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1. Chest CT opportunistic biomarkers for phenotyping high-risk COVID-19 patients: a retrospective multicentre study

Author

Palmisano, A, Gnasso, C, Cereda, A, Vignale, D, Leone, R, Nicoletti, V, Barbieri, S, Toselli, M, Giannini, F, Loffi, M, Patelli, G, Monello, A, Iannopollo, G, Ippolito, D, Mancini, E, Pontone, G, Vignali, L, Scarnecchia, E, Iannaccone, M, Baffoni, L, Spernadio, M, de Carlini, C, Sironi, S, Rapezzi, C, Esposito, A, Palmisano A., Gnasso C., Cereda A., Vignale D., Leone R., Nicoletti V., Barbieri S., Toselli M., Giannini F., Loffi M., Patelli G., Monello A., Iannopollo G., Ippolito D., Mancini E. M., Pontone G., Vignali L., Scarnecchia E., Iannaccone M., Baffoni L., Spernadio M., de Carlini C. C., Sironi S., Rapezzi C., Esposito A., Palmisano, A, Gnasso, C, Cereda, A, Vignale, D, Leone, R, Nicoletti, V, Barbieri, S, Toselli, M, Giannini, F, Loffi, M, Patelli, G, Monello, A, Iannopollo, G, Ippolito, D, Mancini, E, Pontone, G, Vignali, L, Scarnecchia, E, Iannaccone, M, Baffoni, L, Spernadio, M, de Carlini, C, Sironi, S, Rapezzi, C, Esposito, A, Palmisano A., Gnasso C., Cereda A., Vignale D., Leone R., Nicoletti V., Barbieri S., Toselli M., Giannini F., Loffi M., Patelli G., Monello A., Iannopollo G., Ippolito D., Mancini E. M., Pontone G., Vignali L., Scarnecchia E., Iannaccone M., Baffoni L., Spernadio M., de Carlini C. C., Sironi S., Rapezzi C., and Esposito A.

Abstract

Objective: To assess the value of opportunistic biomarkers derived from chest CT performed at hospital admission of COVID-19 patients for the phenotypization of high-risk patients. Methods: In this multicentre retrospective study, 1845 consecutive COVID-19 patients with chest CT performed within 72 h from hospital admission were analysed. Clinical and outcome data were collected by each center 30 and 80 days after hospital admission. Patients with unknown outcomes were excluded. Chest CT was analysed in a single core lab and behind pneumonia CT scores were extracted opportunistic data about atherosclerotic profile (calcium score according to Agatston method), liver steatosis (≤ 40 HU), myosteatosis (paraspinal muscle F < 31.3 HU, M < 37.5 HU), and osteoporosis (D12 bone attenuation < 134 HU). Differences according to treatment and outcome were assessed with ANOVA. Prediction models were obtained using multivariate binary logistic regression and their AUCs were compared with the DeLong test. Results: The final cohort included 1669 patients (age 67.5 [58.5–77.4] yo) mainly men 1105/1669, 66.2%) and with reduced oxygen saturation (92% [88–95%]). Pneumonia severity, high Agatston score, myosteatosis, liver steatosis, and osteoporosis derived from CT were more prevalent in patients with more aggressive treatment, access to ICU, and in-hospital death (always p < 0.05). A multivariable model including clinical and CT variables improved the capability to predict non-critical pneumonia compared to a model including only clinical variables (AUC 0.801 vs 0.789; p = 0.0198) to predict patient death (AUC 0.815 vs 0.800; p = 0.001). Conclusion: Opportunistic biomarkers derived from chest CT can improve the characterization of COVID-19 high-risk patients. Clinical relevance statement: In COVID-19 patients, opportunistic biomarkers of cardiometabolic risk extracted from chest CT improve patient risk stratification. Key Points: • In COVID-19 patients, several i

Published
2023

2. AI-SCoRE (artificial intelligence-SARS CoV2 risk evaluation): a fast, objective and fully automated platform to predict the outcome in COVID-19 patients

