Disease 2019 (COVID-19) infection: Findings and correlation with clinical outcome. Radiol. Med. 2020, 125, 730–737. [Google Scholar] [CrossRef]
- Gatti, M.; Calandri, M.; Barba, M.; Biondo, A.; Geninatti, C.; Gentile, S.; Greco, M.; Morrone, V.; Piatti, C.; Santonocito, A.; et al. Baseline chest X-ray in coronavirus disease 19 (COVID-19) patients: Association with clinical and laboratory data. Radiol. Med. 2020, 125, 1271–1279. [Google Scholar] [CrossRef]
- Granata, V.; Fusco, R.; Vallone, P.; Setola, S.V.; Picone, C.; Grassi, F.; Patrone, R.; Belli, A.; Izzo, F.; Petrillo, A. Not only lymphadenopathy: Case of chest lymphangitis assessed with MRI after COVID 19 vaccine. Infect. Agent Cancer 2022, 17, 8. [Google Scholar] [CrossRef] [PubMed]
- D’Agostino, V.; Caranci, F.; Negro, A.; Piscitelli, V.; Tuccillo, B.; Fasano, F.; Sirabella, G.; Marano, I.; Granata, V.; Grassi, R.; et al. A Rare Case of Cerebral Venous Thrombosis and Disseminated Intravascular Coagulation Temporally Associated to the COVID-19 Vaccine Administration. J. Pers. Med. 2021, 11, 285. [Google Scholar] [CrossRef] [PubMed]
- Granata, V.; Fusco, R.; Setola, S.V.; Galdiero, R.; Picone, C.; Izzo, F.; D’Aniello, R.; Miele, V.; Grassi, R.; Grassi, R.; et al. Lymphadenopathy after BNT162b2 COVID-19 Vaccine: Preliminary Ultrasound Findings. Biology 2021, 10, 214. [Google Scholar] [CrossRef] [PubMed]
- Volz, E.; Hill, V.; McCrone, J.T.; Price, A.; Jorgensen, D.; O’Toole, Á.; Southgate, J.; Johnson, R.; Jackson, B.; Nascimento, F.F.; et al. Evaluating the Effects of SARS-CoV-2 Spike Mutation D614G on Transmissibility and Pathogenicity. Cell 2021, 184, 64–75. [Google Scholar] [CrossRef]
- Zhou, W.; Wang, W. Fast-Spreading SARS-CoV-2 Variants: Challenges to and New Design Strategies of COVID-19 Vaccines. Signal Transduct. Target. Ther. 2021, 6, 226. [Google Scholar] [CrossRef]
- Zhang, J.; Xiao, T.; Cai, Y.; Lavine, C.L.; Peng, H.; Zhu, H.; Anand, K.; Tong, P.; Gautam, A.; Mayer, M.L.; et al. Membrane Fusion and Immune Evasion by the Spike Protein of SARS-CoV-2 Delta Variant. Science 2021, 374, 1353–1360. [Google Scholar] [CrossRef]
- Classification of Omicron (B.1.1.529): SARS-CoV-2 Variant of Concern. Available online: https://www.who.int/news/item/26-11-2021-classification-of-omicron-(b.1.1.529)-sars-cov-2-variant-of-concern (accessed on 17 December 2021).
- Ierardi, A.M.; Gaibazzi, N.; Tuttolomondo, D.; Fusco, S.; La Mura, V.; Peyvandi, F.; Aliberti, S.; Blasi, F.; Cozzi, D.; Carrafiello, G.; et al. Deep vein thrombosis in COVID-19 patients in general wards: Prevalence and association with clinical and laboratory variables. Radiol. Med. 2021, 126, 722–728. [Google Scholar] [CrossRef]
- Turkahia, Y.; Thornlow, B.; Hinrichs, A.; McBroome, J.; Ayala, N.; Ye, C.; De Maio, N.; Haussler, D.; Lanfear, R.; Corbett-Detig, R. Pandemic-Scale Phylogenomics Reveals Elevated Recombination Rates in the SARS-CoV-2 Spike Region. bioRxiv 2021. [Google Scholar] [CrossRef]
- Covin, S.; Rutherford, G.W. Coinfection, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), and Influenza: An Evolving Puzzle. Clin. Infect. Dis. 2021, 72, e993–e994. [Google Scholar] [CrossRef]
- Giannitto, C.; Sposta, F.M.; Repici, A.; Vatteroni, G.; Casiraghi, E.; Casari, E.; Ferraroli, G.M.; Fugazza, A.; Sandri, M.T.; Chiti, A.; et al. Chest CT in patients with a moderate or high pretest probability of COVID-19 and negative swab. Radiol. Med. 2020, 125, 1260–1270. [Google Scholar] [CrossRef]
- Cuadrado-Payán, E.; Montagud-Marrahi, E.; Torres-Elorza, M.; Bodro, M.; Blasco, M.; Poch, E.; Soriano, A.; Piñeiro, G.J. SARS-CoV-2 and Influenza Virus Co-Infection. Lancet 2020, 395, e84. [Google Scholar] [CrossRef]
- Moroni, C.; Cozzi, D.; Albanesi, M.; Cavigli, E.; Bindi, A.; Luvarà, S.; Busoni, S.; Mazzoni, L.N.; Grifoni, S.; Nazerian, P.; et al. Chest X-ray in the emergency department during COVID-19 pandemic descending phase in Italy: Correlation with patients’ outcome. Radiol. Med. 2021, 126, 661–668. [Google Scholar] [CrossRef] [PubMed]
- Di Serafino, M.; Notaro, M.; Rea, G.; Iacobellis, F.; Paoli, V.D.; Acampora, C.; Ianniello, S.; Brunese, L.; Romano, L.; Vallone, G. The lung ultrasound: Facts or artifacts? In the era of COVID-19 outbreak. Radiol. Med. 2020, 125, 738–753. [Google Scholar] [CrossRef]
