{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T16:59:11Z","timestamp":1790787551173,"version":"4.1.0"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T00:00:00Z","timestamp":1788307200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T00:00:00Z","timestamp":1790726400000},"content-version":"vor","delay-in-days":28,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Clin Exp Med"],"DOI":"10.1007\/s10238-026-02305-1","type":"journal-article","created":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T08:36:32Z","timestamp":1788338192000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An interpretable machine learning model for early prediction of subsequent observed CRRT initiation in patients with sepsis-associated acute kidney injury"],"prefix":"10.1007","volume":"26","author":[{"given":"Yaoyao","family":"Tang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuai","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiujun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qi","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liye","family":"Ji","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruoxi","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2604-9932","authenticated-orcid":false,"given":"Mingxing","family":"Fang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,2]]},"reference":[{"issue":"10474","key":"2305_CR1","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1016\/S0140-6736(24)02385-7","volume":"405","author":"M Ostermann","year":"2025","unstructured":"Ostermann M, Lumlertgul N, Jeong R, See E, Joannidis M, James M. Acute kidney injury. Lancet. 2025;405(10474):241\u201356. https:\/\/doi.org\/10.1016\/S0140-6736(24)02385-7.","journal-title":"Lancet"},{"issue":"2","key":"2305_CR2","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1016\/j.ccc.2020.11.010","volume":"37","author":"CL Manrique-Caballero","year":"2021","unstructured":"Manrique-Caballero CL, Del Rio-Pertuz G, Gomez H. Sepsis-Associated Acute Kidney Injury. Crit Care Clin. 2021;37(2):279\u2013301. https:\/\/doi.org\/10.1016\/j.ccc.2020.11.010.","journal-title":"Crit Care Clin"},{"issue":"12","key":"2305_CR3","doi-asserted-by":"publisher","first-page":"e75992","DOI":"10.7759\/cureus.75992","volume":"16","author":"MG Aguilar","year":"2024","unstructured":"Aguilar MG, AlHussen HA, Gandhi PD, et al. Sepsis-associated acute kidney injury: pathophysiology and treatment modalities. Cureus. 2024;16(12):e75992. https:\/\/doi.org\/10.7759\/cureus.75992.","journal-title":"Cureus"},{"issue":"1","key":"2305_CR4","doi-asserted-by":"publisher","first-page":"432","DOI":"10.1186\/s13054-024-05220-7","volume":"28","author":"G Bottari","year":"2024","unstructured":"Bottari G, Ranieri VM, Ince C, et al. Use of extracorporeal blood purification therapies in sepsis: the current paradigm, available evidence, and future perspectives. Crit Care. 2024;28(1):432. https:\/\/doi.org\/10.1186\/s13054-024-05220-7.","journal-title":"Crit Care"},{"issue":"6","key":"2305_CR5","doi-asserted-by":"publisher","first-page":"348","DOI":"10.24875\/RIC.23000221","volume":"75","author":"P Galindo","year":"2023","unstructured":"Galindo P, Neyra JA. Continuous Renal Replacement Therapy: What have we learned and what are key milestones for the years to come? Rev Invest Clin. 2023;75(6):348\u201358. https:\/\/doi.org\/10.24875\/RIC.23000221.","journal-title":"Rev Invest Clin"},{"issue":"6","key":"2305_CR6","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1038\/s41581-023-00683-3","volume":"19","author":"A Zarbock","year":"2023","unstructured":"Zarbock A, Nadim MK, Pickkers P, et al. Sepsis-associated acute kidney injury: consensus report of the 28th Acute Disease Quality Initiative workgroup. Nat Rev Nephrol. 