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Evaluation of novel coronavirus disease (COVID-19) using quantitative lung CT and clinical data: prediction of short-term outcome.
Eur Radiol Exp. 2020 06 26; 4(1):39.ER

Abstract

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.

Authors+Show Affiliations

DISSAL-Department of Health Sciences, University of Genoa, Via Antonio Pastore, 1, 16132, Genova, GE, Italy. jfgavinadematos@gmail.com.Department of Radiology, Galliera Hospital, Genoa, Italy.Department of Radiology, Galliera Hospital, Genoa, Italy.Department of Radiology, Galliera Hospital, Genoa, Italy.Independent Researcher, Plymouth, UK.Department of Radiology, Galliera Hospital, Genoa, Italy.Department of Radiology, Galliera Hospital, Genoa, Italy.Department of Radiology, Galliera Hospital, Genoa, Italy.Department of Critical Care Medicine, Galliera Hospital, Genoa, Italy.Department of Emergency Medicine, Galliera Hospital, Genoa, Italy.Department of Anesthesiology, Galliera Hospital, Genoa, Italy.Department of Geriatric Medicine, Galliera Hospital, Genoa, Italy.Department of Infectious Diseases, Galliera Hospital, Genoa, Italy.Department of Radiology, Galliera Hospital, Genoa, Italy.

Pub Type(s)

Evaluation Study
Journal Article

Language

eng

PubMed ID

32592118

Citation

Matos, João, et al. "Evaluation of Novel Coronavirus Disease (COVID-19) Using Quantitative Lung CT and Clinical Data: Prediction of Short-term Outcome." European Radiology Experimental, vol. 4, no. 1, 2020, p. 39.
Matos J, Paparo F, Mussetto I, et al. Evaluation of novel coronavirus disease (COVID-19) using quantitative lung CT and clinical data: prediction of short-term outcome. Eur Radiol Exp. 2020;4(1):39.
Matos, J., Paparo, F., Mussetto, I., Bacigalupo, L., Veneziano, A., Perugin Bernardi, S., Biscaldi, E., Melani, E., Antonucci, G., Cremonesi, P., Lattuada, M., Pilotto, A., Pontali, E., & Rollandi, G. A. (2020). Evaluation of novel coronavirus disease (COVID-19) using quantitative lung CT and clinical data: prediction of short-term outcome. European Radiology Experimental, 4(1), 39. https://doi.org/10.1186/s41747-020-00167-0
Matos J, et al. Evaluation of Novel Coronavirus Disease (COVID-19) Using Quantitative Lung CT and Clinical Data: Prediction of Short-term Outcome. Eur Radiol Exp. 2020 06 26;4(1):39. PubMed PMID: 32592118.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Evaluation of novel coronavirus disease (COVID-19) using quantitative lung CT and clinical data: prediction of short-term outcome. AU - Matos,João, AU - Paparo,Francesco, AU - Mussetto,Ilaria, AU - Bacigalupo,Lorenzo, AU - Veneziano,Alessio, AU - Perugin Bernardi,Silvia, AU - Biscaldi,Ennio, AU - Melani,Enrico, AU - Antonucci,Giancarlo, AU - Cremonesi,Paolo, AU - Lattuada,Marco, AU - Pilotto,Alberto, AU - Pontali,Emanuele, AU - Rollandi,Gian Andrea, Y1 - 2020/06/26/ PY - 2020/05/07/received PY - 2020/06/10/accepted PY - 2020/6/28/entrez PY - 2020/6/28/pubmed PY - 2020/7/18/medline KW - COVID-19 KW - Lung, SARS-Cov-2 KW - Pneumonia (viral) KW - Support vector machine KW - Tomography (x-ray computed) SP - 39 EP - 39 JF - European radiology experimental JO - Eur Radiol Exp VL - 4 IS - 1 N2 - 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. SN - 2509-9280 UR - https://www.unboundmedicine.com/medline/citation/32592118/Evaluation_of_novel_coronavirus_disease__COVID_19__using_quantitative_lung_CT_and_clinical_data:_prediction_of_short_term_outcome_ L2 - https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/32592118/ DB - PRIME DP - Unbound Medicine ER -