Machine learning-based model for predicting inpatient mortality in adults with traumatic brain injury: a systematic review and meta-analysis

Zhe Wu; Jinqing Lai; Qiaomei Huang; Long Lin; Shu Lin; Xiangrong Chen; Yinqiong Huang

doi:10.3389/fnins.2023.1285904

Machine learning-based model for predicting inpatient mortality in adults with traumatic brain injury: a systematic review and meta-analysis

Front Neurosci. 2023 Dec 14:17:1285904. doi: 10.3389/fnins.2023.1285904. eCollection 2023.

Authors

Zhe Wu¹, Jinqing Lai¹, Qiaomei Huang², Long Lin³, Shu Lin⁴, Xiangrong Chen^{1

4}, Yinqiong Huang^{4

5}

Affiliations

¹ Department of Neuronal Surgery, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China.
² Department of Anesthesiology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China.
³ Department of Neurosurgery, Fuzong Clinical Medical College, Fuzhou, Fujian, China.
⁴ Centre of Neurological and Metabolic Research, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China.
⁵ Department of Endocrinology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China.

Abstract

Background and objective: Predicting mortality from traumatic brain injury facilitates early data-driven treatment decisions. Machine learning has predicted mortality from traumatic brain injury in a growing number of studies, and the aim of this study was to conduct a meta-analysis of machine learning models in predicting mortality from traumatic brain injury.

Methods: This systematic review and meta-analysis included searches of PubMed, Web of Science and Embase from inception to June 2023, supplemented by manual searches of study references and review articles. Data were analyzed using Stata 16.0 software. This study is registered with PROSPERO (CRD2023440875).

Results: A total of 14 studies were included. The studies showed significant differences in the overall sample, model type and model validation. Predictive models performed well with a pooled AUC of 0.90 (95% CI: 0.87 to 0.92).

Conclusion: Overall, this study highlights the excellent predictive capabilities of machine learning models in determining mortality following traumatic brain injury. However, it is important to note that the optimal machine learning modeling approach has not yet been identified.

Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=440875, identifier CRD2023440875.

Keywords: inpatient mortality; machine learning; meta-analysis; mortality predictor; traumatic brain injury.

Publication types

Systematic Review

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported in part by grants from National Natural Science Foundation of China (Grant Nos. 82200871 and 82371390), the Major Scientific Research Program for Young and Middle-aged Health Professionals of Fujian Province, China (Grant No. 2022ZQNZD007), and Fujian Province Scientific Foundation (Grant No. 2023J01725).