{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T17:06:49Z","timestamp":1783357609222,"version":"3.54.6"},"reference-count":30,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T00:00:00Z","timestamp":1746662400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>At present, there are no specialized models for predicting mortality risk in patients with alcoholic cirrhosis complicated by severe acute kidney injury (AKI) in the ICU. This study aims to develop and validate machine learning models to predict the mortality risk of this population during hospitalization.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>A retrospective analysis was conducted on 856 adult patients with alcoholic cirrhosis complicated by severe AKI, utilizing data from the MIMIC-IV database. Within the dataset, 627 patients from the period 2008\u20132016 were designated as the training cohort, whereas 229 patients from 2017 to 2019 comprised the temporal external validation cohort. Feature selection was conducted utilizing LASSO regression, which was subsequently followed by the development of eight distinct machine learning models. The performance of these models in the temporal external validation cohort was rigorously assessed using the area under the receiver operating characteristic curve (AUROC) to determine the optimal model. The model was interpreted using the SHAP method, and nomograms were subsequently constructed. A comprehensive evaluation was performed from the perspectives of discrimination (assessed via AUROC and AUPRC), calibration (using calibration curves), and clinical utility (evaluated through DCA curves).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>LASSO regression identified nine key features: total bilirubin, acute respiratory failure, vasopressin, septic shock, oliguria, AKI stage, lactate, fresh frozen plasma transfusion, and norepinephrine. In the temporal external validation cohort, the Lasso-LR model achieved the highest AUROC value of 0.809, establishing it as the optimal model. We developed both a static nomogram and a web-based dynamic nomogram (<jats:ext-link>https:\/\/zhangjingyu123456.shinyapps.io\/dynnomapp\/<\/jats:ext-link>) for visualization purposes. In the nomogram model, the AUROC for the training cohort and temporal external validation cohort were 0.836 (95% CI: 0.802-0.870) and 0.809 (95% CI: 0.754\u20130.865), respectively. The calibration slope and Brier score for the training cohort were 1.000 and 0.146, respectively; for the temporal external validation cohort, these values were 0.808 and 0.177, respectively. The DCA curves indicate that the model has certain clinical application value.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The Lasso-LR model exhibits robust predictive capability for in-hospital mortality among patients with alcoholic cirrhosis complicated by AKI, offering valuable prognostic insights and individualized treatment decision support for healthcare professionals.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fmed.2025.1570928","type":"journal-article","created":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T05:26:19Z","timestamp":1746681979000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Predicting in-hospital mortality in patients with alcoholic cirrhosis complicated by severe acute kidney injury: development and validation of an explainable machine learning model"],"prefix":"10.3389","volume":"12","author":[{"given":"Meina","family":"Sun","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shihui","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Min","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Zhong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinyu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhian","family":"Du","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,5,8]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"1055137","DOI":"10.3389\/fmed.2023.1055137","article-title":"Development and validation of a dynamic online nomogram for predicting acute kidney injury in cirrhotic patients upon ICU admission.","volume":"10","author":"Feng","year":"2023","journal-title":"Front Med (Lausanne)."},{"key":"B2","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.jceh.2022.09.001","article-title":"Epidemiology and disease burden of alcohol associated liver disease.","volume":"13","author":"Aslam","year":"2023","journal-title":"J Clin Exp Hepatol."