1,207
Views
0
CrossRef citations to date
0
Altmetric
Cardio-renal Physiology and Disease Processes

Machine learning model and nomogram to predict the risk of heart failure hospitalization in peritoneal dialysis patients

, , , , , , & show all
Article: 2324071 | Received 22 Oct 2023, Accepted 22 Feb 2024, Published online: 17 Mar 2024

References

  • Daratha KB, Short RA, Corbett CF, et al. Risks of subsequent hospitalization and death in patients with kidney disease. Clin J Am Soc Nephrol. 2012;7(3):1–11. doi:10.2215/CJN.05070511.
  • Ortiz A, Covic A, Fliser D, et al. Epidemiology, contributors to, and clinical trials of mortality risk in chronic kidney failure. Lancet. 2014;383(9931):1831–1843. doi:10.1016/S0140-6736(14)60384-6.
  • Mauri JM, Cleries M, Vela E, et al. Design and validation of a model to predict early mortality in haemodialysis patients. Nephrol Dial Transplant. 2008;23(5):1690–1696. doi:10.1093/ndt/gfm728.
  • Chua HR, Lau T, Luo N, et al. Predicting first-year mortality in incident dialysis patients with end-stage renal disease – the UREA5 study. Blood Purif. 2014;37(2):85–92. doi:10.1159/000357640.
  • Doi T, Yamamoto S, Morinaga T, et al. Risk score to predict 1-year mortality after haemodialysis initiation in patients with stage 5 chronic kidney disease under predialysis nephrology care. PLOS One. 2015;10(6):e0129180. doi:10.1371/journal.pone.0129180.
  • Floege J, Gillespie IA, Kronenberg F, et al. Development and validation of a predictive mortality risk score from a European Hemodialysis Cohort. Kidney Int. 2015;87(5):996–1008. doi:10.1038/ki.2014.419.
  • Quinn RR, Laupacis A, Hux JE, et al. Predicting the risk of 1-year mortality in incident dialysis patients: accounting for case-mix severity in studies using administrative data. Med Care. 2011;49(3):257–266. doi:10.1097/MLR.0b013e318202aa0b.
  • D’Agostino RBSr., Vasan RS, Pencina MJ, et al. General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation. 2008;117(6):743–753. doi:10.1161/CIRCULATIONAHA.107.699579.
  • Levy WC, Mozaffarian D, Linker DT, et al. The Seattle Heart Failure Model: prediction of survival in heart failure. Circulation. 2006;113(11):1424–1433. doi:10.1161/CIRCULATIONAHA.105.584102.
  • Attia ZI, Kapa S, Lopez-Jimenez F, et al. Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram. Nat Med. 2019;25(1):70–74. doi:10.1038/s41591-018-0240-2.
  • Chen T, Li X, Li Y, et al. Prediction and risk stratification of kidney outcomes in IgA nephropathy. Am J Kidney Dis. 2019;74(3):300–309. doi:10.1053/j.ajkd.2019.02.016.
  • He J, Baxter SL, Xu J, et al. The practical implementation of artificial intelligence technologies in medicine. Nat Med. 2019;25(1):30–36. doi:10.1038/s41591-018-0307-0.
  • Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44–56. doi:10.1038/s41591-018-0300-7.
  • Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115–118. doi:10.1038/nature21056.
  • Gulshan V, Peng L, Coram M, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316(22):2402–2410. doi:10.1001/jama.2016.17216.
  • Weng SF, Reps J, Kai J, et al. Can machine-learning improve cardiovascular risk prediction using routine clinical data? PLOS One. 2017;12(4):e0174944. doi:10.1371/journal.pone.0174944.
  • Young J, Kempton MJ, McGuire P. Using machine learning to predict outcomes in psychosis. Lancet Psychiatry. 2016;3(10):908–909. doi:10.1016/S2215-0366(16)30218-8.
  • Yu KH, Zhang C, Berry GJ, et al. Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features. Nat Commun. 2016;7(1):12474. doi:10.1038/ncomms12474.
  • Jordan MI, Mitchell TM. Machine learning: trends, perspectives, and prospects. Science. 2015;349(6245):255–260. doi:10.1126/science.aaa8415.
  • Song Y, Gao S, Tan W, et al. Multiple machine learnings revealed similar predictive accuracy for prognosis of PNETs from the surveillance, epidemiology, and end result database. J Cancer. 2018;9(21):3971–3978. doi:10.7150/jca.26649.
  • Awan SE, Sohel F, Sanfilippo FM, et al. Machine learning in heart failure: ready for prime time. Curr Opin Cardiol. 2018;33(2):190–195. doi:10.1097/HCO.0000000000000491.
  • Tripoliti EE, Papadopoulos TG, Karanasiou GS, et al. Heart failure: diagnosis, severity estimation and prediction of adverse events through machine learning techniques. Comput Struct Biotechnol J. 2017;15:26–47. doi:10.1016/j.csbj.2016.11.001.
