Infection Control Programme and World Health Organization Collaborating Centre, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland.
Infectious Diseases Division, Central Institute, Valais Hospital, Sion, Switzerland.
Antimicrob Resist Infect Control. 2024 Apr 10;13(1):38. doi: 10.1186/s13756-024-01395-4.
Most surveillance systems for catheter-related bloodstream infections (CRBSI) and central line-associated bloodstream infections (CLABSI) are based on manual chart review. Our objective was to validate a fully automated algorithm for CRBSI and CLABSI surveillance in intensive care units (ICU).
We developed a fully automated algorithm to detect CRBSI, CLABSI and ICU-onset bloodstream infections (ICU-BSI) in patients admitted to the ICU of a tertiary care hospital in Switzerland. The parameters included in the algorithm were based on a recently performed systematic review. Structured data on demographics, administrative data, central vascular catheter and microbiological results (blood cultures and other clinical cultures) obtained from the hospital's data warehouse were processed by the algorithm. Validation for CRBSI was performed by comparing results with prospective manual BSI surveillance data over a 6-year period. CLABSI were retrospectively assessed over a 2-year period.
From January 2016 to December 2021, 854 positive blood cultures were identified in 346 ICU patients. The median age was 61.7 years [IQR 50-70]; 205 (24%) positive samples were collected from female patients. The algorithm detected 5 CRBSI, 109 CLABSI and 280 ICU-BSI. The overall CRBSI and CLABSI incidence rates determined by automated surveillance for the period 2016 to 2021 were 0.18/1000 catheter-days (95% CI 0.06-0.41) and 3.86/1000 catheter days (95% CI: 3.17-4.65). The sensitivity, specificity, positive predictive and negative predictive values of the algorithm for CRBSI, were 83% (95% CI 43.7-96.9), 100% (95% CI 99.5-100), 100% (95% CI 56.5-100), and 99.9% (95% CI 99.2-100), respectively. One CRBSI was misclassified as an ICU-BSI by the algorithm because the same bacterium was identified in the blood culture and in a lower respiratory tract specimen. Manual review of CLABSI from January 2020 to December 2021 (n = 51) did not identify any errors in the algorithm.
A fully automated algorithm for CRBSI and CLABSI detection in critically-ill patients using only structured data provided valid results. The next step will be to assess the feasibility and external validity of implementing it in several hospitals with different electronic health record systems.
大多数导管相关血流感染(CRBSI)和中心静脉相关血流感染(CLABSI)监测系统都基于人工图表审查。我们的目标是验证一种用于重症监护病房(ICU)CRBSI 和 CLABSI 监测的完全自动化算法。
我们开发了一种完全自动化的算法,用于检测瑞士一家三级护理医院 ICU 住院患者的 CRBSI、CLABSI 和 ICU 发病的血流感染(ICU-BSI)。该算法中包含的参数基于最近进行的系统评价。从医院数据仓库中获取的人口统计学、管理数据、中央血管导管和微生物学结果(血培养和其他临床培养)的结构化数据由算法进行处理。通过将 6 年期间前瞻性手动 BSI 监测数据与结果进行比较来验证 CRBSI。在 2 年期间回顾性评估 CLABSI。
从 2016 年 1 月至 2021 年 12 月,在 346 名 ICU 患者中鉴定出 854 份阳性血培养物。中位年龄为 61.7 岁[IQR 50-70];205(24%)份阳性样本取自女性患者。该算法检测到 5 例 CRBSI、109 例 CLABSI 和 280 例 ICU-BSI。2016 年至 2021 年期间,自动监测确定的总体 CRBSI 和 CLABSI 发生率分别为 0.18/1000 导管日(95%CI 0.06-0.41)和 3.86/1000 导管日(95%CI:3.17-4.65)。该算法对 CRBSI 的灵敏度、特异性、阳性预测值和阴性预测值分别为 83%(95%CI 43.7-96.9)、100%(95%CI 99.5-100)、100%(95%CI 56.5-100)和 99.9%(95%CI 99.2-100)。由于在血培养和下呼吸道标本中鉴定出相同的细菌,算法将 1 例 CRBSI 错误分类为 ICU-BSI。对 2020 年 1 月至 2021 年 12 月的 CLABSI(n=51)进行人工审查并未发现算法中的任何错误。
使用仅结构化数据的重症患者 CRBSI 和 CLABSI 检测的完全自动化算法提供了有效结果。下一步将评估在具有不同电子健康记录系统的几家医院实施该算法的可行性和外部有效性。