Abstract
Acute renal failure (ARF) is one of the most common conditions encountered in the intensive care unit (ICU). ARF has a complex pathogenesis and due to the progressive weakening of the structure and function of the kidney, the incidence of ARF increases significantly in the aging group. Therefore, the development of reliable predictive model is of great importance to identify those patients in high risk for ARF, in order to provide timely and effective interventions to improve their prognosis. Extreme gradient boosting (XGBoost) is an efficient integrated learning algorithm with advantages over traditional logistic regression (LR) methods. The purpose of this study was to compare the performance of the two models in predicting 90-day mortality in elderly patients with ARF. Data of elderly patients (> 60years) with ARF in ICU were extracted from MIMIC IV with 90-day mortality as end-point. The performance of the two predictive models was tested and compared by receiver operating characteristic curve and decision curve analysis (DCA). Cumulative residual distribution plot and residual box-plot were then used to determine the fit of the model. Finally, the model with better overall diagnostic value was selected and a breakdown plot was drawn. Data of 7,500 elderly ARF patients were analyzed, of whom 1,150 died within 90 days. Both models showed good discriminatory ability, but the XGBoost model had a larger area under the curve value. DCA results revealed that the net benefit of the XGBoost model had a greater range than the LR model. Moreover, the XGBoost model had the smallest sample residuals and root-mean-square residuals, indicating a better fitting of the XGBoost algorithm. Finally, a breakdown plot based on the XGBoost model was created as an individualized tool for prognosis prediction in elderly patients with ARF. Our study find that the XGBoost algorithm model was a better model for predicting 90-day mortality in elderly ICU patients with ARF compared to the LR model. The model may have clinical applications for elderly patients with ARF and may help healthcare professionals to develop detailed treatment plans as well as provide accurate care.
Data availability
The data that support the findings of this study are openly available on the MIMIC-IV website at https://physionet.org/content/mimiciv/1.0/. The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Abbreviations
- ARF:
-
acute renal failure
- ICU:
-
intensive care unit
- XGBoost:
-
extreme gradient boosting
- LR:
-
logistic regression
- DCA:
-
decision curve analysis
- ML:
-
machine learning
- MIMIC-IV:
-
medical information mart for intensive care-IV
- SOFA:
-
sequential organ failure assessment
- APSIII:
-
acute physiology score-III
- LODS:
-
logistic organ dysfunction system
- OASIS:
-
Oxford acute severity of illness score
- SPASII:
-
simplified acute physiology score-II
- SIRS:
-
systemic inflammatory response syndrome
- WBC:
-
white blood cells
- PT:
-
prothrombin time
- PTT:
-
partial thromboplastin time
- AG:
-
anion gap
- SBP:
-
systolic blood pressure
- DBP:
-
diastolic blood pressure
- SpO2:
-
pulse oxygen saturation
- ROC:
-
receiver operating characteristic curve
- AUC:
-
area under the curve
- CCU:
-
coronary care unit
- SICU:
-
surgical intensive care unit
- MICU:
-
medical intensive care unit
- CVICU:
-
cardiac vascular intensive care unit
- CRRT:
-
continuous renal replacement therapy
- BUN:
-
blood urea nitrogen
- Max:
-
maximum
- Min:
-
minimum
- OR:
-
odds ratio
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Funding
This study was supported in part by grants from the National Natural Science Foundation of China (No. 32270186) and the Huadong Medicine Joint Funds of the Zhejiang Provincial Natural Science Foundation (No. LHDMZ24H040001).
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JZ created the study protocol, performed the statistical analyses and wrote the first manuscript draft. YZ and FY assisted with data collection. QS assisted with data interpretation. HM and JY assisted with manuscript revision. YW conceived the study and critically revised the manuscript. All authors read and approved the final manuscript.
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This study was conducted in accordance with the Declaration of Helsinki. Institutional review board approval and informed consent were not required in current study because MIMIC-IV data is publicly available and all patient data are de-identified. Informed consent of all subjects and/or their legal guardians was obtained when MIMIC-IV was established.
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Zeng, J., Zhu, Y., Ye, F. et al. Application of XGBoost and logistic regression in predicting 90 days mortality for elderly severe acute renal failure patients. Sci Rep (2026). https://doi.org/10.1038/s41598-026-37828-w
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DOI: https://doi.org/10.1038/s41598-026-37828-w
