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Meta-Analysis
. 2023 Jul 29;23(1):142.
doi: 10.1186/s12911-023-02247-8.

Prediction performance of the machine learning model in predicting mortality risk in patients with traumatic brain injuries: a systematic review and meta-analysis

Jue Wang  1 , Ming Jing Yin  1 , Han Chun Wen  2   3
Affiliations
Meta-Analysis

Prediction performance of the machine learning model in predicting mortality risk in patients with traumatic brain injuries: a systematic review and meta-analysis

Jue Wang et al. BMC Med Inform Decis Mak. .

Abstract

Purpose: With the in-depth application of machine learning(ML) in clinical practice, it has been used to predict the mortality risk in patients with traumatic brain injuries(TBI). However, there are disputes over its predictive accuracy. Therefore, we implemented this systematic review and meta-analysis, to explore the predictive value of ML for TBI.

Methodology: We systematically retrieved literature published in PubMed, Embase.com, Cochrane, and Web of Science as of November 27, 2022. The prediction model risk of bias(ROB) assessment tool (PROBAST) was used to assess the ROB of models and the applicability of reviewed questions. The random-effects model was adopted for the meta-analysis of the C-index and accuracy of ML models, and a bivariate mixed-effects model for the meta-analysis of the sensitivity and specificity.

Result: A total of 47 papers were eligible, including 156 model, with 122 newly developed ML models and 34 clinically recommended mature tools. There were 98 ML models predicting the in-hospital mortality in patients with TBI; the pooled C-index, sensitivity, and specificity were 0.86 (95% CI: 0.84, 0.87), 0.79 (95% CI: 0.75, 0.82), and 0.89 (95% CI: 0.86, 0.92), respectively. There were 24 ML models predicting the out-of-hospital mortality; the pooled C-index, sensitivity, and specificity were 0.83 (95% CI: 0.81, 0.85), 0.74 (95% CI: 0.67, 0.81), and 0.75 (95% CI: 0.66, 0.82), respectively. According to multivariate analysis, GCS score, age, CT classification, pupil size/light reflex, glucose, and systolic blood pressure (SBP) exerted the greatest impact on the model performance.

Conclusion: According to the systematic review and meta-analysis, ML models are relatively accurate in predicting the mortality of TBI. A single model often outperforms traditional scoring tools, but the pooled accuracy of models is close to that of traditional scoring tools. The key factors related to model performance include the accepted clinical variables of TBI and the use of CT imaging.

Keywords: Machine learning; Meta-analysis; Mortality rate; Prediction; Systematic review; Traumatic brain injury.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Literature screening process
Fig. 2
Fig. 2
ROB assessment result
Fig. 3
Fig. 3
Forest map of c-index prediction of in-hospital deaths by newly developed ML models and clinically recommended tools
Fig. 4
Fig. 4
Forest map of c-index prediction of out-of-hospital deaths by newly developed ML models and clinically recommended tools
Fig. 5
Fig. 5
a Forest plots of sensitivity of newly developed ML models and clinically recommended tools to predict in-hospital death. b Forest plots of specificity of newly developed ML models and clinically recommended tools to predict in-hospital death
See this image and copyright information in PMC

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