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Abstract：
BACKGROUND: This systematic review aims to assist clinical decision-making in selecting appropriate preoperative prediction methods for difficult tracheal intubation by identifying and synthesizing literature on these methods in adult patients undergoing all types of surgery.
METHODS: A systematic review and meta-analysis were conducted following PRISMA guidelines. Comprehensive electronic searches across multiple databases were completed on March 28, 2023. Two researchers independently screened, selected studies, and extracted data. A total of 227 articles representing 526 studies were included and evaluated for bias using the QUADAS-2 tool. Meta-Disc software computed pooled sensitivity (SEN), specificity (SPC), positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR). Heterogeneity was assessed using the Spearman correlation coefficient, Cochran\'s-Q, and I2 index, with meta-regression exploring sources of heterogeneity. Publication bias was evaluated using Deeks\' funnel plot.
RESULTS: Out of 2906 articles retrieved, 227 met the inclusion criteria, encompassing a total of 686,089 patients. The review examined 11 methods for predicting difficult tracheal intubation, categorized into physical examination, multivariate scoring system, and imaging test. The modified Mallampati test (MMT) showed a SEN of 0.39 and SPC of 0.86, while the thyromental distance (TMD) had a SEN of 0.38 and SPC of 0.83. The upper lip bite test (ULBT) presented a SEN of 0.52 and SPC of 0.84. Multivariate scoring systems like LEMON and Wilson\'s risk score demonstrated moderate sensitivity and specificity. Imaging tests, particularly ultrasound-based methods such as the distance from the skin to the epiglottis (US-DSE), exhibited higher sensitivity (0.80) and specificity (0.77). Significant heterogeneity was identified across studies, influenced by factors such as sample size and study design.
CONCLUSIONS: No single preoperative prediction method shows clear superiority for predicting difficult tracheal intubation. The evidence supports a combined approach using multiple methods tailored to specific patient demographics and clinical contexts. Future research should focus on integrating advanced technologies like artificial intelligence and deep learning to improve predictive models. Standardizing testing procedures and establishing clear cut-off values are essential for enhancing prediction reliability and accuracy. Implementing a multi-modal predictive approach may reduce unanticipated difficult intubations, improving patient safety and outcomes.
METHODS: A systematic review and meta-analysis were conducted following PRISMA guidelines. Comprehensive electronic searches across multiple databases were completed on March 28, 2023. Two researchers independently screened, selected studies, and extracted data. A total of 227 articles representing 526 studies were included and evaluated for bias using the QUADAS-2 tool. Meta-Disc software computed pooled sensitivity (SEN), specificity (SPC), positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR). Heterogeneity was assessed using the Spearman correlation coefficient, Cochran\'s-Q, and I2 index, with meta-regression exploring sources of heterogeneity. Publication bias was evaluated using Deeks\' funnel plot.
RESULTS: Out of 2906 articles retrieved, 227 met the inclusion criteria, encompassing a total of 686,089 patients. The review examined 11 methods for predicting difficult tracheal intubation, categorized into physical examination, multivariate scoring system, and imaging test. The modified Mallampati test (MMT) showed a SEN of 0.39 and SPC of 0.86, while the thyromental distance (TMD) had a SEN of 0.38 and SPC of 0.83. The upper lip bite test (ULBT) presented a SEN of 0.52 and SPC of 0.84. Multivariate scoring systems like LEMON and Wilson\'s risk score demonstrated moderate sensitivity and specificity. Imaging tests, particularly ultrasound-based methods such as the distance from the skin to the epiglottis (US-DSE), exhibited higher sensitivity (0.80) and specificity (0.77). Significant heterogeneity was identified across studies, influenced by factors such as sample size and study design.
CONCLUSIONS: No single preoperative prediction method shows clear superiority for predicting difficult tracheal intubation. The evidence supports a combined approach using multiple methods tailored to specific patient demographics and clinical contexts. Future research should focus on integrating advanced technologies like artificial intelligence and deep learning to improve predictive models. Standardizing testing procedures and establishing clear cut-off values are essential for enhancing prediction reliability and accuracy. Implementing a multi-modal predictive approach may reduce unanticipated difficult intubations, improving patient safety and outcomes.
