{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T01:13:54Z","timestamp":1787015634630,"version":"build-2736575974"},"reference-count":30,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2022,11,18]],"date-time":"2022-11-18T00:00:00Z","timestamp":1668729600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J of Gastro and Hepatol"],"published-print":{"date-parts":[[2023,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background and Aim<\/jats:title>\n                    <jats:p>Data are lacking on predicting inpatient mortality (IM) in patients admitted for inflammatory bowel disease (IBD). IM is a critical outcome; however, difficulty in its prediction exists due to infrequent occurrence. We assessed IM predictors and developed a predictive model for IM using machine\u2010learning (ML).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Using the National Inpatient Sample (NIS) database (2005\u20132017), we extracted adults admitted for IBD. After ML\u2010guided predictor selection, we trained and internally validated multiple algorithms, targeting minimum sensitivity and positive likelihood ratio (+LR)\u00a0\u2265\u00a080% and \u2265\u00a03, respectively. Diagnostic odds ratio (DOR) compared algorithm performance. The best performing algorithm was additionally trained and validated for an IBD\u2010related surgery sub\u2010cohort. External validation was done using NIS 2018.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      In 398\u00a0426 adult IBD admissions, IM was 0.32% overall, and 0.87% among the surgical cohort (\n                      <jats:italic>n<\/jats:italic>\n                      \u00a0=\u00a040\u00a0784). Increasing age, ulcerative colitis, IBD\u2010related surgery, pneumonia, chronic lung disease, acute kidney injury, malnutrition, frailty, heart failure, blood transfusion, sepsis\/septic shock and thromboembolism were associated with increased IM. The QLattice algorithm, provided the highest performance model (+LR: 3.2, 95% CI 3.0\u20133.3; area\u2010under\u2010curve [AUC]:0.87, 85% sensitivity, 73% specificity), distinguishing IM patients by 15.6\u2010fold when comparing high to low\u2010risk patients. The surgical cohort model (+LR: 8.5, AUC: 0.94, 85% sensitivity, 90% specificity), distinguished IM patients by 49\u2010fold. Both models performed excellently in external validation. An online calculator (\n                      <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/clinicalc.ai\/im-ibd\/\">https:\/\/clinicalc.ai\/im\u2010ibd\/<\/jats:ext-link>\n                      ) was developed allowing bedside model predictions.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>An online prediction\u2010model calculator captured &gt;\u00a080% IM cases during IBD\u2010related admissions, with high discriminatory effectiveness. This allows for risk stratification and provides a basis for assessing interventions to reduce mortality in high\u2010risk patients.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/jgh.16029","type":"journal-article","created":{"date-parts":[[2022,10,19]],"date-time":"2022-10-19T00:44:53Z","timestamp":1666140293000},"page":"241-250","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Predicting inpatient mortality in patients with inflammatory bowel disease: A machine learning approach"],"prefix":"10.1111","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5512-4225","authenticated-orcid":false,"given":"Paris","family":"Charilaou","sequence":"first","affiliation":[{"name":"New York Presbyterian Hospital\/Weill\u2010Cornell Medical College \u2010 Jill Roberts Center for Inflammatory Bowel Disease, Weill Cornell Medicine  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sonmoon","family":"Mohapatra","sequence":"additional","affiliation":[{"name":"Division of Gastroenterology and Hepatology Mayo Clinic  Scottsdale Arizona USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sotirios","family":"Doukas","sequence":"additional","affiliation":[{"name":"Department of Medicine Saint Peter's University Hospital\/Rutgers\u2010RWJ Medical School  New Brunswick New Jersey USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maanit","family":"Kohli","sequence":"additional","affiliation":[{"name":"Department of Medicine Icahn School of Medicine at Mount Sinai  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6061-7852","authenticated-orcid":false,"given":"Dhruvil","family":"Radadiya","sequence":"additional","affiliation":[{"name":"Division of Gastroenterology and Hepatology University of Kansas Medical Center  Kansas City Kansas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kalpit","family":"Devani","sequence":"additional","affiliation":[{"name":"Division of Gastroenterology and Hepatology Prisma Health Greenville Memorial Hospital  Greenville South Carolina USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arkady","family":"Broder","sequence":"additional","affiliation":[{"name":"Division of Gastroenterology and Hepatology Saint Peter's University Hospital\/Rutgers\u2010RWJ Medical School  New Brunswick New Jersey USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Olivier","family":"Elemento","sequence":"additional","affiliation":[{"name":"Weill Cornell Medical College \u2010 Caryl and Israel Englander Institute for Precision Medicine, Institute for Computational Biomedicine, Weill Cornell Medicine  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dana J","family":"Lukin","sequence":"additional","affiliation":[{"name":"New York Presbyterian Hospital\/Weill\u2010Cornell Medical College \u2010 Jill Roberts Center for Inflammatory Bowel Disease, Weill Cornell Medicine  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert","family":"Battat","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology and Hepatology Centre Hospitalier de l' Universite de Montreal  Montreal Quebec Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2022,11,18]]},"reference":[{"key":"e_1_2_7_2_1","first-page":"619","article-title":"Prevalence of inflammatory bowel disease in pediatric and adult populations: recent estimates from large national databases in the United States, 2007\u20102016","volume":"26","author":"Ye Y","year":"2020","journal-title":"Inflamm. 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