{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,6]],"date-time":"2026-11-06T07:08:48Z","timestamp":1793948928932,"version":"4.8.0"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T00:00:00Z","timestamp":1762732800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T00:00:00Z","timestamp":1762732800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Research Project of the Fundamental Research Funds for Provincial Universities in Heilongjiang Province"},{"name":"Heilongjiang Provincial Key Research and Development Program"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-025-23037-4","type":"journal-article","created":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T14:52:31Z","timestamp":1762786351000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Machine learning models for predicting short-term progression in patients with stage 4 chronic kidney disease: a multi-center validation study"],"prefix":"10.1038","volume":"15","author":[{"given":"Jingshu","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuanyi","family":"Du","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xue","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinyuan","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangnan","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yisha","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangyan","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuying","family":"Fan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,11,10]]},"reference":[{"key":"23037_CR1","doi-asserted-by":"publisher","first-page":"2052","DOI":"10.1016\/S0140-6736(18)31694-5","volume":"392","author":"KJ Foreman","year":"2018","unstructured":"Foreman, K. J. et al. Forecasting life expectancy, years of life lost, and all-cause and cause-specific mortality for 250 causes of death: reference and alternative scenarios for 2016\u201340 for 195 countries and territories. Lancet 392, 2052\u20132090. https:\/\/doi.org\/10.1016\/S0140-6736(18)31694-5 (2018).","journal-title":"Lancet"},{"key":"23037_CR2","doi-asserted-by":"publisher","first-page":"815","DOI":"10.1016\/s0140-6736(12)60033-6","volume":"379","author":"L Zhang","year":"2012","unstructured":"Zhang, L. et al. Prevalence of chronic kidney disease in china: a cross-sectional survey. Lancet 379, 815\u2013822. https:\/\/doi.org\/10.1016\/s0140-6736(12)60033-6 (2012).","journal-title":"Lancet"},{"key":"23037_CR3","doi-asserted-by":"publisher","first-page":"298","DOI":"10.1001\/jamainternmed.2022.6817","volume":"183","author":"L Wang","year":"2023","unstructured":"Wang, L. et al. Prevalence of chronic kidney disease in china: results from the sixth China chronic disease and risk factor surveillance. JAMA Intern. Med. 183, 298\u2013310. https:\/\/doi.org\/10.1001\/jamainternmed.2022.6817 (2023).","journal-title":"JAMA Intern. Med."},{"key":"23037_CR4","doi-asserted-by":"publisher","first-page":"1157","DOI":"10.1161\/circulationaha.120.050686","volume":"143","author":"J Jankowski","year":"2021","unstructured":"Jankowski, J., Floege, J., Fliser, D., B\u00f6hm, M. & Marx, N. Cardiovascular disease in chronic kidney disease: pathophysiological insights and therapeutic options. Circulation 143, 1157\u20131172. https:\/\/doi.org\/10.1161\/circulationaha.120.050686 (2021).","journal-title":"Circulation"},{"key":"23037_CR5","doi-asserted-by":"publisher","unstructured":"KCWG. KDIGO 2024 clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int. 2024;105:S117\u2013S314. https:\/\/doi.org\/10.1016\/j.kint.2023.10.018  (2024).","DOI":"10.1016\/j.kint.2023.10.018"},{"key":"23037_CR6","doi-asserted-by":"publisher","first-page":"2057","DOI":"10.1007\/s40620-023-01711-y","volume":"36","author":"J Piveteau","year":"2023","unstructured":"Piveteau, J. et al. Pre-dialysis care trajectory and post-dialysis survival and transplantation access in patients with end-stage kidney disease. J. Nephrol. 36, 2057\u20132070. https:\/\/doi.org\/10.1007\/s40620-023-01711-y (2023).","journal-title":"J. Nephrol."