surgical site infection surveillance quality 1179
table 5. Characteristics of 483 Randomly Selected Cases With Originally Reported Infection at the Main Surgical Site and Complete Documentation and Follow-Up Reviewed Between October 1, 2012, and June 26, 2016
Variable All surgeries Total
Appendectomy 37 Colon surgery 213 Rectal surgery
Gastric bypass surgery
Caesarian section
Hysterectomy
Superficial Incisional, No. (%)
Originally Reported Infections Deep
Incisional, No. (%)
Organ- Space, No. (%)
Superficial Incisional, No. (%)
5
Cholecystectomy 44 Herniorrhaphy 21 5
52 0
Hip prostheses 66 Knee prostheses 33 Laminectomiy Cardiac surgery
1 6
79 (37.1) 32 (15.0) 102 (47.9) 79 (37.1) 22 (10.3) 112 (52.6) 0 (0) 2 (40.0) 21 (47.7) 14 (66.7) 2 (40.0)
6 (16.2) 20 (54.1) 11 (29.7) 2 (40.0) 1 (20.0)
5 (11.4) 18 (40.9) 22 (50.0) 6 (28.6) 1 (4.8) 1 (20.0) 2 (40.0)
41 (78.9) 0 (0)
4 (66.7)
5 (9.6) 0 (0)
6 (11.5) 41 (78.9) 0 (0)
0 (0)
18 (27.3) 15 (22.7) 33 (50.0) 17 (25.8) 13 (39.4) 0 (0)
6 (18.2) 14 (42.4) 13 (39.4) 0 (0)
1 (100) 1 (33.3) 1 (33.3)
status (ie, SSI present as compared to absent) and the quality of supervision by a medical supervisor, but not with total validation scores, hospital size, private hospital status, number of operations followed, training of the medical supervisor, full-time equivalents dedicated for surveillance or understaffing. Misclassification of thetypeofinfection (ie, superficial incisional, deep incisional infection, or organ/space infection) was independently associated with lower adequacy of documentation. Taken together, our findings highlight the importance of high-
quality data for interfacility comparisons and, more importantly, public reporting of healthcare-associated surveillance data. Interpretive variation despite uniformsurveillance definitions has been shown previously.26,27 Furthermore, public reporting of HAI surveillance data in a system where there is great disincentive to have unfavorable outcome data may result in exclusion or reclassification of events as opposed to preventing actual negative outcomes.28 Therefore, apart from a standardized methodology, validation of surveillance data, surveillance methods, and operations within participating facilities by an independent party are key for quality assurance under such circumstances. As mentioned previously, the best means to validate a SSI
surveillance module is still unknown. Therefore, various approaches have been proposed in the scientificliterature orare available together with the surveillance methodologies, such as the validation toolkits provided by the NHSN.29 The methods applied to validate surgical site infection surveillance (SSIS) in TheNetherlands have been published in 2007 by Mannien et al.30 Thereby, process validation bymeans of a structured interview as well as a prevalence study were performed. Overall positive pre- dictive values and negative predictive values were then calculated. Similarly, validation of SSIS data was performed in Scotland by McCoubrey et al.12 Validation in terms of structure (ie,
1 3 (50.0)
1 (20.0) 15 (71.4)
2 (40.0)
5 (13.5) 21 (56.8) 0 (0) 2 (40.0) 2 (40.0) 0 (0)
4 (9.1) 18 (40.9) 0 (0) 5 (23.8) 1 (4.8) 0 (0) 1 (20.0) 2 (40.0) 0 (0)
5 (9.6) 0 (0)
6 (11.5) 0 (0) 0 (0)
0 (0)
4 (6.1) 44 (67.7) 1 (1.5) 2 (6.1) 18 (54.6) 0 (0) 0 (0)
0 (0) 0 (0) 2 (33.3) 1 (16.7) 0 (0)
Validation Deep
Incisional, No. (%)
Organ- Space, No. (%)
False
Positive, No. (%)
483 205 (42.4) 79 (16.4) 199 (41.2) 204 (42.2) 52 (10.8) 226 (46.8) 1 (0.2) 11 (29.7)
Misclassification of Type of Infection, No. (%)
46 (9.5) 1 (2.7)
20 (9.4)
2 (40.0) 1 (2.3) 1 (4.8) 0 (0.0)
0 (0.0) 0 (0)
14 (21.2) 6 (18.2) 0 (0)
1 (16.7)
trained personnel and systems for SSIS, systems to ensure complete inclusion, check and confirm the number of operations) and in terms of process (ie, phone interview for identification of the systems for SSIS data collection and man- agement at a local level) were performed. Outcome validation was conducted by calculating sensitivity, specificity, positive predictive value, and negative predictive value of the last 15 cases of SSI and 60 further randomly selected cases. Gastmeier et al10 compared 2 validation methods in a
prevalence survey (Nosokomiale Infektionen in Deutschland Erfassung und Prävention, NIDEP) on nosocomial infections.10 On one hand, as in other previous studies,11,31–33 bedside vali- dation of the 4 physician investigators was performed using 2 supervisors as the gold standard, and sensitivity and specificity were calculated. In addition, the investigators were validated using case studies.33 However, limitations of these approaches—including ours—
are that, first, it remains unclear how and whether results of validation of structure and process by structured interviews translate to the validity of infection outcomes. Second, validation by case studies allows for the assessment of knowledge among the persons performing surveillance, but the conclusion from case study results on SSIS performance is inappropriatewith regard to potential conflicts of interest because people may behave differ- ently in the setting of case studies as compared to real-life situa- tions in their own hospital. Third, there is no consensus about the sensitivity required to consider surveillance results to be valid. And last, given the low prevalence of SSI, large numbers of patient charts need to be reviewed to achieve an adequate level of precision.25,34 In conclusion, validation of process and structure of SSI surveillance and of outcome data helps identify areas for
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