Update on predictive models of severe manifestations and mortality from COVID-19

Eduardo  Garea Llano; Evelio Gonzalez Dalmau

Authors

Eduardo Garea Llano Centro de Neurociencias de Cuba
Evelio Gonzalez Dalmau Centro de Neurociencias de Cuba

Keywords:

COVID-19, Gravedad, Modelos predictivos, Resultados de la enfermedad

Abstract

In this work we present a review of models based on Artificial Intelligence (AI) aimed at predicting serious manifestations caused by the SARS-COV2 virus. The objective of this review was to evaluate the most relevant findings published between 2020 and 2023 that can serve as a basis for the development of our own model adjusted to the conditions of our country characterized by the absence of a well-structured clinical database with the presence of radiological studies based on chest x-ray (CXR) and computed tomography (CT) images. We conducted a systematic review to summarize and critically evaluate available studies that have developed AI-based COVID-19 prognostic models that predict health outcomes, especially models based on CXR and CT images. Three bibliographic databases were searched to identify published articles on prognostic models predicting adverse outcomes in adult patients with COVID-19, including intensive care unit admission, need for mechanical ventilation, and mortality.

The study demonstrated that Deep Learning-based models using CXR or CT images and their combination with non-complex clinical data can achieve significant prediction performance. On this basis, in the work we propose a strategy to address this challenge under the conditions of our country by combining the classification of the degree of severity of lung involvement in CXR images, clinical data on comorbidities and biographical data.

References

Albahri, O.S.; Zaidan, A.A.; Albahri, A.S.; Zaidan, B.B.; Abdulkareem, K.H.; Al-Qaysi, Z.T.; Alamoodi, A.H.; Aleesa, A.M.; Chyad, M.A.; Alesa, R.M.; et al.(2020). Systematic review of artificial intelligence techniques in the detection and classification of COVID-19 medical images in terms of evaluation and benchmarking: Taxonomy analysis, challenges, future solutions and methodological aspects. J. Infect. Public Health 2020, 13, 1381–1396.

Almotairi, K.H.; Hussein, A.M.; Abualigah, L.; Abujayyab, S.K.M.; Mahmoud, E.H.; Ghanem, B.O.; Gandomi, A.H.(2023). Impact of Artificial Intelligence on COVID-19 Pandemic: A Survey of Image Processing, Tracking of Disease, Prediction of Outcomes, and Computational Medicine. Big Data Cogn. Comput. 2023, 7, 11. https://doi.org/10.3390/bdcc7010011

Asteris P. G., Kokoris S., Gavriilaki E. et al (2023). Early prediction of COVID-19 outcome using artificial intelligence techniques and only five laboratory indices. Clinical Immunology 246 (2023) 109218. https://doi.org/10.1016/j.clim.2022.109218.

Bae, J., Kapse, S., Singh, G., Gattu, R., Ali, S., Shah, N., Marshall, C., Pierce. J., Phatak T., Gupta, A., Green, J., Madan, N., Prasanna, P.(2021). Predicting Mechanical Ventilation and Mortality in COVID-19 Using Radiomics and Deep Learning on Chest Radiographs: A Multi- Institutional Study. Diagnostics (Basel). 2021 Sep 30;11(10):1812. doi: 10.3390/diagnostics11101812. PMID: 34679510; PMCID: PMC8535062.

Bansal, A.; Padappayil, R.P.; Garg, C.; Singal, A.; Gupta, M.; Klein, A. (2020). Utility of artificial intelligence amidst the COVID-19 pandemic: A review. J. Med. Syst. 2020, 44, 1–6.

Buttia, C., Llanaj, E., Raeisi-Dehkordi, H. et al. (2023). Prognostic models in COVID-19 infection that predict severity: a systematic review. Eur J Epidemiol 38, 355–372 (2023). https://doi.org/10.1007/s10654-023-00973-x

Duanmu, H., Ren, T., Li, H. et al. (2022). Deep learning of longitudinal chest X-ray and clinical variables predicts duration on ventilator and mortality in COVID-19 patients. BioMed Eng OnLine 21, 77. https://doi.org/10.1186/s12938-022-01045-z

Fu. Y, Zeng L., Huang P., Liao M. et al (2023). Severity-onset prediction of COVID-19 via artificial-intelligence analysis of multivariate factors. Heliyon. V 9, I 8, E18764, 2023. https://doi.org/10.1016/j.heliyon.2023.e18764

Gallo Marin, B., Aghagoli, G., Lavine, K., Yang, L., Siff, E.J., Chiang, S.S., Salazar-Mather, T.P., Dumenco, L., Savaria, M.C., Aung, S.N., Flanigan, T., Michelow, I.C. (2021).Predictors of COVID-19 severity: A literature review. Rev Med Virol. 2021 Jan; 31(1):1-10. doi: 10.1002/rmv.2146. Epub 2020 Jul 30. PMID: 32845042; PMCID: PMC7855377.

