Evaluation of novel coronavirus disease (COVID-19) using quantitative lung CT and clinical data: prediction of short-term outcome
João Matos, Francesco Paparo, Ilaria Mussetto, Lorenzo Bacigalupo, Alessio Veneziano, Silvia Bernardi, Ennio Biscaldi, Enrico Francesco Melani, Giancarlo Antonucci, Paolo Cremonesi, Marco Lattuada, Alberto Pilotto, Emanuele Pontali, Gian Andrea Rollandi
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: (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.