Automated Quantitative Lung CT Improves Prognostication in Non-ICU COVID-19 Patients beyond Conventional Biomarkers of Disease
Pierpaolo Palumbo,
Maria Michela Palumbo,
Federico Bruno,
Giovanna Picchi,
Antonio Iacopino,
Chiara Acanfora,
Ferruccio Sgalambro,
Francesco Arrigoni,
Arturo Ciccullo,
Benedetta Cosimini,
Alessandra Splendiani,
Antonio Barile,
Francesco Masedu,
Alessandro Grimaldi,
Ernesto Di Cesare,
Carlo Masciocchi
Affiliations
Pierpaolo Palumbo
Department of Diagnostic Imaging, Area of Cardiovascular and Interventional Imaging, Abruzzo Health Unit 1, Via Saragat, Località Campo di Pile, 67100 L’Aquila, Italy
Maria Michela Palumbo
Department of Anesthesiology and Intensive Care Medicine, Fondazione Policlinico Universitario A. Gemelli IRCCS, Catholic University of The Sacred Heart, 00168 Rome, Italy
Federico Bruno
Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
Giovanna Picchi
Infectious Disease Unit, San Salvatore Hospital, Via Lorenzo Natali, 1-Località Coppito, 67100 L’Aquila, Italy
Antonio Iacopino
Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, Via Vetoio 1, 67100 L’Aquila, Italy
Chiara Acanfora
Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, Via Vetoio 1, 67100 L’Aquila, Italy
Ferruccio Sgalambro
Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, Via Vetoio 1, 67100 L’Aquila, Italy
Francesco Arrigoni
Department of Diagnostic Imaging, Area of Cardiovascular and Interventional Imaging, Abruzzo Health Unit 1, Via Saragat, Località Campo di Pile, 67100 L’Aquila, Italy
Arturo Ciccullo
Infectious Disease Unit, San Salvatore Hospital, Via Lorenzo Natali, 1-Località Coppito, 67100 L’Aquila, Italy
Benedetta Cosimini
Department of Life, Health and Environmental Sciences, University of L’Aquila, Piazzale Salvatore Tommasi 1, 67100 L’Aquila, Italy
Alessandra Splendiani
Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, Via Vetoio 1, 67100 L’Aquila, Italy
Antonio Barile
Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, Via Vetoio 1, 67100 L’Aquila, Italy
Francesco Masedu
Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, Via Vetoio 1, 67100 L’Aquila, Italy
Alessandro Grimaldi
Infectious Disease Unit, San Salvatore Hospital, Via Lorenzo Natali, 1-Località Coppito, 67100 L’Aquila, Italy
Ernesto Di Cesare
Department of Life, Health and Environmental Sciences, University of L’Aquila, Piazzale Salvatore Tommasi 1, 67100 L’Aquila, Italy
Carlo Masciocchi
Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, Via Vetoio 1, 67100 L’Aquila, Italy
(1) Background: COVID-19 continues to represent a worrying pandemic. Despite the high percentage of non-severe illness, a wide clinical variability is often reported in real-world practice. Accurate predictors of disease aggressiveness, however, are still lacking. The purpose of our study was to evaluate the impact of quantitative analysis of lung computed tomography (CT) on non-intensive care unit (ICU) COVID-19 patients’ prognostication; (2) Methods: Our historical prospective study included fifty-five COVID-19 patients consecutively submitted to unenhanced lung CT. Primary outcomes were recorded during hospitalization, including composite ICU admission for the need of mechanical ventilation and/or death occurrence. CT examinations were retrospectively evaluated to automatically calculate differently aerated lung tissues (i.e., overinflated, well-aerated, poorly aerated, and non-aerated tissue). Scores based on the percentage of lung weight and volume were also calculated; (3) Results: Patients who reported disease progression showed lower total lung volume. Inflammatory indices correlated with indices of respiratory failure and high-density areas. Moreover, non-aerated and poorly aerated lung tissue resulted significantly higher in patients with disease progression. Notably, non-aerated lung tissue was independently associated with disease progression (HR: 1.02; p-value: 0.046). When different predictive models including clinical, laboratoristic, and CT findings were analyzed, the best predictive validity was reached by the model that included non-aerated tissue (C-index: 0.97; p-value: 0.0001); (4) Conclusions: Quantitative lung CT offers wide advantages in COVID-19 disease stratification. Non-aerated lung tissue is more likely to occur with severe inflammation status, turning out to be a strong predictor for disease aggressiveness; therefore, it should be included in the predictive model of COVID-19 patients.
