Predicting ICU mortality in heart failure patients based on blood tests and vital signs
BackgroundCurrently, heart failure has become one of the major complications in the advanced stages of various cardiovascular diseases. Numerous predictive models have been developed to estimate the mortality rate of heart failure patients; however, these models often require the measurement of mult...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Cardiovascular Medicine |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fcvm.2025.1590367/full |
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| Summary: | BackgroundCurrently, heart failure has become one of the major complications in the advanced stages of various cardiovascular diseases. Numerous predictive models have been developed to estimate the mortality rate of heart failure patients; however, these models often require the measurement of multiple indicators and the inclusion of various scoring systems. Critically ill patients are often unsuitable for extensive diagnostic tests, and many primary care hospitals lack comprehensive diagnostic equipment. In contrast, blood tests are not only simpler but also reflect the overall health status of the body. Therefore, using simpler methods to predict mortality in intensive care unit (ICU) patients has become the focus of this study.MethodA total of 5,383 cases from the eICU database were utilized for model development, while 530 cases from the MIMIC-IV database were employed for external testing. The patients were primarily diagnosed with heart failure, and the data included demographic information, blood oxygen saturation, white blood cells, red blood cells, platelets, hemoglobin, electrolytes, lactate, glucose, and other biochemical and physiological indicators collected during the ICU stay. Enhance the accuracy of data analysis and improve the universality of the model, all data underwent rigorous preprocessing prior to training, combined with data standardization. We utilized a variety of machine learning algorithms for modeling purposes, including Logistic Regression (LR), Support Vector Machine (SVM), Decision Trees, Random Forests, Gradient Boosting Machines (GBM), XGBoost, and Neural Networks. The performance of the model was assessed through cross-validation and evaluated using the F1-score.ConclusionThrough feature selection, 15 key variables were ultimately identified. Among the nine machine learning models evaluated, the Multilayer Perceptron (MLP) demonstrated the best overall performance. In predicting mortality (i.e., the deceased population), the MLP achieved an F1 score of 0.54, a recall of 0.71, and a precision of 0.44. The relatively high F1 score of the MLP highlights its potential clinical application value. |
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| ISSN: | 2297-055X |