Type of system | Fully automated |
HAI targeted | Intravascular catheter infections (definitions adapted from ECDC) |
Date sources | Electronic Health Record |
Validation method | Comparison with prospective manual surveillance of BSI conducting according to ECDC definitions |
Comparator | Manual surveillance of bloodstream infections over a 6-year period (2016 to 2021) |
Data type included | Administrative data, microbiology lab data, individual intravascular catheters data extracted from data EHR |
Patient population | All adult patients admitted to the intensive care unit |
Indicators | CRBSI, CLABSI, ICU-onset BSI |
Denominators | Catheter-days and patient-days |
Sensitivity | 83% (95%CI: 43.7–96.9) |
Specificity | 100% (95%CI: 99.5–100) |
Lessons learned | - Several cut-offs for different parameters in the algorithm were set arbitrarily and would require further in-depth sensitivity analyses (e.g., delay between two blood cultures with the same common contaminant to consider the episode; delay to consider positive specimens with the same bacteria as in the blood cultures to exclude a CRBSI). - Some ECDC rules were not transposable in a fully automated algorithm because of the lack of availability or accuracy of the data in the IT system: (e.g., quantitative blood culture ratio CVC blood sample/peripheral blood sample; differential time of blood culture positivity) - Some data are difficult to capture in a fully automated algorithm because of the lack of standardisation (e.g., culture from pus from the insertion site of the catheters are frequently mislabelled and difficult to identify in the microbiology database). |