Clostridioides difficile surveillance: 9-year comparison between automated surveillance and conventional surveillance in acute care hospitals.

Abstract

Objective: To develop and validate an automated surveillance system for healthcare-associated Clostridioides difficile infections (HA-CDI).

Design: Multicenter cohort study.

Setting: 16 acute care hospitals.

Patients: Patients admitted to participating hospitals between 2013 and 2022.

Methods: An automated surveillance system was developed with retrospective extraction from admission/discharge/transfer and laboratory databases and compared with conventional surveillance based on clinical definitions collected prospectively by infection control professionals. Comparison of HA-CDI incidence rates calculated by automated vs conventional surveillances were performed with χ², incidence rate ratios, and linear regression. A subset of discordant cases was further investigated by reviewing medical records.

Results: Overall, conventional surveillance reported 3,211 cases of HA-CDI for an incidence rate of 4.94 per 10,000 patient-days. Automated surveillance detected 4,708 cases, for an incidence rate of 7.24 per 10,000 patient-days (incidence rate ratio, 1.47; 95% CI, 1.40-1.53). Full concordance between both surveillance methods was observed in 62% of cases, while 34% of cases were detected only by automated surveillance, and 4% were detected by conventional surveillance only. Between 2013 and 2022, an identical declining trend in HA-CDI incidence rates of -0.54 cases per 10,000 patient-days was observed with both surveillance methods. A subset of 49 cases detected only by automated surveillance were reviewed; the main reasons for discrepancy were delayed testing (39%), colonization (24%), misclassifications (14%), and interinstitutional transfers (12%).

Conclusions: HA-CDI incidence rates calculated by automated surveillance were higher than those of conventional surveillance, but the overestimation was consistent over time, suggesting that a correction factor could improve precision.