Machine learning algorithm to predict the in-hospital mortality in critically ill patients with chronic kidney disease

References

• GBD Chronic Kidney Disease Collaboration. Global, regional, and national burden of chronic kidney disease, 1990-2017: a systematic analysis for the global burden of disease study 2017. Lancet. 2020;395(10225):1–11.
   PubMed Web of Science ®Google Scholar
• Copur S, Ucku D, Cozzolino M, et al. Hypoxia-inducible factor signaling in vascular calcification in chronic kidney disease patients. J Nephrol. 2022;35(9):2205–2213.
   PubMed Web of Science ®Google Scholar
• Strijack B, Mojica J, Sood M, et al. Outcomes of chronic dialysis patients admitted to the intensive care unit. J Am Soc Nephrol. 2009;20(11):2441–2447.
   PubMed Web of Science ®Google Scholar
• Hutchison CA, Crowe AV, Stevens PE, et al. Case mix, outcome and activity for patients admitted to intensive care units requiring chronic renal dialysis: a secondary analysis of the ICNARC Case Mix Programme Database. Crit Care. 2007;11(2):R50.
   PubMed Web of Science ®Google Scholar
• Denic A, Glassock RJ, Rule AD. Structural and functional changes with the aging kidney. Adv Chronic Kidney Dis. 2016;23(1):19–28.
   PubMed Web of Science ®Google Scholar
• Rimes-Stigare C, Frumento P, Bottai M, et al. Long-term mortality and risk factors for development of end-stage renal disease in critically ill patients with and without chronic kidney disease. Crit Care. 2015;19:383.
   PubMed Web of Science ®Google Scholar
• Alam N, Hobbelink EL, van Tienhoven AJ, et al. The impact of the use of the Early Warning Score (EWS) on patient outcomes: a systematic review. Resuscitation. 2014;85(5):587–594.
   PubMed Web of Science ®Google Scholar
• Hou N, Li M, He L, et al. Predicting 30-days mortality for MIMIC-III patients with sepsis-3: a machine learning approach using XGboost. J Transl Med. 2020;18(1):462.
   PubMed Web of Science ®Google Scholar
• Du M, Haag DG, Lynch JW, et al. Comparison of the tree-based machine learning algorithms to Cox regression in predicting the survival of oral and pharyngeal cancers: analyses based on SEER Database. Cancers. 2020;12(10):2802.
   PubMed Web of Science ®Google Scholar
• Weis C, Cuénod A, Rieck B, et al. Direct antimicrobial resistance prediction from clinical MALDI-TOF mass spectra using machine learning. Nat Med. 2022;28(1):164–174.
   PubMed Web of Science ®Google Scholar
• Hu C, Li L, Huang W, et al. Interpretable machine learning for early prediction of prognosis in sepsis: a discovery and validation study. Infect Dis Ther. 2022;11(3):1117–1132.
   PubMed Web of Science ®Google Scholar
• Zhang Z, Chen L, Xu P, et al. Effectiveness of automated alerting system compared to usual care for the management of sepsis. NPJ Digit Med. 2022;5(1):101.
   PubMed Web of Science ®Google Scholar
• Zhou S, Zeng Z, Wei H, et al. Early combination of albumin with crystalloids administration might be beneficial for the survival of septic patients: a retrospective analysis from MIMIC-IV database. Ann Intensive Care. 2021;11(1):42.
   PubMed Web of Science ®Google Scholar
• Johnson AE, Pollard TJ, Shen L, et al. MIMIC-III, a freely accessible critical care database. Sci Data. 2016;3:160035.
   PubMed Web of Science ®Google Scholar
• Quan H, Sundararajan V, Halfon P, et al. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care. 2005;43(11):1130–1139.
   PubMed Web of Science ®Google Scholar
• Marshall A, Altman DG, Holder RL, et al. Combining estimates of interest in prognostic modelling studies after multiple imputation: current practice and guidelines. BMC Med Res Methodol. 2009;9:57.
