Comparison of artificial intelligence and logistic regression models for mortality prediction in acute respiratory distress syndrome: a systematic review and meta-analysis

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Abstract
Background: The application of artificial intelligence (AI) in predicting the mortality of acute respiratory distress syndrome (ARDS) has garnered significant attention. However, there is still a lack of evidence-based support for its specific diagnostic performance. Thus, this systematic review and meta-analysis was conducted to evaluate the effectiveness of AI algorithms in predicting ARDS mortality. Method: We conducted a comprehensive electronic search across Web of Science, Embase, PubMed, Scopus, and EBSCO databases up to April 28, 2024. The QUADAS-2 tool was used to assess the risk of bias in the included articles. A bivariate mixed-effects model was applied for the meta-analysis. Sensitivity analysis, meta-regression analysis, and tests for heterogeneity were also performed. Results: Eight studies were included in the analysis. The sensitivity, specificity, and summarized receiver operating characteristic (SROC) of the AI-based model in the validation set were 0.89 (95% CI 0.79–0.95), 0.72 (95% CI 0.65–0.78), and 0.84 (95% CI 0.80–0.87), respectively. For the logistic regression (LR) model, the sensitivity, specificity, and SROC were 0.78 (95% CI 0.74–0.82), 0.68 (95% CI 0.60–0.76), and 0.81 (95% CI 0.77–0.84). The AI model demonstrated superior predictive accuracy compared to the LR model. Notably, the predictive model performed better in patients with moderate to severe ARDS (SAUC: 0.84 [95% CI 0.80–0.87] vs. 0.81 [95% CI 0.77–0.84]). Conclusion: The AI algorithms showed superior performance in predicting the mortality of ARDS patients and demonstrated strong potential for clinical application. Additionally, we found that for ARDS, a highly heterogeneous condition, the accuracy of the model is influenced by the severity of the disease.

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