},{"key":"23037_CR7","doi-asserted-by":"publisher","unstructured":"Krishnamurthy, S. et al. Machine learning prediction models for chronic kidney disease using National Health Insurance Claim Data in Taiwan. Healthcare (Basel) 9 (5), 546.  https:\/\/doi.org\/10.3390\/healthcare9050546 (2021).","DOI":"10.3390\/healthcare9050546"},{"issue":"1","key":"23037_CR8","doi-asserted-by":"publisher","first-page":"e0000700","DOI":"10.1371\/journal.pdig.0000700","volume":"4","author":"N Aminnejad","year":"2025","unstructured":"Aminnejad, N., Greiver, M. & Huang, H. Predicting the onset of chronic kidney disease (CKD) for diabetic patients with aggregated longitudinal EMR data. PLOS Digit. Health. 4 (1), e0000700. https:\/\/doi.org\/10.1371\/journal.pdig.0000700 (2025). PMID: 39841679; PMCID: PMC11753645.","journal-title":"PLOS Digit. Health"},{"issue":"1","key":"23037_CR9","doi-asserted-by":"publisher","first-page":"11862","DOI":"10.1038\/s41598-019-48263-5","volume":"9","author":"M Makino","year":"2019","unstructured":"Makino, M. et al. Artificial intelligence predicts the progression of diabetic kidney disease using big data machine learning. Sci. Rep. 9 (1), 11862. https:\/\/doi.org\/10.1038\/s41598-019-48263-5 (2019). PMID: 31413285; PMCID: PMC6694113.","journal-title":"Sci. Rep."},{"key":"23037_CR10","doi-asserted-by":"crossref","unstructured":"Everitt, B. S. & Hothorn, T. An Introduction to Applied Multivariate Analysis with R (2011).","DOI":"10.1007\/978-1-4419-9650-3"},{"key":"23037_CR11","doi-asserted-by":"publisher","first-page":"1925","DOI":"10.1093\/eurheartj\/ehu207","volume":"35","author":"EW Steyerberg","year":"2014","unstructured":"Steyerberg, E. W. & Vergouwe, Y. Towards better clinical prediction models: seven steps for development and an ABCD for validation. Eur. Heart J. 35, 1925\u20131931. https:\/\/doi.org\/10.1093\/eurheartj\/ehu207 (2014).","journal-title":"Eur. Heart J."},{"key":"23037_CR12","doi-asserted-by":"publisher","first-page":"e078378","DOI":"10.1136\/bmj-2023-078378","volume":"385","author":"GS Collins","year":"2024","unstructured":"Collins, G. S. et al. TRIPOD\u2009+\u2009AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. Bmj 385, e078378. https:\/\/doi.org\/10.1136\/bmj-2023-078378 (2024).","journal-title":"Bmj"},{"key":"23037_CR13","doi-asserted-by":"publisher","first-page":"376","DOI":"10.1186\/s12882-023-03424-7","volume":"24","author":"K Takkavatakarn","year":"2023","unstructured":"Takkavatakarn, K., Oh, W., Cheng, E., Nadkarni, G. N. & Chan, L. Machine learning models to predict end-stage kidney disease in chronic kidney disease stage 4. BMC Nephrol. 24, 376. https:\/\/doi.org\/10.1186\/s12882-023-03424-7 (2023).","journal-title":"BMC Nephrol."},{"key":"23037_CR14","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1186\/s12967-019-1860-0","volume":"17","author":"J Xiao","year":"2019","unstructured":"Xiao, J. et al. Comparison and development of machine learning tools in the prediction of chronic kidney disease progression. J. Transl. Med. 17, 119. https:\/\/doi.org\/10.1186\/s12967-019-1860-0 (2019).","journal-title":"J. Transl. Med."},{"key":"23037_CR15","doi-asserted-by":"publisher","unstructured":"Vlad, C. et al. Apolipoproteins A and B and PCSK9: nontraditional cardiovascular risk factors in chronic kidney disease and in End-Stage renal disease. J. Diabetes Res. 2019 https:\/\/doi.org\/10.1155\/2019\/6906278 (2019).","DOI":"10.1155\/2019\/6906278"},{"key":"23037_CR16","doi-asserted-by":"publisher","unstructured":"Kimak, E. & Solski, J. ApoA- and apoB-containing lipoproteins and Lp(a) concentration in non-dialyzed patients with chronic renal failure.  Ren. Fail.   24 (4),  485\u2013492. https:\/\/doi.org\/10.1081\/jdi-120006775  (2002).","DOI":"10.1081\/jdi-120006775"},{"key":"23037_CR17","doi-asserted-by":"publisher","first-page":"2240","DOI":"10.1046\/j.1523-1755.2002.00076.x","volume":"61","author":"GA Kaysen","year":"2002","unstructured":"Kaysen, G. A. et al. Relationships among inflammation nutrition and physiologic mechanisms establishing albumin levels in hemodialysis patients. Kidney Int. 61, 2240\u20132249. https:\/\/doi.org\/10.1046\/j.1523-1755.2002.00076.x (2002).","journal-title":"Kidney Int."