Desafios e Tendências na Predição de Sepse
Resumo
A sepse é uma das principais causas de mortes em Unidades de Terapia Intensiva (UTI) e possui elevados custos para os sistemas de saúde em todo o mundo. Sendo assim, é importante descobrir quais os desafios e tendências sobre este tema para contribuir com pesquisas futuras. Este trabalho apresenta um Mapeamento Sistemático da Literatura (MSL) em que os artigos foram analisados dentro do intervalo de 2016 a 2020 com foco em predição de sepse. Os resultados mostraram, além de outras evidências, que a diversidade de modelos de predição de sepse encontrados revela uma clara necessidade de padronização nesta área.
Referências
Singer M, Deutschman CS, Seymour CW et al (2016) The Third International Consensus definitions for sepsis and septic shock (Sepsis-3). JAMA 315(8):801–810
Chiew, Calvin J., et al. "Heart rate variability based machine learning models for risk prediction of suspected sepsis patients in the emergency department." Medicine 98.6 (2019).
Zabihi, Morteza, Serkan Kiranyaz, and Moncef Gabbouj. "Sepsis prediction in intensive care unit using ensemble of XGboost models." 2019 Computing in Cardiology (CinC). IEEE, 2019.
Abromavičius, Vytautas, et al. "Two-stage monitoring of patients in intensive care unit for sepsis prediction using non-overfitted machine learning models." Electronics 9.7 (2020): 1133. Wickramaratne,
Saqib, Mohammed, Ying Sha, and May D. Wang. "Early prediction of sepsis in EMR records using traditional ML techniques and deep learning LSTM networks." 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2018.
Sajila D., and MD Shaad Mahmud. "Bi-directional gated recurrent unit based ensemble model for the early detection of sepsis." 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2020.
Petersen, Kai, Sairam Vakkalanka, and Ludwik Kuzniarz. "Guidelines for conducting systematic mapping studies in software engineering: An update." Information and software technology 64 (2015): 1-18.
Yan, Melissa Y., Lise Tuset Gustad, and Øystein Nytrø. "Sepsis prediction, early detection, and identification using clinical text for machine learning: a systematic review." Journal of the American Medical Informatics Association 29.3 (2022): 559-575.
Moor, Michael, et al. "Early prediction of sepsis in the ICU using machine learning: a systematic review." Frontiers in medicine 8 (2021): 348.
Schünemann, Holger, et al. "Handbook for grading the quality of evidence and the strength of recommendations using the GRADE approach." Updated October 2013 (2013).
Whiting, Penny F., et al. "QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies." Annals of internal medicine 155.8 (2011): 529-536.
Fleuren, Lucas M., et al. "Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy." Intensive care medicine 46.3 (2020): 383-400.
Moher, David, et al. "Preferred reporting items for systematic reviews and metaanalyses: the PRISMA statement." Annals of internal medicine 151.4 (2009): 264-269.
Kitchenham, Barbara Ann, David Budgen, and Pearl Brereton. Evidence-based software engineering and systematic reviews. Vol. 4. CRC press, 2015.
Stern, Cindy, Zoe Jordan, and Alexa McArthur. "Developing the review question and inclusion criteria." AJN The American Journal of Nursing 114.4 (2014): 53-56.
Da Silva, Martony Demes et al. Levantamento bibliográfico utilizando a ferramenta The End. Sociedade Brasileira de Computação, 2020.
Carletta J. Assessing Agreement on Classification Tasks: The Kappa Statistic. Computational linguistics. 1996; 22(2):249–254.
Honoré, Antoine et al. Hidden Markov Models for sepsis detection in preterm infants. In: ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. p. 1130-1134.
Goldberg, Ori et al. Can we improve early identification of neonatal late-onset sepsis? A validated prediction model. Journal of Perinatology, v. 40, n. 9, p. 1315-1322, 2020.
Thakur, Jyoti; PAHUJA, Sharvan Kumar; PAHUJA, Roop. Performance comparison of systemic inflammatory response syndrome with logistic regression models to predict sepsis in neonates. Children, v. 4, n. 12, p. 111, 2017.
