Huang Rong, Lu Tai-Liang, Liu Ri-Hui
Department of Laboratory, Panyu Hexian Memorial Hospital of Guangzhou, Guangzhou, 511400, China.
Department of Gastrointestinal Surgery, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, 410005, China.
Infection. 2025 Feb;53(1):231-239. doi: 10.1007/s15010-024-02354-2. Epub 2024 Aug 2.
Currently, there are hundreds of hematological parameters used for rapid diagnosis of neonatal sepsis, but there is no network meta-analysis to compare the diagnostic efficacy of these parameters.
We searched for literature on the diagnostic neonatal sepsis and selected 20 of the most common parameters to compare their diagnostic efficacy. We used Bayesian network meta-analysis, Frequentist network meta-analysis, and individual traditional diagnostic meta-analysis to analyze the data and verify the stability of the results. Based on the above analysis, we ranked the diagnostic efficacy of 20 parameters and searched for the optimal indicator. We also conducted subgroup analysis based on different designs. GRADE was used to evaluate the quality of evidence.
311 articles were included in the analysis, of which 206 articles were included in the network meta-analysis. Bayesian models fond the top three of the advantage index were P-SEP, SAA, and CD64. In Individual model, P-SEP, SAA, and CD64 had the best sensitivity; ABC, SAA, and P-SEP had the best specificity. Frequentist model showed that CD64, P-SEP, and IL-10 ranked in the top three for sensitivity, while P-SEP, ABC, and I/M in specificity. Overall, P-SEP, SAA, CD64, and PCT have good sensitivity and specificity among all the three methods. The results of subgroup analysis were consistent with the overall analysis. All evidence was mostly of moderate or low quality.
P-SEP, SAA, CD64, and PCT have good diagnostic efficacy for neonatal sepsis. However, further studies are required to confirm these findings.
目前，有数百种血液学参数用于新生儿败血症的快速诊断，但尚无网络荟萃分析来比较这些参数的诊断效能。
我们检索了有关新生儿败血症诊断的文献，选择了20种最常见的参数来比较它们的诊断效能。我们使用贝叶斯网络荟萃分析、频率学派网络荟萃分析和个体传统诊断荟萃分析来分析数据并验证结果的稳定性。基于上述分析，我们对20个参数的诊断效能进行了排名，并寻找最佳指标。我们还根据不同设计进行了亚组分析。采用GRADE评估证据质量。
分析纳入311篇文章，其中206篇文章纳入网络荟萃分析。贝叶斯模型发现优势指数排名前三的是P-SEP、SAA和CD64。在个体模型中，P-SEP、SAA和CD64具有最佳敏感性；ABC、SAA和P-SEP具有最佳特异性。频率学派模型显示，CD64、P-SEP和IL-10在敏感性方面排名前三，而P-SEP、ABC和I/M在特异性方面排名前三。总体而言，P-SEP、SAA、CD64和PCT在所有三种方法中均具有良好的敏感性和特异性。亚组分析结果与总体分析一致。所有证据大多为中等质量或低质量。
P-SEP、SAA、CD64和PCT对新生儿败血症具有良好的诊断效能。然而，需要进一步研究来证实这些发现。