- Ravikanth, R. Diagnostic accuracy and false-positive rate of chest CT as compared to RT-PCR in coronavirus disease 2019 (COVID-19) pneumonia: A prospective cohort of 612 cases from India and review of literature. Indian J. Radiol. Imaging 2021, 31 (Suppl. 1), S161–S169. [Google Scholar] [CrossRef] [PubMed]
- Grassi, R.; Belfiore, M.P.; Montanelli, A.; Patelli, G.; Urraro, F.; Giacobbe, G.; Fusco, R.; Granata, V.; Petrillo, A.; Sacco, P.; et al. COVID-19 pneumonia: Computer-aided quantification of healthy lung parenchyma, emphysema, ground glass and consolidation on chest computed tomography (CT). Radiol. Med. 2020, 126, 553–560. [Google Scholar] [CrossRef] [PubMed]
- Ippolito, D.; Giandola, T.; Maino, C.; Pecorelli, A.; Capodaglio, C.; Ragusi, M.; Porta, M.; Gandola, D.; Masetto, A.; Drago, S.; et al. Acute pulmonary embolism in hospitalized patients with SARS-CoV-2-related pneumonia: Multicentric experience from Italian endemic area. Radiol. Med. 2021, 126, 669–678. [Google Scholar] [CrossRef]
- Mansbach, R.A.; Chakraborty, S.; Nguyen, K.; Montefiori, D.C.; Korber, B.; Gnanakaran, S. The SARS-CoV-2 Spike Variant D614G Favors an Open Conformational State. Sci. Adv. 2021, 7, eabf3671. [Google Scholar] [CrossRef]
- Shaw, B.; Daskareh, M.; Gholamrezanezhad, A. The lingering manifestations of COVID-19 during and after convalescence: Update on long-term pulmonary consequences of coronavirus disease 2019 (COVID-19). Radiol. Med. 2021, 126, 40–46. [Google Scholar] [CrossRef]
- Rawashdeh, M.A.; Saade, C. Radiation dose reduction considerations and imaging patterns of ground glass opacities in coronavirus: Risk of over exposure in computed tomography. Radiol. Med. 2021, 126, 380–387. [Google Scholar] [CrossRef]
- Teruel, N.; Mailhot, O.; Najmanovich, R.J. Modelling Conformational State Dynamics and Its Role on Infection for SARS-CoV-2 Spike Protein Variants. PLoS Comput. Biol. 2021, 17, e1009286. [Google Scholar] [CrossRef]
- Benton, D.J.; Wrobel, A.G.; Roustan, C.; Borg, A.; Xu, P.; Martin, S.R.; Rosenthal, P.B.; Skehel, J.J.; Gamblin, S.J. The Effect of the D614G Substitution on the Structure of the Spike Glycoprotein of SARS-CoV-2. Proc. Natl. Acad. Sci. USA 2021, 118, e2022586118. [Google Scholar] [CrossRef] [PubMed]
- Chemaitelly, H.; Tang, P.; Hasan, M.R.; AlMukdad, S.; Yassine, H.M.; Benslimane, F.M.; Al Khatib, H.A.; Coyle, P.; Ayoub, H.H.; Al Kanaani, Z.; et al. Waning of BNT162b2 vaccine protection against SARS-CoV-2 infection in Qatar. N. Engl. J. Med. 2021, 385, e83. [Google Scholar] [CrossRef] [PubMed]
- Levin, E.G.; Lustig, Y.; Cohen, C.; Fluss, R.; Indenbaum, V.; Amit, S.; Doolman, R.; Asraf, K.; Mendelson, E.; Ziv, A.; et al. Waning immune humoral response to BNT162b2 covid-19 vaccine over 6 months. N. Engl. J. Med. 2021, 385, e84. [Google Scholar] [CrossRef] [PubMed]
- Andrews, N.; Tessier, E.; Stowe, J.; Gower, C.; Kirsebom, F.; Simmons, R.; Gallagher, E.; Chand, M.; Brown, K.; Ladhani, S.; et al. Vaccine effectiveness and duration of protection of Comirnaty, Vaxzevria and Spikevax against mild and severe COVID-19 in the UK. medRxiv 2021. Available online: https://www.medrxiv.org/content/10.1101/2021.09.15.21263583v2 (accessed on 21 May 2022).
- Goldberg, Y.; Mandel, M.; Bar-On, Y.M.; Bodenheimer, O.; Freedman, L.S.; Haas, E.; Milo, R.; Alroy-Preis, S.; Ash, N.; Huppert, A. Waning immunity of the BNT162b2 vaccine: A nationwide study from Israel. medRxiv 2021. Available online: https://www.medrxiv.org/content/10.1101/2021.08.24.21262423v1 (accessed on 21 May 2022).
- Thomas, S.J.; Moreira, E.D.; Kitchin, N.; Absalon, J.; Gurtman, A.; Lockhart, S.; Perez, J.L.; Marc, G.P.; Polack, F.P.; Zerbini, C.; et al. Six month safety and efficacy of the BNT162b2 mRNA COVID-19 vaccine. medRxiv 2021. Available online: https://www.medrxiv.org/content/10.1101/2021.07.28.21261159v1 (accessed on 21 May 2022).
- COVID-19 Vaccine Booster Doses Administered per 100 People—Our World in Data [Internet]. [Cited 2021 Nov.]. Available online: https://ourworldindata.org/grapher/covid-vaccine-booster-doses-per-capita?country=BRA~CHL~ISR~RUS~USA~URY~OWID_WRL (accessed on 21 May 2022).