   PubMed Web of Science ®Google Scholar
• Bansal N, Fan D, Hsu CY, et al. Incident atrial fibrillation and risk of end-stage renal disease in adults with chronic kidney disease. Circulation. 2013;127(5):569–574.
   PubMed Web of Science ®Google Scholar
• Eddy AA, Neilson EG. Chronic kidney disease progression. J Am Soc Nephrol. 2006;17(11):2964–2966.
   PubMed Web of Science ®Google Scholar
• Meguid El Nahas A, Bello AK. Chronic kidney disease: the global challenge. Lancet. 2005;365(9456):331–340.
   PubMed Web of Science ®Google Scholar
• Go AS, Chertow GM, Fan D, et al. Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization. N Engl J Med. 2004;351(13):1296–1305.
   PubMed Web of Science ®Google Scholar
• Manjunath G, Tighiouart H, Ibrahim H, et al. Level of kidney function as a risk factor for atherosclerotic cardiovascular outcomes in the community. J Am Coll Cardiol. 2003;41(1):47–55.
   PubMed Web of Science ®Google Scholar
• Hotchkiss JR, Palevsky PM. Care of the critically ill patient with advanced chronic kidney disease or end-stage renal disease. Curr Opin Crit Care. 2012;18(6):599–606.
   PubMed Web of Science ®Google Scholar
• Liu J, Wu J, Liu S, et al. Predicting mortality of patients with acute kidney injury in the ICU using XGBoost model. PLOS One. 2021;16(2):e0246306.
   PubMed Web of Science ®Google Scholar
• 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.
   PubMed Web of Science ®Google Scholar
• Wu J, Huang L, He H, et al. Red cell distribution width to platelet ratio is associated with increasing in-hospital mortality in critically ill patients with acute kidney injury. Dis Markers. 2022;2022:4802702.
   PubMed Web of Science ®Google Scholar
• He Y, Xu J, Shang X, et al. Clinical characteristics and risk factors associated with ICU-acquired infections in sepsis: a retrospective cohort study. Front Cell Infect Microbiol. 2022;12:962470.
   PubMed Web of Science ®Google Scholar
• Minne L, Abu-Hanna A, de Jonge E. Evaluation of SOFA-based models for predicting mortality in the ICU: a systematic review. Crit Care. 2008;12(6):R161.
   PubMed Web of Science ®Google Scholar
• Zhu Y, Zhang R, Ye X, et al. SAPS III is superior to SOFA for predicting 28-day mortality in sepsis patients based on sepsis 3.0 criteria. Int J Infect Dis. 2022;114:135–141.
   PubMed Web of Science ®Google Scholar
• Le Gall JR, Lemeshow S, Saulnier F. A new simplified acute physiology score (SAPS II) based on a European/North American Multicenter Study. JAMA. 1993;270(24):2957–2963.
   PubMed Web of Science ®Google Scholar
• Mirzakhani F, Sadoughi F, Hatami M, et al. Which model is superior in predicting ICU survival: artificial intelligence versus conventional approaches. BMC Med Inform Decis Mak. 2022;22(1):167.
   PubMed Web of Science ®Google Scholar
• Barthel P, Wensel R, Bauer A, et al. Respiratory rate predicts outcome after acute myocardial infarction: a prospective cohort study. Eur Heart J. 2013;34(22):1644–1650.
   PubMed Web of Science ®Google Scholar
• Macedo E, Malhotra R, Bouchard J, et al. Oliguria is an early predictor of higher mortality in critically ill patients. Kidney Int. 2011;80(7):760–767.
   PubMed Web of Science ®Google Scholar
• Kellum JA, Sileanu FE, Murugan R, et al. Classifying AKI by urine output versus serum creatinine level. J Am Soc Nephrol. 2015;26(9):2231–2238.
   PubMed Web of Science ®Google Scholar