Lin, Chen et al. Early diagnosis and prediction of sepsis shock by combining static and dynamic information using convolutional-LSTM. In: 2018 IEEE International Conference on Healthcare Informatics (ICHI). IEEE, 2018. p. 219-228.
Norrie, John. The challenge of implementing AI models in the ICU. The Lancet Respiratory Medicine, v. 6, n. 12, p. 886-888, 2018.
Wang, Fei; KAUSHAL, Rainu; KHULLAR, Dhruv. Should health care demand interpretable artificial intelligence or accept “black box” medicine?. Annals of internal medicine, v. 172, n. 1, p. 59-60, 2020.
Calvert, Jacob S. et al. A computational approach to early sepsis detection. Computers in biology and medicine, v. 74, p. 69-73, 2016.
Nemati, Shamim et al. An interpretable machine learning model for accurate prediction of sepsis in the ICU. Critical care medicine, v. 46, n. 4, p. 547, 2018.
Li, Xiang et al. A time-phased machine learning model for real-time prediction of sepsis in critical care. Critical Care Medicine, v. 48, n. 10, p. e884-e888, 2020.
Goldberger, Ary L. et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. circulation, v. 101, n. 23, p.
e215-e220, 2000. Pierrakos, Charalampos; VINCENT, Jean-Louis. Sepsis biomarkers: a review. Critical care, v. 14, p. 1-18, 2010.
Reyna, Matthew A., et al. "Early prediction of sepsis from clinical data: the PhysioNet/Computing in Cardiology Challenge 2019." 2019 Computing in Cardiology (CinC). IEEE, 2019.
Johnson, Alistair EW, et al. "MIMIC-III, a freely accessible critical care database." Scientific data 3.1 (2016): 1-9.
Johnson, Alistair, et al. "Mimic-iv (version 0.4)." PhysioNet (2020).
Machado, Flávia Ribeiro, et al. "Chegando a um consenso: vantagens e desvantagens do Sepsis 3 considerando países de recursos limitados." Revista brasileira de terapia intensiva 28 (2016): 361-365.
Macias, E. et al. Novel imputing method and deep learning techniques for early prediction of sepsis in intensive care units. In: 2019 Computing in Cardiology (CinC). IEEE, 2019. p. 1-4.
Chiew, Calvin J., et al. "Heart rate variability based machine learning models for risk prediction of suspected sepsis patients in the emergency department." Medicine 98.6 (2019).
Zabihi, Morteza, Serkan Kiranyaz, and Moncef Gabbouj. "Sepsis prediction in intensive care unit using ensemble of XGboost models." 2019 Computing in Cardiology (CinC). IEEE, 2019.
Abromavičius, Vytautas, et al. "Two-stage monitoring of patients in intensive care unit for sepsis prediction using non-overfitted machine learning models." Electronics 9.7 (2020): 1133. Wickramaratne,
Saqib, Mohammed, Ying Sha, and May D. Wang. "Early prediction of sepsis in EMR records using traditional ML techniques and deep learning LSTM networks." 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2018.
Sajila D., and MD Shaad Mahmud. "Bi-directional gated recurrent unit based ensemble model for the early detection of sepsis." 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2020.
Petersen, Kai, Sairam Vakkalanka, and Ludwik Kuzniarz. "Guidelines for conducting systematic mapping studies in software engineering: An update." Information and software technology 64 (2015): 1-18.
Yan, Melissa Y., Lise Tuset Gustad, and Øystein Nytrø. "Sepsis prediction, early detection, and identification using clinical text for machine learning: a systematic review." Journal of the American Medical Informatics Association 29.3 (2022): 559-575.
Moor, Michael, et al. "Early prediction of sepsis in the ICU using machine learning: a systematic review." Frontiers in medicine 8 (2021): 348.
Schünemann, Holger, et al. "Handbook for grading the quality of evidence and the strength of recommendations using the GRADE approach." Updated October 2013 (2013).
Whiting, Penny F., et al. "QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies." Annals of internal medicine 155.8 (2011): 529-536.