},{"key":"B3","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1080\/17474124.2024.2380299","article-title":"Incidence and risk factors of acute kidney injury in cirrhosis: A systematic review and meta-analysis of 5,202,232 outpatients, inpatients, and ICU-admitted patients.","volume":"18","author":"Lekakis","year":"2024","journal-title":"Expert Rev Gastroenterol Hepatol."},{"key":"B4","doi-asserted-by":"publisher","first-page":"778","DOI":"10.1111\/apt.18159","article-title":"Outcomes of patients with alcohol-associated hepatitis and acute kidney injury - Results from the HRS Harmony Consortium.","volume":"60","author":"Ma","year":"2024","journal-title":"Aliment Pharmacol Ther."},{"key":"B5","doi-asserted-by":"publisher","first-page":"497","DOI":"10.1097\/MEG.0000000000002520","article-title":"Acute kidney injury development and impact on clinical and economic outcomes in patients with cirrhosis: An observational cohort study over a 10-year period.","volume":"35","author":"Alshogran","year":"2023","journal-title":"Eur J Gastroenterol Hepatol."},{"key":"B6","doi-asserted-by":"publisher","first-page":"135","DOI":"10.14218\/JCTH.2019.00060","article-title":"Incidence, mortality and predictors of acute kidney injury in patients with cirrhosis: A systematic review and meta-analysis.","volume":"8","author":"Tariq","year":"2020","journal-title":"J Clin Transl Hepatol."},{"key":"B7","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1186\/s12882-024-03609-8","article-title":"Development and validation of a nomogram for predicting in-hospital death in cirrhotic patients with acute kidney injury.","volume":"25","author":"Li","year":"2024","journal-title":"BMC Nephrol."},{"key":"B8","doi-asserted-by":"publisher","first-page":"e33074","DOI":"10.1097\/MD.0000000000033074","article-title":"Factors predicting mortality in patients with alcoholic liver cirrhosis visiting the emergency department.","volume":"102","author":"Jeong","year":"2023","journal-title":"Medicine (Baltimore)."},{"key":"B9","doi-asserted-by":"publisher","first-page":"1355","DOI":"10.1016\/j.jhep.2021.07.018","article-title":"Performance of the model for end-stage liver disease score for mortality prediction and the potential role of etiology.","volume":"75","author":"D\u2019Amico","year":"2021","journal-title":"J Hepatol."},{"key":"B10","doi-asserted-by":"publisher","first-page":"1199","DOI":"10.1080\/21505594.2021.1909894","article-title":"High Child-Pugh and CRUB65 scores predict mortality of decompensated cirrhosis patients with COVID-19: A 23-center, retrospective study.","volume":"12","author":"Xiao","year":"2021","journal-title":"Virulence."},{"key":"B11","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1007\/s11739-023-03436-z","article-title":"Development and validation of prognostic nomogram for cirrhotic patients with acute kidney injury upon ICU admission.","volume":"19","author":"Liao","year":"2024","journal-title":"Intern Emerg Med."},{"key":"B12","doi-asserted-by":"publisher","first-page":"3799","DOI":"10.3390\/cancers16223799","article-title":"Mortality prediction modeling for patients with breast cancer based on explainable machine learning.","volume":"16","author":"Park","year":"2024","journal-title":"Cancers (Basel)."},{"key":"B13","doi-asserted-by":"publisher","first-page":"E215","DOI":"10.1161\/01.cir.101.23.e215","article-title":"PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals.","volume":"101","author":"Goldberger","year":"2000","journal-title":"Circulation."},{"key":"B14","doi-asserted-by":"publisher","first-page":"344","DOI":"10.1016\/j.jclinepi.2007.11.008","article-title":"The Strengthening the reporting of observational studies in epidemiology (STROBE) statement: Guidelines for reporting observational studies.","volume":"61","author":"von Elm","year":"2008","journal-title":"J Clin Epidemiol."},{"key":"B15","doi-asserted-by":"publisher","first-page":"c179","DOI":"10.1159\/000339789","article-title":"KDIGO clinical practice guidelines for acute kidney injury.","volume":"120","author":"Khwaja","year":"2012","journal-title":"Nephron Clin Pract."},{"key":"B16","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1007\/s00345-024-05018-w","article-title":"Impact of severe acute kidney injury on short-term mortality in urosepsis.","volume":"42","author":"Fujita","year":"2024","journal-title":"World J. Urol."},{"key":"B17","doi-asserted-by":"publisher","first-page":"463","DOI":"10.1007\/s11739-019-02217-x","article-title":"Electrocardiographic T wave alterations and prediction of hyperkalemia in patients with acute kidney injury.","volume":"15","author":"Regolisti","year":"2020","journal-title":"Internal Emerg Med."