Intensive & critical care nursing · 2025
Effect of artificial intelligence in extracorporeal membrane oxygenation: a systematic review and meta-analysis.
СтатьяСистематический обзорМетаанализНазвание и аннотация: Перевод в очереди
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Chen Shanshan, Liu Lingjuan, Hu Dingji, Zhu Yike, Wu Changde, Fu Haoya, Wu Jing, Zhang Kexin, Chang Maoxiao, Hao Tong, Wang Wan, Liu Songqiao
Аннотация
OBJECTIVES: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
IMPLICATIONS FOR CLINICAL PRACTICE: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
IMPLICATIONS FOR CLINICAL PRACTICE: 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.