{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T21:44:27Z","timestamp":1788299067172,"version":"build-2803163510"},"reference-count":19,"publisher":"European Respiratory Society (ERS)","issue":"2","license":[{"start":{"date-parts":[[2020,5,4]],"date-time":"2020-05-04T00:00:00Z","timestamp":1588550400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.ersjournals.com\/user-licence"}],"funder":[{"DOI":"10.13039\/100000050","name":"National Heart, Lung, and Blood Institute","doi-asserted-by":"publisher","award":["R01 HL134673"],"award-info":[{"award-number":["R01 HL134673"]}],"id":[{"id":"10.13039\/100000050","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["publications.ersnet.org"],"crossmark-restriction":true},"short-container-title":["Eur Respir J"],"accepted":{"date-parts":[[2020,4,22]]},"published-print":{"date-parts":[[2020,8]]},"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Current risk stratification tools in pulmonary arterial hypertension (PAH) are limited in their discriminatory abilities, partly due to the assumption that prognostic clinical variables have an independent and linear relationship to clinical outcomes. We sought to demonstrate the utility of Bayesian network-based machine learning in enhancing the predictive ability of an existing state-of-the-art risk stratification tool, REVEAL 2.0.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We derived a tree-augmented na\u00efve Bayes model (titled PHORA) to predict 1-year survival in PAH patients included in the REVEAL registry, using the same variables and cut-points found in REVEAL 2.0. PHORA models were validated internally (within the REVEAL registry) and externally (in the COMPERA and PHSANZ registries). Patients were classified as low-, intermediate- and high-risk (&lt;5%, 5\u201320% and &gt;10% 12-month mortality, respectively) based on the 2015 European Society of Cardiology\/European Respiratory Society guidelines.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>PHORA had an area under the curve (AUC) of 0.80 for predicting 1-year survival, which was an improvement over REVEAL 2.0 (AUC 0.76). When validated in the COMPERA and PHSANZ registries, PHORA demonstrated an AUC of 0.74 and 0.80, respectively. 1-year survival rates predicted by PHORA were greater for patients with lower risk scores and poorer for those with higher risk scores (p&lt;0.001), with excellent separation between low-, intermediate- and high-risk groups in all three registries.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Our Bayesian network-derived risk prediction model, PHORA, demonstrated an improvement in discrimination over existing models. This is reflective of the ability of Bayesian network-based models to account for the interrelationships between clinical variables on outcome, and tolerance to missing data elements when calculating predictions.<\/jats:p><\/jats:sec>","DOI":"10.1183\/13993003.00008-2020","type":"journal-article","created":{"date-parts":[[2020,5,5]],"date-time":"2020-05-05T00:31:44Z","timestamp":1588638704000},"page":"2000008","update-policy":"https:\/\/doi.org\/10.1183\/ers-crossmark-policy","source":"Crossref","is-referenced-by-count":51,"title":["Risk stratification in pulmonary arterial hypertension using Bayesian analysis"],"prefix":"10.1183","volume":"56","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7280-5268","authenticated-orcid":false,"given":"Manreet K.","family":"Kanwar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mardi","family":"Gomberg-Maitland","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9086-2293","authenticated-orcid":false,"given":"Marius","family":"Hoeper","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christine","family":"Pausch","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1136-2616","authenticated-orcid":false,"given":"David","family":"Pittrow","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Geoff","family":"Strange","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James J.","family":"Anderson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carol","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jacqueline V.","family":"Scott","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marek J.","family":"Druzdzel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jidapa","family":"Kraisangka","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lisa","family":"Lohmueller","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James","family":"Antaki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Raymond