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Initial chest radiographs and artificial intelligence (AI) predict clinical outcomes in COVID-19 patients: analysis of 697 Italian patients.
Eur Radiol. 2021 Mar; 31(3):1770-1779.ER

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.

Authors+Show Affiliations

Clinical and Experimental Radiology Unit, Experimental Imaging Center, IRCCS San Raffaele Scientific Institute, Milan, Italy. Faculty of Medicine and Surgery, Vita-Salute San Raffaele University, Via Olgettina 58, Milan, Italy.Clinical and Experimental Radiology Unit, Experimental Imaging Center, IRCCS San Raffaele Scientific Institute, Milan, Italy. Faculty of Medicine and Surgery, Vita-Salute San Raffaele University, Via Olgettina 58, Milan, Italy.Clinical and Experimental Radiology Unit, Experimental Imaging Center, IRCCS San Raffaele Scientific Institute, Milan, Italy. Faculty of Medicine and Surgery, Vita-Salute San Raffaele University, Via Olgettina 58, Milan, Italy.Clinical and Experimental Radiology Unit, Experimental Imaging Center, IRCCS San Raffaele Scientific Institute, Milan, Italy. Faculty of Medicine and Surgery, Vita-Salute San Raffaele University, Via Olgettina 58, Milan, Italy.Clinical and Experimental Radiology Unit, Experimental Imaging Center, IRCCS San Raffaele Scientific Institute, Milan, Italy.Clinical and Experimental Radiology Unit, Experimental Imaging Center, IRCCS San Raffaele Scientific Institute, Milan, Italy.Clinical and Experimental Radiology Unit, Experimental Imaging Center, IRCCS San Raffaele Scientific Institute, Milan, Italy. Faculty of Medicine and Surgery, Vita-Salute San Raffaele University, Via Olgettina 58, Milan, Italy.Clinical and Experimental Radiology Unit, Experimental Imaging Center, IRCCS San Raffaele Scientific Institute, Milan, Italy. Faculty of Medicine and Surgery, Vita-Salute San Raffaele University, Via Olgettina 58, Milan, Italy.Faculty of Medicine and Surgery, Vita-Salute San Raffaele University, Via Olgettina 58, Milan, Italy. Department of Internal Medicine, IRCCS San Raffaele Scientific Institute, Milan, Italy.Unit of General Medicine and Advanced Care, IRCCS San Raffaele Scientific Institute, Milan, Italy.Faculty of Medicine and Surgery, Vita-Salute San Raffaele University, Via Olgettina 58, Milan, Italy. Department of Anesthesia and Intensive Care, IRCCS San Raffaele Scientific Institute, Milan, Italy.Faculty of Medicine and Surgery, Vita-Salute San Raffaele University, Via Olgettina 58, Milan, Italy. Hematology and Bone Marrow Transplantation, IRCCS San Raffaele Scientific Institute, Milan, Italy.Faculty of Medicine and Surgery, Vita-Salute San Raffaele University, Via Olgettina 58, Milan, Italy. Department of Anesthesia and Intensive Care, IRCCS San Raffaele Scientific Institute, Milan, Italy.Clinical and Experimental Radiology Unit, Experimental Imaging Center, IRCCS San Raffaele Scientific Institute, Milan, Italy. decobelli.francesco@hsr.it. Faculty of Medicine and Surgery, Vita-Salute San Raffaele University, Via Olgettina 58, Milan, Italy. decobelli.francesco@hsr.it.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

32945968

Citation

Mushtaq, Junaid, et al. "Initial Chest Radiographs and Artificial Intelligence (AI) Predict Clinical Outcomes in COVID-19 Patients: Analysis of 697 Italian Patients." European Radiology, vol. 31, no. 3, 2021, pp. 1770-1779.
Mushtaq J, Pennella R, Lavalle S, et al. Initial chest radiographs and artificial intelligence (AI) predict clinical outcomes in COVID-19 patients: analysis of 697 Italian patients. Eur Radiol. 2021;31(3):1770-1779.
Mushtaq, J., Pennella, R., Lavalle, S., Colarieti, A., Steidler, S., Martinenghi, C. M. A., Palumbo, D., Esposito, A., Rovere-Querini, P., Tresoldi, M., Landoni, G., Ciceri, F., Zangrillo, A., & De Cobelli, F. (2021). Initial chest radiographs and artificial intelligence (AI) predict clinical outcomes in COVID-19 patients: analysis of 697 Italian patients. European Radiology, 31(3), 1770-1779. https://doi.org/10.1007/s00330-020-07269-8
Mushtaq J, et al. Initial Chest Radiographs and Artificial Intelligence (AI) Predict Clinical Outcomes in COVID-19 Patients: Analysis of 697 Italian Patients. Eur Radiol. 2021;31(3):1770-1779. PubMed PMID: 32945968.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Initial chest radiographs and artificial intelligence (AI) predict clinical outcomes in COVID-19 patients: analysis of 697 Italian patients. AU - Mushtaq,Junaid, AU - Pennella,Renato, AU - Lavalle,Salvatore, AU - Colarieti,Anna, AU - Steidler,Stephanie, AU - Martinenghi,Carlo M A, AU - Palumbo,Diego, AU - Esposito,Antonio, AU - Rovere-Querini,Patrizia, AU - Tresoldi,Moreno, AU - Landoni,Giovanni, AU - Ciceri,Fabio, AU - Zangrillo,Alberto, AU - De Cobelli,Francesco, Y1 - 2020/09/18/ PY - 2020/06/12/received PY - 2020/09/08/accepted PY - 2020/07/30/revised PY - 2020/9/19/pubmed PY - 2021/2/23/medline PY - 2020/9/18/entrez KW - Artificial intelligence KW - COVID-19 KW - Prognosis KW - Radiography KW - Severe acute respiratory syndrome SP - 1770 EP - 1779 JF - European radiology JO - Eur Radiol VL - 31 IS - 3 N2 - 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. SN - 1432-1084 UR - https://www.unboundmedicine.com/medline/citation/32945968/Initial_chest_radiographs_and_artificial_intelligence__AI__predict_clinical_outcomes_in_COVID_19_patients:_analysis_of_697_Italian_patients_ L2 - https://dx.doi.org/10.1007/s00330-020-07269-8 DB - PRIME DP - Unbound Medicine ER -