Abstract
ObjectiveTo evaluate whether the initial chest X-ray (CXR) severity assessed by an AI system may have prognostic utility in patients with COVID-19.MethodsThis 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.ResultsSix 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.ConclusionAI- 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 registrationClinicalTrials.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.
Highlights
A novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), that causes the coronavirus disease 2019 (COVID-19) was first identified in December 2019 in Wuhan, China [1].Severe complications of COVID-19 include severe pneumonia, acute respiratory distress syndrome, multiple organ failure, and death [2]
artificial intelligence (AI) system–based score ≥ 30 and a Radiographic Assessment of Lung Edema (RALE) score ≥ 12 at chest X-ray (CXR) performed at emergency department (ED) presentation are independent and comparable predictors of death and/or intensive care unit (ICU) admission in COVID-19 patients
All procedures were conducted in agreement with the 1964 Helsinki declaration and its later amendments; informed consent was collected from all patients according to the ethics committee (EC) guidelines
Summary
A novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), that causes the coronavirus disease 2019 (COVID-19) was first identified in December 2019 in Wuhan, China [1].Severe complications of COVID-19 include severe pneumonia, acute respiratory distress syndrome, multiple organ failure, and death [2]. Real-time reverse transcriptase polymerase chain reaction (RT-PCR) remains the reference standard for diagnosis, but its high falsenegative rate, limited testing capacity, and long turnaround times hinder its effectiveness most likely contributing to the spread of the infection within communities [4]. In this scenario, the role of imaging with chest X-ray (CXR) and chest computed tomography (CT) may become fundamental in quickly providing results that can guide in terms of triage and clinical management [5]
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