Initial chest radiographs and artificial intelligence (AI) predict clinical outcomes in COVID-19 patients: analysis of 697 Italian patients
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Open Access
- 18 September 2020
- journal article
- research article
- Published by Springer Science and Business Media LLC in European Radiology
- Vol. 31 (3), 1770-1779
- https://doi.org/10.1007/s00330-020-07269-8
Abstract
Objective To evaluate whether the initial chest X-ray (CXR) severity assessed by an AI system may have prognostic utility in patients with COVID-19. Methods This retrospective single-center study included adult patients presenting to the emergency department (ED) between February 25 and April 9, 2020, with SARS-CoV-2 infection confirmed on real-time reverse transcriptase polymerase chain reaction (RT-PCR). Initial CXRs obtained on ED presentation were evaluated by a deep learning artificial intelligence (AI) system and compared with the Radiographic Assessment of Lung Edema (RALE) score, calculated by two experienced radiologists. Death and critical COVID-19 (admission to intensive care unit (ICU) or deaths occurring before ICU admission) were identified as clinical outcomes. Independent predictors of adverse outcomes were evaluated by multivariate analyses. Results Six hundred ninety-seven 697 patients were included in the study: 465 males (66.7%), median age of 62 years (IQR 52–75). Multivariate analyses adjusting for demographics and comorbidities showed that an AI system-based score ≥ 30 on the initial CXR was an independent predictor both for mortality (HR 2.60 (95% CI 1.69 − 3.99; p < 0.001)) and critical COVID-19 (HR 3.40 (95% CI 2.35–4.94; p < 0.001)). Other independent predictors were RALE score, older age, male sex, coronary artery disease, COPD, and neurodegenerative disease. Conclusion AI- and radiologist-assessed disease severity scores on CXRs obtained on ED presentation were independent and comparable predictors of adverse outcomes in patients with COVID-19. Trial registration ClinicalTrials.gov NCT04318366 (https://clinicaltrials.gov/ct2/show/NCT04318366). Key Points • AI system–based score ≥ 30 and a RALE score ≥ 12 at CXRs performed at ED presentation are independent and comparable predictors of death and/or ICU admission in COVID-19 patients. • Other independent predictors are older age, male sex, coronary artery disease, COPD, and neurodegenerative disease. • The comparable performance of the AI system in relation to a radiologist-assessed score in predicting adverse outcomes may represent a game-changer in resource-constrained settings.This publication has 19 references indexed in Scilit:
- Correlation of Chest CT and RT-PCR Testing for Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 CasesRadiology, 2020
- Coronavirus Disease 2019 (COVID-19) A Perspective from ChinaRadiology, 2020
- Clinical course and mortality risk of severe COVID-19The Lancet, 2020
- Imaging Profile of the COVID-19 Infection: Radiologic Findings and Literature ReviewRadiology: Cardiothoracic Imaging, 2020
- Chest Radiographic and CT Findings of the 2019 Novel Coronavirus Disease (COVID-19): Analysis of Nine Patients Treated in KoreaKorean Journal of Radiology, 2020
- Severity scoring of lung oedema on the chest radiograph is associated with clinical outcomes in ARDSThorax, 2018
- Fleischner Society: Glossary of Terms for Thoracic ImagingRadiology, 2008
- Chest Radiograph Scores as Potential Prognostic Indicators in Severe Acute Respiratory Syndrome (SARS)American Journal of Roentgenology, 2005
- Severe Acute Respiratory Syndrome: Correlation between Clinical Outcome and Radiologic FeaturesRadiology, 2004
- Value of initial chest radiographs for predicting clinical outcomes in patients with severe acute respiratory syndromeThe American Journal of Medicine, 2004