The development and validation of screening tools for semi-automated surveillance of surgical site infection following various surgeries

Pnina Shitrit*, Michal Y. Chowers, Khitam Muhsen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Background: Surveillance of surgical site infections (SSIs) is essential for better prevention. We developed a screening method for SSIs in adults. Methods: The training dataset included data from patients who underwent orthopedic surgeries (N = 1,090), colorectal surgeries (N = 817), and abdominal hysterectomies (N = 523) during 2015–2018. The gold standard for the validation of the screening tool was the presence of SSI as determined by a trained infection control practitioner, via manual full medical record review, using the US Center for Disease Control and Prevention criteria. Using multivariable regression models, we identified the correlates of SSI. Patients who had at least one of these correlates were classified as likely to having SSI and those who did not have any of the correlates were classified as unlikely to have SSI. We calculated the sensitivity and specificity of this tool compared to the gold standard and applied the tool to a validation dataset (N = 1,310, years 2019–2020). Results: SSI was diagnosed by an infection control specialist in 8.2, 5.2, and 31.2% of the patients in the training dataset who underwent hysterectomies, orthopedic surgeries and colorectal surgeries, respectively, vs. 6.2, 6.6, and 25.5%, respectively, in the validation dataset. The correlates of SSI after abdominal hysterectomy were prolonged hospitalization, ordering wound or blood culture, emergency room visit and reoperation; in orthopedic surgery, emergency room visit, wound culture, reoperation, and documentation of SSI, and in colorectal surgeries prolonged hospitalization, readmission, and ordering wound or blood cultures. Area under the curve was >90%. The sensitivity and specificity (95% CI) of the screening tool were 98% (88–100) and 58% (53–62), for abdominal hysterectomy, 91% (81–96) and 82% (80–84) in orthopedic surgeries and 96% (90–98) and 62% (58–66) in colorectal surgeries. The corresponding values for the validation dataset were 89% (67–97) and 75% (69–80) in abdominal hysterectomy; 85% (72–93) and 83% (80–86) in orthopedic surgeries and 98% (93–99) and 59% (53–64) in colorectal surgeries. The number of files needed to be fully reviewed declined by 61–66. Conclusion: The presented semi–automated simple screening tool for SSI surveillance had good sensitivity and specificity and it has great potential of reducing workload and improving SSI surveillance.

Original languageEnglish
Article number1023385
JournalFrontiers in Medicine
Volume10
DOIs
StatePublished - 26 Jan 2023

Funding

FundersFunder number
Tel Aviv University

    Keywords

    • model development
    • screening tool
    • semi-automated surveillance
    • surgical site infection
    • validation

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