{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T00:18:45Z","timestamp":1777853925906,"version":"3.51.4"},"reference-count":38,"publisher":"SAGE Publications","issue":"11","license":[{"start":{"date-parts":[[2025,5,15]],"date-time":"2025-05-15T00:00:00Z","timestamp":1747267200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/100000050","name":"National Heart, Lung, and Blood Institute","doi-asserted-by":"publisher","award":["K23HL151671"],"award-info":[{"award-number":["K23HL151671"]}],"id":[{"id":"10.13039\/100000050","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Mayo Clinic Critical Care Research Committee"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["J Intensive Care Med"],"published-print":{"date-parts":[[2025,11]]},"abstract":"<jats:p>\n                    <jats:bold>Purpose:<\/jats:bold>\n                    To develop and validate an explainable machine learning (ML) tool to help clinicians predict the risk of propofol-associated hypertriglyceridemia in critically ill patients receiving propofol sedation.\n                    <jats:bold>Methods:<\/jats:bold>\n                    Patients from 11 intensive care units (ICUs) across five Mayo Clinic hospitals were included if they met the following criteria: a)\u2009\u2265\u200918 years of age, b) received propofol infusion while on invasive mechanical ventilation for \u226524\u2005h, and c) had a triglyceride level measured. The primary outcome was hypertriglyceridemia (triglyceride &gt;400 mg\/dL) onset within 10 days of propofol initiation. Both COVID-inclusive and COVID-independent modeling pipelines were developed to ensure applicability post-pandemic. Decision thresholds were chosen to maintain model sensitivity &gt;80%. Nested leave-one-site-out cross-validation (LOSO-CV) was used to externally evaluate pipeline performance. Model explainability was assessed using permutation importance and SHapley Additive exPlanations (SHAP).\n                    <jats:bold>Results:<\/jats:bold>\n                    Among 3922 included patients, 769 (19.6%) developed propofol-associated hypertriglyceridemia, and 879 (22.4%) had COVID-19 at ICU admission. During nested LOSO-CV, the COVID-inclusive pipeline achieved an average AUC-ROC of 0.71 (95% confidence interval [CI] 0.70\u20130.72), while the COVID-independent pipeline achieved an average AUC-ROC of 0.69 (95% CI 0.68\u20130.70). Age, initial propofol dose, and BMI were the top three most important features in both models.\n                    <jats:bold>Conclusion:<\/jats:bold>\n                    We developed an explainable ML-based tool with acceptable predictive performance for assessing the risk of propofol-associated hypertriglyceridemia in ICU patients. This tool can aid clinicians in identifying at-risk patients to guide triglyceride monitoring and optimize sedative selection.\n                  <\/jats:p>","DOI":"10.1177\/08850666251342559","type":"journal-article","created":{"date-parts":[[2025,5,15]],"date-time":"2025-05-15T04:05:20Z","timestamp":1747281920000},"page":"1159-1168","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Propofol-associated Hypertriglyceridemia: Development and Multicenter Validation of a Machine-Learning-Based Prediction Tool"],"prefix":"10.1177","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8274-6468","authenticated-orcid":false,"given":"Jiawen","family":"Deng","sequence":"first","affiliation":[{"name":"Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4465-2249","authenticated-orcid":false,"given":"Kiyan","family":"Heybati","sequence":"additional","affiliation":[{"name":"Mayo Clinic Alix School of Medicine, Mayo Clinic, Rochester, MN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Keshav","family":"Poudel","sequence":"additional","affiliation":[{"name":"Mayo Clinic Alix School of Medicine, Mayo Clinic, Rochester, MN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guozhen","family":"Xie","sequence":"additional","affiliation":[{"name":"Mayo Clinic Alix School of Medicine, Mayo Clinic, Rochester, MN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-9754-4149","authenticated-orcid":false,"given":"Eric","family":"Zuberi","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, Mayo Clinic, Jacksonville, FL, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vinaya","family":"Simha","sequence":"additional","affiliation":[{"name":"Division of Endocrinology, Diabetes, Metabolism, and Nutrition, Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1889-2524","authenticated-orcid":false,"given":"Hemang","family":"Yadav","sequence":"additional","affiliation":[{"name":"Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2025,5,15]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1097\/CCM.0000000000003259"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1513\/AnnalsATS.202407-781OC"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1592\/phco.2005.25.10.1348"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1097\/CCE.0000000000000282"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1513\/AnnalsATS.202205-403OC"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/RBME.2020.3007816"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.1126\/scitranslmed.aao5333"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1097\/CCM.0000000000006359"},{"key":"e_1_3_3_10_2","first-page":"4768","volume-title":"A unified approach to interpreting model predictions","author":"Lundberg SM","unstructured":"Lundberg SM, Lee S-I. 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