{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T17:07:45Z","timestamp":1792688865223,"version":"4.5.0"},"reference-count":26,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,4,3]],"date-time":"2023-04-03T00:00:00Z","timestamp":1680480000000},"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. Cardiovasc. Med."],"abstract":"<jats:sec><jats:title>Objective<\/jats:title><jats:p>Risk stratification of patients with congestive heart failure (HF) is vital in clinical practice. The aim of this study was to construct a machine learning model to predict the in-hospital all-cause mortality for intensive care unit (ICU) patients with HF.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>eXtreme Gradient Boosting algorithm (XGBoost) was used to construct a new prediction model (<jats:italic>XGBoost model<\/jats:italic>) from the Medical Information Mart for Intensive Care IV database (MIMIC-IV) (training set). The eICU Collaborative Research Database dataset (eICU-CRD) was used for the external validation (test set). The XGBoost model performance was compared with a logistic regression model and an existing model (Get with the guideline-Heart Failure model) for mortality in the test set. Area under the receiver operating characteristic cure and Brier score were employed to evaluate the discrimination and the calibration of the three models. The SHapley Additive exPlanations (SHAP) value was applied to explain XGBoost model and calculate the importance of its features.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The total of 11,156 and 9,837 patients with congestive HF from the training set and test set, respectively, were included in the study. In-hospital all-cause mortality occurred in 13.3% (1,484\/11,156) and 13.4% (1,319\/9,837) of patients, respectively. In the training set, of 17 features with the highest predictive value were selected into the models with LASSO regression. Acute Physiology Score III (APS III), age and Sequential Organ Failure Assessment (SOFA) were strongest predictors in SHAP. In the external validation, the XGBoost model performance was superior to that of conventional risk predictive methods, with an area under the curve of 0.771 (95% confidence interval, 0.757\u20130.784) and a Brier score of 0.100. In the evaluation of clinical effectiveness, the machine learning model brought a positive net benefit in the threshold probability of 0%\u201390%, prompting evident competitiveness compare to the other two models. This model has been translated into an online calculator which is accessible freely to the public (<jats:ext-link>https:\/\/nkuwangkai-app-for-mortality-prediction-app-a8mhkf.streamlit.app<\/jats:ext-link>).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>This study developed a valuable machine learning risk stratification tool to accurately assess and stratify the risk of in-hospital all-cause mortality in ICU patients with congestive HF. This model was translated into a web-based calculator which access freely.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fcvm.2023.1119699","type":"journal-article","created":{"date-parts":[[2023,4,3]],"date-time":"2023-04-03T04:50:13Z","timestamp":1680497413000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":48,"title":["Machine learning-based in-hospital mortality risk prediction tool for intensive care unit patients with heart 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