COVID-19 mortality prediction in the intensive care unit with deep learning based on longitudinal chest X-rays and clinical data.
Artificial intelligence
Coronavirus
Hospital mortality
Machine learning
Prognosis
Journal
European radiology
ISSN: 1432-1084
Titre abrégé: Eur Radiol
Pays: Germany
ID NLM: 9114774
Informations de publication
Date de publication:
Jul 2022
Jul 2022
Historique:
received:
28
10
2021
accepted:
22
01
2022
revised:
14
12
2021
pubmed:
21
2
2022
medline:
24
6
2022
entrez:
20
2
2022
Statut:
ppublish
Résumé
We aimed to develop deep learning models using longitudinal chest X-rays (CXRs) and clinical data to predict in-hospital mortality of COVID-19 patients in the intensive care unit (ICU). Six hundred fifty-four patients (212 deceased, 442 alive, 5645 total CXRs) were identified across two institutions. Imaging and clinical data from one institution were used to train five longitudinal transformer-based networks applying five-fold cross-validation. The models were tested on data from the other institution, and pairwise comparisons were used to determine the best-performing models. A higher proportion of deceased patients had elevated white blood cell count, decreased absolute lymphocyte count, elevated creatine concentration, and incidence of cardiovascular and chronic kidney disease. A model based on pre-ICU CXRs achieved an AUC of 0.632 and an accuracy of 0.593, and a model based on ICU CXRs achieved an AUC of 0.697 and an accuracy of 0.657. A model based on all longitudinal CXRs (both pre-ICU and ICU) achieved an AUC of 0.702 and an accuracy of 0.694. A model based on clinical data alone achieved an AUC of 0.653 and an accuracy of 0.657. The addition of longitudinal imaging to clinical data in a combined model significantly improved performance, reaching an AUC of 0.727 (p = 0.039) and an accuracy of 0.732. The addition of longitudinal CXRs to clinical data significantly improves mortality prediction with deep learning for COVID-19 patients in the ICU. • Deep learning was used to predict mortality in COVID-19 ICU patients. • Serial radiographs and clinical data were used. • The models could inform clinical decision-making and resource allocation.
Identifiants
pubmed: 35184218
doi: 10.1007/s00330-022-08588-8
pii: 10.1007/s00330-022-08588-8
pmc: PMC8857913
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
4446-4456Subventions
Organisme : NHLBI NIH HHS
ID : T35 HL094308
Pays : United States
Organisme : NHLBI NIH HHS
ID : 5T35HL094308-12
Pays : United States
Organisme : NHLBI NIH HHS
ID : 5T35HL094308-12
Pays : United States
Commentaires et corrections
Type : ErratumIn
Informations de copyright
© 2022. The Author(s), under exclusive licence to European Society of Radiology.
Références
Zhu N, Zhang D, Wang W et al (2020) A novel coronavirus from patients with pneumonia in China. N Engl J Med 382:727–733
doi: 10.1056/NEJMoa2001017
Johns Hopkins University (2021) COVID-19 Map - Johns Hopkins Coronavirus Resource Center. Johns Hopkins University, Baltimore, MD, USA. Available via https://coronavirus.jhu.edu/map.html . Accessed 13 Dec 2021
Huang C, Wang Y, Li X et al (2020) Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 395:497–506
doi: 10.1016/S0140-6736(20)30183-5
Bernal JL, Andrews N, Gower C et al (2021) Effectiveness of COVID-19 vaccines against the B.1.617.2 (Delta) variant. N Engl J Med 385:585–594
doi: 10.1056/NEJMoa2108891
Torjesen I (2021) COVID-19: Delta variant is now UK’s most dominant strain and spreading through schools. BMJ. https://doi.org/10.1136/bmj.n1445
