Assessment of a novel semi-automated dataset for large scale surveillance of Central venous catheter–related mechanical complications

BACKGROUND: The aim of this study was to examine whether a novel semi-automated dataset based on electronic health record documentation can be used for surveillance of central venous catheter-related mechanical complications (failed catheterisation, bleeding, cardiac arrhythmia, pneumothorax and nerve injury) within 24 hours of catheterisation. METHODS: The semi-automated dataset comprised a fully automated extraction of clinical documentation from the electronic health record supplemented with a minor manual review aimed at identifying pneumothoraces as these rarely are diagnosed at the time of insertion but rather after a postprocedural chest X-ray. To assess surveillance performance, we compared the number of mechanical complications between the semi-automated and manually evaluated datasets for the same cohort and study period, focusing on agreement in aggregate counts. Comparisons were made at the group level only, without enforcing insertion-by-insertion matching. RESULTS: A total of 12 667 insertions were included. Minor mechanical complications occurred in 615 (4.9%) of the insertions in the semi-automated dataset and in 645 (5.1%) of the insertions in the manually validated dataset. Major mechanical complications occurred in 44 (0.35%) of the insertions in the semi-automated dataset compared to 48 (0.38%) in the manually validated dataset. CONCLUSION: A semi-automated dataset based on electronic health record documentation provides sufficiently accurate surveillance of central catheterisation related mechanical complications at the group level. Despite minor discrepancies, the semi-automated method enhances efficiency, scalability, and supports continuous real-time quality assurance. The potential underestimation of complication rates is offset by the possibility of robust real-time quality assurance in registries and a substantial analytical power in scientific studies.