},{"key":"23037_CR18","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1053\/j.ajkd.2018.02.364","volume":"72","author":"DN Koye","year":"2018","unstructured":"Koye, D. N. et al. Risk of progression of nonalbuminuric CKD to end-stage kidney disease in people with diabetes: the CRIC (Chronic renal insufficiency Cohort) study. Am. J. Kidney Dis. 72, 653\u2013661. https:\/\/doi.org\/10.1053\/j.ajkd.2018.02.364 (2018).","journal-title":"Am. J. Kidney Dis."},{"key":"23037_CR19","doi-asserted-by":"publisher","first-page":"617","DOI":"10.1093\/ckj\/sfz186","volume":"14","author":"S Kwon","year":"2020","unstructured":"Kwon, S. et al. Apolipoprotein B is a risk factor for end-stage renal disease. Clin. Kidney J. 14, 617\u2013623. https:\/\/doi.org\/10.1093\/ckj\/sfz186 (2020).","journal-title":"Clin. Kidney J."},{"issue":"10","key":"23037_CR20","doi-asserted-by":"publisher","first-page":"1923","DOI":"10.1007\/s11255-020-02550-7","volume":"52","author":"WB Zhao","year":"2020","unstructured":"Zhao, W. B. & Alberto, P. S. M. Serum apolipoprotein B\/apolipoprotein A1 ratio is associated with the progression of diabetic kidney disease to renal replacement therapy. Int. Urol. Nephrol. 52 (10), 1923\u20131928. https:\/\/doi.org\/10.1007\/s11255-020-02550-7 (2020). Epub 2020 Jul 13. PMID: 32661625.","journal-title":"Int. Urol. Nephrol."},{"key":"23037_CR21","doi-asserted-by":"publisher","first-page":"8377","DOI":"10.1038\/s41598-022-12316-z","volume":"12","author":"Q Bai","year":"2022","unstructured":"Bai, Q., Su, C., Tang, W. & Li, Y. Machine learning to predict end stage kidney disease in chronic kidney disease. Sci. Rep. 12, 8377. https:\/\/doi.org\/10.1038\/s41598-022-12316-z (2022).","journal-title":"Sci. Rep."},{"key":"23037_CR22","doi-asserted-by":"publisher","first-page":"562","DOI":"10.1080\/0886022x.2022.2056053","volume":"44","author":"Y Zou","year":"2022","unstructured":"Zou, Y. et al. Development and internal validation of machine learning algorithms for end-stage renal disease risk prediction model of people with type 2 diabetes mellitus and diabetic kidney disease. Ren. Fail. 44, 562\u2013570. https:\/\/doi.org\/10.1080\/0886022x.2022.2056053 (2022).","journal-title":"Ren. Fail."},{"key":"23037_CR23","doi-asserted-by":"publisher","first-page":"1388","DOI":"10.1093\/ckj\/sfaa126","volume":"14","author":"V Garcia-Montemayor","year":"2021","unstructured":"Garcia-Montemayor, V. et al. Predicting mortality in hemodialysis patients using machine learning analysis. Clin. Kidney J. 14, 1388\u20131395. https:\/\/doi.org\/10.1093\/ckj\/sfaa126 (2021).","journal-title":"Clin. Kidney J."},{"issue":"3","key":"23037_CR24","doi-asserted-by":"publisher","first-page":"1556","DOI":"10.34067\/kid.0007012021","volume":"360","author":"S Rankin","year":"2022","unstructured":"Rankin, S. et al. A machine learning model for predicting mortality within 90 days of Dialysis initiation. Kidney 360 (3), 1556\u20131565. https:\/\/doi.org\/10.34067\/kid.0007012021 (2022).","journal-title":"Kidney"},{"key":"23037_CR25","doi-asserted-by":"publisher","first-page":"1442","DOI":"10.1016\/j.kint.2018.01.009","volume":"93","author":"ME Grams","year":"2018","unstructured":"Grams, M. E. et al. Predicting timing of clinical outcomes in patients with chronic kidney disease and severely decreased glomerular filtration rate. Kidney Int. 93, 1442\u20131451. https:\/\/doi.org\/10.1016\/j.kint.2018.01.009 (2018).","journal-title":"Kidney Int."},{"issue":"Suppl 3","key":"23037_CR26","doi-asserted-by":"publisher","first-page":"iii27","DOI":"10.1093\/ndt\/gfs277","volume":"27","author":"KJ Jager","year":"2012","unstructured":"Jager, K. J. et al. The EQUAL study: a European study in chronic kidney disease stage 4 patients. Nephrol. Dial Transpl. 27 (Suppl 3), iii27\u201331. https:\/\/doi.org\/10.1093\/ndt\/gfs277 (2012).","journal-title":"Nephrol. Dial Transpl."