L.","family":"Benza","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"81","published-online":{"date-parts":[[2020,5,4]]},"reference":[{"key":"2024102101374087000_56.2.2000008.1","doi-asserted-by":"publisher","DOI":"10.1378\/chest.11-1460"},{"key":"2024102101374087000_56.2.2000008.2","doi-asserted-by":"publisher","DOI":"10.1183\/13993003.01353-2017"},{"key":"2024102101374087000_56.2.2000008.3","doi-asserted-by":"publisher","DOI":"10.1183\/13993003.01032-2015"},{"key":"2024102101374087000_56.2.2000008.4","doi-asserted-by":"publisher","DOI":"10.1183\/13993003.00740-2017"},{"key":"2024102101374087000_56.2.2000008.5","doi-asserted-by":"publisher","DOI":"10.7326\/0003-4819-115-5-343"},{"key":"2024102101374087000_56.2.2000008.6","doi-asserted-by":"publisher","DOI":"10.1183\/13993003.00889-2017"},{"key":"2024102101374087000_56.2.2000008.7","doi-asserted-by":"crossref","first-page":"4175","DOI":"10.1093\/eurheartj\/ehx257","article-title":"A comprehensive risk stratification at early follow-up determines prognosis in pulmonary arterial hypertension","volume":"39","author":"Kylhammar","year":"2018","journal-title":"Eur Heart J"},{"key":"2024102101374087000_56.2.2000008.8","doi-asserted-by":"crossref","unstructured":"Kraisangka J , Druzdzel MJ , Lohmueller LC , et al. Bayesian network vs. Cox's proportional hazard model of PAH risk: a comparison. In: Ria\u00f1o D , Wilk S , ten Teije A , eds. Artificial Intelligence in Medicine. Cham, Springer International Publishing, 2019.","DOI":"10.1007\/978-3-030-21642-9_19"},{"key":"2024102101374087000_56.2.2000008.9","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/j.chest.2019.02.004","article-title":"Predicting survival in patients with pulmonary arterial hypertension: the REVEAL risk score calculator 2.0 and comparison with ESC\/ERS-based risk assessment strategies","volume":"156","author":"Benza","year":"2019","journal-title":"Chest"},{"key":"2024102101374087000_56.2.2000008.10","doi-asserted-by":"publisher","DOI":"10.4065\/83.8.923"},{"key":"2024102101374087000_56.2.2000008.11","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1023\/A:1007465528199","article-title":"Bayesian network classifiers","volume":"29","author":"Friedman","year":"1997","journal-title":"Machine Learning"},{"key":"2024102101374087000_56.2.2000008.12","doi-asserted-by":"crossref","first-page":"1368","DOI":"10.1016\/j.hlc.2017.08.018","article-title":"Survival of idiopathic pulmonary arterial hypertension patients in the modern era in Australia and New Zealand","volume":"27","author":"Strange","year":"2018","journal-title":"Heart Lung Circ"},{"key":"2024102101374087000_56.2.2000008.13","doi-asserted-by":"publisher","DOI":"10.1016\/j.jchf.2018.03.016"},{"key":"2024102101374087000_56.2.2000008.14","doi-asserted-by":"publisher","DOI":"10.1016\/j.jchf.2016.04.004"},{"key":"2024102101374087000_56.2.2000008.15","doi-asserted-by":"crossref","first-page":"196","DOI":"10.4258\/hir.2016.22.3.196","article-title":"Detection of cardiovascular disease risk's level for adults using naive Bayes classifier","volume":"22","author":"Miranda","year":"2016","journal-title":"Healthc Inform Res"},{"key":"2024102101374087000_56.2.2000008.16","doi-asserted-by":"crossref","first-page":"1158","DOI":"10.18240\/ijo.2019.07.17","article-title":"Artificial intelligence on diabetic retinopathy diagnosis: an automatic classification method based on grey level co-occurrence matrix and naive Bayesian model","volume":"12","author":"Cao","year":"2019","journal-title":"Int J Ophthalmol"},{"key":"2024102101374087000_56.2.2000008.17","doi-asserted-by":"publisher","DOI":"10.1097\/JTO.0b013e3181ec173d"},{"key":"2024102101374087000_56.2.2000008.18","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1111\/j.2517-6161.1972.tb00899.x","article-title":"Regression models and life-tables","volume":"34","author":"Cox","year":"1972","journal-title":"JR Stat Soc B"},{"key":"2024102101374087000_56.2.2000008.19","doi-asserted-by":"publisher","DOI":"10.1097\/EDE.0b013e3181c1ea43"}],"container-title":["European Respiratory Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/syndication.highwire.org\/content\/doi\/10.1183\/13993003.00008-2020","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T07:32:50Z","timestamp":1740382370000},"score":1,"resource":{"primary":{"URL":"https:\/\/publications.ersnet.org\/lookup\/doi\/10.1183\/13993003.00008-2020"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,4]]},"references-count":19,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2020,8,27]]},"published-print":{"date-parts":[[2020,8]]}},"alternative-id":["10.1183\/13993003.00008-2020"],"URL":"https:\/\/doi.org\/10.1183\/13993003.00008-2020","relation":{},"ISSN":["0903-1936","1399-3003"],"issn-type":[{"value":"0903-1936","type":"print"},{"value":"1399-3003","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,5,4]]}}}