NDLI: A machine learning-based risk stratification tool for in-hospital mortality of intensive care unit patients with heart failure

Content Provider	Springer Nature : BioMed Central
Author	Luo, Cida Zhu, Yi Zhu, Zhou Li, Ranxi Chen, Guoqin Wang, Zhang
Abstract	Background Predicting hospital mortality risk is essential for the care of heart failure patients, especially for those in intensive care units. Methods Using a novel machine learning algorithm, we constructed a risk stratification tool that correlated patients’ clinical features and in-hospital mortality. We used the extreme gradient boosting algorithm to generate a model predicting the mortality risk of heart failure patients in the intensive care unit in the derivation dataset of 5676 patients from the Medical Information Mart for Intensive Care III database. The logistic regression model and a common risk score for mortality were used for comparison. The eICU Collaborative Research Database dataset was used for external validation. Results The performance of the machine learning model was superior to that of conventional risk predictive methods, with the area under curve 0.831 (95% CI 0.820–0.843) and acceptable calibration. In external validation, the model had an area under the curve of 0.809 (95% CI 0.805–0.814). Risk stratification through the model was specific when the hospital mortality was very low, low, moderate, high, and very high (2.0%, 10.2%, 11.5%, 21.2% and 56.2%, respectively). The decision curve analysis verified that the machine learning model is the best clinically valuable in predicting mortality risk. Conclusion Using readily available clinical data in the intensive care unit, we built a machine learning-based mortality risk tool with prediction accuracy superior to that of linear regression model and common risk scores. The risk tool may support clinicians in assessing individual patients and making individualized treatment.
Related Links	https://translational-medicine.biomedcentral.com/counter/pdf/10.1186/s12967-022-03340-8.pdf
Ending Page	9
Page Count	9
Starting Page	1
File Format	HTM / HTML
ISSN	14795876
DOI	10.1186/s12967-022-03340-8
Journal	Journal of Translational Medicine
Issue Number	1
Volume Number	20
Language	English
Publisher	BioMed Central
Publisher Date	2022-03-18
Access Restriction	Open
Subject Keyword	Biomedicine Medicine Public Health Machine learning models Heart failure Extreme gradient boosting Medical information mart for intensive care Risk stratification Medicine/Public Health
Content Type	Text
Resource Type	Article
Subject	Biochemistry, Genetics and Molecular Biology Medicine
Journal Impact Factor	6.1/2023
5-Year Journal Impact Factor	6.3/2023

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