{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T14:23:11Z","timestamp":1785421391316,"version":"3.56.0"},"reference-count":74,"publisher":"The Journal of Health Economics and Outcomes Research","issue":"2","license":[{"start":{"date-parts":[[2025,7,17]],"date-time":"2025-07-17T00:00:00Z","timestamp":1752710400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["jheor.org"],"crossmark-restriction":true},"short-container-title":[],"abstract":"<jats:sec id=\"background\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Patients with hereditary angioedema (HA) face a heightened mortality risk due to multiple factors.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"objective\">\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>The purpose of this study was to identify patient demographics or comorbidities associated with higher mortality risk using Bayesian network analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"methods\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Data from the 2021 Nationwide Inpatient Sample were used to identify hospitalized patients with HA. Patient demographics, comorbidities, and severity measures were analyzed, and a Bayesian network model was developed to assess factors contributing to mortality risk. Structure learning was performed using a directed acyclic graph and probability estimating using Bayesian inference. Model performance was validated using a 70\/30 training-testing split and assessed via area under the curve.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"results\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Older HA patients and those with autoimmune conditions, hypertension, or low income were at higher risk of mortality. Elevated risk was also observed across certain racial groups, insurance types, and income levels. Notably, older Black patients from the Midwest exhibited the highest estimated mortality risk.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"conclusion\">\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The Bayesian network demonstrated strong predictive performance, highlighting its potential for identifying high-risk subgroups and supporting targeted clinical interventions.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.36469\/001c.141747","type":"journal-article","created":{"date-parts":[[2025,7,17]],"date-time":"2025-07-17T16:31:16Z","timestamp":1752769876000},"update-policy":"https:\/\/doi.org\/10.36469\/00001","source":"Crossref","is-referenced-by-count":0,"title":["Characteristics and Comorbidities Influencing Mortality Risk Among Hereditary Angioedema Patients"],"prefix":"10.36469","volume":"12","author":[{"given":"Subhan","family":"Khalid","sequence":"first","affiliation":[{"name":"Harrisburg University of Science and Technology, Harrisburg, Pennsylvania","department":["Data Science"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alan T.","family":"Hitch","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/02g0s4z48","id-type":"ROR","asserted-by":"publisher"}],"name":"Harrisburg University of Science and Technology","department":["Data Science"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"22273","published-online":{"date-parts":[[2025,7,17]]},"reference":[{"issue":"6","key":"ref1","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1016\/j.anai.2011.08.015","article-title":"Randomized placebo-controlled trial of the bradykinin B2 receptor antagonist icatibant for the treatment of acute attacks of hereditary angioedema: the FAST-3 trial","volume":"107","author":"W. 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