Effect of artificial intelligence in extracorporeal membrane oxygenation: a systematic review and meta-analysis

To evaluate the effectiveness of Artificial Intelligence (AI) in improving clinical outcomes in Extracorporeal Membrane Oxygenation (ECMO) management, focusing on ECMO initiation, prognosis, and complications.

A meta-analysis following PRISMA guidelines were conducted, with literature searches in PubMed, Embase, and Cochrane Library for studies on AI-based ECMO prediction models between January 1980 and June 2024. Data extraction included AI methodologies, performance metrics, and key findings, with risk of bias was assessed using PROBAST. Meta-analysis used a random-effects model to account for anticipated heterogeneity, with pooled AUCs calculated for ECMO initiation and prognosis prediction.

Of 212 initial records, 36 studies met criteria for inclusion. For ECMO initiation, the pooled AUC was 0.838 (95% CI: 0.804-0.873), and for prognosis prediction, the pooled AUC was 0.776 (95% CI: 0.755-0.797). Significant heterogeneity was observed (I²=96.5 % for ECMO initiation, I²=98.6 % for prognosis). Subgroup analysis revealed single-center studies exhibited higher AUCs for both initiation (AUC=0.888, 95% CI: 0.865-0.910) and prognosis prediction (AUC=0.803, 95% CI: 0.688-0.918) compared to multi-center studies in initiation (AUC=0.823, 95% CI: 0.782-0.864) and prognosis prediction (AUC=0.772, 95% CI: 0.752-0.792). Key AI applications included patient identification, mortality prediction, enhancing resource allocation and decision-making. However, due to data variability and limited external validation, the pooled findings should be interpreted in light of the limitations identified.

AI has shown promise, albeit with significant heterogeneity, in improving ECMO management by providing predictions for initiation timing and patient outcomes. Future research should focus on enhancing model generalizability through multi-center validation, and standardizing data to reduce heterogeneity.

AI models could enhance ECMO management by identifying high-risk patients, predicting adverse events, and guiding timely interventions. Healthcare providers should consider integrating AI tools to ECMO management, while considering its limitations in data and validation.