Evaluation of novel coronavirus disease (COVID-19) using quantitative lung CT and clinical data: prediction of short-term outcome
Posted on 2020-06-27 - 03:34
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.
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Matos, João; Paparo, Francesco; Mussetto, Ilaria; Bacigalupo, Lorenzo; Veneziano, Alessio; Perugin Bernardi, Silvia; et al. (2020). Evaluation of novel coronavirus disease (COVID-19) using quantitative lung CT and clinical data: prediction of short-term outcome. figshare. Collection. https://doi.org/10.6084/m9.figshare.c.5042171.v1
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AUTHORS (14)
JM
João Matos
FP
Francesco Paparo
IM
Ilaria Mussetto
LB
Lorenzo Bacigalupo
AV
Alessio Veneziano
SP
Silvia Perugin Bernardi
EB
Ennio Biscaldi
EM
Enrico Melani
GA
Giancarlo Antonucci
PC
Paolo Cremonesi
ML
Marco Lattuada
AP
Alberto Pilotto
EP
Emanuele Pontali
GR
Gian Andrea Rollandi