Background: Surgical site infections (SSIs) complicate ~2% of primary total hip (THAs) or total knee arthroplasties (TKAs). Accurate and timely identification through surveillance is essential for targeted implementation and monitoring of preventive interventions. Electronic health records (EHR) facilitate (semi)-automated surveillance and enable upscaling. A validated algorithm is a prerequisite for broader implementation of semiautomated surveillance. Objectives: To validate a previously published algorithm for semiautomated surveillance of deep SSI after THA or TKA in 4 independent regional Dutch hospitals. The algorithm was developed and implemented in the University Medical Centre Utrecht and relies on retrospective routine care data. Methods: For this multicenter retrospective cohort study, the following data required for the algorithm were extracted from the EHR from all patients under THA and TKA surveillance: microbiology results, antibiotics, (re)admissions, and surgical procedures within the 120 days following the primary surgery. Patients were retrospectively classified with a low or high probability of having developed a deep SSI after THA or TKA, according to the algorithm. Sensitivity, positive predictive value (PPV), and workload reduction (defined as the proportion of manuals requiring review) were calculated compared to the traditional (manual) surveillance results, as reported to the national surveillance PREZIES. Discrepancy analyses were performed to understand algorithm results. Results: Data from 8,378 total THA and TKA surgeries (deep SSI n = 95, 1.1%) performed between 2012 and 2018 were extracted by 4 hospitals (Table 1). Sensitivity ranged across centers from 93.6% to 100%, with a PPV from 55.8% to 72.2%. In all hospitals, the algorithm resulted in >98% workload reduction. Cases missed by the algorithm could be explained by incomplete data extraction. Conclusions: This study shows that the surveillance algorithm performance is good in general Dutch hospitals. Broader implementation of this semiautomated surveillance for SSIs after THA or TKA may be possible in the near future and will result in a substantial workload reduction.

Funding: This work was supported by the Regional Healthcare Network Antibiotic Resistance Utrecht with a subsidy of the Dutch Ministry of Health, Welfare and Sport (grant number 326835).

Disclosures: None