Author

Palmisano, A, Vignale, D, Boccia, E, Nonis, A, Gnasso, C, Leone, R, Montagna, M, Nicoletti, V, Bianchi, A, Brusamolino, S, Dorizza, A, Moraschini, M, Veettil, R, Cereda, A, Toselli, M, Giannini, F, Loffi, M, Patelli, G, Monello, A, Iannopollo, G, Ippolito, D, Mancini, E, Pontone, G, Vignali, L, Scarnecchia, E, Iannacone, M, Baffoni, L, Sperandio, M, de Carlini, C, Sironi, S, Rapezzi, C, Antiga, L, Jagher, V, Di Serio, C, Furlanello, C, Tacchetti, C, Esposito, A, Palmisano A., Vignale D., Boccia E., Nonis A., Gnasso C., Leone R., Montagna M., Nicoletti V., Bianchi A. G., Brusamolino S., Dorizza A., Moraschini M., Veettil R., Cereda A., Toselli M., Giannini F., Loffi M., Patelli G., Monello A., Iannopollo G., Ippolito D., Mancini E. M., Pontone G., Vignali L., Scarnecchia E., Iannacone M., Baffoni L., Sperandio M., de Carlini C. C., Sironi S., Rapezzi C., Antiga L., Jagher V., Di Serio C., Furlanello C., Tacchetti C., Esposito A., Palmisano, A, Vignale, D, Boccia, E, Nonis, A, Gnasso, C, Leone, R, Montagna, M, Nicoletti, V, Bianchi, A, Brusamolino, S, Dorizza, A, Moraschini, M, Veettil, R, Cereda, A, Toselli, M, Giannini, F, Loffi, M, Patelli, G, Monello, A, Iannopollo, G, Ippolito, D, Mancini, E, Pontone, G, Vignali, L, Scarnecchia, E, Iannacone, M, Baffoni, L, Sperandio, M, de Carlini, C, Sironi, S, Rapezzi, C, Antiga, L, Jagher, V, Di Serio, C, Furlanello, C, Tacchetti, C, Esposito, A, Palmisano A., Vignale D., Boccia E., Nonis A., Gnasso C., Leone R., Montagna M., Nicoletti V., Bianchi A. G., Brusamolino S., Dorizza A., Moraschini M., Veettil R., Cereda A., Toselli M., Giannini F., Loffi M., Patelli G., Monello A., Iannopollo G., Ippolito D., Mancini E. M., Pontone G., Vignali L., Scarnecchia E., Iannacone M., Baffoni L., Sperandio M., de Carlini C. C., Sironi S., Rapezzi C., Antiga L., Jagher V., Di Serio C., Furlanello C., Tacchetti C., and Esposito A.

Abstract

Purpose: To develop and validate an effective and user-friendly AI platform based on a few unbiased clinical variables integrated with advanced CT automatic analysis for COVID-19 patients’ risk stratification. Material and Methods: In total, 1575 consecutive COVID-19 adults admitted to 16 hospitals during wave 1 (February 16-April 29, 2020), submitted to chest CT within 72 h from admission, were retrospectively enrolled. In total, 107 variables were initially collected; 64 extracted from CT. The outcome was survival. A rigorous AI model selection framework was adopted for models selection and automatic CT data extraction. Model performances were compared in terms of AUC. A web–mobile interface was developed using Microsoft PowerApps environment. The platform was externally validated on 213 COVID-19 adults prospectively enrolled during wave 2 (October 14-December 31, 2020). Results: The final cohort included 1125 patients (292 non-survivors, 26%) and 24 variables. Logistic showed the best performance on the complete set of variables (AUC = 0.839 ± 0.009) as in models including a limited set of 13 and 5 variables (AUC = 0.840 ± 0.0093 and AUC = 0.834 ± 0.007). For non-inferior performance, the 5 variables model (age, sex, saturation, well-aerated lung parenchyma and cardiothoracic vascular calcium) was selected as the final model and the extraction of CT-derived parameters was fully automatized. The fully automatic model showed AUC = 0.842 (95% CI: 0.816–0.867) on wave 1 and was used to build a 0–100 scale risk score (AI-SCoRE). The predictive performance was confirmed on wave 2 (AUC 0.808; 95% CI: 0.7402–0.8766). Conclusions: AI-SCoRE is an effective and reliable platform for automatic risk stratification of COVID-19 patients based on a few unbiased clinical data and CT automatic analysis.