- Lombardi, A.F.; Afsahi, A.M.; Gupta, A.; Gholamrezanezhad, A. Severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), influenza, and COVID-19, beyond the lungs: A review article. Radiol. Med. 2021, 126, 561–569. [Google Scholar] [CrossRef]
- Alballa, N.; Al-Turaiki, I. Machine learning approaches in COVID-19 diagnosis, mortality, and severity risk prediction: A review. Inform. Med. Unlocked 2021, 24, 100564. [Google Scholar] [CrossRef]
- Cozzi, D.; Bindi, A.; Cavigli, E.; Grosso, A.M.; Luvarà, S.; Morelli, N.; Moroni, C.; Piperio, R.; Miele, V.; Bartolucci, M. Exogenous lipoid pneumonia: When radiologist makes the difference. Radiol. Med. 2021, 126, 22–28. [Google Scholar] [CrossRef]
- Palmisano, A.; Scotti, G.M.; Ippolito, D.; Morelli, M.J.; Vignale, D.; Gandola, D.; Sironi, S.; De Cobelli, F.; Ferrante, L.; Spessot, M.; et al. Chest CT in the emergency department for suspected COVID-19 pneumonia. Radiol. Med. 2021, 126, 498–502. [Google Scholar] [CrossRef]
- Bar-On, Y.M.; Goldberg, Y.; Mandel, M.; Bodenheimer, O.; Freedman, L.; Kalkstein, N.; Mizrahi, B.; Alroy-Preis, S.; Ash, N.; Milo, R.; et al. Protection of BNT162b2 Vaccine Booster against COVID-19 in Israel. N. Engl. J. Med. 2021, 385, 1393–1400. [Google Scholar] [CrossRef]
- Waxman, J.G.; Makov-Assif, M.; Reis, B.Y.; Netzer, D.; Balicer, R.D.; Dagan, N.; Barda, N. Comparing COVID-19-related hospitalization rates among individuals with infection-induced and vaccine-induced immunity in Israel. Nat. Commun. 2022, 13, 2202. [Google Scholar] [CrossRef] [PubMed]
- Caruso, D.; Polici, M.; Zerunian, M.; Pucciarelli, F.; Polidori, T.; Guido, G.; Rucci, C.; Bracci, B.; Muscogiuri, E.; De Dominicis, C.; et al. Quantitative Chest CT analysis in discriminating COVID-19 from non-COVID-19 patients. Radiol. Med. 2020, 126, 243–249. [Google Scholar] [CrossRef] [PubMed]
- Cardobi, N.; Benetti, G.; Cardano, G.; Arena, C.; Micheletto, C.; Cavedon, C.; Montemezzi, S. CT radiomic models to distinguish COVID-19 pneumonia from other interstitial pneumonias. Radiol. Med. 2021, 126, 1037–1043. [Google Scholar] [CrossRef] [PubMed]
- Caruso, D.; Pucciarelli, F.; Zerunian, M.; Ganeshan, B.; De Santis, D.; Polici, M.; Rucci, C.; Polidori, T.; Guido, G.; Bracci, B.; et al. Chest CT texture-based radiomics analysis in differentiating COVID-19 from other interstitial pneumonia. Radiol. Med. 2021, 126, 1415–1424. [Google Scholar] [CrossRef] [PubMed]
- Masselli, G.; Almberger, M.; Tortora, A.; Capoccia, L.; Dolciami, M.; D’Aprile, M.R.; Valentini, C.; Avventurieri, G.; Bracci, S.; Ricci, P. Role of CT angiography in detecting acute pulmonary embolism associated with COVID-19 pneumonia. Radiol. Med. 2021, 126, 1553–1560. [Google Scholar] [CrossRef] [PubMed]
- Pecoraro, M.; Cipollari, S.; Marchitelli, L.; Messina, E.; Del Monte, M.; Galea, N.; Ciardi, M.R.; Francone, M.; Catalano, C.; Panebianco, V. Cross-sectional analysis of follow-up chest MRI and chest CT scans in patients previously affected by COVID-19. Radiol. Med. 2021, 126, 1273–1281. [Google Scholar] [CrossRef]
- Granata, V.; Ianniello, S.; Fusco, R.; Urraro, F.; Pupo, D.; Magliocchetti, S.; Albarello, F.; Campioni, P.; Cristofaro, M.; Di Stefano, F.; et al. Quantitative Analysis of Residual COVID-19 Lung CT Features: Consistency among Two Commercial Software. J. Pers. Med. 2021, 11, 1103. [Google Scholar] [CrossRef]
- Spinato, G.; Fabbris, C.; Conte, F.; Menegaldo, A.; Franz, L.; Gaudioso, P.; Cinetto, F.; Agostini, C.; Costantini, G.; Boscolo-Rizzo, P. COVID-Q: Validation of the first COVID-19 questionnaire based on patient-rated symptom gravity. Int. J. Clin. Pract. 2021, 75, e14829. [Google Scholar] [CrossRef]
- Bertolini, M.; Brambilla, A.; Dallasta, S.; Colombo, G. High-quality chest CT segmentation to assess the impact of COVID-19 disease. Int. J. Comput. Assist. Radiol. Surg. 2021, 16, 1737–1747. [Google Scholar] [CrossRef]
- Zieda, A.; Sbardella, S.; Patel, M.; Smith, R. Diagnostic Bias in the COVID-19 Pandemic: A Series of Short Cases. Eur. J. Case Rep. Intern. Med. 2021, 8, 002575. [Google Scholar] [CrossRef]
- Giannakis, A.; Móré, D.; Erdmann, S.; Kintzelé, L.; Fischer, R.M.; Vogel, M.N.; Mangold, D.L.; von Stackelberg, O.; Schnitzler, P.; Zimmermann, S.; et al. COVID-19 pneumonia and its lookalikes: How radiologists perform in differentiating atypical pneumonias. Eur. J. Radiol. 2021, 144, 110002. [Google Scholar] [CrossRef] [PubMed]
