Machine learning algorithm to predict the in-hospital mortality in critically ill patients with chronic kidney disease

Abstract

Background

This study aimed to establish and validate a machine learning (ML) model for predicting in-hospital mortality in critically ill patients with chronic kidney disease (CKD).

Methods

This study collected data on CKD patients from 2008 to 2019 using the Medical Information Mart for Intensive Care IV. Six ML approaches were used to build the model. Accuracy and area under the curve (AUC) were used to choose the best model. In addition, the best model was interpreted using SHapley Additive exPlanations (SHAP) values.

Results

There were 8527 CKD patients eligible for participation; the median age was 75.1 (interquartile range: 65.0–83.5) years, and 61.7% (5259/8527) were male. We developed six ML models with clinical variables as input factors. Among the six models developed, the eXtreme Gradient Boosting (XGBoost) model had the highest AUC, at 0.860. According to the SHAP values, the sequential organ failure assessment score, urine output, respiratory rate, and simplified acute physiology score II were the four most influential variables in the XGBoost model.

Conclusions

In conclusion, we successfully developed and validated ML models for predicting mortality in critically ill patients with CKD. Among all ML models, the XGBoost model is the most effective ML model that can help clinicians accurately manage and implement early interventions, which may reduce mortality in critically ill CKD patients with a high risk of death.

Keywords:

• Chronic kidney disease
• machine learning
• mortality
• intensive care unit
• critically care

Acknowledgements

The authors thank all the participants for their contributions.

Ethical approval

MIMIC IV was set up with the approval of the Institutional Review Board at the Massachusetts Institute of Technology. All participant data were anonymized to safeguard their privacy. Due to the use of anonymized health records, ethical approval was not required. This study adheres to the ethical criteria outlined in the Helsinki Declaration of 1964.

Consent form

Due to the use of anonymized health records, informed consent was not required.

Author contributions

Conceptualization: Xunliang Li, Yuyu Zhu, Haifeng Pan, and Deguang Wang; methodology: Xunliang Li and Yuyu Zhu; formal analysis and investigation: Xunliang Li, Wenman Zhao, Rui Shi, Yuyu Zhu, and Zhijuan Wang; funding acquisition: Haifeng Pan and Deguang Wang; supervision: Haifeng Pan and Deguang Wang. All authors read and approved the final manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The datasets presented in the current study are available in the MIMIC-IV database (https://physionet.org/content/mimiciv/1.0/).

Additional information

Funding

This work was supported by grants from the Natural Science Foundation of Anhui Province [2008085MH244], Incubation Program of National Natural Science Foundation of China of The Second Hospital of Anhui Medical University [2020GMFY04], Clinical Research Incubation Program of The Second Hospital of Anhui Medical University [2020LCZD01], Co-construction Project of Clinical and Preliminary Disciplines of Anhui Medical University in 2020 [2020lcxk02], and Co-construction Project of Clinical and Preliminary Disciplines of Anhui Medical University in 2021 [2021lcxk032]. No funding bodies had any role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.