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Critical Care Nephrology and Continuous Kidney Replacement Therapy

Machine learning-based prediction of in-hospital mortality for critically ill patients with sepsis-associated acute kidney injury

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Article: 2316267 | Received 23 Nov 2023, Accepted 03 Feb 2024, Published online: 18 Feb 2024

References

  • Peerapornratana S, Manrique-Caballero CL, Gómez H, et al. Acute kidney injury from sepsis: current concepts, epidemiology, pathophysiology, prevention and treatment. Kidney Int. 2019;96(5):1–10. doi: 10.1016/j.kint.2019.05.026.
  • Zhi DY, Lin J, Zhuang HZ, et al. Acute kidney injury in critically ill patients with sepsis: clinical characteristics and outcomes. J Invest Surg. 2019;32(8):689–696. doi: 10.1080/08941939.2018.1453891.
  • Mehta RL, Bouchard J, Soroko SB, et al. Sepsis as a cause and consequence of acute kidney injury: program to improve care in acute renal disease. Intensive Care Med. 2011;37(2):241–248. doi: 10.1007/s00134-010-2089-9.
  • Sood MM, Shafer LA, Ho J, et al. Early reversible acute kidney injury is associated with improved survival in septic shock. J Crit Care. 2014;29(5):711–717. doi: 10.1016/j.jcrc.2014.04.003.
  • Coelho S, Cabral G, Lopes JA, et al. Renal regeneration after acute kidney injury. Nephrology (Carlton). 2018;23(9):805–814. doi: 10.1111/nep.13256.
  • Manrique-Caballero CL, Del Rio-Pertuz G, Gomez H. Sepsis-associated acute kidney injury. Crit Care Clin. 2021;37(2):279–301. doi: 10.1016/j.ccc.2020.11.010.
  • Xin Q, Xie T, Chen R, et al. A predictive model based on inflammatory and coagulation indicators for sepsis-induced acute kidney injury. J Inflamm Res. 2022;15:4561–4571 doi: 10.2147/JIR.S372246.
  • Järvisalo MJ, Hellman T, Uusalo P. Mortality and associated risk factors in patients with blood culture positive sepsis and acute kidney injury requiring continuous renal replacement therapy-A retrospective study. PLoS One. 2021;16(4):e0249561. doi: 10.1371/journal.pone.0249561.
  • Chen V, Li J, Kim JS, et al. Interpretable machine learning: Moving from mythos to diagnostics. 2022:arXiv:2103.06254.
  • Song X, Liu X, Liu F, et al. Comparison of machine learning and logistic regression models in predicting acute kidney injury: a systematic review and meta-analysis. Int J Med Inform. 2021;151:104484. doi: 10.1016/j.ijmedinf.2021.104484.
  • Yue S, Li S, Huang X, et al. Machine learning for the prediction of acute kidney injury in patients with sepsis. J Transl Med. 2022;20(1):215. doi: 10.1186/s12967-022-03364-0.
  • Katz S, Suijker J, Hardt C, et al. Decision support system and outcome prediction in a cohort of patients with necrotizing soft-tissue infections. Int J Med Inform. 2022;167:104878. doi: 10.1016/j.ijmedinf.2022.104878.
  • Hong S, Hou X, Jing J, et al. Predicting risk of mortality in pediatric ICU based on ensemble step-Wise feature selection. Health Data Sci. 2021;2021:7. doi: 10.34133/2021/9365125.
  • Johnson A, Bulgarelli L, Pollard T, et al. MIMIC-IV (version 1.0). PhysioNet. 2021. doi: 10.13026/s6n6-xd98.
  • Singer M, Deutschman CS, Seymour CW, et al. The third international consensus definitions for sepsis and septic shock (sepsis-3). JAMA. 2016;315(8):801–810. doi: 10.1001/jama.2016.0287.
  • Khwaja A. KDIGO clinical practice guidelines for acute kidney injury. Nephron Clin Pract. 2012;120(4):c179–184. doi: 10.1159/000339789.
  • Lee KJ, Simpson JA. Introduction to multiple imputation for dealing with missing data. Respirology. 2014;19(2):162–167. doi: 10.1111/resp.12226.
  • Fang Y, Middaugh CR, Fang J. In silico classification of proteins from acidic and neutral cytoplasms. PLoS One. 2012;7(9):e45585. doi: 10.1371/journal.pone.0045585.
  • Garcia-Carretero R, Vigil-Medina L, Mora-Jimenez I, et al. Use of a K-nearest neighbors model to predict the development of type 2 diabetes within 2 years in an obese, hypertensive population. Med Biol Eng Comput. 2020;58(5):991–1002. doi: 10.1007/s11517-020-02132-w.
