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Quantitative Evaluation of COVID-19 Pneumonia Lung Extension by Specific Software and Correlation with Patient Clinical Outcome.
Annoni, Andrea Daniele; Conte, Edoardo; Mancini, Maria Elisabetta; Gigante, Carlo; Agalbato, Cecilia; Formenti, Alberto; Muscogiuri, Giuseppe; Mushtaq, Saima; Guglielmo, Marco; Baggiano, Andrea; Bonomi, Alice; Pepi, Mauro; Pontone, Gianluca; Andreini, Daniele.
  • Annoni AD; Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy.
  • Conte E; Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy.
  • Mancini ME; Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy.
  • Gigante C; Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy.
  • Agalbato C; Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy.
  • Formenti A; Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy.
  • Muscogiuri G; Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy.
  • Mushtaq S; Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy.
  • Guglielmo M; Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy.
  • Baggiano A; Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy.
  • Bonomi A; Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy.
  • Pepi M; Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy.
  • Pontone G; Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy.
  • Andreini D; Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy.
Diagnostics (Basel) ; 11(2)2021 Feb 09.
Article in English | MEDLINE | ID: covidwho-1134020
ABSTRACT
Lung infection named as COVID-19 is an infectious disease caused by the most recently discovered coronavirus 2 (SARS-CoV-2). CT (computed tomography) has been shown to have good sensitivity in comparison with RT-PCR, particularly in early stages. However, CT findings appear to not always be related to a certain clinical severity. The aim of this study is to evaluate a correlation between the percentage of lung parenchyma volume involved with COVID-19 infection (compared to the total lung volume) at baseline diagnosis and correlated to the patient's clinical course (need for ventilator assistance and or death). All patients with suspected COVID-19 lung disease referred to our imaging department for Chest CT from 24 February to 6 April 2020were included in the study. Specific CT features were assessed including the amount of high attenuation areas (HAA) related to lung infection. HAA, defined as the percentage of lung parenchyma above a predefined threshold of -650 (HAA%, HAA/total lung volume), was automatically calculated using a dedicated segmentation software. Lung volumes and CT findings were correlated with patient's clinical course. Logistic regressions were performed to assess the predictive value of clinical, inflammatory and CT parameters for the defined outcome. In the overall population we found an average infected lung volume of 31.4 ± 26.3% while in the subgroup of patients who needed ventilator assistance and who died as well as the patients who died without receiving ventilator assistance the volume of infected lung was significantly higher 41.4 ± 28.5 and 72.7 ± 36.2 (p < 0.001). In logistic regression analysis best predictors for ventilation and death were the presence of air bronchogram (p = 0.006), crazy paving (p = 0.007), peripheral distribution (p < 0.001), age (p = 0.002), fever at admission (p = 0.007), dyspnea (p = 0.002) and cardiovascular comorbidities (p < 0.001). In multivariable analysis, quantitative CT parameters and features added incremental predictive value beyond a model with only clinical parameters (area under the curve, 0.78 vs. 0.74, p = 0.02). Our study demonstrates that quantitative evaluation of lung volume involved by COVID-19 pneumonia helps to predict patient's clinical course.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies / Prognostic study Language: English Year: 2021 Document Type: Article Affiliation country: Diagnostics11020265

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies / Prognostic study Language: English Year: 2021 Document Type: Article Affiliation country: Diagnostics11020265