Published
2022

3. Coronary and total thoracic calcium scores predict mortality and provides pathophysiologic insights in COVID-19 patients

Author

Giannini, F, Toselli, M, Palmisano, A, Cereda, A, Vignale, D, Leone, R, Nicoletti, V, Gnasso, C, Monello, A, Manfrini, M, Khokhar, A, Sticchi, A, Biagi, A, Turchio, P, Tacchetti, C, Landoni, G, Boccia, E, Campo, G, Scoccia, A, Ponticelli, F, Danzi, G, Loffi, M, Muri, M, Pontone, G, Andreini, D, Mancini, E, Casella, G, Iannopollo, G, Nannini, T, Ippolito, D, Bellani, G, Franzesi, C, Patelli, G, Besana, F, Costa, C, Vignali, L, Benatti, G, Sverzellati, N, Scarnecchia, E, Lombardo, F, Anastasio, F, Iannaccone, M, Vaudano, P, Pacielli, A, Baffoni, L, Gardi, I, Cesini, E, Sperandio, M, Micossi, C, De Carlini, C, Spreafico, C, Maggiolini, S, Bonaffini, P, Iacovoni, A, Sironi, S, Senni, M, Fominskiy, E, De Cobelli, F, Maggioni, A, Rapezzi, C, Ferrari, R, Colombo, A, Esposito, A, Giannini, Francesco, Toselli, Marco, Palmisano, Anna, Cereda, Alberto, Vignale, Davide, Leone, Riccardo, Nicoletti, Valeria, Gnasso, Chiara, Monello, Alberto, Manfrini, Marco, Khokhar, Arif, Sticchi, Alessandro, Biagi, Andrea, Turchio, Piergiorgio, Tacchetti, Carlo, Landoni, Giovanni, Boccia, Edda, Campo, Gianluca, Scoccia, Alessandra, Ponticelli, Francesco, Danzi, Gian Battista, Loffi, Marco, Muri, Margherita, Pontone, Gianluca, Andreini, Daniele, Mancini, Elisabetta Maria, Casella, Gianni, Iannopollo, Gianmarco, Nannini, Tommaso, Ippolito, Davide, Bellani, Giacomo, Franzesi, Camillo Talei, Patelli, Gianluigi, Besana, Francesca, Costa, Claudia, Vignali, Luigi, Benatti, Giorgio, Sverzellati, Nicola, Scarnecchia, Elisa, Lombardo, Francesco Paolo, Anastasio, Fabio, Iannaccone, Mario, Vaudano, Paolo Giacomo, Pacielli, Alberto, Baffoni, Lucio, Gardi, Iljia, Cesini, Elisabetta, Sperandio, Massimiliano, Micossi, Chiara, De Carlini, Caterina Chiara, Spreafico, Cristiano, Maggiolini, Stefano, Bonaffini, Pietro Andrea, Iacovoni, Attilio, Sironi, Sandro, Senni, Michele, Fominskiy, Evgeny, De Cobelli, Francesco, Maggioni, Aldo Pietro, Rapezzi, Claudio, Ferrari, Roberto, Colombo, Antonio, Esposito, Antonio, Giannini, F, Toselli, M, Palmisano, A, Cereda, A, Vignale, D, Leone, R, Nicoletti, V, Gnasso, C, Monello, A, Manfrini, M, Khokhar, A, Sticchi, A, Biagi, A, Turchio, P, Tacchetti, C, Landoni, G, Boccia, E, Campo, G, Scoccia, A, Ponticelli, F, Danzi, G, Loffi, M, Muri, M, Pontone, G, Andreini, D, Mancini, E, Casella, G, Iannopollo, G, Nannini, T, Ippolito, D, Bellani, G, Franzesi, C, Patelli, G, Besana, F, Costa, C, Vignali, L, Benatti, G, Sverzellati, N, Scarnecchia, E, Lombardo, F, Anastasio, F, Iannaccone, M, Vaudano, P, Pacielli, A, Baffoni, L, Gardi, I, Cesini, E, Sperandio, M, Micossi, C, De Carlini, C, Spreafico, C, Maggiolini, S, Bonaffini, P, Iacovoni, A, Sironi, S, Senni, M, Fominskiy, E, De Cobelli, F, Maggioni, A, Rapezzi, C, Ferrari, R, Colombo, A, Esposito, A, Giannini, Francesco, Toselli, Marco, Palmisano, Anna, Cereda, Alberto, Vignale, Davide, Leone, Riccardo, Nicoletti, Valeria, Gnasso, Chiara, Monello, Alberto, Manfrini, Marco, Khokhar, Arif, Sticchi, Alessandro, Biagi, Andrea, Turchio, Piergiorgio, Tacchetti, Carlo, Landoni, Giovanni, Boccia, Edda, Campo, Gianluca, Scoccia, Alessandra, Ponticelli, Francesco, Danzi, Gian Battista, Loffi, Marco, Muri, Margherita, Pontone, Gianluca, Andreini, Daniele, Mancini, Elisabetta Maria, Casella, Gianni, Iannopollo, Gianmarco, Nannini, Tommaso, Ippolito, Davide, Bellani, Giacomo, Franzesi, Camillo Talei, Patelli, Gianluigi, Besana, Francesca, Costa, Claudia, Vignali, Luigi, Benatti, Giorgio, Sverzellati, Nicola, Scarnecchia, Elisa, Lombardo, Francesco Paolo, Anastasio, Fabio, Iannaccone, Mario, Vaudano, Paolo Giacomo, Pacielli, Alberto, Baffoni, Lucio, Gardi, Iljia, Cesini, Elisabetta, Sperandio, Massimiliano, Micossi, Chiara, De Carlini, Caterina Chiara, Spreafico, Cristiano, Maggiolini, Stefano, Bonaffini, Pietro Andrea, Iacovoni, Attilio, Sironi, Sandro, Senni, Michele, Fominskiy, Evgeny, De Cobelli, Francesco, Maggioni, Aldo Pietro, Rapezzi, Claudio, Ferrari, Roberto, Colombo, Antonio, and Esposito, Antonio

