{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T01:15:43Z","timestamp":1787015743591,"version":"build-2736575974"},"reference-count":25,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T00:00:00Z","timestamp":1767312000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T00:00:00Z","timestamp":1767312000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82302323"],"award-info":[{"award-number":["82302323"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Liver International"],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background and Aims<\/jats:title>\n                    <jats:p>\n                      While remeasuring portacaval pressure gradient (\n                      <jats:styled-content style=\"fixed-case\">PPG<\/jats:styled-content>\n                      ) after transjugular intrahepatic portosystemic shunt (\n                      <jats:styled-content style=\"fixed-case\">TIPS<\/jats:styled-content>\n                      ), it provides superior prognostic information, and its clinical utility is limited by invasiveness. We aimed to develop and validate an explainable machine learning (\n                      <jats:styled-content style=\"fixed-case\">ML<\/jats:styled-content>\n                      ) model for predicting short\u2010term\n                      <jats:styled-content style=\"fixed-case\">PPG<\/jats:styled-content>\n                      changes and improving prognostic value of\n                      <jats:styled-content style=\"fixed-case\">PPG<\/jats:styled-content>\n                      in cirrhotic patients undergoing\n                      <jats:styled-content style=\"fixed-case\">TIPS<\/jats:styled-content>\n                      .\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We enrolled 328 and 128 patients (2018\u20132023) in retrospective training and prospective validation cohorts, respectively. PPG was measured pre\u2010TIPS, immediately (imePPG) and 2\u20134\u2009days post\u2010TIPS (delPPG). All ML models were developed using hyperparameter tuning with 5\u2010fold cross\u2010validation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Support vector regression model (SVR_RBF) demonstrated superior performance across feature subsets, with its 6\u2010feature model achieving optimal balance between robustness and simplicity. In the validation cohort, the final model significantly improved PPG agreement (R\u2010squared: 0.265\u20130.617, mean square error: 16.64\u20134.90, mean absolute error: 3.45\u20131.65, intraclass correlation coefficient: 0.533\u20130.841). SHapley Additive exPlanation analysis identified imePPG reduction from baseline as the primary determinant of subsequent PPG changes. Restricted cubic spline analyses revealed significant nonlinear relationships between relative PPG reduction (but not absolute values) and 2\u2010year further decompensation in the training cohort. Optimal reduction thresholds were defined as U\u2010shaped curves with hazard ratio (HR) nodes at 1 (imePPG: 30%\u201370%, delPPG: 20%\u201355%), which were validated in the prospective cohort showing significant associations for both true (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.003, HR\u2009=\u20090.45) and predicted (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.044, HR\u2009=\u20090.57) delPPG, but not for imePPG (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.196).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The six\u2010feature SVR_RBF model enhances the accuracy and prognostic utility of PPG measurements before TIPS completion, supporting clinical decision\u2010making without additional invasive assessments.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/liv.70505","type":"journal-article","created":{"date-parts":[[2026,1,3]],"date-time":"2026-01-03T07:05:01Z","timestamp":1767423901000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["An Explainable Machine Learning Model for Predicting Short\u2010Term Haemodynamic Changes Post\u2010\n                    <scp>TIPS<\/scp>\n                    With Prognostic Implications"],"prefix":"10.1111","volume":"46","author":[{"given":"Li","family":"Ma","sequence":"first","affiliation":[{"name":"Shanghai Institute of Medical Imaging, Fudan University  Shanghai China"},{"name":"Department of Interventional Radiology Zhongshan Hospital, Fudan University  Shanghai China"},{"name":"National Clinical Research Center for Interventional Medicine, Zhongshan Hospital, Fudan University  Shanghai 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