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A Machine Learning Approach for the Prediction of Severe Acute Kidney Injury Following Traumatic Brain Injury

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Abstract

Background

Acute kidney injury (AKI), a prevalent non-neurological complication following traumatic brain injury (TBI), is a major clinical issue with an unfavorable prognosis. This study aimed to develop and validate machine learning models to predict severe AKI (stage 3 or greater) incidence in patients with TBI.

Methods

A retrospective cohort study was conducted by using two public databases: the Medical Information Mart for Intensive Care IV (MIMIC)-IV and the eICU Collaborative Research Database (eICU-CRD). Recursive feature elimination was used to select candidate predictors obtained within 24 h of intensive care unit admission. The area under the curve and decision curve analysis curves were used to determine the discriminatory ability. On the other hand, the calibration curve was employed to evaluate the calibrated performance of the newly developed machine learning models.

Results

In the MIMIC-IV database, there were 808 patients diagnosed with moderate and severe TBI (msTBI) (msTBI is defined as Glasgow Coma Score < 12). Of these, 60 (7.43%) patients experienced severe AKI. External validation in the eICU-CRD indicated that the random forest (RF) model had the highest area under the curve of 0.819 (95% confidence interval 0.783–0.851). Furthermore, in the calibration curve, the RF model was well calibrated (P = 0.795).

Conclusions

In this study, the RF model demonstrated better discrimination in predicting severe AKI than other models. An online calculator could facilitate its application, potentially improving the early detection of severe AKI and subsequently improving the clinical outcomes among patients with msTBI.

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Acknowledgements

We would like to thank the Massachusetts Institute of Technology and the Beth Israel Deaconess Medical Center for the Medical Information Mart for Intensive Care project. We would also like to thank the Philips eICU Research Institute and Philips Healthcare for their contributions to the eICU-CRD project.

Funding

This study received funding from the National Key Research and Development Program of China (No. 2017YFC0908005), the San Hang Program of the Second Military Medical University, Three-Year Action Plan for Strengthening Public Health System in Shanghai (2020–2022) Key Discipline Construction Project (NO. GWV-10.1-XK05), and Military Key Disciplines Construction Project -03.

Author information

Author notes

  1. Chi Peng, Fan Yang, Lulu Li and Liwei Peng contributed equally to this work and are co-first authors.

Authors and Affiliations

  1. Department of Health Statistics, Second Military Medical University, Shanghai, China

    Chi Peng, Jian Yu & Zhichao Jin

  2. Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Army Medical University) and Key Laboratory of Tumor Immunopathology, Ministry of Education of China, Chongqing, China

    Fan Yang

  3. Department of Orthopedics, Changhai Hospital, Second Military Medical University, Shanghai, China

    Lulu Li

  4. Department of Neurosurgery, Tangdu Hospital, Fourth Military Medical University, Xi’an, China

    Liwei Peng & Peng Wang

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Contributions

CP, FY, LL, and LP conceived and designed this study; CP and JY performed the modeling and statistical analysis; all authors contributed to the acquisition, analysis, or interpretation of data; FY drafted the article; all authors revised the article for important intellectual content, and ZJ obtained funding. The final manuscript was approved by all authors.

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Correspondence to Zhichao Jin.

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The authors are accountable for all aspects of the work, ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. Since this deidentified database is publicly available, the institutional review board approval and need for written informed consent are waived.

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Peng, C., Yang, F., Li, L. et al. A Machine Learning Approach for the Prediction of Severe Acute Kidney Injury Following Traumatic Brain Injury. Neurocrit Care 38, 335–344 (2023). https://doi.org/10.1007/s12028-022-01606-z

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