A structured-data algorithm for semiautomated surveillance of surgical site infection after colorectal surgery: A...

Manual surveillance of surgical site infections (SSIs) after colorectal surgery is resource-intensive, limiting scalability. Semiautomated algorithms based on structured electronic health record (EHR) data may maintain high case-finding sensitivity while reducing workload.

A retrospective diagnostic-accuracy study was conducted in a teaching hospital participating in a nationwide SSI surveillance programme. All elective colorectal procedures performed between January 2010 and December 2023 were included. SSIs were classified according to CDC-NHSN/ECDC criteria. Eight binary EHR-derived "alerts" were combined into a composite rule (any alert positive). Manual surveillance served as the reference standard. Performance was assessed overall, by SSI depth (superficial, deep, organ/space), and by procedure type (colon vs rectal). Discrimination (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated with 95 % confidence intervals (CIs).

A total of 1213 patients (1085 colon; 128 rectal) were included. The overall SSI incidence was 11.2 % (3.1 % superficial, 1.2 % deep, 6.8 % organ/space). The composite alert achieved an AUC of 0.859 (95 % CI 0.838-0.878) for any SSI, with sensitivity 0.721, specificity 0.876, PPV 0.424, and NPV 0.961. At this operating point, 19 % of procedures would be flagged for manual verification, corresponding to an estimated 81 % reduction in full chart reviews. Discrimination was highest for organ/space infections (AUC 0.919; sensitivity 0.831; specificity 0.911). Performance for deep SSI was intermediate (AUC 0.805), and for superficial SSI, more limited (AUC 0.571). Sensitivity was higher for colon surgery (AUC 0.853) and specificity higher for rectal surgery (AUC 0.881).

The structured-data algorithm demonstrated strong overall discrimination and excellent performance for organ/space infections, supporting the feasibility of semiautomated surveillance without compromising detection quality. External and prospective validation, definition of diagnostic safety thresholds, and workload-reduction analyses are required to optimise implementation. Exploration of NLP add-ons may be considered where resources permit.

gov: NCT07130656.