{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T23:53:35Z","timestamp":1789084415125,"version":"build-2803163510"},"reference-count":32,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"The Tangshan City Municipal-level Science and Technology Project","award":["25120204C"],"award-info":[{"award-number":["25120204C"]}]},{"name":"the Hebei Province \u201c333 Talent Project\u201d","award":["C2024071"],"award-info":[{"award-number":["C2024071"]}]},{"name":"the Hebei Provincial Medical Scientific Research Project Plan","award":["20231803"],"award-info":[{"award-number":["20231803"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["DIGITAL HEALTH"],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Critically ill patients with digestive system tumors are at high risk of acute kidney injury (AKI), a complication strongly associated with increased mortality and adverse outcomes. However, early AKI risk identification in the intensive care unit (ICU) remains challenging. This study aimed to develop and externally validate an interpretable model for early AKI risk prediction in critically ill patients with digestive system tumors.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We retrospectively analyzed 3,821 patients with digestive system tumors from the MIMIC-IV 3.0 database. Routine clinical variables from the first 24 hours after ICU admission were extracted. Least absolute shrinkage and selection operator (LASSO) regression and multivariable logistic regression were used for feature selection, followed by the development and comparison of six machine learning models. Model performance was evaluated using the AUC, calibration analysis, and decision curve analysis. The optimal model was interpreted using Shapley Additive Explanations (SHAP), and an online interactive prediction tool was developed to facilitate clinical translation. External validation was conducted in three independent cohorts from the United States and China, including the eICU Collaborative Research Database (eICU), the Tangshan tumor-related AKI cohort (TS-TAKI), and the Beijing Acute Kidney Injury cohort (BAKIT).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Result<\/jats:title>\n                    <jats:p>\n                      The incidence of AKI was 75.8%. Among all models, extreme gradient boosting (XGBoost) showed the best overall performance, with an AUC of 0.765 (95%\n                      <jats:italic toggle=\"yes\">CI<\/jats:italic>\n                      0.745\u20130.786) in the training set and 0.742 (95%\n                      <jats:italic toggle=\"yes\">CI<\/jats:italic>\n                      0.710\u20130.773) in the validation set, low Brier score, good calibration, and meaningful clinical net benefit. SHAP analysis identified the SOFA score, mechanical ventilation, WBC count, age, serum potassium level, sepsis, and vasoactive drug use as key predictors, with consistent interpretability at both the population and individual levels. In external validation, the model demonstrated stable discrimination across multicenter populations (AUCs of 0.719 in eICU, 0.769 in TS-TAKI, and 0.616 in BAKIT); however, calibration performance was notably affected by population heterogeneity, suggesting the need for recalibration using local data in real-world application.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This interpretable XGBoost-based model, based on routine ICU data, enables early AKI risk stratification in critically ill patients with digestive system tumors. The model achieves a balance between predictive performance, transparency, and clinical feasibility. It exhibited stable discriminative performance across multicenter and cross-population cohorts, while calibration was clearly affected by population heterogeneity, highlighting the need for local validation and recalibration in real-world application. An online prediction tool further supports its potential for clinical translation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/20552076261461368","type":"journal-article","created":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T18:33:43Z","timestamp":1781548423000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Multicenter validation of an explainable machine learning model for early prediction of acute kidney injury in critically ill patients with digestive system tumors"],"prefix":"10.1177","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-0269-4997","authenticated-orcid":false,"given":"DunZhu","family":"Guo","sequence":"first","affiliation":[{"name":"Tangshan People\u2019s Hospital"},{"name":"North China University of Science and Technology, Clinical Medical College, Tangshan, Hebei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Bai","sequence":"additional","affiliation":[{"name":"North China University of Science and Technology Affiliated Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Zhang","sequence":"additional","affiliation":[{"name":"Tangshan People\u2019s Hospital"},{"name":"North China University of Science and Technology, Clinical Medical College, Tangshan, Hebei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiuming","family":"Xi","sequence":"additional","affiliation":[{"name":"Fuxing Hospital, Capital Medical University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"YuJuan","family":"Chen","sequence":"additional","affiliation":[{"name":"Hospital Administration Office, Tangshan Vocational and Technical College Affiliated Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"ZhiPeng","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Computer and Artificial Intelligence, Southwest Jiaotong University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai","family":"Feng","sequence":"additional","affiliation":[{"name":"North China University of Science and Technology Affiliated Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"JiangWei","family":"Zeng","sequence":"additional","affiliation":[{"name":"North China University of Science and