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. 2024 Aug 20;11(1):57.
doi: 10.1186/s40779-024-00558-z.

A prediction model for moderate to severe acute kidney injury in people with heart failure

Yu-Qi Yang #  1 , Jing-Jing Da #  1 , Sheng Nie #  2 , Jing Yuan  1 , Bi-Cheng Liu  3 , Hua-Feng Liu  4 , Qiong-Qiong Yang  5 , Hua Li  6 , Gang Xu  7 , Jian-Ping Weng  8 , Yao-Zhong Kong  9 , Qi-Jun Wan  10 , Gui-Sen Li  11 , Chun-Bo Chen  12 , Hong Xu  13 , Ying Hu  14 , Yong-Jun Shi  15 , Yi-Lun Zhou  16 , Guo-Bin Su  17 , Ying Tang  18 , Fan-Fan Hou  19 , Yan Zha  20
Affiliations

A prediction model for moderate to severe acute kidney injury in people with heart failure

Yu-Qi Yang et al. Mil Med Res. .
No abstract available

Keywords: Acute kidney injury; Heart failure; Machine learning; Prediction model.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Development and validation of heart failure (HF)-related acute kidney injury (AKI) prediction model. a Study design. The area under the receiver operating characteristic curves (AUCs) for moderate to severe AKI (b, d) and AKI requiring dialysis (c, e) in the internal validation and external validation cohorts. f SHapley Additive exPlanation (SHAP) summary plot of the XGBoost model. The plot depicts the dot estimation on the model output of the XGBoost model. Each dot represents an individual patient from the dataset. Red represents the higher SHAP value of specific features; blue represents the lower SHAP value of specific features. The higher the SHAP values, the greater the risk of developing AKI development. LR logistic regression, RF random forest, SVM supported vector machine, XGBoost eXtreme gradient boosting, proBNP pro-brain natriuretic peptide, eGFR estimated glomerular filtration rate, LDH lactate dehydrogenase
See this image and copyright information in PMC

References

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