- Borghesi, A.; Sverzellati, N.; Polverosi, R.; Balbi, M.; Baratella, E.; Busso, M.; Calandriello, L.; Cortese, G.; Farchione, A.; Iezzi, R.; et al. Impact of the COVID-19 pandemic on the selection of chest imaging modalities and reporting systems: A survey of Italian radiologists. Radiol. Med. 2021, 126, 1258–1272. [Google Scholar] [CrossRef] [PubMed]
- Cozzi, D.; Bicci, E.; Bindi, A.; Cavigli, E.; Danti, G.; Galluzzo, M.; Granata, V.; Pradella, S.; Trinci, M.; Miele, V. Role of Chest Imaging in Viral Lung Diseases. Int. J. Environ. Res. Public Health 2021, 18, 6434. [Google Scholar] [CrossRef] [PubMed]
- Fusco, R.; Grassi, R.; Granata, V.; Setola, S.V.; Grassi, F.; Cozzi, D.; Pecori, B.; Izzo, F.; Petrillo, A. Artificial Intelligence and COVID-19 Using Chest CT Scan and Chest X-ray Images: Machine Learning and Deep Learning Approaches for Diagnosis and Treatment. J. Pers. Med. 2021, 11, 993. [Google Scholar] [CrossRef]
- Reginelli, A.; Grassi, R.; Feragalli, B.; Belfiore, M.P.; Montanelli, A.; Patelli, G.; La Porta, M.; Urraro, F.; Fusco, R.; Granata, V.; et al. Coronavirus Disease 2019 (COVID-19) in Italy: Double Reading of Chest CT Examination. Biology 2021, 10, 89. [Google Scholar] [CrossRef]
- Grassi, R.; Fusco, R.; Belfiore, M.P.; Montanelli, A.; Patelli, G.; Urraro, F.; Petrillo, A.; Granata, V.; Sacco, P.; Mazzei, M.A.; et al. Coronavirus disease 2019 (COVID-19) in Italy: Features on chest computed tomography using a structured report system. Sci. Rep. 2020, 10, 17236, Erratum in Sci. Rep. 2021, 11, 4231. [Google Scholar] [CrossRef]
- Masci, G.M.; Iafrate, F.; Ciccarelli, F.; Pambianchi, G.; Panebianco, V.; Pasculli, P.; Ciardi, M.R.; Mastroianni, C.M.; Ricci, P.; Catalano, C.; et al. Tocilizumab effects in COVID-19 pneumonia: Role of CT texture analysis in quantitative assessment of response to therapy. Radiol. Med. 2021, 126, 1170–1180. [Google Scholar] [CrossRef]
- Francolini, G.; Desideri, I.; Stocchi, G.; Ciccone, L.P.; Salvestrini, V.; Garlatti, P.; Aquilano, M.; Greto, D.; Bonomo, P.; Meattini, I.; et al. Impact of COVID-19 on workload burden of a complex radiotherapy facility. Radiol. Med. 2021, 126, 717–721. [Google Scholar] [CrossRef]
- Cellini, F.; Di Franco, R.; Manfrida, S.; Borzillo, V.; Maranzano, E.; Pergolizzi, S.; Morganti, A.G.; Fusco, V.; Deodato, F.; Santarelli, M.; et al. Palliative radiotherapy indications during the COVID-19 pandemic and in future complex logistic settings: The NORMALITY model. Radiol. Med. 2021, 126, 1619–1656. [Google Scholar] [CrossRef]
- De Felice, F.; D’Angelo, E.; Ingargiola, R.; Iacovelli, N.A.; Alterio, D.; Franco, P.; Bonomo, P.; Merlotti, A.; Bacigalupo, A.; Maddalo, M.; et al. A snapshot on radiotherapy for head and neck cancer patients during the COVID-19 pandemic: A survey of the Italian Association of Radiotherapy and Clinical Oncology (AIRO) head and neck working group. Radiol. Med. 2020, 126, 343–347. [Google Scholar] [CrossRef]
- Ortiz, S.; Rojas, F.; Valenzuela, O.; Herrera, L.J.; Rojas, I. Determination of the Severity and Percentage of COVID-19 Infection through a Hierarchical Deep Learning System. J. Pers. Med. 2022, 12, 535. [Google Scholar] [CrossRef] [PubMed]
- Mohiuddin Chowdhury, A.T.M.; Kamal, A.; Abbas, K.U.; Talukder, S.; Karim, M.R.; Ali, M.A.; Nuruzzaman, M.; Li, Y.; He, S. Efficacy and Outcome of Remdesivir and Tocilizumab Combination Against Dexamethasone for the Treatment of Severe COVID-19: A Randomized Controlled Trial. Front. Pharmacol. 2022, 13, 690726. [Google Scholar] [CrossRef] [PubMed]
- Ibrahim, H.M.; Mohammad, A.A.; Fouda, E.; Abouelfotouh, K.; Habeeb, N.M.; Rezk, A.R.; Magdy, S.; Allam, A.M.; Mahmoud, S.A. Clinical Characteristics and Pulmonary Computerized Imaging Findings of Critically Ill Egyptian Patients with Multisystem Inflammatory Syndrome in Children. Glob. Pediatr. Health 2022, 9, 2333794X221085386. [Google Scholar] [CrossRef] [PubMed]
- Lorent, N.; Vande Weygaerde, Y.; Claeys, E.; Guler Caamano Fajardo, I.; De Vos, N.; De Wever, W.; Salhi, B.; Gyselinck, I.; Bosteels, C.; Lambrecht, B.N.; et al. Prospective longitudinal evaluation of hospitalised COVID-19 survivors 3 and 12 months after discharge. ERJ Open Res. 2022, 8, 00004–2022. [Google Scholar] [CrossRef] [PubMed]
