Pulmonary parenchymal and vascular damage are frequently reported in COVID-19 patients and can be assessed with unenhanced chest computed tomography (CT), widely used as a triaging exam. Integrating clinical data, chest CT features, and CT-derived vascular metrics, we aimed to build a predictive model of in-hospital mortality using univariate analysis (Mann–Whitney U test) and machine learning models (support vectors machines (SVM) and multilayer perceptrons (MLP)). Patients with RT-PCR-confirmed SARS-CoV-2 infection and unenhanced chest CT performed on emergency department admission were included after retrieving their outcome (discharge or death), with an 85/15% training/test dataset split. Out of 897 patients, the 229 (26%) patients who died during hospitalization had higher median pulmonary artery diameter (29.0 mm) than patients who survived (27.0 mm, p < 0.001) and higher median ascending aortic diameter (36.6 mm versus 34.0 mm, p < 0.001). SVM and MLP best models considered the same ten input features, yielding a 0.747 (precision 0.522, recall 0.800) and 0.844 (precision 0.680, recall 0.567) area under the curve, respectively. In this model integrating clinical and radiological data, pulmonary artery diameter was the third most important predictor after age and parenchymal involvement extent, contributing to reliable in-hospital mortality prediction, highlighting the value of vascular metrics in improving patient stratification.

Machine Learning to Predict In-Hospital Mortality in COVID-19 Patients Using Computed Tomography-Derived Pulmonary and Vascular Features / S. Schiaffino, M. Codari, A. Cozzi, D. Albano, M. Alì, R. Arioli, E. Avola, C. Bnà, M. Cariati, S. Carriero, M. Cressoni, P.S.C. Danna, G. Della Pepa, G. Di Leo, F. Dolci, Z. Falaschi, N. Flor, R.A. Foà, S. Gitto, G. Leati, V. Magni, A.E. Malavazos, G. Mauri, C. Messina, L. Monfardini, A. Paschè, F. Pesapane, L.M. Sconfienza, F. Secchi, E. Segalini, A. Spinazzola, V. Tombini, S. Tresoldi, A. Vanzulli, I. Vicentin, D. Zagaria, D. Fleischmann, F. Sardanelli. - In: JOURNAL OF PERSONALIZED MEDICINE. - ISSN 2075-4426. - 11:6(2021), pp. 501.1-501.14.

Machine Learning to Predict In-Hospital Mortality in COVID-19 Patients Using Computed Tomography-Derived Pulmonary and Vascular Features

M. Codari;A. Cozzi
;
M. Alì;E. Avola;S. Carriero;M. Cressoni;G. Della Pepa;S. Gitto;V. Magni;A.E. Malavazos;G. Mauri;C. Messina;F. Pesapane;L.M. Sconfienza;F. Secchi;V. Tombini;S. Tresoldi;A. Vanzulli;I. Vicentin;F. Sardanelli
Ultimo
2021

Abstract

Pulmonary parenchymal and vascular damage are frequently reported in COVID-19 patients and can be assessed with unenhanced chest computed tomography (CT), widely used as a triaging exam. Integrating clinical data, chest CT features, and CT-derived vascular metrics, we aimed to build a predictive model of in-hospital mortality using univariate analysis (Mann–Whitney U test) and machine learning models (support vectors machines (SVM) and multilayer perceptrons (MLP)). Patients with RT-PCR-confirmed SARS-CoV-2 infection and unenhanced chest CT performed on emergency department admission were included after retrieving their outcome (discharge or death), with an 85/15% training/test dataset split. Out of 897 patients, the 229 (26%) patients who died during hospitalization had higher median pulmonary artery diameter (29.0 mm) than patients who survived (27.0 mm, p < 0.001) and higher median ascending aortic diameter (36.6 mm versus 34.0 mm, p < 0.001). SVM and MLP best models considered the same ten input features, yielding a 0.747 (precision 0.522, recall 0.800) and 0.844 (precision 0.680, recall 0.567) area under the curve, respectively. In this model integrating clinical and radiological data, pulmonary artery diameter was the third most important predictor after age and parenchymal involvement extent, contributing to reliable in-hospital mortality prediction, highlighting the value of vascular metrics in improving patient stratification.
COVID-19; lung; pulmonary artery; tomography; X-ray computed; machine learning; support vector machine; neural networks; computer; prognosis
Settore MED/36 - Diagnostica per Immagini e Radioterapia
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/848594
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