Machine learning algorithm for predict the in-hospital mortality in critically ill patients with congestive heart failure combined with chronic kidney disease

Abstract

Background

The objective of this study was to develop and validate a machine learning (ML) model for predict in-hospital mortality among critically ill patients with congestive heart failure (CHF) combined with chronic kidney disease (CKD).

Methods

After employing least absolute shrinkage and selection operator regression for feature selection, six distinct methodologies were employed in the construction of the model. The selection of the optimal model was based on the area under the curve (AUC). Furthermore, the interpretation of the chosen model was facilitated through the utilization of SHapley Additive exPlanation (SHAP) values and the Local Interpretable Model-Agnostic Explanations (LIME) algorithm.

Results

This study collected data and enrolled 5041 patients on CHF combined with CKD from 2008 to 2019, utilizing the Medical Information Mart for Intensive Care Unit. After selection, 22 of the 47 variables collected post-intensive care unit admission were identified as mortality-associated and subsequently utilized in the development of ML models. Among the six models generated, the eXtreme Gradient Boosting (XGBoost) model demonstrated the highest AUC at 0.837. Notably, the SHAP values highlighted the sequential organ failure assessment score, age, simplified acute physiology score II, and urine output as the four most influential variables in the XGBoost model. In addition, the LIME algorithm explains the individualized predictions.

Conclusions

In conclusion, our study accomplished the successful development and validation of ML models for predicting in-hospital mortality in critically ill patients with CHF combined with CKD. Notably, the XGBoost model emerged as the most efficacious among all the ML models employed.

Keywords:

• Congestive heart failure
• chronic kidney disease
• machine learning
• mortality
• critically care

Acknowledgments

The authors would like to acknowledge our previous work titled ‘Machine learning algorithm to predict the in-hospital mortality in critically ill patients with chronic kidney disease’ by Li et al. (Renal Failure, 45(1), 2212790), which shares some similarities in study design and sections with the current manuscript. It is essential to note that this manuscript is not derived from the previously published work and presents unique contributions focused on critically ill patients with congestive heart failure combined with chronic kidney disease.

Author contributions

Conceptualization: Xunliang Li, Zhijuan Wang, Haifeng Pan, Deguang Wang; Methodology: Xunliang Li, Zhijuan Wang; Formal analysis and investigation: Xunliang Li, Wenman Zhao, Rui Shi, Yuyu Zhu, Zhijuan Wang. Funding acquisition: Haifeng Pan, Deguang Wang; Supervision: Haifeng Pan, Deguang Wang. All authors read and approved the final manuscript.

Ethics approval and consent to participate

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 and informed consent were not required. This study adheres to the ethical criteria outlined in the Declaration of Helsinki of 1964.

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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.