- Ohno, Y.; Aoyagi, K.; Arakita, K.; Doi, Y.; Kondo, M.; Banno, S.; Kasahara, K.; Ogawa, T.; Kato, H.; Hase, R.; et al. Newly developed artificial intelligence algorithm for COVID-19 pneumonia: Utility of quantitative CT texture analysis for prediction of favipiravir treatment effect. Jpn. J. Radiol. 2022, 9, 1–14. [Google Scholar] [CrossRef] [PubMed]
- Yanamandra, U.; Shobhit, S.; Paul, D.; Aggarwal, B.; Kaur, P.; Duhan, G.; Singh, A.; Srinath, R.; Saxena, P.; Menon, A.S. Relationship of Computed Tomography Severity Score with Patient Characteristics and Survival in Hypoxemic COVID-19 Patients. Cureus 2022, 14, e22847. [Google Scholar] [CrossRef]
- Ghafuri, L.; Hamzehzadeh Alamdari, A.; Roustaei, S.; Golshani Beheshti, A.; Nayerpour, A. Predicting Severity of Novel Coronavirus (COVID-19) Pneumonia Based upon Admission Clinical, Laboratory, and Imaging Findings. Tanaffos 2021, 20, 232–239. [Google Scholar]
- Karthik, R.; Menaka, R.; Hariharan, M.; Won, D. CT-based severity assessment for COVID-19 using weakly supervised non-local CNN. Appl. Soft Comput. 2022, 121, 108765. [Google Scholar] [CrossRef]
- Vargas Centanaro, G.; Calle Rubio, M.; Álvarez-Sala Walther, J.L.; Martinez-Sagasti, F.; Albuja Hidalgo, A.; Herranz Hernández, R.; Rodríguez Hermosa, J.L. Long-term Outcomes and Recovery of Patients who Survived COVID-19: LUNG INJURY COVID-19 Study. Open Forum Infect. Dis. 2022, 9, ofac098. [Google Scholar] [CrossRef]
- Küçük, M.; Ergan, B.; Yakar, M.N.; Ergün, B.; Akdoğan, Y.; Cantürk, A.; Gezer, N.S.; Kalkan, F.; Yaka, E.; Cömert, B.; et al. The Predictive Values of Respiratory Rate Oxygenation Index and Chest Computed Tomography Severity Score for High-Flow Nasal Oxygen Failure in Critically Ill Patients with Coronavirus Disease-2019. Balkan Med. J. 2022, 39, 140–147. [Google Scholar] [CrossRef]
- Özel, M.; Aslan, A.; Araç, S. Use of the COVID-19 Reporting and Data System (CO-RADS) classification and chest computed tomography involvement score (CT-IS) in COVID-19 pneumonia. Radiol. Med. 2021, 126, 679–687. [Google Scholar] [CrossRef] [PubMed]
- Cereser, L.; Girometti, R.; Da Re, J.; Marchesini, F.; Como, G.; Zuiani, C. Inter-reader agreement of high-resolution computed tomography findings in patients with COVID-19 pneumonia: A multi-reader study. Radiol. Med. 2021, 126, 577–584. [Google Scholar] [CrossRef] [PubMed]
- Cappabianca, S.; Fusco, R.; de Lisio, A.; Paura, C.; Clemente, A.; Gagliardi, G.; Lombardi, G.; Giacobbe, G.; Russo, G.M.; Belfiore, M.P.; et al. Correction to: Clinical and laboratory data, radiological structured report findings and quantitative evaluation of lung involvement on baseline chest CT in COVID-19 patients to predict prognosis. Radiol. Med. 2021, 126, 643, Erratum in Radiol. Med. 2021, 126, 29–39. [Google Scholar] [CrossRef] [PubMed]
- Cartocci, G.; Colaiacomo, M.C.; Lanciotti, S.; Andreoli, C.; De Cicco, M.L.; Brachetti, G.; Pugliese, S.; Capoccia, L.; Tortora, A.; Scala, A.; et al. Correction to: Chest CT for early detection and management of coronavirus disease (COVID-19): A report of 314 patients admitted to Emergency Department with suspected pneumonia. Radiol. Med. 2021, 126, 642, Erratum in Radiol. Med. 2020, 125, 931–942. [Google Scholar] [CrossRef] [PubMed]
- Bianchi, A.; Mazzoni, L.N.; Busoni, S.; Pinna, N.; Albanesi, M.; Cavigli, E.; Cozzi, D.; Poggesi, A.; Miele, V.; Fainardi, E.; et al. Assessment of cerebrovascular disease with computed tomography in COVID-19 patients: Correlation of a novel specific visual score with increased mortality risk. Radiol. Med. 2021, 126, 570–576. [Google Scholar] [CrossRef] [PubMed]
- Kovács, A.; Palásti, P.; Veréb, D.; Bozsik, B.; Palkó, A.; Kincses, Z.T. The sensitivity and specificity of chest CT in the diagnosis of COVID-19. Eur. Radiol. 2021, 31, 2819–2824. [Google Scholar] [CrossRef] [PubMed]
- Li, K.; Wu, J.; Wu, F.; Guo, D.; Chen, L.; Fang, Z.; Li, C. The clinical and chest CT features associated with severe and critical COVID-19 pneumonia. Investig. Radiol. 2020, 55, 327–331. [Google Scholar] [CrossRef]
- Hansell, D.M.; Bankier, A.A.; MacMahon, H.; McLoud, T.C.; Muller, N.L.; Remy, J. Fleischner Society: Glossary of terms for thoracic imaging. Radiology 2008, 246, 697–722. [Google Scholar] [CrossRef] [Green Version]
- Available online: https://who.maps.arcgis.com/apps/dashboards/ead3c6475654481ca51c248d52ab9c61 (accessed on 21 May 2022).