  • Chen T, Guestrin C. XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’16). New York, NY, USA: Association for Computing Machinery; 2016. p. 785–794. doi: 10.1145/2939672.2939785.
  • Langarizadeh M, Moghbeli F. Applying naive Bayesian networks to disease prediction: a systematic review. Acta Inform Med. 2016;24(5):364–369. doi: 10.5455/aim.2016.24.364-369.
  • Hastie T, Tibshirani R, Friedman J. The elements of statistical learning: data mining, inference, and prediction. New York (NY): Springer; 2009. p. 20.
  • Cortes C, Vapnik V. Support-vector networks. Mach Learn. 1995;20(3):273–297. doi: 10.1007/BF00994018.
  • Breiman L. Random forests. Machine Learning. 2001;45(1):5–32. doi: 10.1023/A:1010933404324.
  • Fitzmaurice GM, Laird NM. Multivariate analysis: Discrete variables (Logistic Regression). IESBS. 2001:10221–10228.
  • Handelman G, Kok H, Chandra R, et al. eDoctor: machine learning and the future of medicine. J Intern Med. 2018;284(6):603–619. doi: 10.1111/joim.12822.
  • Pedregosa F, Varoquaux G, Gramfort A, et al. Scikit-learn: machine learning in python. J Mach Learn Res. 2011;12:2825–2830.
  • Nohara Y, Matsumoto K, Soejima H, et al. Explanation of machine learning models using shapley additive explanation and application for real data in hospital. Comput Methods Programs Biomed. 2022;214:106584. doi: 10.1016/j.cmpb.2021.106584.
  • Hu H, Li L, Zhang Y, et al. A prediction model for assessing prognosis in critically ill patients with sepsis-associated acute kidney injury. Shock. 2021;56(4):564–572. doi: 10.1097/SHK.0000000000001768.
  • Li X, Wu R, Zhao W, et al. Machine learning algorithm to predict mortality in critically ill patients with sepsis-associated acute kidney injury. Sci Rep. 2023;13(1):5223. doi: 10.1038/s41598-023-32160-z.
  • Luo XQ, Yan P, Duan SB, et al. Development and validation of machine learning models for Real-Time mortality prediction in critically ill patients with sepsis-associated acute kidney injury. Front Med (Lausanne). 2022;9:853102. doi: 10.3389/fmed.2022.853102.
  • Xiao W, Lu Z, Liu Y, et al. Influence of the initial neutrophils to lymphocytes and platelets ratio on the incidence and severity of sepsis-associated acute kidney injury: a double robust estimation based on a large public database. Front Immunol. 2022;13:925494. doi: 10.3389/fimmu.2022.925494.
  • Rivera-Fernández R, Nap R, Vázquez-Mata G, et al. Analysis of physiologic alterations in intensive care unit patients and their relationship with mortality. J Crit Care. 2007;22(2):120–128. doi: 10.1016/j.jcrc.2006.09.005.
  • McCarthy K, Conway R, Byrne D, et al. Hyponatraemia during an emergency medical admission as a marker of illness severity & case complexity. Eur J Intern Med. 2019;59:60–64. doi: 10.1016/j.ejim.2018.08.002.
  • O’Sullivan M, McCarthy KF. Sodium: sign, signifier, or signified, of sepsis? Eur J Intern Med. 2021;83:10–11. doi: 10.1016/j.ejim.2020.12.002.
  • Heffernan DS, Monaghan SF, Thakkar RK, et al. Failure to normalize lymphopenia following trauma is associated with increased mortality, independent of the leukocytosis pattern. Crit Care. 2012;16(1):R12. doi: 10.1186/cc11157.
  • Moreno-Torres V, Royuela A, Múñez-Rubio E, et al. Red blood cell distribution width as prognostic factor in sepsis: a new use for a classical parameter. J Crit Care. 2022;71:154069. doi: 10.1016/j.jcrc.2022.154069.
  • Lin K, Hu Y, Kong G. Predicting in-hospital mortality of patients with acute kidney injury in the ICU using random Forest model. Int J Med Inform. 2019;125:55–61. doi: 10.1016/j.ijmedinf.2019.02.002.
  • Desautels T, Calvert J, Hoffman J, et al. Prediction of sepsis in the intensive care unit with minimal electronic health record data: a machine learning approach. JMIR Med Inform. 2016;4(3):e28. doi: 10.2196/medinform.5909.
  • Dong Z, Wang Q, Ke Y, et al. Prediction of 3-year risk of diabetic kidney disease using machine learning based on electronic medical records. J Transl Med. 2022;20(1):143. doi: 10.1186/s12967-022-03339-1.