2023;19(6):401\u201317. https:\/\/doi.org\/10.1038\/s41581-023-00683-3.","journal-title":"Nat Rev Nephrol"},{"issue":"5","key":"2305_CR7","doi-asserted-by":"publisher","first-page":"649","DOI":"10.1053\/j.ajkd.2013.02.349","volume":"61","author":"PM Palevsky","year":"2013","unstructured":"Palevsky PM, Liu KD, Brophy PD, et al. KDOQI US commentary on the 2012 KDIGO clinical practice guideline for acute kidney injury. Am J Kidney Dis. 2013;61(5):649\u201372. https:\/\/doi.org\/10.1053\/j.ajkd.2013.02.349.","journal-title":"Am J Kidney Dis"},{"issue":"4","key":"2305_CR8","doi-asserted-by":"publisher","first-page":"564","DOI":"10.1097\/SHK.0000000000001768","volume":"56","author":"H Hu","year":"2021","unstructured":"Hu H, Li L, Zhang Y, et al. A prediction model for assessing prognosis in critically ill patients with sepsis-associated acute kidney injury. Shock. 2021;56(4):564\u201372. https:\/\/doi.org\/10.1097\/SHK.0000000000001768.","journal-title":"Shock"},{"issue":"10","key":"2305_CR9","doi-asserted-by":"publisher","first-page":"e2019209","DOI":"10.1001\/jamanetworkopen.2020.19209","volume":"3","author":"M Ostermann","year":"2020","unstructured":"Ostermann M, Zarbock A, Goldstein S, et al. Recommendations on acute kidney injury biomarkers from the acute disease quality initiative consensus conference: a consensus statement. JAMA Netw Open. 2020;3(10):e2019209. https:\/\/doi.org\/10.1001\/jamanetworkopen.2020.19209.","journal-title":"JAMA Netw Open"},{"key":"2305_CR10","doi-asserted-by":"publisher","first-page":"1500995","DOI":"10.3389\/fmed.2024.1500995","volume":"11","author":"H Xu","year":"2024","unstructured":"Xu H, Mo R, Liu Y, Niu H, Cai X, He P. L-shaped association between triglyceride-glucose body mass index and short-term mortality in ICU patients with sepsis-associated acute kidney injury. Front Med (Lausanne). 2024;11:1500995. https:\/\/doi.org\/10.3389\/fmed.2024.1500995.","journal-title":"Front Med (Lausanne)"},{"issue":"2","key":"2305_CR11","doi-asserted-by":"publisher","first-page":"371","DOI":"10.34067\/KID.0004912020","volume":"2","author":"JA Neyra","year":"2020","unstructured":"Neyra JA, Yessayan L, Thompson Bastin ML, Wille KM, Tolwani AJ. How to prescribe and troubleshoot continuous renal replacement therapy: a case-based review. Kidney360. 2020;2(2):371\u201384. https:\/\/doi.org\/10.34067\/KID.0004912020.","journal-title":"Kidney360"},{"issue":"1","key":"2305_CR12","doi-asserted-by":"publisher","first-page":"30","DOI":"10.5005\/jp-journals-10071-24595","volume":"28","author":"AKA Taha","year":"2024","unstructured":"Taha AKA, Shigidi MMT, Abdulfatah NM, Alsayed RK. The use of sustained low-efficiency dialysis in the treatment of sepsis-associated acute kidney injury in a low-income country: a Prospective Cohort Study. Indian J Crit Care Med. 2024;28(1):30\u20135. https:\/\/doi.org\/10.5005\/jp-journals-10071-24595.","journal-title":"Indian J Crit Care Med"},{"issue":"1","key":"2305_CR13","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1093\/ndt\/gfad142","volume":"39","author":"A Zarbock","year":"2023","unstructured":"Zarbock A, Koyner JL, Gomez H, Pickkers P, Forni L, Acute Disease Quality Initiative Group. Sepsis-associated acute kidney injury\u2014treatment standard. Nephrol Dial Transpl. 2023;39(1):26\u201335. https:\/\/doi.org\/10.1093\/ndt\/gfad142.","journal-title":"Nephrol Dial Transpl"},{"issue":"1","key":"2305_CR14","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1016\/j.cell.2020.03.022","volume":"181","author":"J Goecks","year":"2020","unstructured":"Goecks J, Jalili V, Heiser LM, Gray JW. How machine learning will transform biomedicine. Cell. 2020;181(1):92\u2013101. https:\/\/doi.org\/10.1016\/j.cell.2020.03.022.","journal-title":"Cell"},{"issue":"1","key":"2305_CR15","doi-asserted-by":"publisher","first-page":"282","DOI":"10.1186\/s12911-025-03132-2","volume":"25","author":"D Topalo\u011flu","year":"2025","unstructured":"Topalo\u011flu D, Polat O. Trends and methods in intensive care unit (ICU) research using machine learning: latent dirichlet allocation (LDA)-based thematic literature