Garea Llano, E., Castellanos Loaces, H. A., Martínez Montes, E., & González Dalmau, E. (2021a). Estimación del Grado de Afectación Pulmonar por COVID-19 Mediante la Clasificación Supervisada de la Imagen de Rayos X. Revista Cubana De Transformación Digital, 2(3), 4–18. https://doi.org/10.5281/zenodo.5545908.

Garea-Llano, E., Castellanos-Loaces, H.A., Martinez-Montes, E., Gonzalez-Dalmau, E. (2021b). A Machine Learning Based Approach for Estimation of the Lung Affectation Degree in CXR Images of COVID-19 Patients. In: Hernández Heredia, Y., Milián Núñez, V., Ruiz Shulcloper, J. (eds) Progress in Artificial Intelligence and Pattern Recognition. IWAIPR 2021. Lecture Notes in Computer Science(), vol 13055. Springer, Cham. https://doi.org/10.1007/978-3-030-89691-1_2

Garea-Llano, E., Martinez-Montes, E., Gonzalez-Dalmaus, E. (2022). Affectation index and severity degree by COVID-19 in Chest X-ray images using artificial intelligence. Int Rob Auto J. 2022; 8(3):103‒107. DOI: 10.15406/iratj.2022.08.00252.

Garea-Llano, E., Diaz-Berenguer, A., Sahli, H., Gonzalez-Dalmau, E. (2023). Chest X-Ray Imaging Severity Score of COVID-19 Pneumonia. In: Rodríguez-González, A.Y., Pérez- Espinosa, H., Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A., Olvera-López, J.A. (eds) Pattern Recognition. MCPR 2023. Lecture Notes in Computer Science, vol 13902. Springer, Cham. https://doi.org/10.1007/978-3-031-33783-3_20.

Gourdeau, D., Potvin, O., Biem, J.H. et al. (2022). Deep learning of chest X-rays can predict mechanical ventilation outcome in ICU-admitted COVID-19 patients. Sci Rep 12, 6193 (2022). https://doi.org/10.1038/s41598-022-10136-9

Hassan, A.; Prasad, D.; Rani, S.; Alhassan, M. (2022). Gauging the Impact of Artificial Intelligence and Mathematical Modeling in Response to the COVID-19 Pandemic: A Systematic Review. BioMed Res. Int. 2022, 2022, 7731618.

Heidari, A., Jafari Navimipour, N., Unal, M. et al. (2022). Machine learning applications for COVID-19 outbreak management. Neural Comput & Applic 34, 15313–15348 (2022). https://doi.org/10.1007/s00521-022-07424-w.

Heo, J., Han, D., Kim, H. J., Kim, D., Lee, Y. K., Lim, D. Seog, W. (2021). Prediction of patients requiring intensive care for COVID-19: development and validation of an integerbased score using data from Centers for Disease Control and Prevention of South Korea. J Intensive Care, 9(1), 16. doi:10.1186/s40560-021-00527-x

Ho, T. T., Park, J., Kim, T., Park, B., Lee, J., Kim, J. Y., Choi, S. (2021). Deep Learning Models for Predicting Severe Progression in COVID-19-Infected Patients: Retrospective Study. JMIR Med Inform, 9(1), e24973. doi:10.2196/24973

Jalaber, C.; Lapotre, T.; Morcet-Delattre, T.; Ribet, F.; Jouneau, S.; Lederlin, M.(2020). Chest CT in COVID-19 pneumonia: A review of current knowledge. Diagn. Interv. Imaging 2020, 101, 431–437.

Jamshidi, M.B.; Roshani, S.; Talla, J.; Lalbakhsh, A.; Peroutka, Z.; Roshani, S.; Sabet, A.; Dehghani, M.; Lotfi, S.; Hadjilooei, F.; et al.(2022). A Review on Potentials of Artificial Intelligence Approaches to Forecasting COVID-19 Spreading. AI 2022, 3, 493–511.

Kumar, A.; Gupta, P.K.; Srivastava, A. (2020). A review of modern technologies for tackling COVID-19 pandemic. Diabetes Metab. Syndr. Clin. Res. Rev. 2020, 14, 569–573.

Liang, W., Liang, H., Ou, L., Chen, B., Chen, A., Li, C. China Medical Treatment Expert Group for, C. (2020). Development and Validation of a Clinical Risk Score to Predict the Occurrence of Critical Illness in Hospitalized Patients With COVID-19. JAMA Intern Med, 180(8), 1081-1089. doi:10.1001/jamainternmed.2020.2033 (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7218676/)

Lieveld, A. W. E., Azijli, K., Teunissen, B. P., van Haaften, R. M., Kootte, R. S., van den Berk, I.

