Utilization of interpretable machine learning model to forecast the risk of major adverse kidney events in elderly patients in critical care

References

• Becker S, Müller J, de Heer G, et al. Clinical characteristics and outcome of very elderly patients ≥90 years in intensive care: a retrospective observational study. Ann Intensive Care. 2015;5(1):1.
   PubMedGoogle Scholar
• Roedl K, Daniels R, Theile P, et al. The independent impact of peripheral arterial disease on mortality in nonagenarians and centenarians who were treated in an intensive care unit: a consecutive cohort of 1108 patients. Eur J Vasc Endovasc Surg. 2023;65(4):582–9.
   PubMed Web of Science ®Google Scholar
• Xin Q, Xie T, Chen R, et al. Predictive nomogram model for major adverse kidney events within 30 days in sepsis patients with type 2 diabetes mellitus. Front Endocrinol. 2022;13:1024500.
   PubMedGoogle Scholar
• Semler MW, Rice TW, Shaw AD, et al. Identification of major adverse kidney events within the electronic health record. J Med Syst. 2016;40(7):167.
   PubMed Web of Science ®Google Scholar
• McKown AC, Wang L, Wanderer JP, et al. Predicting major adverse kidney events among critically ill adults using the electronic health record. J Med Syst. 2017;41(10):156.
   PubMed Web of Science ®Google Scholar
• Semler MW, Wanderer JP, Ehrenfeld JM, et al. Balanced crystalloids versus saline in the intensive care unit. The SALT randomized trial. Am J Respir Crit Care Med. 2017;195(10):1362–1372.
   PubMed Web of Science ®Google Scholar
• Spiegel R, Teeter W, Sullivan S, et al. The use of venous doppler to predict adverse kidney events in a general ICU cohort. Crit Care. 2020;24(1):615.
   PubMed Web of Science ®Google Scholar
• Zhou D, Jiang J, Zhang J, et al. Increase in chloride is associated with major adverse kidney events in critically ill patients. Shock. 2023;59(3):338–343.
   PubMed Web of Science ®Google Scholar
• Semler MW, Self WH, Wanderer JP, et al. Balanced crystalloids versus saline in critically ill adults. N Engl J Med. 2018;378(9):829–839.
   PubMed Web of Science ®Google Scholar
• Boutin L, Legrand M, Sadoune M, et al. Elevated plasma galectin-3 is associated with major adverse kidney events and death after ICU admission. Crit Care. 2022;26(1):13.
   PubMed Web of Science ®Google Scholar
• Fuchs L, Chronaki CE, Park S, et al. ICU admission characteristics and mortality rates among elderly and very elderly patients. Intensive Care Med. 2012;38(10):1654–1661.
   PubMed Web of Science ®Google Scholar
• Ihra GC, Lehberger J, Hochrieser H, et al. Development of demographics and outcome of very old critically ill patients admitted to intensive care units. Intensive Care Med. 2012;38(4):620–626.
   PubMed Web of Science ®Google Scholar
• Flannery AH, Ortiz-Soriano V, Li X, et al. Serum renin and major adverse kidney events in critically ill patients: a multicenter prospective study. Crit Care. 2021;25(1):294.
   PubMedGoogle Scholar
• Mele A, Cerminara E, Häbel H, et al. Fluid accumulation and major adverse kidney events in sepsis: a multicenter observational study. Ann Intensive Care. 2022;12(1):62.
   PubMed Web of Science ®Google Scholar
• Neyra JA, Ortiz-Soriano V, Liu LJ, et al. Prediction of mortality and major adverse kidney events in critically ill patients with acute kidney injury. Am J Kidney Dis. 2023;81(1):36–47.
   PubMed Web of Science ®Google Scholar
• Zhang X, Ye B, Mao W, et al. Major adverse kidney events within 30 days in patients with acute pancreatitis: a tertiary-center cohort study. HPB. 2022;24(2):169–175.
   PubMed Web of Science ®Google Scholar
• Vinclair C, De Montmollin E, Sonneville R, et al. Factors associated with major adverse kidney events in patients who underwent veno-arterial extracorporeal membrane oxygenation. Ann Intensive Care. 2020;10(1):44.
   PubMed Web of Science ®Google Scholar
• Venerito V, Emmi G, Cantarini L, et al. Validity of machine learning in predicting giant cell arteritis flare after glucocorticoids tapering. Front Immunol. 2022;13:860877.
   PubMed Web of Science ®Google Scholar
• Tai AMY, Albuquerque A, Carmona NE, et al. Machine learning and big data: implications for disease modeling and therapeutic discovery in psychiatry. Artif Intell Med. 2019;99:101704.
   PubMed Web of Science ®Google Scholar
• Luo XQ, Kang YX, Duan SB, et al. Machine learning-based prediction of acute kidney injury following pediatric cardiac surgery: model development and validation study. J Med Internet Res. 2023;25:e41142.
   PubMed Web of Science ®Google Scholar
• Abe D, Inaji M, Hase T, et al. A prehospital triage system to detect traumatic intracranial hemorrhage using machine learning algorithms. JAMA Netw Open. 2022;5(6):e2216393.
   PubMed Web of Science ®Google Scholar
• Lee SW, Lee HC, Suh J, et al. Multi-center validation of machine learning model for preoperative prediction of postoperative mortality. NPJ Digit Med. 2022;5(1):91.
   PubMed Web of Science ®Google Scholar
• Collins GS, Reitsma JB, Altman DG, et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. Ann Intern Med. 2015;162(1):55–63.
