Artificial Intelligence in Predicting Clinical Outcome in COVID-19 Patients from Clinical, Biochemical and a Qualitative Chest X-Ray Scoring System
Authors Esposito A, Casiraghi E, Chiaraviglio F, Scarabelli A, Stellato E, Plensich G, Lastella G, Di Meglio L, Fusco S, Avola E, Jachetti A, Giannitto C, Malchiodi D, Frasca M, Beheshti A, Robinson PN, Valentini G, Forzenigo L, Carrafiello G
Received 16 November 2020
Accepted for publication 28 February 2021
Published 12 March 2021 Volume 2021:14 Pages 27—39
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 3
Editor who approved publication: Professor Tarik Massoud
Andrea Esposito,1 Elena Casiraghi,2 Francesca Chiaraviglio,1 Alice Scarabelli,3 Elvira Stellato,3 Guido Plensich,3 Giulia Lastella,1 Letizia Di Meglio,3 Stefano Fusco,3 Emanuele Avola,3 Alessandro Jachetti,4 Caterina Giannitto,5 Dario Malchiodi,2 Marco Frasca,2 Afshin Beheshti,6,7 Peter N Robinson,8,9 Giorgio Valentini,2 Laura Forzenigo,1 Gianpaolo Carrafiello1
1Radiology Department, Foundation IRCCS Ospedale Maggiore Policlinico Hospital, Milan, 20122, Italy; 2Anacleto Lab, Computer Science Department, University of Milan, Milan, 20133, Italy; 3Postgraduate School of Diagnostic and Interventional Radiology, University of Milan, Milan, 20122, Italy; 4Accident and Emergency Department, Foundation IRCCS Ospedale Maggiore Policlinico Hospital, Milan, 20122, Italy; 5Radiology Department, Humanitas Research Hospital, Milan, 20013, Italy; 6KBR, Space Biosciences Division, NASA Ames Research Center, Moffett Field, CA, 94035, USA; 7Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA; 8The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA; 9Institute for Systems Genomics, University of Connecticut, Farmington, CT, 06030, USA
Correspondence: Elvira Stellato; Alice Scarabelli Email [email protected]; [email protected]
Purpose: To determine the performance of a chest radiograph (CXR) severity scoring system combined with clinical and laboratory data in predicting the outcome of COVID-19 patients.
Materials and Methods: We retrospectively enrolled 301 patients who had reverse transcriptase-polymerase chain reaction (RT-PCR) positive results for COVID-19. CXRs, clinical and laboratory data were collected. A CXR severity scoring system based on a qualitative evaluation by two expert thoracic radiologists was defined. Based on the clinical outcome, the patients were divided into two classes: moderate/mild (patients who did not die or were not intubated) and severe (patients who were intubated and/or died). ROC curve analysis was applied to identify the cut-off point maximizing the Youden index in the prediction of the outcome. Clinical and laboratory data were analyzed through Boruta and Random Forest classifiers.
Results: The agreement between the two radiologist scores was substantial (kappa = 0.76). A radiological score ≥ 9 predicted a severe class: sensitivity = 0.67, specificity = 0.58, accuracy = 0.61, PPV = 0.40, NPV = 0.81, F1 score = 0.50, AUC = 0.65. Such performance was improved to sensitivity = 0.80, specificity = 0.86, accuracy = 0.84, PPV = 0.73, NPV = 0.90, F1 score = 0.76, AUC= 0.82, combining two clinical variables (oxygen saturation [SpO2]), the ratio of arterial oxygen partial pressure to fractional inspired oxygen [P/F ratio] and three laboratory test results (C-reactive protein, lymphocytes [%], hemoglobin).
Conclusion: Our CXR severity score assigned by the two radiologists, who read the CXRs combined with some specific clinical data and laboratory results, has the potential role in predicting the outcome of COVID-19 patients.
Keywords: radiography, thoracic, COVID-19, artificial intelligence, prognosis
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