{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T13:02:14Z","timestamp":1790341334981,"version":"4.1.0"},"reference-count":39,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T00:00:00Z","timestamp":1790294400000},"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. Nutr."],"abstract":"<jats:sec>\n                    <jats:title>Background and aims<\/jats:title>\n                    <jats:p>Refeeding syndrome (RFS) is a common and serious metabolic reaction during nutritional support in critically ill patients, associated with substantially increased mortality risk and poor prognosis. Existing studies mainly focus on predicting its occurrence, while predictive models for 28-day mortality risk remain lacking. This study aimed to develop and validate an interpretable machine learning model to support short-term prognostic assessment in clinical practice.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Data for critically ill patients with RFS were extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV, 2008\u20132019) as the internal cohort. A total of 8,705 patients were randomly assigned to training and test sets at a 7:3 ratio. Thirty-eight candidate variables were initially included, and feature selection was performed using least absolute shrinkage and selection operator (LASSO) logistic regression. Three machine learning models were subsequently constructed, and the optimal model was validated using two independent datasets. Model interpretability was enhanced using Shapley additive explanations (SHAP). Kaplan\u2013Meier curves were used to compare survival between the high- and low-risk, and nomograms were constructed to facilitate clinical application.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Nine variables were ultimately selected to construct the logistic regression model. The model demonstrated excellent discriminative performance and generalizability, with area under the receiver operating characteristic curve (AUC) values of 0.821, 0.809, and 0.731 in the internal test set, external validation set I (MIMIC-III), and external validation set II (the Affiliated Hospital of Yangzhou University), respectively. SHAP analysis identified partial pressure of oxygen, mean blood glucose level, and age as the key prognostic factors and further revealed nonlinear effects among the variables. Kaplan\u2013Meier survival curves confirmed that the model significantly distinguished between the high- and low-risk groups across all cohorts (all\n                      <jats:italic>p<\/jats:italic>\n                      \u202f&amp;lt;\u202f0.05). Decision curve analysis showed that the model provided favorable clinical net benefit in both external validation cohorts.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The interpretable machine learning model developed in this study effectively predicts the 28-day mortality risk of RFS in critically ill patients, provides an objective reference for individualized clinical decision-making, and may help improve patient prognosis.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fnut.2026.1888609","type":"journal-article","created":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T12:43:02Z","timestamp":1790340182000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Construction and validation of a 28-day mortality prediction model for refeeding syndrome in critically ill patients"],"prefix":"10.3389","volume":"13","author":[{"given":"Jiajia","family":"Pan","sequence":"first","affiliation":[{"name":"Department of Intensive Care Unit, Affiliated Hospital of Yangzhou University","place":["Yangzhou, Jiangsu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuanle","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Intensive Care Unit, Affiliated Hospital of Yangzhou University","place":["Yangzhou, Jiangsu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nina","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Intensive Care Unit, Affiliated Hospital of Yangzhou University","place":["Yangzhou, Jiangsu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangfa","family":"Wei","sequence":"additional","affiliation":[{"name":"Department of Intensive Care Unit, Affiliated Hospital of Yangzhou