Abstract
The pandemic of coronavirus disease 2019 (COVID-19) has severely impacted the world. Several studies suggest an increased risk for COVID-19 patients with underlying cardiovascular diseases (CVD). However, it is challenging to quantify such risk factors and integrate them into patient condition evaluation. This paper presents machine learning methods to assess CVD risk scores from chest computed tomography together with laboratory data, demographics, and deep learning extracted lung imaging features to increase the outcome prediction accuracy for COVID-19 patients. The experimental results demonstrate an overall increase in prediction performance when the CVD severity score was added to the feature set. The machine learning methods obtained their best performance when all categories of the features were used for the patient outcome prediction. With the best attained area under the curve of 0.888, the presented research may assist physicians in clinical decision-making process on managing COVID-19 patients.
This work was partially supported by National Institute of Biomedical Imaging and Bioengineering (NIBIB) under award R21EB028001 and National Heart, Lung, and Blood Institute (NHLBI) under award R56HL145172.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bartlett, M.S.: Properties of sufficiency and statistical tests. Proc. Roy. Soc. London Ser. A Math. Phys. Sci. 160(901), 268–282 (1937)
Chao, H., et al.: Integrative analysis for COVID-19 patient outcome prediction. Med. Image Anal. 67, 101844 (2020)
Chao, H., et al.: Deep learning predicts cardiovascular disease risks from lung cancer screening low dose computed tomography. arXiv:2008.06997 [cs, eess] (2020)
Fang, X., et al.: Association of AI quantified COVID-19 chest CT and patient outcome. Int. J. Comput. Assist. Radiol. Surg. 16(3), 435–445 (2021). https://doi.org/10.1007/s11548-020-02299-5
Fang, X., Yan, P.: Multi-organ segmentation over partially labeled datasets with multi-scale feature abstraction. IEEE Trans. Med. Imaging 39(11), 3619–3629 (2020). https://doi.org/10.1109/TMI.2020.3001036
Figliozzi, S., et al.: Predictors of adverse prognosis in COVID-19: a systematic review and meta-analysis. Eur. J. Clin. Invest. 50(10) (2020)
Guan, et al.: Clinical characteristics of coronavirus disease 2019 in China. N. Engl. J. Med. 382(18), 1708–1720 (2020). https://doi.org/10.1056/NEJMoa2002032
Jain, V., Yuan, J.-M.: Predictive symptoms and comorbidities for severe COVID-19 and intensive care unit admission: a systematic review and meta-analysis. Int. J. Public Health 65(5), 533–546 (2020). https://doi.org/10.1007/s00038-020-01390-7
Kaiser, H.F., Rice, J.: Little jiffy, mark iv. Educ. Psychol. Measur. 34(1), 111–117 (1974). https://doi.org/10.1177/001316447403400115
Liu, J., et al.: A comparative overview of COVID-19, MERS and SARS: review article. Int. J. Surg. 81, 1–8 (2020). https://doi.org/10.1016/j.ijsu.2020.07.032
Moore, D.H.: Classification and regression trees, by Leo Breiman, Jerome H. Friedman, Richard A. Olshen, and Charles J. Stone. Brooks/Cole Publishing, Monterey, 1984,358 Pages, \$27.95. Cytometry 8(5), 534–535 (1987)
Nishiga, M., Wang, D.W., Han, Y., Lewis, D.B., Wu, J.C.: COVID-19 and cardiovascular disease: from basic mechanisms to clinical perspectives. Nat. Rev. Cardiol. 17(9), 543–558 (2020). https://doi.org/10.1038/s41569-020-0413-9
North, B.J., Sinclair, D.A.: The intersection between aging and cardiovascular disease. Circ. Res. 110(8), 1097–1108 (2012)
Shi, F., et al.: Review of artificial intelligence techniques in imaging data acquisition, segmentation, and diagnosis for COVID-19. IEEE Rev. Biomed. Eng. 14, 4–15 (2021). https://doi.org/10.1109/RBME.2020.2987975
Siddiqi, H.K., Mehra, M.R.: COVID-19 illness in native and immunosuppressed states: a clinical-therapeutic staging proposal. J. Heart Lung Transplant. 39(5), 405–407 (2020). https://doi.org/10.1016/j.healun.2020.03.012
Spearman, C.: “general intelligence,” objectively determined and measured. Am. J. Psychol. 15(2), 201 (1904)
Ssentongo, P., Ssentongo, A.E., Heilbrunn, E.S., Ba, D.M., Chinchilli, V.M.: Association of cardiovascular disease and 10 other pre-existing comorbidities with COVID-19 mortality: a systematic review and meta-analysis. PLOS ONE 15(8), e0238215 (2020). https://doi.org/10.1371/journal.pone.0238215
Tang, Z., et al.: Severity assessment of coronavirus disease 2019 (COVID-19) using quantitative features from chest CT images. arXiv:2003.11988 [cs, eess] (2020)
Yang, J., et al.: Prevalence of comorbidities and its effects in patients infected with SARS-CoV-2: a systematic review and meta-analysis. Int. J. Infect. Dis. 94, 91–95 (2020). https://doi.org/10.1016/j.ijid.2020.03.017
Zhou, F., et al.: Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet 395(10229), 1054–1062 (2020). https://doi.org/10.1016/S0140-6736(20)30566-3
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Machado Reyes, D., Chao, H., Homayounieh, F., Hahn, J., Kalra, M.K., Yan, P. (2021). Cardiovascular Disease Risk Improves COVID-19 Patient Outcome Prediction. In: Lian, C., Cao, X., Rekik, I., Xu, X., Yan, P. (eds) Machine Learning in Medical Imaging. MLMI 2021. Lecture Notes in Computer Science(), vol 12966. Springer, Cham. https://doi.org/10.1007/978-3-030-87589-3_48
Download citation
DOI: https://doi.org/10.1007/978-3-030-87589-3_48
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-87588-6
Online ISBN: 978-3-030-87589-3
eBook Packages: Computer ScienceComputer Science (R0)
