PDF(Pubmed)
Abstract：
Intravascular catheter infections are associated with adverse clinical outcomes. However, a significant proportion of these infections are preventable. Evaluations of the performance of automated surveillance systems for adequate monitoring of central-line associated bloodstream infection (CLABSI) or catheter-related bloodstream infection (CRBSI) are limited.
We evaluated the predictive performance of automated algorithms for CLABSI/CRBSI detection, and investigated which parameters included in automated algorithms provide the greatest accuracy for CLABSI/CRBSI detection.
We performed a meta-analysis based on a systematic search of published studies in PubMed and EMBASE from 1 January 2000 to 31 December 2021. We included studies that evaluated predictive performance of automated surveillance algorithms for CLABSI/CRBSI detection and used manually collected surveillance data as reference. We estimated the pooled sensitivity and specificity of algorithms for accuracy and performed a univariable meta-regression of the different parameters used across algorithms.
The search identified five full text studies and 32 different algorithms or study populations were included in the meta-analysis. All studies analysed central venous catheters and identified CLABSI or CRBSI as an outcome. Pooled sensitivity and specificity of automated surveillance algorithm were 0.88 [95%CI 0.84-0.91] and 0.86 [95%CI 0.79-0.92] with significant heterogeneity (I2 = 91.9, p < 0.001 and I2 = 99.2, p < 0.001, respectively). In meta-regression, algorithms that include results of microbiological cultures from specific specimens (respiratory, urine and wound) to exclude non-CRBSI had higher specificity estimates (0.92, 95%CI 0.88-0.96) than algorithms that include results of microbiological cultures from any other body sites (0.88, 95% CI 0.81-0.95). The addition of clinical signs as a predictor did not improve performance of these algorithms with similar specificity estimates (0.92, 95%CI 0.88-0.96).
Performance of automated algorithms for detection of intravascular catheter infections in comparison to manual surveillance seems encouraging. The development of automated algorithms should consider the inclusion of results of microbiological cultures from specific specimens to exclude non-CRBSI, while the inclusion of clinical data may not have an added-value. Trail Registration Prospectively registered with International prospective register of systematic reviews (PROSPERO ID CRD42022299641; January 21, 2022). https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022299641.
We evaluated the predictive performance of automated algorithms for CLABSI/CRBSI detection, and investigated which parameters included in automated algorithms provide the greatest accuracy for CLABSI/CRBSI detection.
We performed a meta-analysis based on a systematic search of published studies in PubMed and EMBASE from 1 January 2000 to 31 December 2021. We included studies that evaluated predictive performance of automated surveillance algorithms for CLABSI/CRBSI detection and used manually collected surveillance data as reference. We estimated the pooled sensitivity and specificity of algorithms for accuracy and performed a univariable meta-regression of the different parameters used across algorithms.
The search identified five full text studies and 32 different algorithms or study populations were included in the meta-analysis. All studies analysed central venous catheters and identified CLABSI or CRBSI as an outcome. Pooled sensitivity and specificity of automated surveillance algorithm were 0.88 [95%CI 0.84-0.91] and 0.86 [95%CI 0.79-0.92] with significant heterogeneity (I2 = 91.9, p < 0.001 and I2 = 99.2, p < 0.001, respectively). In meta-regression, algorithms that include results of microbiological cultures from specific specimens (respiratory, urine and wound) to exclude non-CRBSI had higher specificity estimates (0.92, 95%CI 0.88-0.96) than algorithms that include results of microbiological cultures from any other body sites (0.88, 95% CI 0.81-0.95). The addition of clinical signs as a predictor did not improve performance of these algorithms with similar specificity estimates (0.92, 95%CI 0.88-0.96).
Performance of automated algorithms for detection of intravascular catheter infections in comparison to manual surveillance seems encouraging. The development of automated algorithms should consider the inclusion of results of microbiological cultures from specific specimens to exclude non-CRBSI, while the inclusion of clinical data may not have an added-value. Trail Registration Prospectively registered with International prospective register of systematic reviews (PROSPERO ID CRD42022299641; January 21, 2022). https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022299641.