review. BMC Med Inf Decis Mak. 2025;25(1):282. https:\/\/doi.org\/10.1186\/s12911-025-03132-2.","journal-title":"BMC Med Inf Decis Mak"},{"issue":"5","key":"2305_CR16","doi-asserted-by":"publisher","first-page":"e35293","DOI":"10.2196\/35293","volume":"10","author":"C Barboi","year":"2022","unstructured":"Barboi C, Tzavelis A, Muhammad LN. Comparison of severity of illness scores and artificial intelligence models that are predictive of intensive care unit mortality: meta-analysis and review of the literature. JMIR Med Inf. 2022;10(5):e35293. https:\/\/doi.org\/10.2196\/35293.","journal-title":"JMIR Med Inf"},{"issue":"1","key":"2305_CR17","doi-asserted-by":"publisher","first-page":"478","DOI":"10.1186\/s13054-020-03179-9","volume":"24","author":"PY Tseng","year":"2020","unstructured":"Tseng PY, Chen YT, Wang CH, et al. Prediction of the development of acute kidney injury following cardiac surgery by machine learning. Crit Care. 2020;24(1):478. https:\/\/doi.org\/10.1186\/s13054-020-03179-9.","journal-title":"Crit Care"},{"issue":"1","key":"2305_CR18","doi-asserted-by":"publisher","first-page":"2388709","DOI":"10.1080\/07853890.2024.2388709","volume":"56","author":"Y Wang","year":"2024","unstructured":"Wang Y, Sun X, Lu J, Zhong L, Yang Z. Construction and evaluation of a mortality prediction model for patients with acute kidney injury undergoing continuous renal replacement therapy based on machine learning algorithms. Ann Med. 2024;56(1):2388709. https:\/\/doi.org\/10.1080\/07853890.2024.2388709.","journal-title":"Ann Med"},{"issue":"1","key":"2305_CR19","doi-asserted-by":"publisher","first-page":"2561794","DOI":"10.1080\/07853890.2025.2561794","volume":"57","author":"L Zhong","year":"2025","unstructured":"Zhong L, Liang Y, Xu L, Ji XW, Xie B, Yang XH. Construction and evaluation of prediction model for renal function recovery in acute kidney injury patients undergoing continuous renal replacement therapy based on machine learning algorithms. Ann Med. 2025;57(1):2561794. https:\/\/doi.org\/10.1080\/07853890.2025.2561794.","journal-title":"Ann Med"},{"key":"2305_CR20","doi-asserted-by":"publisher","first-page":"853102","DOI":"10.3389\/fmed.2022.853102","volume":"9","author":"XQ Luo","year":"2022","unstructured":"Luo XQ, Yan P, Duan SB, et al. Development and validation of machine learning models for real-time mortality prediction in critically ill patients with sepsis-associated acute kidney injury. Front Med (Lausanne). 2022;9:853102. https:\/\/doi.org\/10.3389\/fmed.2022.853102.","journal-title":"Front Med (Lausanne)"},{"issue":"1","key":"2305_CR21","doi-asserted-by":"publisher","first-page":"406","DOI":"10.1186\/s12967-023-04205-4","volume":"21","author":"Z Fan","year":"2023","unstructured":"Fan Z, Jiang J, Xiao C, et al. Construction and validation of prognostic models in critically ill patients with sepsis-associated acute kidney injury: interpretable machine learning approach. J Transl Med. 2023;21(1):406. https:\/\/doi.org\/10.1186\/s12967-023-04205-4.","journal-title":"J Transl Med"},{"issue":"5","key":"2305_CR22","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1038\/s42256-019-0048-x","volume":"1","author":"C Rudin","year":"2019","unstructured":"Rudin C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat Mach Intell. 2019;1(5):206\u201315. https:\/\/doi.org\/10.1038\/s42256-019-0048-x.","journal-title":"Nat Mach Intell"},{"key":"2305_CR23","doi-asserted-by":"publisher","first-page":"1483710","DOI":"10.3389\/fmed.2024.1483710","volume":"11","author":"C Zhuang","year":"2025","unstructured":"Zhuang C, Hu R, Li K, et al. Machine learning prediction models for mortality risk in sepsis-associated acute kidney injury: evaluating early versus late CRRT initiation. Front Med (Lausanne). 