PDF(Pubmed)
Abstract：
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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 (p < 0.05). Additionally, investigations involving non-intensive care unit patients demonstrated significantly greater predictive specificity (p < 0.001).
UNASSIGNED: 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.
UNASSIGNED: https://www.crd.york.ac.uk/prospero/ with the Unique Identifier CRD42023431537.
UNASSIGNED: 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.
UNASSIGNED: 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 (p < 0.05). Additionally, investigations involving non-intensive care unit patients demonstrated significantly greater predictive specificity (p < 0.001).
UNASSIGNED: 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.
UNASSIGNED: https://www.crd.york.ac.uk/prospero/ with the Unique Identifier CRD42023431537.
摘要：
■COVID-19引起的肺炎已成为持续的健康问题，严重病例对患者生命构成重大威胁。然而,人工智能(AI)在帮助医师预测严重COVID-19患者预后方面的潜力尚不清楚.
■为了获得相关研究，两名研究人员对PubMed进行了全面搜索，WebofScience,和Embase数据库，包括截至2023年10月31日发表的所有利用人工智能预测严重COVID-19患者死亡率的研究。PROBAST2019工具用于评估纳入研究中的潜在偏差，Stata16用于荟萃分析，出版偏见评估，和敏感性分析。
■总共19项研究，由26个模型组成，包括在分析中。其中,纳入临床和放射学数据的模型表现出最高的性能.这些模型的总体灵敏度为0.81(0.64-0.91)，特异性为0.77（0.71-0.82），曲线下面积(AUC)为0.88(0.85-0.90)。亚组分析显示显著发现。在发达国家进行的研究对放射学模型和组合模型均表现出明显更高的预测特异性(p<0.05)。此外,涉及非重症监护病房患者的调查显示了更高的预测特异性(p<0.001).
■当前证据表明，人工智能预测模型在预测严重COVID-19患者的预后方面显示出有希望的性能。然而,由于不同模型对特定人群的适用性不同，尚不确定它们是否可以完全应用于临床实践。他们的预测能力还有改进的空间,需要未来的研究和开发努力。
■https://www.crd.约克。AC.带有唯一标识符CRD42023431537的uk/prospro/。
■为了获得相关研究，两名研究人员对PubMed进行了全面搜索，WebofScience,和Embase数据库，包括截至2023年10月31日发表的所有利用人工智能预测严重COVID-19患者死亡率的研究。PROBAST2019工具用于评估纳入研究中的潜在偏差，Stata16用于荟萃分析，出版偏见评估，和敏感性分析。
■总共19项研究，由26个模型组成，包括在分析中。其中,纳入临床和放射学数据的模型表现出最高的性能.这些模型的总体灵敏度为0.81(0.64-0.91)，特异性为0.77（0.71-0.82），曲线下面积(AUC)为0.88(0.85-0.90)。亚组分析显示显著发现。在发达国家进行的研究对放射学模型和组合模型均表现出明显更高的预测特异性(p<0.05)。此外,涉及非重症监护病房患者的调查显示了更高的预测特异性(p<0.001).
■当前证据表明，人工智能预测模型在预测严重COVID-19患者的预后方面显示出有希望的性能。然而,由于不同模型对特定人群的适用性不同，尚不确定它们是否可以完全应用于临床实践。他们的预测能力还有改进的空间,需要未来的研究和开发努力。
■https://www.crd.约克。AC.带有唯一标识符CRD42023431537的uk/prospro/。