- Yek, C.; Warner, S.; Wiltz, J.L.; Sun, J.; Adjei, S.; Mancera, A.; Silk, B.J.; Gundlapalli, A.V.; Harris, A.M.; Boehmer, T.K.; et al. Risk Factors for Severe COVID-19 Outcomes among Persons Aged ≥ 18 Years Who Completed a Primary COVID-19 Vaccination Series—465 Health Care Facilities, United States, December 2020–October 2021. MMWR Morb. Mortal. Wkly. Rep. 2022, 71, 19–25. [Google Scholar] [CrossRef]
- Pairo-Castineira, E.; Clohisey, S.; Klaric, L.; Bretherick, A.D.; Rawlik, K.; Pasko, D.; Walker, S.; Parkinson, N.; Fourman, M.H.; Russell, C.D.; et al. Genetic mechanisms of critical illness in COVID-19. Nature 2021, 591, 92–98. [Google Scholar] [CrossRef]
- Perico, L.; Benigni, A.; Casiraghi, F.; Ng, L.F.P.; Renia, L.; Remuzzi, G. Immunity, endothelial injury and complement-induced coagulopathy in COVID-19. Nat. Rev. Nephrol. 2021, 17, 46–64. [Google Scholar] [CrossRef] [PubMed]
- Gupta, S.; Wang, W.; Hayek, S.S.; Chan, L.; Mathews, K.S.; Melamed, M.L.; Brenner, S.K.; Leonberg-Yoo, A.; Schenck, E.J.; Radbel, J.; et al. Association Between Early Treatment with Tocilizumab and Mortality Among Critically Ill Patients with COVID-19. JAMA Intern. Med. 2021, 181, 41–51, Erratum in JAMA Intern. Med. 2021, 181, 570. [Google Scholar] [CrossRef] [PubMed]
- Leentjens, J.; van Haaps, T.F.; Wessels, P.F.; Schutgens, R.E.G.; Middeldorp, S. COVID-19-associated coagulopathy and antithrombotic agents-lessons after 1 year. Lancet Haematol. 2021, 8, e524–e533. [Google Scholar] [CrossRef]
- Scobie, H.M.; Johnson, A.G.; Suthar, A.B.; Severson, R.; Alden, N.B.; Balter, S.; Bertolino, D.; Blythe, D.; Brady, S.; Cadwell, B.; et al. Monitoring Incidence of COVID-19 Cases, Hospitalizations, and Deaths, by Vaccination Status—13 U.S. Jurisdictions, April 4–July 17, 2021. MMWR Morb. Mortal. Wkly. Rep. 2021, 70, 1284–1290. [Google Scholar] [CrossRef] [PubMed]
- Mallapaty, S. China’s COVID vaccines have been crucial—Now immunity is waning. Nature 2021, 598, 398–399. [Google Scholar] [CrossRef] [PubMed]
- Barda, N.; Dagan, N.; Cohen, C.; Hernán, M.A.; Lipsitch, M.; Kohane, I.S.; Reis, B.Y.; Balicer, R.D. Effectiveness of a third dose of the BNT162b2 mRNA COVID-19 vaccine for preventing severe outcomes in Israel: An observational study. Lancet 2021, 398, 2093–2100. [Google Scholar] [CrossRef]
- Callaway, E. The race for coronavirus vaccines: A graphical guide. Nature 2020, 580, 576–577. [Google Scholar] [CrossRef]
- Dai, L.; Gao, G.F. Viral targets for vaccines against COVID-19. Nat. Rev. Immunol. 2021, 21, 73–82. [Google Scholar] [CrossRef]
- Chen, J.; Wang, R.; Gilby, N.B.; Wei, G.W. Omicron (B.1.1.529): Infectivity, vaccine breakthrough, and antibody resistance. arXiv 2021. Update in J. Chem. Inf. Model. 2022, 62, 412–422. [Google Scholar]
- Ren, S.Y.; Wang, W.B.; Gao, R.D.; Zhou, A.M. Omicron variant (B.1.1.529) of SARS-CoV-2: Mutation, infectivity, transmission, and vaccine resistance. World J. Clin. Cases. 2022, 10, 1–11. [Google Scholar] [CrossRef]
- Araf, Y.; Akter, F.; Tang, Y.D.; Fatemi, R.; Parvez, M.S.A.; Zheng, C.; Hossain, M.G. Omicron variant of SARS-CoV-2: Genomics, transmissibility, and responses to current COVID-19 vaccines. J. Med. Virol. 2022, 94, 1825–1832. [Google Scholar] [CrossRef] [PubMed]
- Lusvarghi, S.; Pollett, S.D.; Neerukonda, S.N.; Wang, W.; Wang, R.; Vassell, R.; Epsi, N.J.; Fries, A.C.; Agan, B.K.; Lindholm, D.A.; et al. SARS-CoV-2 Omicron neutralization by therapeutic antibodies, convalescent sera, and post-mRNA vaccine booster. bioRxiv 2021. [Google Scholar] [CrossRef]
- Rampado, O.; Depaoli, A.; Marchisio, F.; Gatti, M.; Racine, D.; Ruggeri, V.; Ruggirello, I.; Darvizeh, F.; Fonio, P.; Ropolo, R. Ef-fects of different levels of CT iterative reconstruction on low-contrast detectability and radiation dose in patients of different sizes: An anthropomorphic phantom study. Radiol. Med. 2021, 126, 55–62. [Google Scholar] [CrossRef] [PubMed]
- Schicchi, N.; Fogante, M.; Palumbo, P.; Agliata, G.; Esposto Pirani, P.; Di Cesare, E.; Giovagnoni, A. The sub-millisievert era in CTCA: The technical basis of the new radiation dose approach. Radiol. Med. 2020, 125, 1024–1039. [Google Scholar] [CrossRef]