2025;11:1483710. https:\/\/doi.org\/10.3389\/fmed.2024.1483710.","journal-title":"Front Med (Lausanne)"},{"issue":"1","key":"2305_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41597-022-01899-x","volume":"10","author":"AEW Johnson","year":"2023","unstructured":"Johnson AEW, Bulgarelli L, Shen L, et al. MIMIC-IV, a freely accessible electronic health record dataset. Sci Data. 2023;10(1):1. https:\/\/doi.org\/10.1038\/s41597-022-01899-x.","journal-title":"Sci Data"},{"key":"2305_CR25","doi-asserted-by":"publisher","first-page":"180178","DOI":"10.1038\/sdata.2018.178","volume":"5","author":"TJ Pollard","year":"2018","unstructured":"Pollard TJ, Johnson AEW, Raffa JD, Celi LA, Mark RG, Badawi O. The eICU Collaborative Research Database, a freely available multi-center database for critical care research. Sci Data. 2018;5:180178. https:\/\/doi.org\/10.1038\/sdata.2018.178.","journal-title":"Sci Data"},{"issue":"8","key":"2305_CR26","doi-asserted-by":"publisher","first-page":"801","DOI":"10.1001\/jama.2016.0287","volume":"315","author":"M Singer","year":"2016","unstructured":"Singer M, Deutschman CS, Seymour CW, et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA. 2016;315(8):801\u201310. https:\/\/doi.org\/10.1001\/jama.2016.0287.","journal-title":"JAMA"},{"key":"2305_CR27","doi-asserted-by":"publisher","first-page":"m3919","DOI":"10.1136\/bmj.m3919","volume":"371","author":"Y Li","year":"2020","unstructured":"Li Y, Sperrin M, Ashcroft DM, van Staa TP. Consistency of variety of machine learning and statistical models in predicting clinical risks of individual patients: longitudinal cohort study using cardiovascular disease as exemplar. BMJ. 2020;371:m3919. https:\/\/doi.org\/10.1136\/bmj.m3919.","journal-title":"BMJ"},{"issue":"1","key":"2305_CR28","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1186\/s12911-025-03224-z","volume":"25","author":"ME Samadi","year":"2025","unstructured":"Samadi ME, Nikulina K, Fritsch SJ, Schuppert A. GPT-4o and the quest for machine learning interpretability in ICU risk of death prediction. BMC Med Inf Decis Mak. 2025;25(1):373. https:\/\/doi.org\/10.1186\/s12911-025-03224-z.","journal-title":"BMC Med Inf Decis Mak"},{"issue":"6","key":"2305_CR29","doi-asserted-by":"publisher","first-page":"1241","DOI":"10.1002\/bimj.202000128","volume":"63","author":"LE Bantis","year":"2021","unstructured":"Bantis LE, Nakas CT, Reiser B. Statistical inference for the difference between two maximized Youden indices obtained from correlated biomarkers. Biom J. 2021;63(6):1241\u201353. https:\/\/doi.org\/10.1002\/bimj.202000128.","journal-title":"Biom J"},{"issue":"1","key":"2305_CR30","doi-asserted-by":"publisher","first-page":"10922","DOI":"10.1038\/s41598-024-84435-8","volume":"15","author":"M Nuermaimaiti","year":"2025","unstructured":"Nuermaimaiti M, Wang M, Lou R, et al. The impact of initiation timing of continuous renal replacement therapy on outcomes in critically ill patients with acute kidney injury: a retrospective study from the MIMIC-IV database. Sci Rep. 2025;15(1):10922. https:\/\/doi.org\/10.1038\/s41598-024-84435-8.","journal-title":"Sci Rep"},{"issue":"5","key":"2305_CR31","doi-asserted-by":"publisher","first-page":"397","DOI":"10.1159\/000517281","volume":"51","author":"R Murugan","year":"2022","unstructured":"Murugan R, Kerti SJ, Chang CH, et al. Association between net ultrafiltration rate and renal recovery among critically ill adults with acute kidney injury receiving continuous renal replacement therapy: an observational cohort study. Blood Purif. 2022;51(5):397\u2013409. https:\/\/doi.org\/10.1159\/000517281.","journal-title":"Blood Purif"},{"key":"2305_CR32","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.jcrc.2020.07.012","volume":"60","author":"P Hansrivijit","year":"2020","unstructured":"Hansrivijit P, Yarlagadda K, Puthenpura MM, et al. A meta-analysis of clinical predictors for renal recovery and overall mortality in acute kidney injury requiring continuous renal replacement therapy. J Crit Care. 2020;60:13\u201322. https:\/\/doi.org\/10.1016\/j.jcrc.2020.07.012.","journal-title":"J Crit Care"},{"issue":"1","key":"2305_CR33","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1186\/s12882-023-03063-y","volume":"24","author":"SG Kim","year":"2023","unstructured":"Kim SG, Lee J, Yun D, et al. Hyperlactatemia is a predictor of mortality in patients undergoing continuous renal replacement therapy for acute kidney injury. BMC Nephrol. 