},{"key":"B18","doi-asserted-by":"publisher","first-page":"1817","DOI":"10.1007\/s11845-024-03663-z","article-title":"Study of prevalence, risk factors for acute kidney injury, and mortality in liver cirrhosis patients.","volume":"193","author":"Mohan","year":"2024","journal-title":"Ir J Med Sci."},{"key":"B19","doi-asserted-by":"publisher","first-page":"1103","DOI":"10.1038\/ajg.2017.122","article-title":"Acute kidney injury in cirrhosis: Baseline serum creatinine predicts patient outcomes.","volume":"112","author":"Wong","year":"2017","journal-title":"Am J Gastroenterol."},{"key":"B20","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1159\/000496066","article-title":"Hyperbilirubinemia induces pro-apoptotic effects and aggravates renal ischemia reperfusion injury.","volume":"142","author":"Yuan","year":"2019","journal-title":"Nephron."},{"key":"B21","doi-asserted-by":"publisher","first-page":"e2133094","DOI":"10.1001\/jamanetworkopen.2021.33094","article-title":"Association of oliguria with acute kidney injury diagnosis, severity assessment, and mortality among patients with critical illness.","volume":"4","author":"Bianchi","year":"2021","journal-title":"JAMA Netw Open."},{"key":"B22","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1186\/s13054-022-04086-x","article-title":"Acute kidney injury in critical COVID-19: A multicenter cohort analysis in seven large hospitals in Belgium.","volume":"26","author":"Schaubroeck","year":"2022","journal-title":"Crit Care."},{"key":"B23","doi-asserted-by":"publisher","first-page":"1055","DOI":"10.1097\/MEG.0000000000001184","article-title":"Serum sodium, model for end-stage liver disease, and a recent invasive procedure are risk factors for severe acute-on-chronic liver failure and death in cirrhotic patients hospitalized with bacterial infection.","volume":"30","author":"Lopes-Secundo","year":"2018","journal-title":"Eur J Gastroenterol Hepatol."},{"key":"B24","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1186\/s12950-021-00270-y","article-title":"Overexpression of SP1 restores autophagy to alleviate acute renal injury induced by ischemia-reperfusion through the miR-205\/PTEN\/Akt pathway.","volume":"18","author":"Huang","year":"2021","journal-title":"J Inflamm (Lond)."},{"key":"B25","doi-asserted-by":"publisher","first-page":"801","DOI":"10.1001\/jama.2016.0287","article-title":"The third international consensus definitions for sepsis and septic shock (Sepsis-3).","volume":"315","author":"Singer","year":"2016","journal-title":"JAMA."},{"key":"B26","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1186\/s12871-020-01171-y","article-title":"Pulse oximetry-based capillary refilling evaluation predicts postoperative outcomes in liver transplantation: A prospective observational cohort study.","volume":"20","author":"Yamamoto","year":"2020","journal-title":"BMC Anesthesiol."},{"key":"B27","doi-asserted-by":"publisher","first-page":"596","DOI":"10.3892\/etm.2021.10028","article-title":"Microcirculatory disturbance in acute liver injury.","volume":"21","author":"Kuwano","year":"2021","journal-title":"Exp Ther Med."},{"key":"B28","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1186\/s13613-022-01037-1","article-title":"Association between acute kidney injury and norepinephrine use following cardiac surgery: A retrospective propensity score-weighted analysis.","volume":"12","author":"Huette","year":"2022","journal-title":"Ann Intensive Care."},{"key":"B29","doi-asserted-by":"publisher","first-page":"738","DOI":"10.1016\/j.dld.2020.12.009","article-title":"Hyperkalemia influences the outcome of patients with cirrhosis with acute decompensation (AD) and acute-on-chronic liver failure (ACLF).","volume":"53","author":"Mezzano","year":"2021","journal-title":"Dig Liver Dis."},{"key":"B30","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1097\/LVT.0000000000000008","article-title":"The prognostic impact of acute kidney injury recovery patterns in critically ill patients with cirrhosis.","volume":"29","author":"Worden","year":"2023","journal-title":"Liver Transpl."}],"container-title":["Frontiers in Medicine"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fmed.2025.1570928\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T05:26:23Z","timestamp":1746681983000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fmed.2025.1570928\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,8]]},"references-count":30,"alternative-id":["10.3389\/fmed.2025.1570928"],"URL":"https:\/\/doi.org\/10.3389\/fmed.2025.1570928","relation":{},"ISSN":["2296-858X"],"issn-type":[{"value":"2296-858X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,8]]},"article-number":"1570928"}}