- Palumbo, P.; Cannizzaro, E.; Bruno, F.; Schicchi, N.; Fogante, M.; Agostini, A.; De Donato, M.C.; De Cataldo, C.; Giovagnoni, A.; Barile, A.; et al. Coronary artery disease (CAD) extension-derived risk strati-fication for asymptomatic diabetic patients: Usefulness of low-dose coronary computed tomography angiography (CCTA) in detecting high-risk profile patients. Radiol. Med. 2020, 125, 1249–1259. [Google Scholar] [CrossRef]
- Hussein, M.A.M.; Cafarelli, F.P.; Paparella, M.T.; Rennie, W.J.; Guglielmi, G. Phosphaturic mesenchymal tumors: Radiological aspects and suggested imaging pathway. Radiol. Med. 2021, 126, 1609–1618. [Google Scholar] [CrossRef]
- Danti, G.; Flammia, F.; Matteuzzi, B.; Cozzi, D.; Berti, V.; Grazzini, G.; Pradella, S.; Recchia, L.; Brunese, L.; Miele, V. Gastrointestinal neuroendocrine neoplasms (GI-NENs): Hot topics in morphological, functional, and prognostic imaging. Radiol. Med. 2021, 126, 1497–1507. [Google Scholar] [CrossRef]
- Karmazanovsky, G.; Gruzdev, I.; Tikhonova, V.; Kondratyev, E.; Revishvili, A. Computed tomography-based radiomics approach in pancreatic tumors characterization. Radiol. Med. 2021, 126, 1388–1395. [Google Scholar] [CrossRef]
- Fusco, R.; Petrillo, M.; Granata, V.; Filice, S.; Sansone, M.; Catalano, O.; Petrillo, A. Magnetic Resonance Imaging Evaluation in Neoadjuvant Therapy of Locally Advanced Rectal Cancer: A Systematic Review. Radiol. Oncol. 2017, 51, 252–262. [Google Scholar] [CrossRef]
- Fusco, R.; Sansone, M.; Granata, V.; Setola, S.V.; Petrillo, A. A systematic review on multiparametric MR imaging in prostate cancer detection. Infect. Agent Cancer 2017, 12, 57. [Google Scholar] [CrossRef] [Green Version]
- Granata, V.; Fusco, R.; Avallone, A.; Filice, F.; Tatangelo, F.; Piccirillo, M.; Grassi, R.; Izzo, F.; Petrillo, A. Critical analysis of the major and ancillary imaging features of LI-RADS on 127 proven HCCs evaluated with functional and morphological MRI: Lights and shadows. Oncotarget 2017, 8, 51224–51237. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Granata, V.; Grassi, R.; Fusco, R.; Belli, A.; Cutolo, C.; Pradella, S.; Grazzini, G.; La Porta, M.; Brunese, M.C.; De Muzio, F.; et al. Diagnostic evaluation and ablation treatments assessment in hepatocellular carcinoma. Infect. Agent Cancer 2021, 16, 53. [Google Scholar] [CrossRef]
- Barabino, M.; Gurgitano, M.; Fochesato, C.; Angileri, S.A.; Franceschelli, G.; Santambrogio, R.; Mariani, N.M.; Opocher, E.; Carrafiello, G. LI-RADS to categorize liver nodules in patients at risk of HCC: Tool or a gadget in daily practice? Radiol. Med. 2021, 126, 5–13. [Google Scholar] [CrossRef] [PubMed]
- Granata, V.; Fusco, R.; Filice, S.; Catalano, O.; Piccirillo, M.; Palaia, R.; Izzo, F.; Petrillo, A. The current role and future prospectives of functional parameters by diffusion weighted imaging in the assessment of histologic grade of HCC. Infect. Agent Cancer 2018, 3, 23. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Orlacchio, A.; Chegai, F.; Roma, S.; Merolla, S.; Bosa, A.; Francioso, S. Degradable starch microspheres transarterial chemoembolization (DSMs-TACE) in patients with unresectable hepatocellular carcinoma (HCC): Long-term results from a single-center 137-patient cohort prospective study. Radiol. Med. 2020, 125, 98–106. [Google Scholar] [CrossRef]
Characteristic | Alpha Variant n = 20 | Unvaccinated Delta Variant n = 20 | Unvaccinated Delta Variant n = 18 | Unvaccinated Omicron Variant n = 20 | Vaccinated Omicron Variant n = 13 | p Value |
---|---|---|---|---|---|---|
Age (y) | ||||||
Mean | 62 | 58 | 64 | 69 | 75 | 0.07 |
Range | 43–78 | 37–83 | 35–87 | 42–88 | 55–94 | |
Sex, no. (%) of patients | ||||||
Male | 14 | 17 | 15 | 13 | 12 | 0.43 |
Female | 6 | 3 | 3 | 7 | 1 | |
CT Findings | ||||||
GGO | 19 | 20 | 16 | 19 | 13 | 0.89 |
Crazy Paving | 17 | 20 | 14 | 16 | 11 | 0.10 |
Consolidation | 15 | 17 | 11 | 16 | 11 | 0.70 |
Exitus | 5 | 5 | 6 | 4 | 5 | 0.95 |
Unvaccinated | Vaccinated with 2 Doses | Vaccinated with 3 Doses | p Value | ||
---|---|---|---|---|---|