摘要：
背景：本系统综述旨在帮助临床决策，为困难的气管插管选择合适的术前预测方法，通过识别和综合有关接受所有类型手术的成年患者的这些方法的文献。
方法：按照PRISMA指南进行系统评价和荟萃分析。2023年3月28日完成了跨多个数据库的全面电子搜索。两名研究人员独立筛选，选定的研究，并提取数据。共纳入227篇文章，代表526项研究，并使用QUADAS-2工具评估偏倚。元光盘软件计算合并灵敏度(SEN)，特异性（SPC），正似然比(PLR)，负似然比(NLR)，和诊断比值比(DOR)。使用Spearman相关系数评估异质性，Cochran\'s-Q,和I2指数，利用元回归探索异质性来源。使用Deeks漏斗图评估出版偏倚。
结果：在检索到的2906篇文章中，227符合纳入标准，包括总共686,089名患者。该综述检查了11种预测气管插管困难的方法，分为体格检查，多元评分系统，和成像测试。改良的Mallampati测试（MMT）显示SEN为0.39，SPC为0.86，而甲状腺距离（TMD）的SEN为0.38，SPC为0.83。上唇咬伤测试（ULBT）的SEN为0.52，SPC为0.84。多变量评分系统如LEMON和Wilson的风险评分显示出中等的敏感性和特异性。成像测试,特别是基于超声的方法，如从皮肤到会厌的距离（US-DSE），表现出更高的敏感性（0.80）和特异性（0.77）。在研究中发现了显著的异质性，受样本量和研究设计等因素的影响。
结论：没有单一的术前预测方法在预测气管插管困难方面具有明显的优越性。证据支持使用针对特定患者人口统计学和临床背景量身定制的多种方法的组合方法。未来的研究应该集中在整合人工智能和深度学习等先进技术，以改进预测模型。标准化测试程序和建立明确的截止值对于提高预测的可靠性和准确性至关重要。实施多模式预测方法可以减少意想不到的困难插管，改善患者安全和预后。
方法：按照PRISMA指南进行系统评价和荟萃分析。2023年3月28日完成了跨多个数据库的全面电子搜索。两名研究人员独立筛选，选定的研究，并提取数据。共纳入227篇文章，代表526项研究，并使用QUADAS-2工具评估偏倚。元光盘软件计算合并灵敏度(SEN)，特异性（SPC），正似然比(PLR)，负似然比(NLR)，和诊断比值比(DOR)。使用Spearman相关系数评估异质性，Cochran\'s-Q,和I2指数，利用元回归探索异质性来源。使用Deeks漏斗图评估出版偏倚。
结果：在检索到的2906篇文章中，227符合纳入标准，包括总共686,089名患者。该综述检查了11种预测气管插管困难的方法，分为体格检查，多元评分系统，和成像测试。改良的Mallampati测试（MMT）显示SEN为0.39，SPC为0.86，而甲状腺距离（TMD）的SEN为0.38，SPC为0.83。上唇咬伤测试（ULBT）的SEN为0.52，SPC为0.84。多变量评分系统如LEMON和Wilson的风险评分显示出中等的敏感性和特异性。成像测试,特别是基于超声的方法，如从皮肤到会厌的距离（US-DSE），表现出更高的敏感性（0.80）和特异性（0.77）。在研究中发现了显著的异质性，受样本量和研究设计等因素的影响。
结论：没有单一的术前预测方法在预测气管插管困难方面具有明显的优越性。证据支持使用针对特定患者人口统计学和临床背景量身定制的多种方法的组合方法。未来的研究应该集中在整合人工智能和深度学习等先进技术，以改进预测模型。标准化测试程序和建立明确的截止值对于提高预测的可靠性和准确性至关重要。实施多模式预测方法可以减少意想不到的困难插管，改善患者安全和预后。