University","place":["Yangzhou, Jiangsu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuyan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Intensive Care Unit, Affiliated Hospital of Yangzhou University","place":["Yangzhou, Jiangsu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qining","family":"Zheng","sequence":"additional","affiliation":[{"name":"Department of Intensive Care Unit, Affiliated Hospital of Yangzhou University","place":["Yangzhou, Jiangsu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Intensive Care Unit, Affiliated Hospital of Yangzhou University","place":["Yangzhou, Jiangsu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weili","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Intensive Care Unit, Affiliated Hospital of Yangzhou University","place":["Yangzhou, Jiangsu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,25]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1002\/ncp.11259","article-title":"A deeper look into the history of refeeding syndrome","volume":"40","author":"Mohajir","year":"2025","journal-title":"Nutr Clin Pract"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"36198","DOI":"10.34117\/bjdv7n4-199","article-title":"S\u00edndrome de realimenta\u00e7\u00e3o em pacientes hospitalizados \u2013 uma condi\u00e7\u00e3o que requer vigil\u00e2ncia \/ refeeding syndrome in hospitalized patients \u2013 a condition that requires surveillance","volume":"7","author":"Dias","year":"2021","journal-title":"Braz J Dev"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1002\/ncp.10474","article-title":"ASPEN consensus recommendations for refeeding syndrome","volume":"35","author":"da Silva","year":"2020","journal-title":"Nutr Clin Pract"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"1866","DOI":"10.3390\/nu17111866","article-title":"Understanding refeeding syndrome in critically ill patients: a narrative review","volume":"17","author":"Borriello","year":"2025","journal-title":"Nutrients"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"112562","DOI":"10.1016\/j.nut.2024.112562","article-title":"Nutritional therapy in intensive care unit inpatients at risk for refeeding syndrome: a systematic review","volume":"128","author":"Alencar","year":"2024","journal-title":"Nutrition"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"1009","DOI":"10.1016\/j.amjmed.2021.03.010","article-title":"Impact of refeeding syndrome on short- and medium-term all-cause mortality: a systematic review and meta-analysis","volume":"134","author":"Bioletto","year":"2021","journal-title":"Am J Med"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1097\/MCO.0000000000000449","article-title":"Is refeeding syndrome relevant for critically ill patients?","volume":"21","author":"Koekkoek","year":"2018","journal-title":"Curr Opin Clin Nutr Metab Care"},{"key":"ref8","doi-asserted-by":"publisher","first-page":"1495","DOI":"10.1136\/bmj.a301","article-title":"Refeeding syndrome: what it is, and how to prevent and treat it","volume":"336","author":"Mehanna","year":"2008","journal-title":"BMJ"},{"key":"ref9","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.nut.2017.09.007","article-title":"Management and prevention of refeeding syndrome in medical inpatients: an evidence-based and consensus-supported algorithm","volume":"47","author":"Friedli","year":"2018","journal-title":"Nutrition"},{"key":"ref10","doi-asserted-by":"publisher","first-page":"e002173","DOI":"10.1136\/bmjopen-2012-002173","article-title":"Occurrence of refeeding syndrome in adults started on artificial nutrition support: prospective cohort study","volume":"3","author":"Rio","year":"2013","journal-title":"BMJ Open"},{"key":"ref11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41597-022-01899-x","article-title":"MIMIC-IV, a freely accessible electronic health record dataset","volume":"10","author":"Johnson","year":"2023","journal-title":"Sci Data"},{"key":"ref12","doi-asserted-by":"publisher","first-page":"994359","DOI":"10.3389\/fcvm.2022.994359","article-title":"Interpretable machine learning for 28-day all-cause in-hospital mortality prediction in critically ill patients with heart failure combined with hypertension: a retrospective cohort study based on medical information mart for intensive care database-IV and eICU databases","volume":"9","author":"Peng","year":"2022","journal-title":"Front