Fleuren, Lucas M., et al. "Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy." Intensive care medicine 46.3 (2020): 383-400.
Moher, David, et al. "Preferred reporting items for systematic reviews and metaanalyses: the PRISMA statement." Annals of internal medicine 151.4 (2009): 264-269.
Kitchenham, Barbara Ann, David Budgen, and Pearl Brereton. Evidence-based software engineering and systematic reviews. Vol. 4. CRC press, 2015.
Stern, Cindy, Zoe Jordan, and Alexa McArthur. "Developing the review question and inclusion criteria." AJN The American Journal of Nursing 114.4 (2014): 53-56.
Da Silva, Martony Demes et al. Levantamento bibliográfico utilizando a ferramenta The End. Sociedade Brasileira de Computação, 2020.
Carletta J. Assessing Agreement on Classification Tasks: The Kappa Statistic. Computational linguistics. 1996; 22(2):249–254.
Honoré, Antoine et al. Hidden Markov Models for sepsis detection in preterm infants. In: ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. p. 1130-1134.
Goldberg, Ori et al. Can we improve early identification of neonatal late-onset sepsis? A validated prediction model. Journal of Perinatology, v. 40, n. 9, p. 1315-1322, 2020.
Thakur, Jyoti; PAHUJA, Sharvan Kumar; PAHUJA, Roop. Performance comparison of systemic inflammatory response syndrome with logistic regression models to predict sepsis in neonates. Children, v. 4, n. 12, p. 111, 2017.
Lin, Chen et al. Early diagnosis and prediction of sepsis shock by combining static and dynamic information using convolutional-LSTM. In: 2018 IEEE International Conference on Healthcare Informatics (ICHI). IEEE, 2018. p. 219-228.
Norrie, John. The challenge of implementing AI models in the ICU. The Lancet Respiratory Medicine, v. 6, n. 12, p. 886-888, 2018.
Wang, Fei; KAUSHAL, Rainu; KHULLAR, Dhruv. Should health care demand interpretable artificial intelligence or accept “black box” medicine?. Annals of internal medicine, v. 172, n. 1, p. 59-60, 2020.
Calvert, Jacob S. et al. A computational approach to early sepsis detection. Computers in biology and medicine, v. 74, p. 69-73, 2016.
Nemati, Shamim et al. An interpretable machine learning model for accurate prediction of sepsis in the ICU. Critical care medicine, v. 46, n. 4, p. 547, 2018.
Li, Xiang et al. A time-phased machine learning model for real-time prediction of sepsis in critical care. Critical Care Medicine, v. 48, n. 10, p. e884-e888, 2020.
Goldberger, Ary L. et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. circulation, v. 101, n. 23, p.
e215-e220, 2000. Pierrakos, Charalampos; VINCENT, Jean-Louis. Sepsis biomarkers: a review. Critical care, v. 14, p. 1-18, 2010.
Reyna, Matthew A., et al. "Early prediction of sepsis from clinical data: the PhysioNet/Computing in Cardiology Challenge 2019." 2019 Computing in Cardiology (CinC). IEEE, 2019.
Johnson, Alistair EW, et al. "MIMIC-III, a freely accessible critical care database." Scientific data 3.1 (2016): 1-9.
Johnson, Alistair, et al. "Mimic-iv (version 0.4)." PhysioNet (2020).
Machado, Flávia Ribeiro, et al. "Chegando a um consenso: vantagens e desvantagens do Sepsis 3 considerando países de recursos limitados." Revista brasileira de terapia intensiva 28 (2016): 361-365.
Macias, E. et al. Novel imputing method and deep learning techniques for early prediction of sepsis in intensive care units. In: 2019 Computing in Cardiology (CinC). IEEE, 2019. p. 1-4.
Publicado
27/06/2023
Como Citar
SILVA JR., Lourival G.; ANDRADE, Rossana M. C.; CELESTINO JR., Joaquim.
Desafios e Tendências na Predição de Sepse. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 23. , 2023, São Paulo/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 455-466.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2023.230163.