A. H., Nanayakkara, P. W. B. (2021). Chest CT in COVID-19 at the ED: Validation of the COVID-19 Reporting and Data System (CO-RADS) and CT Severity Score: A Prospective, Multicenter,Observational Study. Chest, 159(3), 1126-1135. doi:10.1016/j.chest.2020.11.026.

Matsumoto, T., Walston, S.L., Walston, M. et al. (2022). Deep Learning–Based Time-to-Death Prediction Model for COVID-19 Patients Using Clinical Data and Chest Radiographs. J Digit Imaging (2022). https://doi.org/10.1007/s10278-022-00691-y

Mbunge, E.; Akinnuwesi, B.; Fashoto, S.G.; Metfula, A.S.; Mashwama, P. (2021). A critical review of emerging technologies for tackling COVID-19 pandemic. Hum. Behav. Emerg. Technol. 2021, 3, 25–39.

Moher D, Liberati A, Tetzlaff J, et al. (2009). Reprint--preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Phys Ther 2009;89:873−80.

Naudé,W.(2020). Artificial Intelligence against COVID-19: An Early Review; Institute of Labor Economics: Bonn, Germany, 2020.

Okoli C (2015). A guide to conducting a standalone systematic literature review. Commun Assoc Inf Syst 2015;37:43.

Olowolayemo, A., Yasin, M., Raashid S. M. (2023). Predicting Mortality Risk of Covid-19 Patients Using Chest X-Rays. International Journal on Perceptive and Cognitive Computing, 9(1), 33–43.

Ortiz, A., Trivedi, A., Desbiens, J. et al. (2022). Effective deep learning approaches for predicting COVID-19 outcomes from chest computed tomography volumes. Sci Rep 12, 1716 (2022). https://doi.org/10.1038/s41598-022-05532-0

Panagiotis G. Asteris, Styliani Kokoris, Eleni Gavriilaki, et al (2023). Early prediction of COVID- 19 outcome using artificial intelligence techniques and only five laboratory indices, Clinical Immunology, Volume 246, 2023, 109218, ISSN 1521-6616, https://doi.org/10.1016/j.clim.2022.109218.

Portal-Diaz, J.A., Lovelle-Enríquez, O., Perez-Diaz, M. et al. (2022). New patch-based strategy for COVID-19 automatic identification using chest x-ray images. Health Technol. 12, 1117–1132 (2022). https://doi.org/10.1007/s12553-022-00704-4

Quiroz, J. C., Feng, Y. Z., Cheng, Z. Y., Rezazadegan, D., Chen, P. K., Lin, Q. T., Cai, X. R. (2021). Development and Validation of a Machine Learning Approach for Automated Severity Assessment of COVID-19 Based on Clinical and Imaging Data: Retrospective Study. JMIR Med Inform, 9(2), e24572. doi:10.2196/24572 (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7879715/).

Rasheed, J.; Jamil, A.; Hameed, A.A.; Al-Turjman, F.; Rasheed, A. (2021). COVID-19 in the Age of Artificial Intelligence: A Comprehensive Review. Interdiscip. Sci. Comput. Life Sci. 2021, 13, 153–175

Rahimi, I.; Chen, F.; Gandomi, A.H. (2021). A review on COVID-19 forecasting models. Neural Comput. Appl. 2021, 1–11.

Shi, F.; Wang, J.; Shi, J.; Wu, Z.; Wang, Q.; Tang, Z.; He, K.; Shi, Y.; Shen, D. (2020). Review of artificial intelligence techniques in imaging data acquisition, segmentation, and diagnosis for COVID-19. IEEE Rev. Biomed. Eng. 2020, 14, 4–15

Swapnarekha, H.; Behera, H.S.; Nayak, J.; Naik, B. (2020). Role of intelligent computing in COVID-19 prognosis: A state-of-the-art review. Chaos Solitons Fractals 2020, 138, 109947.

Tanboga, I. H., Canpolat, U., Cetin, E. H. O., Kundi, H., Celik, O., Caglayan, M., . . .Topaloglu, S. (2021). Development and validation of clinical prediction model to estimate the probability of death in hospitalized patients with COVID-19: Insights from a nationwide database. J Med Virol, 93(5), 3015-3022. doi:10.1002/jmv.26844.

Tranfield D, Denyer D, Smart P. (2003). Towards a methodology for developing evidence-informed management knowledge by means of systematic review. Br J Manag 2003;14:207−22.

Verzellesi L, Botti A, Bertolini M, Trojani V, Carlini G, Nitrosi A, Monelli F, Besutti G, Castellani G, Remondini D, et al. Machine and Deep Learning Algorithms for COVID-19 Mortality Prediction Using Clinical and Radiomic Features. Electronics. 2023; 12(18):3878. https://doi.org/10.3390/electronics12183878

Wendland. P., Schmitt V., Zimmermann J , Hager L., Gopel S., Schenkel-Hager C., Kschischo M (2023). Machine learning models for predicting severe COVID-19