摘要：
背景：血管内导管感染与不良临床结局相关。然而,这些感染中有很大一部分是可以预防的。用于充分监测中心线相关血流感染（CLABSI）或导管相关血流感染（CRBSI）的自动监测系统的性能评估有限。
目的：我们评估了CLABSI/CRBSI检测自动算法的预测性能，并研究自动算法中包含的哪些参数为CLABSI/CRBSI检测提供了最大的准确性。
方法：我们基于2000年1月1日至2021年12月31日在PubMed和EMBASE上发表的研究的系统检索进行了荟萃分析。我们纳入了评估CLABSI/CRBSI检测自动监测算法预测性能的研究，并使用手动收集的监测数据作为参考。我们估计了算法的准确性的合并敏感性和特异性，并对算法中使用的不同参数进行了单变量元回归。
结果：搜索确定了5个全文研究，32个不同的算法或研究群体被纳入荟萃分析。所有研究都分析了中心静脉导管，并将CLABSI或CRBSI确定为结果。自动监测算法的集合敏感性和特异性分别为0.88[95CI0.84-0.91]和0.86[95CI0.79-0.92]，具有显著异质性（I2=91.9，p<0.001和I2=99.2，p<0.001）。在元回归中，包括来自特定标本的微生物培养结果的算法(呼吸，尿液和伤口）排除非CRBSI的特异性估计值（0.92，95CI0.88-0.96）高于包括来自任何其他身体部位的微生物培养结果的算法（0.88，95％CI0.81-0.95）。添加临床体征作为预测因子并没有改善这些算法的性能，具有相似的特异性估计（0.92，95CI0.88-0.96）。
结论：与人工监测相比，自动算法检测血管内导管感染的表现似乎令人鼓舞。自动算法的开发应考虑将来自特定标本的微生物培养结果纳入其中，以排除非CRBSI，而纳入临床数据可能没有附加值。跟踪注册与国际前瞻性系统评价注册（PROSPEROIDCRD42022299641;2022年1月21日）。https://www.crd.约克。AC.uk/prospro/display_record.php?ID=CRD42022299641。
目的：我们评估了CLABSI/CRBSI检测自动算法的预测性能，并研究自动算法中包含的哪些参数为CLABSI/CRBSI检测提供了最大的准确性。
方法：我们基于2000年1月1日至2021年12月31日在PubMed和EMBASE上发表的研究的系统检索进行了荟萃分析。我们纳入了评估CLABSI/CRBSI检测自动监测算法预测性能的研究，并使用手动收集的监测数据作为参考。我们估计了算法的准确性的合并敏感性和特异性，并对算法中使用的不同参数进行了单变量元回归。
结果：搜索确定了5个全文研究，32个不同的算法或研究群体被纳入荟萃分析。所有研究都分析了中心静脉导管，并将CLABSI或CRBSI确定为结果。自动监测算法的集合敏感性和特异性分别为0.88[95CI0.84-0.91]和0.86[95CI0.79-0.92]，具有显著异质性（I2=91.9，p<0.001和I2=99.2，p<0.001）。在元回归中，包括来自特定标本的微生物培养结果的算法(呼吸，尿液和伤口）排除非CRBSI的特异性估计值（0.92，95CI0.88-0.96）高于包括来自任何其他身体部位的微生物培养结果的算法（0.88，95％CI0.81-0.95）。添加临床体征作为预测因子并没有改善这些算法的性能，具有相似的特异性估计（0.92，95CI0.88-0.96）。
结论：与人工监测相比，自动算法检测血管内导管感染的表现似乎令人鼓舞。自动算法的开发应考虑将来自特定标本的微生物培养结果纳入其中，以排除非CRBSI，而纳入临床数据可能没有附加值。跟踪注册与国际前瞻性系统评价注册（PROSPEROIDCRD42022299641;2022年1月21日）。https://www.crd.约克。AC.uk/prospro/display_record.php?ID=CRD42022299641。