Li Y, Xia L (2020) Coronavirus disease 2019 (COVID-19): role of chest CT in diagnosis and management. AJR Am J Roentgenol 214:1280–1286
Bernheim A, Mei X, Huang M et al (2021) Chest CT findings in coronavirus disease 2019 (COVID-19): relationship to duration of infection. Radiology 295:685–691
Borghesi A, Maroldi R (2020) COVID-19 outbreak in Italy: experimental chest X-ray scoring system for quantifying and monitoring disease progression. Radiol Med 125:509–513
doi: 10.1007/s11547-020-01200-3
Lomoro P, Verde F, Zerboni F et al (2020) COVID-19 pneumonia manifestations at the admission on chest ultrasound, radiographs, and CT: single-center study and comprehensive radiologic literature review. Eur J Radiol Open 7:100231
doi: 10.1016/j.ejro.2020.100231
Wong HYF, Lam HYS, Fong AH-T et al (2020) Frequency and distribution of chest radiographic findings in patients positive for COVID-19. Radiology 296:E72–E78
doi: 10.1148/radiol.2020201160
Cohen JP, Dan L, Roth K et al (2020) Predicting COVID-19 pneumonia severity on chest X-ray with deep learning. Cureus 12:e9448
pubmed: 32864270
pmcid: 7451075
Yang W, Sirajuddin A, Zhang X et al (2020) The role of imaging in 2019 novel coronavirus pneumonia (COVID-19). Eur Radiol 30:4874–4882
doi: 10.1007/s00330-020-06827-4
Bai X, Wang R, Xiong Z et al (2020) Artificial intelligence augmentation of radiologist performance in distinguishing COVID-19 from pneumonia of other origin at chest CT. Radiology 296:E156–E165
doi: 10.1148/radiol.2020201491
Xu Q, Zhan X, Zhou Z et al (2021) AI-based analysis of CT images for rapid triage of COVID-19 patients. NPJ Digit Med 4:1–11
Borkowski A, Viswanadhan NA, Thomas LB, Guzman RD, Deland LA, Mastorides SM (2020) Using artificial intelligence for COVID-19 chest X-ray diagnosis. Fed Pract 37:398–404
pubmed: 33029064
pmcid: 7535959
Jiao Z, Choi JW, Halsey K et al (2021) Prognostication of patients with COVID-19 using artificial intelligence based on chest x-rays and clinical data: a retrospective study. Lancet Digit Heal 3:e286–e294
doi: 10.1016/S2589-7500(21)00039-X
Wang R, Jiao Z, Yang L et al (2021) Artificial intelligence for prediction of COVID-19 progression using CT imaging and clinical data. Eur Radiol 1:205–212
Bai HX, Hsieh B, Xiong Z et al (2020) Performance of radiologists in differentiating COVID-19 from non-COVID-19 viral pneumonia at chest CT. Radiology 296:E46–E54
doi: 10.1148/radiol.2020200823
Bai HX, Wang R, Xiong Z et al (2020) Artificial intelligence augmentation of radiologist performance in distinguishing COVID-19 from pneumonia of other origin at chest CT. Radiology 296:E156–E165
doi: 10.1148/radiol.2020201491
Rigatti S (2017) Random forest. J Insur Med 47:31–39
doi: 10.17849/insm-47-01-31-39.1
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition vols 2016-December 770–778 (IEEE Computer Society, 2016). https://doi.org/10.1109/CVPR.2016.90
Dosovitskiy A, Beyer L, Kolesnikov A et al (2020) An image is worth 16x16 words: transformers for image recognition at scale. arXiv. DOI: arxiv:2010.11929
American College of Radiology (2021) ACR recommendations for the use of chest radiography and computed tomography (CT) for suspected COVID-19 infection. American College of Radiology, Virginia, USA. Available via https://www.acr.org/Advocacy-and-Economics/ACR-Position-Statements/Recommendations-for-Chest-Radiography-and-CT-for-Suspected-COVID19-Infection . Accessed 13 Dec 2021
Fang X, Kruger U, Homayounieh F et al (2021) Association of AI quantified COVID-19 chest CT and patient outcome. Int J Comput Assist Radiol Surg 16:435–445
doi: 10.1007/s11548-020-02299-5
