Evaluation of novel coronavirus disease (COVID-19) using quantitative lung CT and clinical data: prediction of short-term outcome
| Autor: | Ilaria Mussetto, Giancarlo Antonucci, Francesco Paparo, Paolo Cremonesi, Alessio Veneziano, E. Melani, Silvia Perugin Bernardi, Marco Lattuada, Gian Andrea Rollandi, Ennio Biscaldi, João Matos, Lorenzo Bacigalupo, Emanuele Pontali, Alberto Pilotto |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Adult
Male lcsh:Medical physics. Medical radiology. Nuclear medicine medicine.medical_specialty Lung SARS-Cov-2 Support vector machine medicine.medical_treatment lcsh:R895-920 Pneumonia Viral Disease Real-Time Polymerase Chain Reaction Tomography (x-ray computed) 030218 nuclear medicine & medical imaging Betacoronavirus 03 medical and health sciences 0302 clinical medicine Predictive Value of Tests Linear regression medicine Humans Radiology Nuclear Medicine and imaging Lung Pandemics Aged Neuroradiology Aged 80 and over Mechanical ventilation medicine.diagnostic_test SARS-CoV-2 business.industry Area under the curve Reproducibility of Results COVID-19 Interventional radiology Middle Aged Prognosis medicine.disease Pneumonia (viral) Patient Outcome Assessment Pneumonia Evaluation Studies as Topic 030220 oncology & carcinogenesis Predictive value of tests Original Article Female Radiology Coronavirus Infections Tomography X-Ray Computed business |
| Zdroj: | European Radiology Experimental, Vol 4, Iss 1, Pp 1-10 (2020) European Radiology Experimental |
| ISSN: | 2509-9280 |
| Popis: | Background Computed tomography (CT) enables quantification of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, helping in outcome prediction. Methods From 1 to 22 March 2020, patients with pneumonia symptoms, positive lung CT scan, and confirmed SARS-CoV-2 on reverse transcription-polymerase chain reaction (RT-PCR) were consecutively enrolled. Clinical data was collected. Outcome was defined as favourable or adverse (i.e., need for mechanical ventilation or death) and registered over a period of 10 days following CT. Volume of disease (VoD) on CT was calculated semi-automatically. Multiple linear regression was used to predict VoD by clinical/laboratory data. To predict outcome, important features were selected using a priori analysis and subsequently used to train 4 different models. Results A total of 106 consecutive patients were enrolled (median age 63.5 years, range 26–95 years; 41/106 women, 38.7%). Median duration of symptoms and C-reactive protein (CRP) was 5 days (range 1–30) and 4.94 mg/L (range 0.1–28.3), respectively. Median VoD was 249.5 cm3 (range 9.9–1505) and was predicted by lymphocyte percentage (p = 0.008) and CRP (p < 0.001). Important variables for outcome prediction included CRP (area under the curve [AUC] 0.77), VoD (AUC 0.75), age (AUC 0.72), lymphocyte percentage (AUC 0.70), coronary calcification (AUC 0.68), and presence of comorbidities (AUC 0.66). Support vector machine had the best performance in outcome prediction, yielding an AUC of 0.92. Conclusions Measuring the VoD using a simple CT post-processing tool estimates SARS-CoV-2 burden. CT and clinical data together enable accurate prediction of short-term clinical outcome. |
| Databáze: | OpenAIRE |
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