{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T07:54:30Z","timestamp":1791618870912,"version":"4.3.4"},"reference-count":24,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"1","content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,1]]},"abstract":"<jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>Feeding intolerance in patients with sepsis is associated with a lower enteral nutrition (EN) intake and worse clinical outcomes. The aim of this study was to develop and validate a predictive model for enteral feeding intolerance in the intensive care unit patients with sepsis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>In this dual-center, retrospective, case-control study, a total of 195 intensive care unit patients with sepsis were enrolled from June 2018 to June 2020. Data of 124 patients for 27 clinical indicators from one hospital were used to train the model, and data from 71 patients from another hospital were used to assess the external predictive performance. The predictive models included logistic regression, naive Bayesian, random forest, gradient boosting tree, and deep learning (multilayer artificial neural network) models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>Eighty-six (44.1%) patients were diagnosed with enteral feeding intolerance. The deep learning model achieved the best performance, with areas under the receiver operating characteristic curve of 0.82 (95% confidence interval = 0.74\u20130.90) and 0.79 (95% confidence interval = 0.68\u20130.89) in the training and external sets, respectively. The deep learning model showed good calibration; based on the decision curve analysis, the model's clinical benefit was considered useful. Lower respiratory tract infection was the most important contributing factor, followed by peptide EN and shock.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions:<\/jats:title>\n                    <jats:p>The new prediction model based on deep learning can effectively predict enteral feeding intolerance in intensive care unit patients with sepsis. Simple clinical information such as infection site, nutrient type, and septic shock can be useful in stratifying a septic patient's risk of EN intolerance.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.4103\/sjg.sjg_286_21","type":"journal-article","created":{"date-parts":[[2021,9,13]],"date-time":"2021-09-13T08:34:16Z","timestamp":1631522056000},"page":"32-38","update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":33,"title":["Development and validation of a predictive model for feeding intolerance in intensive care unit patients with sepsis"],"prefix":"10.4103","volume":"28","author":[{"given":"Kunlin","family":"Hu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin lei","family":"Deng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lin","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shulin","family":"Xiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Xiong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liao","family":"Pinhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","reference":[{"key":"R1-4-20240829","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1001\/jama.2016.0287","article-title":"The third international consensus definitions for sepsis and septic shock (Sepsis-3)","volume":"315","author":"Singer","year":"2016","journal-title":"JAMA"},{"key":"R2-4-20240829","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1016\/j.ccm.2016.01.011","article-title":"Metabolism, metabolomics, and nutritional support of patients with sepsis","volume":"37","author":"Englert","year":"2016","journal-title":"Clin Chest Med"},{"key":"R3-4-20240829","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1007\/s00134-016-4665-0","article-title":"Early enteral nutrition in critically ill patients: ESICM clinical practice guidelines","volume":"43","author":"Reintam Blaser","year":"2017","journal-title":"Intensive Care Med"},{"key":"R4-4-20240829","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1097\/CCM.0000000000004712","article-title":"Incidence, risk factors, and clinical consequence of enteral feeding intolerance in the mechanically ventilated critically ill: An analysis of a multicenter, multiyear database","volume":"49","author":"Heyland","year":"2021","journal-title":"Crit Care Med"},{"key":"R5-4-20240829","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1007\/s00134-011-2459-y","article-title":"Gastrointestinal function in intensive care patients: Terminology, definitions and management. 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