Identification of Sepsis Subphenotypes via ICU Data Clustering
Resumo
Este estudo tem como objetivo identificar subfenótipos de sepse em pacientes de UTI usando técnicas de clusterização em dados clínicos do MIMIC-IV. Dados ausentes foram imputados usando MissForest. Aplicamos UMAP (trustworthiness = 0,97) para redução de dimensionalidade e K-means (K = 5, silhouette score = 0,30) para clusterização, revelando cinco subfenótipos distintos com características clínicas únicas. Nossos achados sugerem que esses subfenótipos podem orientar diagnósticos mais eficientes e tratamentos personalizados, superando as limitações do escore SOFA atual. Os resultados destacam a necessidade de explorar variáveis adicionais para melhorar o diagnóstico e tratamento da sepse.
Palavras-chave:
Agrupamento, Sepse, UTI
Referências
Fohner, A. E., Greene, J. D., Lawson, B. L., Chen, J. H., Kipnis, P., Escobar, G. J., and Liu, V. X. (2019). Assessing clinical heterogeneity in sepsis through treatment patterns and machine learning. Journal of the American Medical Informatics Association, 26(12):1466–1477.
Hu, C., Li, Y., Wang, F., et al. (2022). Application of machine learning for clinical subphenotype identification in sepsis. Infectious Disease Therapy, 11:1949–1964.
Ibrahim, Z. M., Wu, H., Hamoud, A., Stappen, L., Dobson, R. J. B., and Agarossi, A. (2020). On classifying sepsis heterogeneity in the icu: Insight using machine learning. Journal of the American Medical Informatics Association, 27(3):437–443.
Johnson, A. E. W., Bulgarelli, L., Shen, L., Gayles, A., Shammout, A., Horng, S., Pollard, T. J., Hao, S., Moody, B., Gow, B., Lehman, L. H., Celi, L. A., and Mark, R. G. (2023). Mimic-iv, a freely accessible electronic health record dataset. Scientific Data, 10(1):1.
Koutroulis, I., Velez, T., Wang, T., Yohannes, S., Galarraga, J. E., Morales, J. A., Freishtat, R. J., and Chamberlain, J. M. (2022). Pediatric sepsis phenotypes for enhanced therapeutics: An application of clustering to electronic health records. Journal of the American College of Emergency Physicians Open, 3(1):e12660.
McInnes, L., Healy, J., and Melville, J. (2018). Umap: Uniform manifold approximation and projection. Journal of Open Source Software, 3(29):861.
Sakr, Y., Jaschinski, U., Wittebole, X., Szakmany, T., Lipman, J., Ñamendys Silva, S. A., Martin-Loeches, I., Leone, M., Lupu, M.-N., Vincent, J.-L., and Investigators, I. (2018). Sepsis in intensive care unit patients: Worldwide data from the intensive care over nations audit. Open Forum Infectious Diseases, 5(12).
Seymour, C. W., Kennedy, J. N., Wang, S., et al. (2019). Derivation, validation, and potential treatment implications of novel clinical phenotypes for sepsis. JAMA, 321(20):2003–2017.
Singer, M., Deutschman, C., Seymour, C., Shankar-Hari, M., Annane, D., Bauer, M., Bellomo, R., Bernard, G., Chiche, J., Coopersmith, C., Hotchkiss, R., Levy, M., Marshall, J., Martin, G., Opal, S., Rubenfeld, G., van der Poll, T., Vincent, J., and Angus, D. (2016). The third international consensus definitions for sepsis and septic shock (sepsis-3). JAMA, 315(8):801–810.
Hu, C., Li, Y., Wang, F., et al. (2022). Application of machine learning for clinical subphenotype identification in sepsis. Infectious Disease Therapy, 11:1949–1964.
Ibrahim, Z. M., Wu, H., Hamoud, A., Stappen, L., Dobson, R. J. B., and Agarossi, A. (2020). On classifying sepsis heterogeneity in the icu: Insight using machine learning. Journal of the American Medical Informatics Association, 27(3):437–443.
Johnson, A. E. W., Bulgarelli, L., Shen, L., Gayles, A., Shammout, A., Horng, S., Pollard, T. J., Hao, S., Moody, B., Gow, B., Lehman, L. H., Celi, L. A., and Mark, R. G. (2023). Mimic-iv, a freely accessible electronic health record dataset. Scientific Data, 10(1):1.
Koutroulis, I., Velez, T., Wang, T., Yohannes, S., Galarraga, J. E., Morales, J. A., Freishtat, R. J., and Chamberlain, J. M. (2022). Pediatric sepsis phenotypes for enhanced therapeutics: An application of clustering to electronic health records. Journal of the American College of Emergency Physicians Open, 3(1):e12660.
McInnes, L., Healy, J., and Melville, J. (2018). Umap: Uniform manifold approximation and projection. Journal of Open Source Software, 3(29):861.
Sakr, Y., Jaschinski, U., Wittebole, X., Szakmany, T., Lipman, J., Ñamendys Silva, S. A., Martin-Loeches, I., Leone, M., Lupu, M.-N., Vincent, J.-L., and Investigators, I. (2018). Sepsis in intensive care unit patients: Worldwide data from the intensive care over nations audit. Open Forum Infectious Diseases, 5(12).
Seymour, C. W., Kennedy, J. N., Wang, S., et al. (2019). Derivation, validation, and potential treatment implications of novel clinical phenotypes for sepsis. JAMA, 321(20):2003–2017.
Singer, M., Deutschman, C., Seymour, C., Shankar-Hari, M., Annane, D., Bauer, M., Bellomo, R., Bernard, G., Chiche, J., Coopersmith, C., Hotchkiss, R., Levy, M., Marshall, J., Martin, G., Opal, S., Rubenfeld, G., van der Poll, T., Vincent, J., and Angus, D. (2016). The third international consensus definitions for sepsis and septic shock (sepsis-3). JAMA, 315(8):801–810.
Publicado
17/11/2024
Como Citar
ASSIS, Giovana; MELLO, Victoria F.; RIBEIRO, Haniel B.; BARROS, Alexandre G.; PAPPA, Gisele L.; MEIRA JR., Wagner.
Identification of Sepsis Subphenotypes via ICU Data Clustering. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 21. , 2024, Belém/PA.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 204-215.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2024.245281.
