WHO (2020) The top 10 causes of death. WHO. https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death. Accessed 31 Aug 2023

Junaid Nazar CM, Kindratt TB, Ahmad SM, Ahmed M, Anderson J (2014) Barriers to the successful practice of chronic kidney diseases at the primary health care level; a systematic review. J Renal Inj Prev 3:61–67. https://doi.org/10.12861/jrip.2014.20

Article  PubMed  PubMed Central  Google Scholar 

Couser WG, Remuzzi G, Mendis S, Tonelli M (2011) The contribution of chronic kidney disease to the global burden of major noncommunicable diseases. Kidney Int 80:1258–1270. https://doi.org/10.1038/ki.2011.368

Article  PubMed  Google Scholar 

SCORE2 working group and ESC Cardiovascular risk collaboration (2021) SCORE2 risk prediction algorithms: new models to estimate 10-year risk of cardiovascular disease in Europe. Eur Heart J 42:2439–2454. https://doi.org/10.1093/eurheartj/ehab309

Article  CAS  Google Scholar 

Hippisley-Cox J, Coupland C, Brindle P (2017) Development and validation of QRISK3 risk prediction algorithms to estimate future risk of cardiovascular disease: prospective cohort study. BMJ 357:j2099. https://doi.org/10.1136/bmj.j2099

Article  PubMed  PubMed Central  Google Scholar 

Hansrivijit P, Chen YJ, Lnu K, Trongtorsak A, Puthenpura MM, Thongprayoon C, Bathini T, Mao MA, Cheungpasitporn W (2021) Prediction of mortality among patients with chronic kidney disease: a systematic review. World J Nephrol 10:59–75. https://doi.org/10.5527/wjn.v10.i4.59

Article  PubMed  PubMed Central  Google Scholar 

Bansal N, Katz R, De Boer IH, Peralta CA, Fried LF, Siscovick DS, Rifkin DE, Hirsch C, Cummings SR, Harris TB, Kritchevsky SB, Sarnak MJ, Shlipak MG, Ix JH (2015) Development and validation of a model to predict 5-year risk of death without ESRD among older adults with CKD. Clin J Am Soc Nephrol 10:363–371. https://doi.org/10.2215/cjn.04650514

Article  CAS  PubMed  PubMed Central  Google Scholar 

Danial M, Hassali MA, Meng OL, Kin YC, Khan AH (2019) Development of a mortality score to assess risk of adverse drug reactions among hospitalized patients with moderate to severe chronic kidney disease. BMC Pharmacol Toxicol 20:41. https://doi.org/10.1186/s40360-019-0318-6

Article  CAS  PubMed  PubMed Central  Google Scholar 

Gan GCH, Kadappu KK, Bhat A, Fernandez F, Gu KH, Cai L, Byth K, Eshoo S, Thomas L (2021) Left atrial strain is the best predictor of adverse cardiovascular outcomes in patients with chronic kidney disease. J Am Soc Echocardiog 34:166–175. https://doi.org/10.1016/j.echo.2020.09.015

Article  Google Scholar 

Jagadeswaran D, Indhumathi E, Hemamalini AJ, Sivakumar V, Soundararajan P, Jayakumar M (2018) Inflammation and nutritional status assessment by malnutrition inflammation score and its outcome in pre-dialysis chronic kidney disease patients. Clin Nutr 38:341–347. https://doi.org/10.1016/j.clnu.2018.01.001

Article  CAS  PubMed  Google Scholar 

Ramspek CL, Jager KJ, Dekker FW, Zoccali C, van Diepen M (2021) External validation of prognostic models: what, why, how, when and where? Clin Kidney J 14:49–58. https://doi.org/10.1093/ckj/sfaa188

Article  PubMed  Google Scholar 

Steyerberg EW (2019) Clinical prediction models: a practical approach to development, validation, and updating. Springer Link, New York

Book  Google Scholar 

Tran NTD, Balezeaux M, Granal M, Fouque D, Ducher M, Fauvel JP (2022) Prediction of all-cause mortality for chronic kidney disease patients using four models of machine learnings. Nephrol Dial Transplant 38:1691–1699. https://doi.org/10.1093/ndt/gfac316

Article  CAS  Google Scholar 

Fouque D, Roth H, Darne B, Bouchet JL, Daugas E, Drüeke TB, Hannedouche T, Jean G, London GM (2018) Achievement of 2009 and 2017 kidney disease: improving global outcomes mineral and bone targets and survival in a French cohort of chronic kidney disease Stages 4 and 5 non-dialysis patients. Clin Kidney J 11:710–719. https://doi.org/10.1093/ckj/sfy015

Article  CAS  PubMed  PubMed Central  Google Scholar 

Schisterman EF, Faraggi D, Reiser B, Hu J (2008) Youden Index and the optimal threshold for markers with mass at zero. Stat Med 27:297–315.

RapidMiner (2023). https://rapidminer.com/. Accessed 31 Aug 2023

Riley RD, Debray TPA, Collins GS, Archer L, Ensor J, van Smeden M, Snell KIE (2021) Minimum sample size for external validation of a clinical prediction model with a binary outcome. Stat Med 40:4230–4425. https://doi.org/10.1002/sim.9025

Article  PubMed  Google Scholar 

Bland JM, Altman DG (2004) The logrank test. BMJ 328:1073. https://doi.org/10.1136/bmj.328.7447.1073

Article  PubMed  PubMed Central  Google Scholar 

Ramspek CL, de Jong Y, Dekker FW, van Diepen M (2020) Towards the best kidney failure prediction tool: a systematic review and selection aid. Nephrol Dial Transplant 35:1527–1538. https://doi.org/10.1093/ndt/gfz018

Article  PubMed  Google Scholar 

Sanmarchi F, Fanconi C, Golinelli D, Gori D, Hernandez-Boussard T, Capodici A (2023) Predict, diagnose, and treat chronic kidney disease with machine learning: a systematic literature review. J Nephrol 36:1101–1117. https://doi.org/10.1007/s40620-023-01573-4

Article  PubMed  PubMed Central  Google Scholar 

Tangri N, Kitsios GD, Inker LA, Griffith J, Naimark DM, Walker S, Rigatto C, Uhlig K, Kent DM, Levey AS (2013) Risk prediction models for patients with chronic kidney disease: a systematic review. Ann Intern Med 158:596–603. https://doi.org/10.7326/0003-4819-158-8-201304160-00004

Article  PubMed  Google Scholar 

Lei N, Zhang X, Wei M, Lao B, Xu X, Zhang M, Chen H, Xu Y, Xia B, Zhang D, Dong C, Fu L, Tang F, Wu Y (2022) Machine learning algorithms’ accuracy in predicting kidney disease progression: a systematic review and meta-analysis. BMC Med Inform Decis Mak 22:205. https://doi.org/10.1186/s12911-022-01951-1

Article  PubMed  PubMed Central  Google Scholar 

Tuena C, Semonella M, Fernández-Álvarez J, Colombo D, Cipresso P (2020) Predictive precision medicine: towards the computational challenge. Springer, Cham

Google Scholar 

Vogenberg FR (2009) Predictive and prognostic models: implications for healthcare decision-making in a modern recession. Am Health Drug Benefits 2:218–222

PubMed  PubMed Central  Google Scholar