| Title: |
Superimposed Pressure for Predicting Mortality in Acute Respiratory Failure During Spontaneous Breathing: Insights From the CT-COVID19 Multicenter Study Group |
| Authors: |
Rezoagli, Emanuele; Signori, Davide; Xin, Yi; Gerard, Sarah; Magliocca, Aurora; Graziano, Francesca; Vitale, Giovanni; Mussoni, Linda; Montomoli, Jonathan; Subert, Matteo; Ponti, Alessandra; Spadaro, Savino; Poli, Giancarla; Casola, Francesco; Garberi, Roberta; Cominesi, Davide Raimondi; Nova, Alice; Giani, Marco; Foti, Giuseppe; Laffey, John; Cereda, Maurizio; Cazzaniga, Matteo; Lorini, Ferdinando Luca; Bonaffini, Pietro; Ottaviani, Irene; Borgo, Asia; Tavola, Mario; Ferraris, Livio; Bellani, Giacomo; Gatti, Stefano; Restivo, Andrea; Serra, Filippo; Ippolito, Davide; Arlotti, Massimo; Gatti, Marino; Tamagnini, Beatrice; Cavagna, Enrico; Gamberini, Emiliano; De Ponti, Davide; Galbiati, Giuseppe; Giacomini, Matteo |
| Contributors: |
Rezoagli, E; Signori, D; Xin, Y; Gerard, S; Magliocca, A; Graziano, F; Vitale, G; Mussoni, L; Montomoli, J; Subert, M; Ponti, A; Spadaro, S; Poli, G; Casola, F; Garberi, R; Cominesi, D; Nova, A; Giani, M; Foti, G; Laffey, J; Cereda, M; Cazzaniga, M; Lorini, F; Bonaffini, P; Ottaviani, I; Borgo, A; Tavola, M; Ferraris, L; Bellani, G; Gatti, S; Restivo, A; Serra, F; Ippolito, D; Arlotti, M; Gatti, M; Tamagnini, B; Cavagna, E; Gamberini, E; De Ponti, D; Galbiati, G; Giacomini, M |
| Publisher Information: |
American College of Chest Physicians; US |
| Publication Year: |
2026 |
| Collection: |
Università degli Studi di Milano-Bicocca: BOA (Bicocca Open Archive) |
| Subject Terms: |
artificial intelligence; computed tomography; COVID-19; gas/tissue ratio; hounsfield unit; laterality; mortality; quadrant; respiratory failure; spontaneous breathing; superimposed pressure |
| Description: |
Background: Early lung imaging may improve patient management and prognostication in acute respiratory failure (ARF). Research Question: Does quantitative assessment of lung injury by CT imaging predict outcome in spontaneously breathing patients with COVID-19 ARF? Study Design and Methods: This was a large retrospective, multicenter cohort study including patients seeking treatment at the emergency department with a clinical diagnosis of COVID-19 respiratory failure and undergoing early lung CT imaging at hospital admission. Lung injury was characterized by the severity of lung involvement as follows: (1) absence, unilateral, or bilateral infiltrates; (2) number of lung quadrants affected by infiltrates (0-4); and level of global and regional (3) superimposed pressure (SP) and (4) gas to tissue (GT) ratio. Baseline, laboratory, and clinical characteristics were described by the presence or absence of laterality of lung infiltrates. Association of 90-day mortality and lung CT scan characterization was explored using Cox multivariable models and areas under receiving operating characteristic curves. Subphenotypes including CT scan assessment were explored by latent class analyses. Results: Eight hundred eight patients were included. Bilateral infiltrates were associated with higher global and regional SP and GT ratio and a higher 90-day mortality (38%) compared with unilateral infiltrates (18%) or no lung infiltrates (11%). Involvement by laterality, quadrants, degree of global SP, and GT ratio all were associated with the degree of hypoxemia on admission and 90-day mortality. Among other CT scan-derived variables of lung injury, SP characterized a subphenotype with a robust relationship with 90-day mortality. Interpretation: Characterization of lung injury severity by early lung CT imaging findings was shown to describe the severity of hypoxemia. The adjunct of CT scan global SP to clinical and laboratory parameters identified a subphenotype with high 90-day mortality prediction. Early lung CT imaging may ... |
| Document Type: |
article in journal/newspaper |
| File Description: |
ELETTRONICO |
| Language: |
English |
| Relation: |
volume:4; issue:1; journal:CHEST CRITICAL CARE; https://hdl.handle.net/10281/582962 |
| DOI: |
10.1016/j.chstcc.2025.100231 |
| Availability: |
https://hdl.handle.net/10281/582962; https://doi.org/10.1016/j.chstcc.2025.100231; https://www.chestcc.org/article/S2949-7884(25)00104-2/fulltext |
| Rights: |
info:eu-repo/semantics/openAccess ; license:Creative Commons ; license uri:http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| Accession Number: |
edsbas.35EB7D23 |
| Database: |
BASE |