Technology Affiliated Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"MengXin","family":"Zhang","sequence":"additional","affiliation":[{"name":"North China University of Science and Technology Affiliated Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"WeiQin","family":"Dong","sequence":"additional","affiliation":[{"name":"North China University of Science and Technology Affiliated Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"XinXin","family":"Xu","sequence":"additional","affiliation":[{"name":"North China University of Science and Technology Affiliated Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Wang","sequence":"additional","affiliation":[{"name":"North China University of Science and Technology Affiliated Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Tangshan People\u2019s Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,6,15]]},"reference":[{"key":"e_1_3_7_2_2","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21660"},{"key":"e_1_3_7_3_2","doi-asserted-by":"publisher","DOI":"10.4103\/ija.ija_98_24"},{"issue":"7","key":"e_1_3_7_4_2","first-page":"e89155","article-title":"Incidence and Clinical Outcomes of Postoperative Acute Kidney Injury in Relation to Etiological Factors and Surgical Procedures","volume":"17","author":"Tahir M","year":"2025","unstructured":"Tahir M, Nawaz H, Iqbal A, et al. Incidence and Clinical Outcomes of Postoperative Acute Kidney Injury in Relation to Etiological Factors and Surgical Procedures. Cureus 2025; 17(7): e89155. https:\/\/doi.org\/10.7759\/cureus.89155","journal-title":"Cureus"},{"key":"e_1_3_7_5_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12885-023-11561-3"},{"key":"e_1_3_7_6_2","doi-asserted-by":"publisher","DOI":"10.1093\/jnci\/djy167"},{"key":"e_1_3_7_7_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-39735-9"},{"key":"e_1_3_7_8_2","doi-asserted-by":"publisher","DOI":"10.1159\/000339789"},{"key":"e_1_3_7_9_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12882-025-04630-1"},{"key":"e_1_3_7_10_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12882-023-03324-w"},{"key":"e_1_3_7_11_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-022-00597-7"},{"key":"e_1_3_7_12_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-024-02537-9"},{"key":"e_1_3_7_13_2","doi-asserted-by":"publisher","DOI":"10.3389\/fcvm.2022.964894"},{"key":"e_1_3_7_14_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-022-01899-x"},{"key":"e_1_3_7_15_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12967-022-03364-0"},{"key":"e_1_3_7_16_2","doi-asserted-by":"publisher","DOI":"10.1080\/0886022X.2024.2303395"},{"key":"e_1_3_7_17_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-025-02944-6"},{"key":"e_1_3_7_18_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00134-015-3934-7"},{"key":"e_1_3_7_19_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10238-025-01873-y"},{"key":"e_1_3_7_20_2","doi-asserted-by":"publisher","DOI":"10.2196\/62932"},{"key":"e_1_3_7_21_2","doi-asserted-by":"publisher","DOI":"10.2196\/72349"},{"key":"e_1_3_7_22_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-90459-5"},{"key":"e_1_3_7_23_2","doi-asserted-by":"publisher","DOI":"10.3389\/fendo.2024.1462330"},{"issue":"6","key":"e_1_3_7_24_2","first-page":"567","article-title":"[A multicenter clinical study of critically ill patients with sepsis complicated with acute kidney injury in Beijing: incidence, clinical characteristics and outcomes]","volume":"36","author":"Gao N","year":"2024","unstructured":"Gao N, Wang M, Jiang L, et al. [A multicenter clinical study of critically ill patients with sepsis complicated with acute kidney injury in Beijing: incidence, clinical characteristics and outcomes]. Zhonghua Wei Zhong Bing Ji Jiu Yi Xue 2024; 36(6): 567\u2013573. https:\/\/doi.org\/10.3760\/cma.j.cn121430-20240210-00124","journal-title":"Zhonghua Wei Zhong Bing Ji Jiu Yi Xue"},{"key":"e_1_3_7_25_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41551-018-0304-0"},{"key":"e_1_3_7_26_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.aucc.2022.06.001"},{"issue":"5","key":"e_1_3_7_27_2","first-page":"720","article-title":"Predictive Biomarkers of Acute Kidney Injury in COVID-19: Distinct Inflammatory Pathways in Patients with and Without Pre-Existing Chronic Kidney Disease","volume":"15","author":"Carollo C","year":"2025","unstructured":"Carollo C, Benfante A, Sorce A, et al. Predictive Biomarkers of Acute Kidney Injury in COVID-19: Distinct Inflammatory Pathways in Patients with and Without Pre-Existing Chronic Kidney Disease. Life (Basel) 2025; 15(5): 720. https:\/\/doi.org\/10.3390\/life15050720","journal-title":"Life (Basel)"},{"key":"e_1_3_7_28_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.iccn.2025.104118"},{"key":"e_1_3_7_29_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12882-020-01974-8"},{"key":"e_1_3_7_30_2","doi-asserted-by":"publisher","DOI":"10.3389\/fmolb.2025.1603392"},{"key":"e_1_3_7_31_2","doi-asserted-by":"publisher","DOI":"10.1177\/20552076241311173"},{"key":"e_1_3_7_32_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-023-00832-9"},{"key":"e_1_3_7_33_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-025-03045-0"}],"container-title":["DIGITAL HEALTH"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/20552076261461368","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/20552076261461368","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/20552076261461368","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T04:46:35Z","timestamp":1781757995000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/20552076261461368"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2]]},"references-count":32,"alternative-id":["10.1177\/20552076261461368"],"URL":"https:\/\/doi.org\/10.1177\/20552076261461368","relation":{},"ISSN":["2055-2076","2055-2076"],"issn-type":[{"value":"2055-2076","type":"print"},{"value":"2055-2076","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2]]},"article-number":"20552076261461368"}}