Machine learning-based prediction of in-hospital mortality for critically ill patients with sepsis-associated acute kidney injury

Abstract

Objectives

This study aims to develop and validate a prediction model in-hospital mortality in critically ill patients with sepsis-associated acute kidney injury (SA-AKI) based on machine learning algorithms.

Methods

Patients who met the criteria for inclusion were identified in the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database and divided according to the validation (n = 2440) and development (n = 9756, 80%) queues. Ensemble stepwise feature selection method was used to screen for effective features. The prediction models of short-term mortality were developed by seven machine learning algorithms. Ten-fold cross-validation was used to verify the performance of the algorithm in the development queue. The area under the receiver operating characteristic curve (ROC-AUC) was used to evaluate the differentiation accuracy and performance of the prediction model in the validation queue. The best-performing model was interpreted by Shapley additive explanations (SHAP).

Results

A total of 12,196 patients were enrolled in this study. Eleven variables were finally chosen to develop the prediction model. The AUC of the random forest (RF) model was the highest value both in the Ten-fold cross-validation and evaluation (AUC: 0.798, 95% CI: 0.774–0.821). According to the SHAP plots, old age, low Glasgow Coma Scale (GCS) score, high AKI stage, reduced urine output, high Simplified Acute Physiology Score (SAPS II), high respiratory rate, low temperature, low absolute lymphocyte count, high creatinine level, dysnatremia, and low body mass index (BMI) increased the risk of poor prognosis.

Conclusions

The RF model developed in this study is a good predictor of in-hospital mortality for patients with SA-AKI in the intensive care unit (ICU), which may have potential applications in mortality prediction.

Keywords:

• Sepsis
• acute kidney injury
• prediction model of prognosis
• machine learning algorithms

Acknowledgments

Not application.

Authors’ contributions

L.P.: study design, data analysis, and revised the manuscript; T.G. and Z.N: study design, data extraction, and writing the manuscript; Y.L. and M.M.: study design, revised the manuscript; Z.C. and Z.Y.: data extraction and revised the manuscript. All authors read and approved the final manuscript.

Ethics approval and consent to participate

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee at which the studies were conducted and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The study was approved by the Clinical Research Ethics Committee of The First Affiliated Hospital of Guangxi Medical University [institutional review board approval number 2019 (KY-E-028)]. The establishment of MIMIC-IV (v1.0) was approved by the institutional review boards of the Beth Israel Deaconess Medical Center (Boston, MA) and the Institutional Review Boards of Massachusetts Institute of Technology (Cambridge, MA). Informed consent was waived because of the study design.

Consent for publication

Not Applicable.

Patient and public involvement

Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

Disclosure statement

The authors have declared no conflicts of interest.

Availability of data and materials

The datasets analyzed for this study can be found in the MIMIC-IV (https://mimic.mit.edu/) [11].

Additional information

Funding

This work was supported by the Guangxi Natural Science Foundation (2018GXNSFBA050040, 2022GXNSFAA035458), National Natural Science Foundation of China (81960135), the Scientific Research and Technological Development Program of Guangxi (No. GuiKeGong 1598011-6), the Guangxi Medical and Health Care Suitable Technology Project of Guangxi Zhuang Autonomous Region Health Committee (S2018045), and the Guangxi Zhuang Autonomous Region Health Committee Self-funded Scientific Research Project (Z20191097).