人工智能在预测严重 COVID-19 预后方面的表现：系统评价和荟萃分析。
Performance of artificial intelligence in predicting the prognossis of severe COVID-19: a systematic review and meta-analysis.
机构信息
School of Public Health, Zhejiang Chinese Medical University, Hangzhou, China.
Department of Critical Care Medicine, Tongde Hospital of Zhejiang Province, Hangzhou, China.
出版信息
Front Public Health. 2024 Jul 31;12:1371852. doi: 10.3389/fpubh.2024.1371852. eCollection 2024.
BACKGROUND
COVID-19-induced pneumonia has become a persistent health concern, with severe cases posing a significant threat to patient lives. However, the potential of artificial intelligence (AI) in assisting physicians in predicting the prognosis of severe COVID-19 patients remains unclear.
METHODS
To obtain relevant studies, two researchers conducted a comprehensive search of the PubMed, Web of Science, and Embase databases, including all studies published up to October 31, 2023, that utilized AI to predict mortality rates in severe COVID-19 patients. The PROBAST 2019 tool was employed to assess the potential bias in the included studies, and Stata 16 was used for meta-analysis, publication bias assessment, and sensitivity analysis.
RESULTS
A total of 19 studies, comprising 26 models, were included in the analysis. Among them, the models that incorporated both clinical and radiological data demonstrated the highest performance. These models achieved an overall sensitivity of 0.81 (0.64-0.91), specificity of 0.77 (0.71-0.82), and an overall area under the curve (AUC) of 0.88 (0.85-0.90). Subgroup analysis revealed notable findings. Studies conducted in developed countries exhibited significantly higher predictive specificity for both radiological and combined models ( < 0.05). Additionally, investigations involving non-intensive care unit patients demonstrated significantly greater predictive specificity ( < 0.001).
CONCLUSION
The current evidence suggests that artificial intelligence prediction models show promising performance in predicting the prognosis of severe COVID-19 patients. However, due to variations in the suitability of different models for specific populations, it is not yet certain whether they can be fully applied in clinical practice. There is still room for improvement in their predictive capabilities, and future research and development efforts are needed.
SYSTEMATIC REVIEW REGISTRATION
https://www.crd.york.ac.uk/prospero/ with the Unique Identifier CRD42023431537.
背景
COVID-19 引起的肺炎已成为持续存在的健康问题，重症病例对患者生命构成重大威胁。然而，人工智能（AI）在帮助医生预测重症 COVID-19 患者预后方面的潜力尚不清楚。
方法
为了获取相关研究，两位研究人员全面检索了 PubMed、Web of Science 和 Embase 数据库，包括截至 2023 年 10 月 31 日发表的所有使用 AI 预测重症 COVID-19 患者死亡率的研究。使用 PROBAST 2019 工具评估纳入研究的潜在偏倚，使用 Stata 16 进行荟萃分析、发表偏倚评估和敏感性分析。
结果
共纳入 19 项研究，包含 26 个模型。其中，同时纳入临床和影像学数据的模型表现最佳。这些模型的总体敏感性为 0.81（0.64-0.91），特异性为 0.77（0.71-0.82），总体曲线下面积（AUC）为 0.88（0.85-0.90）。亚组分析显示了显著结果。在发达国家进行的研究中，影像学和综合模型的预测特异性均显著更高（<0.05）。此外，针对非重症监护病房患者的研究显示，预测特异性显著更高（<0.001）。
结论
目前的证据表明，人工智能预测模型在预测重症 COVID-19 患者预后方面表现出良好的性能。然而，由于不同模型对特定人群的适用性存在差异，尚不确定它们是否能够在临床实践中得到充分应用。它们的预测能力仍有改进的空间，需要进一步的研究和开发工作。
系统评价注册
https://www.crd.york.ac.uk/prospero/，注册号为 CRD42023431537。
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