   PubMed Web of Science ®Google Scholar
• Brown RM, Wang L, Coston TD, et al. Balanced crystalloids versus saline in sepsis. A secondary analysis of the SMART clinical trial. Am J Respir Crit Care Med. 2019;200(12):1487–1495.
   PubMed Web of Science ®Google Scholar
• Bian Y, Xu T, Le Y, et al. The efficacy and safety of sodium bicarbonate ringer’s solution in critically ill patients: a retrospective cohort study. Front Pharmacol. 2022;13:829394.
   PubMed Web of Science ®Google Scholar
• Zhang H, Jiang L, Fu R, et al. Impact of levosimendan on efficacy and renal function in acute heart failure according to renal function: a perspective, multi-center, real-world registry. Front Cardiovasc Med. 2022;9:986039.
   PubMed Web of Science ®Google Scholar
• Eckardt KU, Kasiske BL. Kidney disease: improving global outcomes. Nat Rev Nephrol. 2009;5(11):650–657.
   PubMed Web of Science ®Google Scholar
• Stekhoven DJ, Bühlmann P. MissForest–non-parametric missing value imputation for mixed-type data. Bioinformatics. 2012;28(1):112–118.
   PubMed Web of Science ®Google Scholar
• Lundberg S, Lee SI. A unified approach to interpreting model predictions. arXiv. 2017.
   Google Scholar
• Loh HW, Ooi CP, Seoni S, et al. Application of explainable artificial intelligence for healthcare: a systematic review of the last decade (2011–2022). Comput Methods Programs Biomed. 2022;226:107161.
   PubMed Web of Science ®Google Scholar
• Zou Y, Shi Y, Sun F, et al. Extreme gradient boosting model to assess risk of central cervical lymph node metastasis in patients with papillary thyroid carcinoma: individual prediction using SHapley additive exPlanations. Comput Methods Programs Biomed. 2022;225:107038.
   PubMed Web of Science ®Google Scholar
• Yuan Y, Wang X, Shi M, et al. Performance comparison of RGB and multispectral vegetation indices based on machine learning for estimating hopea hainanensis SPAD values under different shade conditions. Front Plant Sci. 2022;13:928953.
   PubMed Web of Science ®Google Scholar
• Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115–118.
   PubMed Web of Science ®Google Scholar
• Liang H, Tsui BY, Ni H, et al. Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence. Nat Med. 2019;25(3):433–438.
   PubMed Web of Science ®Google Scholar
• Chilamkurthy S, Ghosh R, Tanamala S, et al. Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study. Lancet. 2018;392(10162):2388–2396.
   PubMed Web of Science ®Google Scholar
• Al’Aref SJ, Maliakal G, Singh G, et al. Machine learning of clinical variables and coronary artery calcium scoring for the prediction of obstructive coronary artery disease on coronary computed tomography angiography: analysis from the CONFIRM registry. Eur Heart J. 2020;41(3):359–367.
   PubMed Web of Science ®Google Scholar
• Huang J, Jin W, Duan X, et al. Twenty-eight-day in-hospital mortality prediction for elderly patients with ischemic stroke in the intensive care unit: interpretable machine learning models. Front Public Health. 2022;10:1086339.
   PubMed Web of Science ®Google Scholar
• Li J, Liu S, Hu Y, et al. Predicting mortality in intensive care unit patients with heart failure using an interpretable machine learning model: retrospective cohort study. J Med Internet Res. 2022;24(8):e38082.
   PubMed Web of Science ®Google Scholar
• Raphael KL. Metabolic acidosis and subclinical metabolic acidosis in CKD. J Am Soc Nephrol. 2018;29(2):376–382.
   PubMed Web of Science ®Google Scholar
• Wesson DE, Buysse JM, Bushinsky DA. Mechanisms of metabolic acidosis-induced kidney injury in chronic kidney disease. J Am Soc Nephrol. 2020;31(3):469–482.
   PubMed Web of Science ®Google Scholar
• Zhu A, Whitlock RH, Ferguson TW, et al. Metabolic acidosis is associated with acute kidney injury in patients with CKD. Kidney Int Rep. 2022;7(10):2219–2229.
   PubMed Web of Science ®Google Scholar
• Huang S, Peng Y, Wang L, et al. Effectiveness of sodium bicarbonate infusion on mortality for elderly septic patients with acute metabolic acidosis. Front Pharmacol. 2022;13:974271.
   PubMed Web of Science ®Google Scholar
• Han JH, Kwak JY, Lee SS, et al. Markedly elevated aspartate aminotransferase from non-hepatic causes. JCM. 2022;12(1):310.
   Google Scholar
• Sheng S, Zhang YH, Ma HK, et al. Albumin levels predict mortality in sepsis patients with acute kidney injury undergoing continuous renal replacement therapy: a secondary analysis based on a retrospective cohort study. BMC Nephrol. 2022;23(1):52.
   PubMed Web of Science ®Google Scholar
• Takegawa R, Kabata D, Shimizu K, et al. Serum albumin as a risk factor for death in patients with prolonged sepsis: an observational study. J Crit Care. 2019;51:139–144.
   PubMed Web of Science ®Google Scholar
• Teipel SJ, Grothe MJ, Metzger CD, et al. Robust detection of impaired resting state functional connectivity networks in Alzheimer’s disease using elastic net regularized regression. Front Aging Neurosci. 2016;8:318.
   PubMed Web of Science ®Google Scholar
• Bledsoe J, Peltan ID, Bunnell RJ, et al. Order substitutions and education for balanced crystalloid solution use in an integrated health care system and association with major adverse kidney events. JAMA Netw Open. 2022;5(5):e2210046.
   PubMed Web of Science ®Google Scholar