Revisão Narrativa sobre Modelos de Alocação de Recursos em UTI durante Pandemias: Abordagens Computacionais e Desafios Éticos
Resumo
A pandemia de COVID-19 destacou o desafio de alocar pacientes em UTIs. Este trabalho revisa modelos computacionais propostos para apoiar essa decisão, abrangendo desde abordagens baseadas em teoria da utilidade até métodos recentes de Decision-Focused Learning. Observa-se uma tendência a modelos que tratam explicitamente a incerteza e buscam aplicabilidade prática, embora persistam desafios quanto à validação clínica, equidade e transparência ética.
Palavras-chave:
Inteligência Artificial, Otimização, COVID-19
Referências
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Frej, E. A., Roselli, L. R. P., Ferreira, R. J. P., Alberti, A. R., and de Almeida, A. T. (2021). Decision model for allocation of intensive care unit beds for suspected covid-19 patients under scarce resources. Computational and Mathematical Methods in Medicine, 2021:8853787.
Ghanbari, V., Ardalan, A., Zareiyan, A., Nejati, A., Hanfling, D., and Bagheri, A. (2019). Ethical prioritization of patients during disaster triage: a systematic review. International Emergency Nursing, 43:126–132.
Gomes, C., Clímaco, G., Carvalho, J., and Quintanilha, D. (2024). Modelagem matemática para a alocação de leitos de uti com diferentes tipos de leitos e taxa de ocupação. In Anais do XXIV Simpósio Brasileiro de Computação Aplicada à Saúde, pages 226–237, Porto Alegre, RS, Brasil. SBC.
Guan, W.-j., Ni, Z.-y., Hu, Y., Liang, W.-h., Ou, C.-q., He, J.-x., et al. (2020). Clinical characteristics of coronavirus disease 2019 in china. New England Journal of Medicine, 382(18):1708–1720.
Knaus, W. A., Draper, E. A., Wagner, D. P., and Zimmerman, J. E. (1985). Apache ii: A severity of disease classification system. Critical Care Medicine, 13(10):818–829.
Knaus, W. A., Wagner, D. P., Draper, E. A., Zimmerman, J. E., Bergner, M., Bastos, P. G., et al. (1991). The apache iii prognostic system. Chest, 100(6):1619–1636.
Le Gall, J.-R., Lemeshow, S., and Saulnier, F. (1993). A new simplified acute physiology score (saps ii). JAMA, 270(24):2957–2963.
Manca, D., Caldirola, D., and Storti, E. (2020). A simplified math approach to predict icu beds and mortality rate for hospital emergency planning under covid-19 pandemic. Computers and Chemical Engineering, 140:106945.
Mandi, J., Kotary, J., Berden, S., Mulamba, M., Bucarey, V., Guns, T., and Fioretto, F. (2024). Decision-focused learning: Foundations, state of the art, benchmark and future opportunities. Journal of Artificial Intelligence Research, 81:1623–1701.
McGuire, A. and McConnell, P. C. (2019). Resource allocation in icu. Current Opinion in Anaesthesiology, 32(2):190–194.
Minne, L., Abu-Hanna, A., and de Jonge, E. (2008). Evaluation of sofa-based models for predicting mortality in the icu: A systematic review. Critical Care, 12(6):R161.
Oerlemans, A. J. M., van Sluisveld, N., van Leeuwen, E. S. J., Wollersheim, H., Dekkers, W. J. M., and Zegers, M. (2015). Ethical problems in intensive care unit admission and discharge decisions: a qualitative study. BMC Medical Ethics, 16(1):1–11.
Ouyang, H., Argon, N. T., and Ziya, S. (2020). Allocation of intensive care unit beds in periods of high demand. Operations Research, 68(2):591–608.
Ranzani, O. T., Singer, M., Salluh, J. I. F., Shankar-Hari, M., Pilcher, D., et al. (2025). Development and validation of the sofa-2 score. JAMA.
Wikman-Jorgensen, P. E., Ruiz, A., Galva, V. G., Lienas-García, J., Segui-Ripoll, J. M., Serrano, J. M. S., et al. (2022). Hospitalization forecast to inform covid-19 pandemic planning and resource allocation using mathematical models. medRxiv.
Zhou, F., Yu, T., Du, R., Fan, G., Liu, Y., Liu, Z., et al. (2020). Clinical course and risk factors for mortality of adult inpatients with covid-19. The Lancet, 395(10229):1054–1062.
