Department of Nursing, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Qiaokou District, 1095 Jiefang Road, Wuhan, Hubei Province, China.
School of Nursing, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Ital J Pediatr. 2023 Oct 2;49(1):133. doi: 10.1186/s13052-023-01538-0.
Extubation failure (EF) is a significant concern in mechanically ventilated newborns, and predicting its occurrence is an ongoing area of research. To investigate the predictors of EF in newborns undergoing planned extubation, we conducted a systematic review and meta-analysis. A systematic literature search was conducted in PubMed, Web of Science, Embase, and Cochrane Library for studies published in English from the inception of each database to March 2023. The PRISMA guidelines were followed in all phases of this systematic review. The Risk of Bias Assessment for Nonrandomized Studies tool was used to assess methodological quality. Thirty-four studies were included, 10 of which were overall low risk of bias, 15 of moderate risk of bias, and 9 of high risk of bias. The studies reported 43 possible predictors in six broad categories (intrinsic factors; maternal factors; diseases and adverse conditions of the newborn; treatment of the newborn; characteristics before and after extubation; and clinical scores and composite indicators). Through a qualitative synthesis of 43 predictors and a quantitative meta-analysis of 19 factors, we identified five definite factors, eight possible factors, and 22 unclear factors related to EF. Definite factors included gestational age, sepsis, pre-extubation pH, pre-extubation FiO, and respiratory severity score. Possible factors included age at extubation, anemia, inotropic use, mean airway pressure, pre-extubation PCO, mechanical ventilation duration, Apgar score, and spontaneous breathing trial. With only a few high-quality studies currently available, well-designed and more extensive prospective studies investigating the predictors affecting EF are still needed. In the future, it will be important to explore the possibility of combining multiple predictors or assessment tools to enhance the accuracy of predicting extubation outcomes in clinical practice.
拔管失败(EF)是机械通气新生儿的一个重要关注点,预测其发生是一个持续的研究领域。为了研究计划拔管的新生儿 EF 的预测因素,我们进行了系统评价和荟萃分析。在 PubMed、Web of Science、Embase 和 Cochrane Library 中进行了系统的文献检索,检索了从每个数据库建立到 2023 年 3 月发表的英文研究。本系统评价的所有阶段均遵循 PRISMA 指南。非随机研究风险评估工具用于评估方法学质量。共纳入 34 项研究,其中 10 项总体上为低偏倚风险,15 项为中度偏倚风险,9 项为高偏倚风险。这些研究报告了 6 个广泛类别中的 43 个可能的预测因素(内在因素;产妇因素;新生儿疾病和不良状况;新生儿治疗;拔管前后的特征;临床评分和综合指标)。通过对 43 个预测因素进行定性综合分析和对 19 个因素进行定量荟萃分析,我们确定了与 EF 相关的五个确定因素、八个可能因素和二十两个不确定因素。确定因素包括胎龄、败血症、拔管前 pH 值、拔管前 FiO 和呼吸严重程度评分。可能的因素包括拔管时的年龄、贫血、正性肌力药物的使用、平均气道压力、拔管前 PCO、机械通气持续时间、Apgar 评分和自主呼吸试验。由于目前只有少数高质量的研究,因此仍需要设计良好、更广泛的前瞻性研究来研究影响 EF 的预测因素。未来,探索将多个预测因素或评估工具相结合以提高预测临床实践中拔管结果的准确性将非常重要。