Maroldi R, Rondi P, Agazzi GM, Ravanelli M, Borghesi A, Farina D (2020) Which role for chest x-ray score in predicting the outcome in COVID-19 pneumonia? Eur Radiol 31:4016–4022
doi: 10.1007/s00330-020-07504-2
Wang S, Rondi P, Agazzi GM, Ravanelli M, Borghesi A, Farina D et al (2020) A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis. Eur Respir J 56:4016–4022
doi: 10.1183/13993003.00775-2020
Zhu J, Ge P, Jiang C et al (2020) Deep-learning artificial intelligence analysis of clinical variables predicts mortality in COVID-19 patients. J Am Coll Emerg Physicians Open 1:1364–1373
doi: 10.1002/emp2.12205
Hu C, Liu Z, Jiang Y et al (2020) Early prediction of mortality risk among patients with severe COVID-19, using machine learning. Int J Epidemiol 49:1918–1929
doi: 10.1093/ije/dyaa171
Ko H, Chung H, Kang WS et al (2020) An artificial intelligence model to predict the mortality of COVID-19 patients at hospital admission time using routine blood samples: development and validation of an ensemble model. J Med Internet Res 22:e25442
doi: 10.2196/25442
Gao Y, Cai G-Y, Fang W et al (2020) Machine learning based early warning system enables accurate mortality risk prediction for COVID-19. Nat Commun 11:1–10
doi: 10.1038/s41467-020-18684-2
Vaid A, Somani S, Russak A et al (2020) Machine learning to predict mortality and critical events in a cohort of patients with COVID-19 in New York City: model development and validation. J Med Internet Res 20:e24018
doi: 10.2196/24018
Sánchez-Montañés M, Rodríguez-Belenguer P, Serrano-López AJ, Soria-Olivas E, Alakhdar-Mohmara Y (2020) Machine learning for mortality analysis in patients with COVID-19. Int J Environ Res Public Health 17:1–20
doi: 10.3390/ijerph17228386
Abdulaal A, Patel A, Charani E, Denny S, Mughal N, Moore L (2020) Prognostic modeling of COVID-19 using artificial intelligence in the United Kingdom: model development and validation. J Med Internet Res 22:e20259
doi: 10.2196/20259
Guan X, Zhang B, Fu M et al (2021) Clinical and inflammatory features based machine learning model for fatal risk prediction of hospitalized COVID-19 patients: results from a retrospective cohort study. Ann Med 53:257–266
doi: 10.1080/07853890.2020.1868564
Ikemura K, Bellin E, Yagi Y et al (2021) Using automated machine learning to predict the mortality of patients with COVID-19: prediction model development study. J Med Internet Res 23:e23458
doi: 10.2196/23458
Ma X, Ng M, Xu S et al (2020) Development and validation of prognosis model of mortality risk in patients with COVID-19. Epidemiol Infect 148:e168
doi: 10.1017/S0950268820001727
Pourhomayoun M, Shakibi M (2021) Predicting mortality risk in patients with COVID-19 using machine learning to help medical decision-making. Smart Heal 20:100178
doi: 10.1016/j.smhl.2020.100178
Booth AL, Abels E, McCaffrey P (2021) Development of a prognostic model for mortality in COVID-19 infection using machine learning. Mod Pathol 34:522–531
doi: 10.1038/s41379-020-00700-x
Mushtaq J, Pennella R, Lavalle S et al (2021) Initial chest radiographs and artificial intelligence (AI) predict clinical outcomes in COVID-19 patients: analysis of 697 Italian patients. Eur Radiol 31:1770–1779
doi: 10.1007/s00330-020-07269-8
American Journal of Managad Care (2021) A timeline of COVID-19 vaccine developments in 2021. The American Journal of Managed Care, Cranbury, NJ, USA. Available via https://www.ajmc.com/view/a-timeline-of-covid-19-vaccine-developments-in-2021 . Accessed 13 Dec 2021
Faust JS, Du C, Maye KD et al (2021) Absence of excess mortality in a highly vaccinated population during the initial COVID-19 Delta period. medRxiv. https://doi.org/10.1101/2021.09.16.21263477