Infection Control & Hospital Epidemiology
Table 1. Detection Sensitivity (Classified by SSI Type) Using Semiautomated Surveillance Conventional SSI Surveillance
No. of SSI Variable Total (n=40,516)
Clean surgery (n=19,672) Superficial incisional SSI Deep incisional SSI Organ/space SSI
Clean-contaminated surgery (n=20,736) Superficial incisional SSI Deep incisional SSI Organ/space SSI
Contaminated or dirty-contaminated surgery (n=108) Superficial incisional SSI Deep incisional SSI
NOTE. SSI, surgical site infection; CI, confidence interval.
microbial culture, and ID specialist consultation) can detect SSI events across the various categories of surgery. We also verified the validity of the semiautomated SSI surveillance system by analyzing cases according to wound class, type of SSI, and SSI risk index. A systematic review on the impact of electronic surveillance
Fig. 1. Venn diagram showing the number of surgical cases flagged using each criterion of the electronic screening algorithms. NOTE. SSI, surgical site infection; ID, infectious disease.
Discussion
In this study, we used a large, conventional SSI surveillance dataset with 38 categories of surgery to validate a semiautomated SSI surveillance system based on electronic screening algorithms. We confirmed that the semiautomated system can detect SSI cases with high sensitivity while significantly reducing the workload of IPs. In contrast to previous studies that only covered limited types of surgery,11–15 our study included 38 categories of surgery. Validation of the semiautomated surveillance system was possible because of the large, conventional SSI surveillance database that had already been built, which was used as a refer- ence standard. That dataset includes SSI surveillance data for 40,516 surgical operations in 38 NHSN categories, which accounts for ~50% of all the surgeries performed during the study period. Current conventional surveillance methods, which are dependent on chart review, are so resource intensive that most hospitals monitor only select surgical procedures. Our study shows that a semiautomated surveillance system using electronic screening algorithms and select criteria (ie, antibiotic prescription,
system on IP resources demonstrated a reduction in IP staff time to undertake surveillance in 13 studies ranging from 12.5% to 98.4%.18 Our results are consistent with previous studies demonstrating that physician requests for wound cultures or antibiotic prescriptions are useful for monitoring SSIs electro- nically.19–21 The 3 criteria used in our study produced a high sensitivity of 96.7%, although the specificity was low (67.6%). In validation studies using electronic surveillance methods for SSI, sensitivity ranged from 60.0% to 97.8%.22 A systematic review also demonstrated that recent electronic HAI detection systems showed a bias toward higher sensitivity at the expense of speci- ficity.23 The PPV in our study was very low when SSI surveillance depended solely on screening algorithms without the manual chart review of flagged cases. When using antibiotic prescription as a criterion, false-positive cases may include those in which antibiotics are administered to treat infections other than SSI (eg, postoperative pneumonia or urinary tract infection) or those in which prophylactic antibiotics are given for a prolonged duration. The criterion of microbial culture may also flag false-positive cases, including those in which microbial culture is performed for the etiologic diagnosis of an infection other than SSI. The cri- terion of consultation with an ID specialist provided a lower SSI detection sensitivity than the other criteria, but provided the highest PPV. This might be because surgeons tend to request consultation with an ID specialist only for severe cases of SSI. Some previous studies have focused on the use of billing codes to help improve SSI surveillance, although they were limited to certain types of surgery.12,15 However, a Korean study regarding feasibility of using administrative data to identify HAI showed that the rates of HAI was significantly underestimated compared
Cases Identified % (95% CI) 575 131 56 15 60
433 139 50
244 11 8 2
1.42 (1.30–1.54) 0.67 (0.56–0.78) 0.28 (0.24–0.32) 0.08 (0.04–0.12) 0.31 (0.23–0.39) 2.09 (1.90–2.28) 0.67 (0.56–0.78) 0.24 (0.17–0.30) 1.18 (0.43–1.93) 10.19 (4.48–15.90) 7.41 (2.47–12.35) 1.85 (0.57–5.05)
933
Semiautomated SSI Surveillance
No. of SSI Cases Identified
556 129 54 15 60
416 122 50
244 11 8 2
% (95% CI)
1.37 (1.31–1.43) 0.66 (0.58–0.74) 0.27 (0.22–0.32) 0.08 (0.04–0.12) 0.31 (0.23–0.39) 2.01 (1.82–2.20) 0.59 (0.49–0.69) 0.24 (0.17–0.30) 1.18 (0.43–1.93) 10.19 (4.48–15.90) 7.41 (2.47–12.35) 1.85 (0.57–5.05)
Sensitivity, % 96.7 98.5 96.4
100 100
96.1 88.5
100 100 100 100 100
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