Patients with Alpha Variant | Number of patients | 20 | 0 | 0 | 0.001 |
Patients with Delta variant | 20 | 16 | 2 | ||
Patients with Omicron | 20 | 8 | 5 | ||
Patients with Alpha Variant | Median value of (range) of Aerated residual lung volume [%] | 39.95 (19.40–67.50) | - | - | 0.05 |
Patients with Delta variant | 46.7 (13.60–75.60) | 67.10 (17.10–89.80) | 52.00 (19.40–84.50) | ||
Patients with Omicron | 48.35 (8.20–83.30) | 38.30 (18.90–73.30) | 61.9 (31.60–73.60) |
Aerated Residual Volume % | Right Upper Lobe Volume % | Right Lower Lobe Volume % | Medium Lobe Volume % | Left Upper Lobe Volume % | Left Lower Lobe Volume % | |
---|---|---|---|---|---|---|
Alpha | 39.95 | 47.30 | 26.00 | 64.40 | 55.00 | 25.05 |
Unvaccinated | 39.95 | 47.30 | 26.00 | 64.40 | 55.00 | 25.05 |
Delta | 55.25 | 56.2 | 58.35 | 72.9 | 32.75 | 56 |
Unvaccinated | 46.70 | 39.20 | 51.30 | 60.15 | 23.45 | 46.65 |
Vaccinated | 67.10 | 66.50 | 71.55 | 83.50 | 57.00 | 66.80 |
Omicron | 46.4 | 46.8 | 59 | 68.4 | 26.9 | 50 |
Unvaccinated | 48.35 | 42.2 | 54.2 | 53.65 | 28.65 | 51.65 |
Vaccinated | 46.4 | 49.8 | 61.4 | 70.1 | 25.7 | 45.1 |
p value at Kruskal Wallis test | 0.03 | 0.06 | 0.06 | 0.04 | 0.004 | 0.12 |
Overall Radiological SCORE | Aerated Residual Volume % | Right Upper Lobe Volume % | Right Lower Lobe Volume % | Medium Lobe Volume % | Left Upper Lobe Volume % | Left Lower Lobe Volume % | |
---|---|---|---|---|---|---|---|
Alpha | 2 | 57.50 | 65.40 | 48.85 | 70.50 | 59.50 | 26.30 |
3 | 47.37 | 72.07 | 35.27 | 80.50 | 66.67 | 33.03 | |
4 | 39.51 | 40.20 | 23.73 | 54.84 | 49.61 | 27.15 | |
5 | 36.96 | 39.06 | 23.71 | 57.19 | 44.23 | 25.16 | |
Delta | 1 | 82.17 | 86.83 | 73.63 | 87.37 | 84.87 | 71.67 |
2 | 76.06 | 74.02 | 68.02 | 86.20 | 83.06 | 66.08 | |
3 | 65.25 | 68.04 | 54.91 | 75.65 | 72.97 | 56.29 | |
4 | 43.40 | 47.19 | 21.51 | 63.44 | 61.50 | 20.47 | |
5 | 26.86 | 29.34 | 11.54 | 38.18 | 45.60 | 15.18 | |
Omicron | 1 | 77.73 | 80.83 | 69.03 | 78.43 | 81.53 | 75.93 |
2 | 67.97 | 72.62 | 51.53 | 78.83 | 75.50 | 57.17 | |
3 | 47.87 | 55.15 | 25.83 | 65.77 | 60.47 | 29.38 | |
4 | 46.64 | 52.84 | 29.56 | 57.07 | 59.79 | 31.39 | |
5 | 34.00 | 47.12 | 16.17 | 51.25 | 44.08 | 15.88 | |
p value at Kruskal Wallis test | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 |
Overall Radiological Severity Score | Alpha Variant n = 20 | Delta Variant n = 38 | Omicronvariant n = 33 | p Value at Chi Square Test | |
---|---|---|---|---|---|
≤65 years | ≤5 | 0 | 2 | 3 | 0.55 |
>65 years | 0 | 1 | 0 | ||
Total | 0 | 3 | 3 | ||
≤65 years | 6–10 | 1 | 2 | 5 | 0.32 |
>65 years | 1 | 3 | 1 | ||
Total | 2 | 5 | 6 | ||
≤65 years | 11–15 | 0 | 7 | 4 | 0.11 |
>65 years | 3 | 4 | 2 | ||
Total | 3 | 11 | 6 | ||
≤65 years | 16–20 | 4 | 3 | 6 | 0.06 |
>65 years | 4 | 8 | 1 | ||
Total | 8 | 11 | 7 | ||
≤65 years | 21–25 | 3 | 3 | 8 | 0.25 |
>65 years | 4 | 5 | 3 | ||
Total | 7 | 8 | 11 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Granata, V.; Fusco, R.; Villanacci, A.; Magliocchetti, S.; Urraro, F.; Tetaj, N.; Marchioni, L.; Albarello, F.; Campioni, P.; Cristofaro, M.; et al. Imaging Severity COVID-19 Assessment in Vaccinated and Unvaccinated Patients: Comparison of the Different Variants in a High Volume Italian Reference Center. J. Pers. Med. 2022, 12, 955. https://doi.org/10.3390/jpm12060955
Granata V, Fusco R, Villanacci A, Magliocchetti S, Urraro F, Tetaj N, Marchioni L, Albarello F, Campioni P, Cristofaro M, et al. Imaging Severity COVID-19 Assessment in Vaccinated and Unvaccinated Patients: Comparison of the Different Variants in a High Volume Italian Reference Center. Journal of Personalized Medicine. 2022; 12(6):955. https://doi.org/10.3390/jpm12060955
Chicago/Turabian StyleGranata, Vincenza, Roberta Fusco, Alberta Villanacci, Simona Magliocchetti, Fabrizio Urraro, Nardi Tetaj, Luisa Marchioni, Fabrizio Albarello, Paolo Campioni, Massimo Cristofaro, and et al. 2022. "Imaging Severity COVID-19 Assessment in Vaccinated and Unvaccinated Patients: Comparison of the Different Variants in a High Volume Italian Reference Center" Journal of Personalized Medicine 12, no. 6: 955. https://doi.org/10.3390/jpm12060955