Infection Control Program and WHO Collaborating Centre, Geneva University Hospitals and Faculty of Medicine, Service PCI, Rue Gabrielle-Perret-Gentil 4, 1205, Geneve, Switzerland.
Division of Infectious Diseases & Hospital Epidemiology, University Hospital Basel and University of Basel, Basel, Switzerland.
Antimicrob Resist Infect Control. 2023 Aug 31;12(1):87. doi: 10.1186/s13756-023-01286-0.
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 (I = 91.9, p < 0.001 and I = 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.
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。评估自动化监测系统在监测中心静脉导管相关血流感染（CLABSI）或导管相关血流感染（CRBSI）方面的性能的研究有限。
我们评估了用于 CLABSI/CRBSI 检测的自动化算法的预测性能，并研究了自动化算法中包含的哪些参数对 CLABSI/CRBSI 检测具有最高的准确性。
我们进行了一项荟萃分析，基于对 2000 年 1 月 1 日至 2021 年 12 月 31 日期间在 PubMed 和 EMBASE 上发表的研究进行了系统搜索。我们纳入了评估自动化监测算法用于 CLABSI/CRBSI 检测的预测性能并使用手动收集的监测数据作为参考的研究。我们估计了算法的准确性的汇总敏感性和特异性，并对算法中使用的不同参数进行了单变量荟萃回归分析。
搜索确定了五项全文研究，32 种不同的算法或研究人群被纳入荟萃分析。所有研究均分析了中心静脉导管，并将 CLABSI 或 CRBSI 作为结局。自动化监测算法的汇总敏感性和特异性分别为 0.88 [95%CI 0.84-0.91] 和 0.86 [95%CI 0.79-0.92]，存在显著异质性（I=91.9，p<0.001 和 I=99.2，p<0.001）。在荟萃回归中，包含特定标本（呼吸道、尿液和伤口）微生物培养结果以排除非 CRBSI 的算法具有更高的特异性估计值（0.92，95%CI 0.88-0.96），而包含任何其他身体部位微生物培养结果的算法（0.88，95%CI 0.81-0.95）。添加临床症状作为预测因子并没有提高这些算法的性能，特异性估计值相似（0.92，95%CI 0.88-0.96）。
与手动监测相比，用于检测血管内导管感染的自动化算法的性能似乎令人鼓舞。在开发自动化算法时，应考虑包含特定标本的微生物培养结果以排除非 CRBSI，而包含临床数据可能没有附加值。
前瞻性注册于国际前瞻性系统评价注册库（PROSPERO ID CRD42022299641；2022 年 1 月 21 日）。https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022299641。
