{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,11]],"date-time":"2026-10-11T17:46:25Z","timestamp":1791740785312,"version":"4.3.4"},"reference-count":36,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T00:00:00Z","timestamp":1791417600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Technology and Health Care"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Postoperative delirium is a serious complication in elderly ICU patients after gastric surgery, increasing mortality and healthcare burden. Early identification of high-risk patients may support timely prevention and optimise perioperative care.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To develop and externally validate an interpretable machine learning model for predicting postoperative delirium in elderly ICU patients undergoing gastric surgery.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This retrospective multicohort study used three perioperative and critical care databases. MIMIC-IV supported model development and internal validation, while eICU and MOVER supported external validation. Feature selection used LASSO regression, followed by comparison of six machine learning algorithms. Logistic regression was selected using the MIMIC-IV internal validation cohort based on discrimination, recall, and clinical interpretability; the locked model was then evaluated in eICU and MOVER. SHAP analysis quantified predictor contributions and enhanced model transparency.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The final population included four cohorts: MIMIC-IV training (\n                      <jats:italic toggle=\"yes\">N<\/jats:italic>\n                      \u2009=\u20094316), MIMIC-IV internal validation (\n                      <jats:italic toggle=\"yes\">N<\/jats:italic>\n                      \u2009=\u20091851), eICU external testing (\n                      <jats:italic toggle=\"yes\">N<\/jats:italic>\n                      \u2009=\u20091745), and MOVER external testing (\n                      <jats:italic toggle=\"yes\">N<\/jats:italic>\n                      \u2009=\u20091152). The internally selected logistic regression model achieved AUCs of 0.819 in eICU and 0.827 in MOVER, with recalls of 0.827 and 0.936, respectively. SHAP analysis identified chloride, sodium, bicarbonate, and anion gap as dominant predictors across cohorts.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study presents an interpretable postoperative delirium risk-prediction model in elderly ICU patients following gastric surgery. Although the model achieved high recall, marked class imbalance and low outcome prevalence were associated with low precision and a substantial false-positive burden. Prospective validation, threshold optimisation, and assessment of clinical utility are required before implementation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/09287329261495038","type":"journal-article","created":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T12:29:44Z","timestamp":1791462584000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Targeting delirium in gastric surgery: An\u00a0interpretable predictive model and\u00a0statistical analysis in critical care"],"prefix":"10.1177","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-6895-5036","authenticated-orcid":false,"given":"Junqi","family":"Cui","sequence":"first","affiliation":[{"name":"Vanke School of Public Health, Tsinghua University, Beijing, PR China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-6349-4531","authenticated-orcid":false,"given":"Enoch Chi Ngai","family":"Lim","sequence":"additional","affiliation":[{"name":"Translational Research Department, Specialist Medical Services Group, Earlwood, NSW, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4448-8154","authenticated-orcid":false,"given":"Chi Eung Danforn","family":"Lim","sequence":"additional","affiliation":[{"name":"Translational Research Department, Specialist Medical Services Group, Earlwood, NSW, Australia"},{"name":"NICM Health Research Institute, Western Sydney University, Westmead, NSW, Australia"},{"name":"Data Science Institute, University of Technology Sydney, Ultimo, NSW, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,10,8]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41572-020-00223-4"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0193-953X(05)70299-9"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1002\/gps.4823"},{"key":"e_1_3_3_5_2","volume-title":"Delirium: acute confusional states in palliative medicine","author":"Caraceni A","year":"2011","unstructured":"Caraceni A, Grassi L. 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Phenylephrine\u2019s heterogeneous renal effects in non-cardiac surgery: A causal machine learning study. 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