Abstract

Background: Coronavirus disease 2019 (COVID-19) has spread worldwide determining dramatic impacts on healthcare systems. Early identification of high-risk parameters is required in order to provide the best therapeutic approach. Coronary, thoracic aorta and aortic valve calcium can be measured from a non-gated chest computer tomography (CT) and are validated predictors of cardiovascular events and all-cause mortality. However, their prognostic role in acute systemic inflammatory diseases, such as COVID-19, has not been investigated. Objectives: The aim was to evaluate the association of coronary artery calcium and total thoracic calcium on in-hospital mortality in COVID-19 patients. Methods: 1093 consecutive patients from 16 Italian hospitals with a positive swab for COVID-19 and an admission chest CT for pneumonia severity assessment were included. At CT, coronary, aortic valve and thoracic aorta calcium were qualitatively and quantitatively evaluated separately and combined together (total thoracic calcium) by a central Core-lab blinded to patients’ outcomes. Results: Non-survivors compared to survivors had higher coronary artery [Agatston (467.76 ​± ​570.92 vs 206.80 ​± ​424.13 ​mm2, p ​< ​0.001); Volume (487.79 ​± ​565.34 vs 207.77 ​± ​406.81, p ​< ​0.001)], aortic valve [Volume (322.45 ​± ​390.90 vs 98.27 ​± ​250.74 mm2, p ​< ​0.001; Agatston 337.38 ​± ​414.97 vs 111.70 ​± ​282.15, p ​< ​0.001)] and thoracic aorta [Volume (3786.71 ​± ​4225.57 vs 1487.63 ​± ​2973.19 mm2, p ​< ​0.001); Agatston (4688.82 ​± ​5363.72 vs 1834.90 ​± ​3761.25, p ​< ​0.001)] calcium values. Coronary artery calcium (HR 1.308; 95% CI, 1.046–1.637, p ​= ​0.019) and total thoracic calcium (HR 1.975; 95% CI, 1.200–3.251, p ​= ​0.007) resulted to be independent predictors of in-hospital mortality. Conclusion: Coronary, aortic valve and thoracic aortic calcium assessment on admission non-gated CT permits to stratify the COVID-19 patients in-hospital mortality risk.

Published
2021

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