Frej, E. A., Roselli, L. R. P., Ferreira, R. J. P., Alberti, A. R., and de Almeida, A. T. (2021). Decision model for allocation of intensive care unit beds for suspected covid-19 patients under scarce resources. Computational and Mathematical Methods in Medicine, 2021:8853787.
Ghanbari, V., Ardalan, A., Zareiyan, A., Nejati, A., Hanfling, D., and Bagheri, A. (2019). Ethical prioritization of patients during disaster triage: a systematic review. International Emergency Nursing, 43:126–132.
Gomes, C., Clímaco, G., Carvalho, J., and Quintanilha, D. (2024). Modelagem matemática para a alocação de leitos de uti com diferentes tipos de leitos e taxa de ocupação. In Anais do XXIV Simpósio Brasileiro de Computação Aplicada à Saúde, pages 226–237, Porto Alegre, RS, Brasil. SBC.
Guan, W.-j., Ni, Z.-y., Hu, Y., Liang, W.-h., Ou, C.-q., He, J.-x., et al. (2020). Clinical characteristics of coronavirus disease 2019 in china. New England Journal of Medicine, 382(18):1708–1720.
Knaus, W. A., Draper, E. A., Wagner, D. P., and Zimmerman, J. E. (1985). Apache ii: A severity of disease classification system. Critical Care Medicine, 13(10):818–829.
Knaus, W. A., Wagner, D. P., Draper, E. A., Zimmerman, J. E., Bergner, M., Bastos, P. G., et al. (1991). The apache iii prognostic system. Chest, 100(6):1619–1636.
Le Gall, J.-R., Lemeshow, S., and Saulnier, F. (1993). A new simplified acute physiology score (saps ii). JAMA, 270(24):2957–2963.
Manca, D., Caldirola, D., and Storti, E. (2020). A simplified math approach to predict icu beds and mortality rate for hospital emergency planning under covid-19 pandemic. Computers and Chemical Engineering, 140:106945.
Mandi, J., Kotary, J., Berden, S., Mulamba, M., Bucarey, V., Guns, T., and Fioretto, F. (2024). Decision-focused learning: Foundations, state of the art, benchmark and future opportunities. Journal of Artificial Intelligence Research, 81:1623–1701.
McGuire, A. and McConnell, P. C. (2019). Resource allocation in icu. Current Opinion in Anaesthesiology, 32(2):190–194.
Minne, L., Abu-Hanna, A., and de Jonge, E. (2008). Evaluation of sofa-based models for predicting mortality in the icu: A systematic review. Critical Care, 12(6):R161.
Oerlemans, A. J. M., van Sluisveld, N., van Leeuwen, E. S. J., Wollersheim, H., Dekkers, W. J. M., and Zegers, M. (2015). Ethical problems in intensive care unit admission and discharge decisions: a qualitative study. BMC Medical Ethics, 16(1):1–11.
Ouyang, H., Argon, N. T., and Ziya, S. (2020). Allocation of intensive care unit beds in periods of high demand. Operations Research, 68(2):591–608.
Ranzani, O. T., Singer, M., Salluh, J. I. F., Shankar-Hari, M., Pilcher, D., et al. (2025). Development and validation of the sofa-2 score. JAMA.
Wikman-Jorgensen, P. E., Ruiz, A., Galva, V. G., Lienas-García, J., Segui-Ripoll, J. M., Serrano, J. M. S., et al. (2022). Hospitalization forecast to inform covid-19 pandemic planning and resource allocation using mathematical models. medRxiv.
Zhou, F., Yu, T., Du, R., Fan, G., Liu, Y., Liu, Z., et al. (2020). Clinical course and risk factors for mortality of adult inpatients with covid-19. The Lancet, 395(10229):1054–1062.
Publicado
04/12/2025
Como Citar
GOMES, Carlos Eduardo V.; DE CARVALHO, João Augusto F. Nunes; DE PAIVA, Fael Faray; CLÍMACO, Francisco Glaubos N..
Revisão Narrativa sobre Modelos de Alocação de Recursos em UTI durante Pandemias: Abordagens Computacionais e Desafios Éticos. In: ESCOLA REGIONAL DE COMPUTAÇÃO DO CEARÁ, MARANHÃO E PIAUÍ (ERCEMAPI), 13. , 2025, São Luís/MA.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 101-110.
DOI: https://doi.org/10.5753/ercemapi.2025.17613.
