{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T13:16:11Z","timestamp":1788182171207,"version":"build-2803163510"},"reference-count":30,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T00:00:00Z","timestamp":1787788800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["82272180"],"award-info":[{"award-number":["82272180"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["82472243"],"award-info":[{"award-number":["82472243"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Zhejiang Provincial Science and Technology Program for Disease Control and Prevention","award":["2026JKZ042"],"award-info":[{"award-number":["2026JKZ042"]}]},{"name":"National Administration of Traditional Chinese Medicine","award":["GZY-ZJ-KJ-24082"],"award-info":[{"award-number":["GZY-ZJ-KJ-24082"]}]},{"name":"the Key Research & Development Project of Zhejiang Province","award":["2024C03240"],"award-info":[{"award-number":["2024C03240"]}]},{"name":"the Project of Zhejiang University Longquan Innovation Center","award":["ZJDXLQCXZCJBGS2024016"],"award-info":[{"award-number":["ZJDXLQCXZCJBGS2024016"]}]},{"name":"the Prevention and control of Emerging and Major Infectious Diseases-National Science and Technology Major Project","award":["2025ZD01902500"],"award-info":[{"award-number":["2025ZD01902500"]}]},{"name":"the Prevention and control of Emerging and Major Infectious Diseases-National Science and Technology Major Project","award":["2025ZD01902501"],"award-info":[{"award-number":["2025ZD01902501"]}]},{"name":"the General Health Science and Technology Program of Zhejiang Province","award":["2024KY1099"],"award-info":[{"award-number":["2024KY1099"]}]},{"name":"Wu Jieping Medical Foundation Special Research Grant","award":["320.6750.2024-23-07"],"award-info":[{"award-number":["320.6750.2024-23-07"]}]},{"name":"the China National Key Research and Development Program","award":["2023YFC3603104"],"award-info":[{"award-number":["2023YFC3603104"]}]},{"name":"Beijing Natural Science Foundation","award":["7252298"],"award-info":[{"award-number":["7252298"]}]},{"name":"the Huadong Medicine Joint Funds of the Zhejiang Provincial Natural Science Foundation of China","award":["LHDMD24H150001"],"award-info":[{"award-number":["LHDMD24H150001"]}]},{"name":"the Fundamental Research Funds for the Central Universities","award":["226-2025-00024"],"award-info":[{"award-number":["226-2025-00024"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["J Intensive Care Med"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Sepsis-associated acute kidney injury (SA-AKI) is a common and severe complication in critically ill patients, with poor prognosis. Diabetes may further increase adverse outcomes through infection susceptibility, immune dysfunction, and renal vulnerability. However, mortality prediction models for patients with diabetes complicated by SA-AKI remain limited. This study aimed to develop and validate a machine learning-based model for early in-hospital mortality prediction in this population.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A total of 6929 patients with SA-AKI and diabetes were identified from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database and randomly divided into training and validation sets at a ratio of 7:3. Ninety-four variables, including demographics, diagnoses, clinical parameters, and medication records within the first 24\u2005h after ICU admission, were extracted. Twelve machine learning algorithms were developed and compared, and the optimal model was selected. Recursive feature elimination was used to identify key predictors, while SHapley Additive exPlanations were applied for model interpretation. The final model was deployed as a web-based tool and externally tested using the eICU Collaborative Research Database.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Thirty-two key predictors were ultimately selected, including urine output rate, platelet count, lactate, weight, blood glucose, SOFA score, pH, blood urea nitrogen, vital signs, coagulation indices, vasopressor use, and other clinically relevant variables. The categorical boosting algorithm model presented better predictive performance [receiver operating characteristic (AUC): 0.828] than other models [accuracy (ACC): 70.9%, sensitivity: 78.7%, specificity: 69%, F1 score: 0.509, positive predictive value (PPV): 33.7%, and negative predictive value (NPV): 93.1%]. External testing using data from the eICU database was also well validated (AUC: 0.793).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>A CatBoost-based machine learning model incorporating 32 clinically accessible variables showed good predictive performance for in-hospital mortality in patients with diabetes and SA-AKI, supporting early risk stratification and clinical decision-making.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/08850666261479295","type":"journal-article","created":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T15:16:05Z","timestamp":1787843765000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Development and Validation of an Interpretable Machine Learning Model for Predicting In-Hospital Mortality in Diabetic Patients with Sepsis-Associated Acute Kidney Injury"],"prefix":"10.1177","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-4651-2120","authenticated-orcid":false,"given":"Yanni","family":"Wang","sequence":"first","affiliation":[{"name":"the Second Affiliated Hospital of Anhui Medical University"},{"name":"School of Medicine, Zhejiang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongjie","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Medicine, Zhejiang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shengze","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Medicine, Zhejiang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suibi","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Medicine, Zhejiang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feng","family":"Guo","sequence":"additional","affiliation":[{"name":"Zhejiang University"},{"name":"School of Medicine, Zhejiang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3484-2447","authenticated-orcid":false,"given":"Min","family":"Yang","sequence":"additional","affiliation":[{"name":"the Second Affiliated Hospital of Anhui Medical University"},{"name":"Laboratory of Cardiopulmonary Resuscitation and Critical Illness, the Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhongheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"the Second Affiliated Hospital of Anhui Medical University"},{"name":"School of Medicine, Zhejiang University"},{"name":"School of Medicine, Shaoxing University, Shaoxing, Zhejiang, P.R. 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