Cardiovasc Med"},{"key":"ref13","doi-asserted-by":"publisher","first-page":"7141216","DOI":"10.1155\/2022\/7141216","article-title":"The hemoglobin-to-red cell distribution width ratio to predict all-cause mortality in patients with sepsis-associated encephalopathy in the MIMIC-IV database","volume":"2022","author":"Huang","year":"2022","journal-title":"Int J Clin Pract"},{"key":"ref14","doi-asserted-by":"publisher","first-page":"2303395","DOI":"10.1080\/0886022X.2024.2303395","article-title":"Predictive model of acute kidney injury in critically ill patients with acute pancreatitis: a machine learning approach using the MIMIC-IV database","volume":"46","author":"Lin","year":"2024","journal-title":"Ren Fail"},{"key":"ref15","doi-asserted-by":"publisher","first-page":"897611","DOI":"10.3389\/fnagi.2022.897611","article-title":"Machine learning prediction models for postoperative stroke in elderly patients: analyses of the MIMIC database","volume":"14","author":"Zhang","year":"2022","journal-title":"Front Aging Neurosci"},{"key":"ref16","doi-asserted-by":"publisher","first-page":"1322","DOI":"10.1016\/j.cjca.2020.11.010","article-title":"Missing data in clinical research: a tutorial on multiple imputation","volume":"37","author":"Austin","year":"2021","journal-title":"Can J Cardiol"},{"key":"ref17","doi-asserted-by":"publisher","first-page":"1327","DOI":"10.1093\/cvr\/cvae123","article-title":"Sex inequalities in cardiovascular risk prediction","volume":"120","author":"Elliott","year":"2024","journal-title":"Cardiovasc Res"},{"key":"ref18","doi-asserted-by":"publisher","first-page":"108589","DOI":"10.1016\/j.cmpb.2025.108589","article-title":"Machine learning-based 28-day mortality prediction model for elderly neurocritically ill patients","volume":"260","author":"Yuan","year":"2025","journal-title":"Comput Methods Prog Biomed"},{"key":"ref19","doi-asserted-by":"publisher","first-page":"796","DOI":"10.1016\/j.eururo.2018.08.038","article-title":"Reporting and interpreting decision curve analysis: a guide for investigators","volume":"74","author":"Van Calster","year":"2018","journal-title":"Eur Urol"},{"key":"ref20","doi-asserted-by":"publisher","first-page":"647","DOI":"10.1007\/s10115-013-0679-x","article-title":"Explaining prediction models and individual predictions with feature contributions","volume":"41","author":"\u0160trumbelj","year":"2014","journal-title":"Knowl Inf Syst"},{"key":"ref21","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1186\/s12933-025-03073-0","article-title":"Joint association of estimated glucose disposal rate and aggregate index of systemic inflammation with mortality in general population: a nationwide prospective cohort study","volume":"25","author":"Yuan","year":"2026","journal-title":"Cardiovasc Diabetol"},{"key":"ref22","doi-asserted-by":"publisher","first-page":"110281","DOI":"10.1016\/j.isci.2024.110281","article-title":"Explainable machine learning for predicting 30-day readmission in acute heart failure patients","volume":"27","author":"Zhang","year":"2024","journal-title":"iScience"},{"key":"ref23","doi-asserted-by":"publisher","first-page":"909","DOI":"10.1007\/s11739-024-03732-2","article-title":"Early sepsis mortality prediction model based on interpretable machine learning approach: development and validation study","volume":"20","author":"Wang","year":"2025","journal-title":"Intern Emerg Med"},{"key":"ref24","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1186\/s13054-023-04579-3","article-title":"Elevated MMP-8 levels, inversely associated with BMI, predict mortality in mechanically ventilated patients: an observational multicenter study","volume":"27","author":"Ruan","year":"2023","journal-title":"Crit Care"},{"key":"ref25","doi-asserted-by":"publisher","first-page":"2623","DOI":"10.1097\/CCM.0000000000001310","article-title":"Being overweight is associated with greater survival in ICU patients: results from the intensive care over nations audit","volume":"43","author":"Sakr","year":"2015","journal-title":"Crit Care Med"},{"key":"ref26","doi-asserted-by":"publisher","first-page":"2859","DOI":"10.3390\/nu14142859","article-title":"Refeeding syndrome: a critical reality in patients with chronic