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Greater efficiency in SSI surveillance

Greater efficiency in SSI surveillance

Identification of post-operative wound infections

Surveillance of surgical site infections (SSIs) is the first step to their prevention [1, 2]. In practice, however, recording and analysing SSIs is time-consuming and labour-intensive [3, 4]. Comprehensive surveillance often requires the simultaneous monitoring of hundreds of patients, or the retrospective review of thousands of patient records. A recent study therefore investigated a new approach in the form of the sAReS-SSI (Semi-Automated Retrospective Surveillance of Surgical Site Infections) algorithm, which is designed to retrospectively identify potential SSIs using routine hospital data [4].

In particular, the algorithm utilises ICD-10 and OPS codes, including time-related data. By linking diagnoses and procedures, as well as their chronological sequence, on a patient-by-patient basis, the algorithm aims to detect signs of SSIs. As a pre-screening tool sAReS-SSI reduces the number of patient records requiring review as only cases identified as suspected SSIs are subsequently reviewed manually [4].

Screening based on available routine data

sAReS-SSI was validated using routine data from the HygArzt study, which involved prospective SSI surveillance with manual confirmation across three orthopaedic and trauma surgery wards at a university hospital. Of the 65 SSIs previously identified in the routine data, sAReS-SSI detected 61. Subsequent manual analysis of cases initially classified as false positives revealed a further ten SSIs that had not been recorded in the HygArzt study. A total of 75 SSIs were identified in this way, 71 of which were detected by sAReS-SSI [4].

Conserving resources through fewer false-positive results

Of the 2,298 patients, the sAReS-SSI algorithm flagged 198 as potential SSI cases. Compared with a reconstituted NWIF algorithm, the sAReS-SSI algorithm produced fewer false-positive results (127 versus 203). This reduced the number of cases that subsequently had to be assessed manually. According to the authors, reviewing such a case took an average of around 30 minutes [4].

sAReS-SSI as a complement to established surveillance?

Compared with the OP-KISS surgical module of the Hospital Infection Surveillance System, which is run by the National Reference Centre (NRZ), sAReS-SSI provides a broader overview. While OP-KISS is limited to specific indicator procedures, sAReS-SSI can, in principle, track all surgical procedures over an observation period of up to 365 days. In the cohort studied, OP-KISS captured more than half of the operations, but only around 37% of the potential SSIs identified by sAReS-SSI. The algorithm could therefore complement established surveillance [4].

Considerations regarding limitations

The results show that the sAReS-SSI algorithm can support SSI monitoring, making it more efficient. It utilises routinely collected hospital data to help identify cases requiring closer scrutiny. The algorithm is not intended to replace established monitoring methods or clinical judgement, but rather to complement them. Further validation in other hospitals and surgical specialties is required, but the results provide a promising basis for more targeted use of resources in infection prevention [4].

Sources

  1. KRINKO (2018) Prevention of postoperative wound infections. Recommendations of the Commission for Hospital Hygiene and Infection Prevention (KRINKO) at the Robert Koch Institute. Bundesgesundheitsbl 61: 448–473.

  1. Centres for Disease Control (CDC) (1988) Guidelines for Evaluating Surveillance Systems. MMWR Suppl; 37(5): 1–18.

  1. WHO (2018) Global guidelines for the prevention of surgical site infection. World Health Organisation 2018.

  1. Sons D et al. (2026) Antimicrobial Resistance & Infection Control 15: 112.

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