  • Collins AJ, Foley RN, Chavers B, et al. United States Renal Data System 2011 Annual Data Report: atlas of chronic kidney disease & end-stage renal disease in the United States. Am J Kidney Dis. 2012;59(1 Suppl. 1):A7, e1–420.
  • Heart Failure Group of Chinese Society of Cardiology of Chinese Medical A, Chinese Heart Failure Association of Chinese medical doctor A, editorial board of Chinese journal of C. Chinese guidelines for the diagnosis and treatment of heart failure 2018. Zhonghua Xin Xue Guan Bing Za Zhi. 2018;46(10):760–789.
  • Perez-Moreno AC, Jhund PS, MacDonald MR, et al. Fatigue as a predictor of outcome in patients with heart failure: analysis of CORONA (Controlled Rosuvastatin Multinational Trial in Heart Failure). JACC Heart Fail. 2014;2(2):187–197. doi:10.1016/j.jchf.2014.01.001.
  • Kobayashi M, Watanabe M, Coiro S, et al. Mid-term prognostic impact of residual pulmonary congestion assessed by radiographic scoring in patients admitted for worsening heart failure. Int J Cardiol. 2019;289:91–98. doi:10.1016/j.ijcard.2019.01.091.
  • Melenovsky V, Andersen MJ, Andress K, et al. Lung congestion in chronic heart failure: haemodynamic, clinical, and prognostic implications. Eur J Heart Fail. 2015;17(11):1161–1171. doi:10.1002/ejhf.417.
  • Caldentey G, Khairy P, Roy D, et al. Prognostic value of the physical examination in patients with heart failure and atrial fibrillation: insights from the AF-CHF trial (atrial fibrillation and chronic heart failure). JACC Heart Fail. 2014;2(1):15–23. doi:10.1016/j.jchf.2013.10.004.
  • Charlson ME, Pompei P, Ales KL, et al. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373–383. doi:10.1016/0021-9681(87)90171-8.
  • Woythaler JN, Singer SL, Kwan OL, et al. Accuracy of echocardiography versus electrocardiography in detecting left ventricular hypertrophy: comparison with postmortem mass measurements. J Am Coll Cardiol. 1983;2(2):305–311. doi:10.1016/s0735-1097(83)80167-3.
  • Ellouali F, Berkchi F, Bayahia R, et al. Comparison of the effects of dialysis methods (haemodialysis vs peritoneal dialysis) on diastolic left ventricular function dialysis methods and diastolic function. Open Cardiovasc Med J. 2016;10(1):171–178. doi:10.2174/1874192401610010171.
  • Essig M, Escoubet B, de Zuttere D, et al. Cardiovascular remodelling and extracellular fluid excess in early stages of chronic kidney disease. Nephrol Dial Transplant. 2008;23(1):239–248. doi:10.1093/ndt/gfm542.
  • Cunha FM, Pereira J, Ribeiro A, et al. Age affects the prognostic impact of diabetes in chronic heart failure. Acta Diabetol. 2018;55(3):271–278. doi:10.1007/s00592-017-1092-9.
  • Moskalev AA, Shaposhnikov MV, Plyusnina EN, et al. The role of DNA damage and repair in aging through the prism of Koch-like criteria. Ageing Res Rev. 2013;12(2):661–684. doi:10.1016/j.arr.2012.02.001.
  • Fried L, Bernardini J, Piraino B. Charlson Comorbidity Index as a predictor of outcomes in incident peritoneal dialysis patients. Am J Kidney Dis. 2001;37(2):337–342. doi:10.1053/ajkd.2001.21300.
  • van Manen JG, Korevaar JC, Dekker FW, et al. How to adjust for comorbidity in survival studies in ESRD patients: a comparison of different indices. Am J Kidney Dis. 2002;40(1):82–89. doi:10.1053/ajkd.2002.33916.
  • Pulliam J, Li NC, Maddux F, et al. First-year outcomes of incident peritoneal dialysis patients in the United States. Am J Kidney Dis. 2014;64(5):761–769. doi:10.1053/j.ajkd.2014.04.025.
  • Saran R, Li Y, Robinson B, et al. US Renal Data System 2015 Annual Data Report: epidemiology of kidney disease in the United States. Am J Kidney Dis. 2016;67(3 Suppl. 1): Svii, S1–305. doi:10.1053/j.ajkd.2015.12.014.
  • Desai RJ, Wang SV, Vaduganathan M, et al. Comparison of machine learning methods with traditional models for use of administrative claims with electronic medical records to predict heart failure outcomes. JAMA Netw Open. 2020;3(1):e1918962. doi:10.1001/jamanetworkopen.2019.18962.
  • Angraal S, Mortazavi BJ, Gupta A, et al. Machine learning prediction of mortality and hospitalization in heart failure with preserved ejection fraction. JACC Heart Fail. 2020;8(1):12–21. doi:10.1016/j.jchf.2019.06.013.
  • Kwon JM, Kim KH, Jeon KH, et al. Artificial intelligence algorithm for predicting mortality of patients with acute heart failure. PLOS One. 2019;14(7):e0219302. doi:10.1371/journal.pone.0219302.