{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T06:21:27Z","timestamp":1783059687588,"version":"3.54.6"},"reference-count":33,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T00:00:00Z","timestamp":1783036800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF) is a life-threatening syndrome, the condition can deteriorate rapidly, and the 90-day mortality rate is high. Due to the rapid changes in the clinical course, early and accurate risk stratification is crucial for timely decision-making and resource allocation in the ICU. This study has developed and verified a machine learning framework that integrates the C-reactive protein-albumin-lymphocyte (CALLY) index to predict the 90-day mortality rate of HBV-ACLF patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We conducted a retrospective single-center study on 471 patients with HBV-ACLF who met the 2018 Chinese liver failure diagnosis criteria and were hospitalized in the Western Theater General Hospital from 2015 to 2023. We randomly divide the constructed data set into training set (\n                      <jats:italic>n<\/jats:italic>\n                      = 329) and internal verification set (\n                      <jats:italic>n<\/jats:italic>\n                      = 142) according to the ratio of 7:3. In addition, we performed temporal external validation using a later cohort of 46 patients with HBV-ACLF admitted between 2024 and 2025. Six machine-learning algorithms including logistic regression, support vector machine, K-nearest neighbors, Extra Trees, XGBoost, and LightGBM, were trained to develop predictive models. SHAP was applied to assess feature importance and model interpretability.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Among the evaluated algorithms, the LightGBM model showed the strongest overall discriminative performance, with AUCs of 0.940 (95% CI, 0.916\u20130.964) in the training set, 0.825 (95% CI, 0.757\u20130.894) in the internal validation set, and 0.804 (95% CI, 0.669\u20130.939) in the external validation set. SHAP analysis identified international normalized ratio (INR) as the strongest predictor of mortality, followed by the CALLY index, log-transformed total bilirubin (TBIL), age, and creatinine. The CALLY-based model significantly improved risk stratification compared with the Model for End-Stage Liver Disease (MELD) score, as demonstrated by a continuous net reclassification index of 0.6223 and an integrated discrimination improvement of 0.0937. To facilitate clinical implementation, a user-friendly online tool was created to predict mortality rates.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Our machine-learning framework integrating the CALLY index provides a high-precision and transparent decision-support tool for predicting 90-day mortality with HBV-ACLF patients. By quantifying the immune\u2013nutritional\u2013inflammatory axis, this approach may facilitate earlier risk stratification and personalized interventions, potentially improving survival in high-risk patients.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2026.1814799","type":"journal-article","created":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T05:38:09Z","timestamp":1783057089000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A machine learning model for 90-day mortality prediction in hepatitis B virus-related acute-on-chronic liver failure: the pivotal role of CALLY index"],"prefix":"10.3389","volume":"13","author":[{"given":"Yijun","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Gastroenterology, The General Hospital of Western Theater Command","place":["Chengdu, Sichuan, 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Sichuan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, The General Hospital of Western Theater Command","place":["Chengdu, Sichuan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shanhong","family":"Tang","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, The General Hospital of Western Theater Command","place":["Chengdu, Sichuan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,3]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1186\/s12876-020-01191-5","article-title":"Artificial neural network-based models used for predicting 28- and 90-day mortality of patients with hepatitis B-associated acute-on-chronic liver failure","volume":"20","author":"Hou","year":"2020","journal-title":"BMC 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