disease","volume":"14","author":"Krutkyte","year":"2022","journal-title":"Nutrients"},{"key":"ref27","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1111\/1747-0080.70003","article-title":"The Australasian Society of Parenteral and Enteral Nutrition: consensus statements on refeeding syndrome","volume":"82","author":"Matthews-Rensch","year":"2025","journal-title":"Nutr Diet"},{"key":"ref28","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1177\/0115426505020006625","article-title":"Review of the refeeding syndrome","volume":"20","author":"Kraft","year":"2005","journal-title":"Nutr Clin Pract"},{"key":"ref29","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1016\/j.nut.2009.11.017","article-title":"Refeeding syndrome: treatment considerations based on collective analysis of literature case reports","volume":"26","author":"Boateng","year":"2010","journal-title":"Nutrition"},{"key":"ref30","author":"Persaud-Sharma","year":"2025"},{"key":"ref31","doi-asserted-by":"publisher","first-page":"3008","DOI":"10.1097\/CCM.0b013e31818b38d2","article-title":"Glycemic variability: a strong independent predictor of mortality in critically ill patients","volume":"36","author":"Krinsley","year":"2008","journal-title":"Crit Care Med"},{"key":"ref32","doi-asserted-by":"publisher","first-page":"796","DOI":"10.1186\/s12872-025-05257-8","article-title":"Stress hyperglycemia ratio and mortality in critically ill patients with heart failure: a retrospective cohort study from the MIMIC-IV database","volume":"25","author":"Jiang","year":"2025","journal-title":"BMC Cardiovasc Disord"},{"key":"ref33","doi-asserted-by":"publisher","first-page":"255","DOI":"10.2147\/CEG.S136429","article-title":"Refeeding syndrome in the frail elderly population: prevention, diagnosis and management","volume":"11","author":"Aubry","year":"2018","journal-title":"Clin Exp Gastroenterol"},{"key":"ref34","doi-asserted-by":"publisher","first-page":"6233","DOI":"10.2147\/IJGM.S544989","article-title":"Development and internal validation of a clinical prediction model for refeeding syndrome in adult intensive care unit patients: a retrospective observational study","volume":"18","author":"Zhan","year":"2025","journal-title":"Int J Gen Med"},{"key":"ref35","doi-asserted-by":"publisher","first-page":"282","DOI":"10.1016\/j.clnu.2025.11.006","article-title":"Development and validation of a risk prediction model for refeeding syndrome in adults with critical illness: a prospective observational study","volume":"55","author":"Jing","year":"2025","journal-title":"Clin Nutr"},{"key":"ref36","doi-asserted-by":"publisher","first-page":"1083483","DOI":"10.3389\/fnut.2023.1083483","article-title":"Development and validation of risk prediction model for refeeding syndrome in neurocritical patients","volume":"10","author":"Zhang","year":"2023","journal-title":"Front Nutr"},{"key":"ref37","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1016\/j.clnesp.2021.08.022","article-title":"Explainable machine learning model to predict refeeding hypophosphatemia","volume":"45","author":"Choi","year":"2021","journal-title":"Clinical Nutrition ESPEN"},{"key":"ref38","doi-asserted-by":"publisher","first-page":"102946","DOI":"10.1016\/j.clnesp.2026.102946","article-title":"Interpretable machine learning model for predicting refeeding syndrome after colorectal cancer surgery","volume":"72","author":"Jin","year":"2026","journal-title":"Clin Nutr ESPEN"},{"key":"ref39","doi-asserted-by":"publisher","first-page":"1741052","DOI":"10.3389\/fnut.2025.1741052","article-title":"Explainable machine learning model to predict refeeding syndrome in patients with severe acute pancreatitis","volume":"12","author":"Wu","year":"2025","journal-title":"Front Nutr"}],"container-title":["Frontiers in Nutrition"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnut.2026.1888609\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T12:43:03Z","timestamp":1790340183000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnut.2026.1888609\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,25]]},"references-count":39,"alternative-id":["10.3389\/fnut.2026.1888609"],"URL":"https:\/\/doi.org\/10.3389\/fnut.2026.1888609","relation":{},"ISSN":["2296-861X"],"issn-type":[{"value":"2296-861X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,25]]},"article-number":"1888609"}}