2023;24(1):11. https:\/\/doi.org\/10.1186\/s12882-023-03063-y.","journal-title":"BMC Nephrol"},{"key":"2305_CR34","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1186\/s40560-015-0105-4","volume":"3","author":"J Chertoff","year":"2015","unstructured":"Chertoff J, Chisum M, Garcia B, Lascano J. Lactate kinetics in sepsis and septic shock: a review of the literature and rationale for further research. J Intensive Care. 2015;3:39. https:\/\/doi.org\/10.1186\/s40560-015-0105-4.","journal-title":"J Intensive Care"},{"key":"2305_CR35","doi-asserted-by":"publisher","first-page":"997853","DOI":"10.3389\/fimmu.2022.997853","volume":"13","author":"H Wu","year":"2022","unstructured":"Wu H, Liao B, Cao T, Ji T, Huang J, Ma K. Diagnostic value of RDW for the prediction of mortality in adult sepsis patients: a systematic review and meta-analysis. Front Immunol. 2022;13:997853. https:\/\/doi.org\/10.3389\/fimmu.2022.997853.","journal-title":"Front Immunol"},{"issue":"3","key":"2305_CR36","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1016\/j.jointm.2023.02.005","volume":"3","author":"H Lai","year":"2023","unstructured":"Lai H, Wu G, Zhong Y, et al. Red blood cell distribution width improves the prediction of 28-day mortality for patients with sepsis-induced acute kidney injury: A retrospective analysis from MIMIC-IV database using propensity score matching. J Intensive Med. 2023;3(3):275\u201382. https:\/\/doi.org\/10.1016\/j.jointm.2023.02.005.","journal-title":"J Intensive Med"},{"key":"2305_CR37","doi-asserted-by":"publisher","first-page":"2301","DOI":"10.2147\/DMSO.S377650","volume":"15","author":"M Mo","year":"2022","unstructured":"Mo M, Huang Z, Huo D, et al. Influence of red blood cell distribution width on all-cause death in critical diabetic patients with acute kidney injury. Diabetes Metab Syndr Obes. 2022;15:2301\u20139. https:\/\/doi.org\/10.2147\/DMSO.S377650.","journal-title":"Diabetes Metab Syndr Obes"}],"container-title":["Clinical and Experimental Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10238-026-02305-1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10238-026-02305-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10238-026-02305-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T16:04:06Z","timestamp":1790784246000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10238-026-02305-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,2]]},"references-count":37,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["2305"],"URL":"https:\/\/doi.org\/10.1007\/s10238-026-02305-1","relation":{},"ISSN":["1591-9528"],"issn-type":[{"value":"1591-9528","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,2]]},"assertion":[{"value":"29 January 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 August 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 September 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The study was conducted in accordance with the Declaration of Helsinki. This study used data from the Medical Information Mart for Intensive Care IV (MIMIC\u2013IV) and the eICU Collaborative Research Database (eICU\u2013CRD), both of which contain fully de-identified patient information. As all data were anonymized and publicly available, ethical approval and informed consent were waived according to institutional and national guidelines. Access to the databases was granted after completion of the required data use agreements and training.","order":2,"name":"Ethics","label":"Ethics Statement","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"345"}}