},{"key":"23037_CR27","doi-asserted-by":"publisher","first-page":"540","DOI":"10.1093\/ndt\/gfab002","volume":"37","author":"A Rao","year":"2022","unstructured":"Rao, A. et al. Using datasets to ascertain the generalizability of clinical cohorts: the example of the European quality study on the treatment of advanced chronic kidney disease. Nephrol. Dial Transpl. 37, 540\u2013547. https:\/\/doi.org\/10.1093\/ndt\/gfab002 (2022).","journal-title":"Nephrol. Dial Transpl."},{"key":"23037_CR28","doi-asserted-by":"publisher","first-page":"e0204586","DOI":"10.1371\/journal.pone.0204586","volume":"13","author":"MJ Lee","year":"2018","unstructured":"Lee, M. J. et al. Can we predict when to start renal replacement therapy in patients with chronic kidney disease using 6 months of clinical data? PLoS One. 13, e0204586. https:\/\/doi.org\/10.1371\/journal.pone.0204586 (2018).","journal-title":"PLoS One"},{"key":"23037_CR29","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1007\/s10916-017-0732-5","volume":"41","author":"LC Cheng","year":"2017","unstructured":"Cheng, L. C., Hu, Y. H. & Chiou, S. H. Applying the temporal abstraction technique to the prediction of chronic kidney disease progression. J. Med. Syst. 41, 85. https:\/\/doi.org\/10.1007\/s10916-017-0732-5 (2017).","journal-title":"J. Med. Syst."},{"key":"23037_CR30","doi-asserted-by":"publisher","unstructured":"Eckardt, K. U. et al. Improving the prognosis of patients with severely decreased glomerular filtration rate (CKD G4+): conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) Controversies Conference. Kidney Int.  93, 1281\u20131292. https:\/\/doi.org\/10.1016\/j.kint.2018.02.006  (2018).","DOI":"10.1016\/j.kint.2018.02.006"},{"key":"23037_CR31","doi-asserted-by":"publisher","first-page":"1553","DOI":"10.1001\/jama.2011.451","volume":"305","author":"N Tangri","year":"2011","unstructured":"Tangri, N. et al. A predictive model for progression of chronic kidney disease to kidney failure. Jama 305, 1553\u20131559. https:\/\/doi.org\/10.1001\/jama.2011.451 (2011).","journal-title":"Jama"},{"key":"23037_CR32","doi-asserted-by":"publisher","first-page":"87","DOI":"10.2215\/cjn.01290216","volume":"12","author":"EB Schroeder","year":"2017","unstructured":"Schroeder, E. B. et al. Predicting 5-Year risk of RRT in stage 3 or 4 CKD: development and external validation. Clin. J. Am. Soc. Nephrol. 12, 87\u201394. https:\/\/doi.org\/10.2215\/cjn.01290216 (2017).","journal-title":"Clin. J. Am. Soc. Nephrol."},{"key":"23037_CR33","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1053\/j.ajkd.2021.05.018","volume":"79","author":"HU Zacharias","year":"2022","unstructured":"Zacharias, H. U. et al. A predictive model for progression of CKD to kidney failure based on routine laboratory tests. Am. J. Kidney Dis. 79, 217\u2013230. https:\/\/doi.org\/10.1053\/j.ajkd.2021.05.018 (2022).","journal-title":"Am. J. Kidney Dis."},{"key":"23037_CR34","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1016\/j.ekir.2018.01.002","volume":"3","author":"M Evans","year":"2018","unstructured":"Evans, M. et al. Risk factors for prognosis in patients with severely decreased GFR. Kidney Int. Rep. 3, 625\u2013637. https:\/\/doi.org\/10.1016\/j.ekir.2018.01.002 (2018).","journal-title":"Kidney Int. Rep."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-23037-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-23037-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-23037-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T02:04:38Z","timestamp":1763172278000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-23037-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,10]]},"references-count":34,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["23037"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-23037-4","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,10]]},"assertion":[{"value":"6 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 November 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"39285"}}