PDF(Pubmed)
Abstract：
OBJECTIVE: To develop and evaluate the predictive value of a simplified lung ultrasound (LUS) method for forecasting respiratory support in term infants.
METHODS: This observational, prospective, diagnostic accuracy study was conducted in a tertiary academic hospital between June and December 2023. A total of 361 neonates underwent LUS examination within 1 h of birth. The proportion of each LUS sign was utilized to predict their respiratory outcomes and compared with the LUS score model. After identifying the best predictive LUS sign, simplified models were created based on different scan regions. The optimal simplified model was selected by comparing its accuracy with both the full model and the LUS score model.
RESULTS: After three days of follow-up, 91 infants required respiratory support, while 270 remained healthy. The proportion of confluent B-lines demonstrated high predictive accuracy for respiratory support, with an area under the curve (AUC) of 89.1% (95% confidence interval [CI]: 84.5-93.7%). The optimal simplified model involved scanning the R/L 1-4 region, yielding an AUC of 87.5% (95% CI: 82.6-92.3%). Both the full model and the optimal simplified model exhibited higher predictive accuracy compared to the LUS score model. The optimal cut-off value for the simplified model was determined to be 15.9%, with a sensitivity of 76.9% and specificity of 91.9%.
CONCLUSIONS: The proportion of confluent B-lines in LUS can effectively predict the need for respiratory support in term infants shortly after birth and offers greater reliability than the LUS score model.
METHODS: This observational, prospective, diagnostic accuracy study was conducted in a tertiary academic hospital between June and December 2023. A total of 361 neonates underwent LUS examination within 1 h of birth. The proportion of each LUS sign was utilized to predict their respiratory outcomes and compared with the LUS score model. After identifying the best predictive LUS sign, simplified models were created based on different scan regions. The optimal simplified model was selected by comparing its accuracy with both the full model and the LUS score model.
RESULTS: After three days of follow-up, 91 infants required respiratory support, while 270 remained healthy. The proportion of confluent B-lines demonstrated high predictive accuracy for respiratory support, with an area under the curve (AUC) of 89.1% (95% confidence interval [CI]: 84.5-93.7%). The optimal simplified model involved scanning the R/L 1-4 region, yielding an AUC of 87.5% (95% CI: 82.6-92.3%). Both the full model and the optimal simplified model exhibited higher predictive accuracy compared to the LUS score model. The optimal cut-off value for the simplified model was determined to be 15.9%, with a sensitivity of 76.9% and specificity of 91.9%.
CONCLUSIONS: The proportion of confluent B-lines in LUS can effectively predict the need for respiratory support in term infants shortly after birth and offers greater reliability than the LUS score model.
摘要：
目的：开发并评估一种简化的肺部超声（LUS）方法对足月儿呼吸支持的预测价值。
方法：这种观察，prospective,2023年6月至12月在一家三级学术医院进行了诊断准确性研究.共有361例新生儿在出生后1小时内接受了LUS检查。每个LUS体征的比例被用来预测他们的呼吸结果，并与LUS评分模型进行比较。在确定了最佳预测LUS标志后，基于不同的扫描区域创建简化模型。通过将其精度与完整模型和LUS评分模型进行比较，选择了最佳简化模型。
结果：经过三天的随访，91名婴儿需要呼吸支持，270人保持健康。汇合B线的比例对呼吸支持具有较高的预测准确性，曲线下面积（AUC）为89.1%（95%置信区间[CI]：84.5-93.7%）。最佳简化模型涉及扫描R/L1-4区域，产生87.5%的AUC（95%CI：82.6-92.3%）。与LUS评分模型相比，完整模型和最佳简化模型均表现出更高的预测准确性。简化模型的最佳临界值确定为15.9%，敏感性为76.9%，特异性为91.9%。
结论：LUS中融合的B线比例可以有效预测足月婴儿出生后不久对呼吸支持的需求，并且比LUS评分模型具有更高的可靠性。
方法：这种观察，prospective,2023年6月至12月在一家三级学术医院进行了诊断准确性研究.共有361例新生儿在出生后1小时内接受了LUS检查。每个LUS体征的比例被用来预测他们的呼吸结果，并与LUS评分模型进行比较。在确定了最佳预测LUS标志后，基于不同的扫描区域创建简化模型。通过将其精度与完整模型和LUS评分模型进行比较，选择了最佳简化模型。
结果：经过三天的随访，91名婴儿需要呼吸支持，270人保持健康。汇合B线的比例对呼吸支持具有较高的预测准确性，曲线下面积（AUC）为89.1%（95%置信区间[CI]：84.5-93.7%）。最佳简化模型涉及扫描R/L1-4区域，产生87.5%的AUC（95%CI：82.6-92.3%）。与LUS评分模型相比，完整模型和最佳简化模型均表现出更高的预测准确性。简化模型的最佳临界值确定为15.9%，敏感性为76.9%，特异性为91.9%。
结论：LUS中融合的B线比例可以有效预测足月婴儿出生后不久对呼吸支持的需求，并且比LUS评分模型具有更高的可靠性。
