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Diagnostic algorithm simplifies surveillance of deep SSIs
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A five-parameter diagnostic model identified 98.5% of patients with a deep surgical site infection, or SSI, and demonstrated a 99.8% negative predictive value, according to findings published in Infection Control & Hospital Epidemiology.
“SSI surveillance is traditionally performed by extensive manual review of medical records,” Tessa Mulder, MD, a PhD candidate in infectious disease epidemiology at the University Medical Center Utrecht, and colleagues wrote. “The objective of this study was to develop an algorithm for the surveillance of deep SSIs after colorectal surgery to reduce the number of medical records that require full manual review.”
Mulder and colleagues conducted a retrospective cohort study at Amphia Hospital — a Dutch teaching hospital — where deep SSIs are defined using CDC criteria and identified through a manual review of medical records. They used surveillance data from January 2012 to December 2015. The researchers included consecutive patients who underwent colorectal surgery and excluded any patient who had a contaminated wound at the time of surgery.
They used a logistic regression model to identify five predictors of deep SSIs: postoperative length of stay, wound class, readmission, reoperation and 30-day mortality. A total of 1,606 patients were included in the analysis, with 8% acquiring a deep SSI. The diagnostic model demonstrated 68.7% specificity and 98.5% sensitivity.
“We set the model threshold at an excellent sensitivity so that it could detect a high percentage of patients with SSI,” Mulder and colleagues wrote. “The trade-off for this high sensitivity was a relatively high rate of false positives due to a moderate specificity, which we accepted because the model would be used to identify the patients with a high probability of SSI whose records would then be reviewed.”
According to the researchers, the model also achieved an area under the receiver operator characteristic curve of 0.95 (95% CI, 0.932-0.969). The model had a 21.5% positive predictive value and a 99.8% negative predictive value, according to the study. The algorithm resulted in a 63.4% reduction in the number of records requiring full manual review, Mulder and colleagues reported.
“These results can be used to develop semiautomatic surveillance of deep SSIs after colorectal surgery,” they wrote. – by Marley Ghizzone
Disclosures: The authors report no relevant financial disclosures.
Perspective
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Keith S. Kaye, MD, MPH
The electronic health record is a powerful tool. By maintaining excellent and timely documentation of patient care, clinicians can help to improve infection surveillance and prevention. Semiautomated surveillance methods using the EHR are powerful tools that are becoming increasingly common.
With appropriate data management and analytics skills, the EHR can be used to improve the efficiency of infection surveillance and potentially reduce the effort necessary to conduct SSI surveillance compared with traditional manual surveillance.
Semiautomated surveillance methods like the one described in this article represent a growing part of infection prevention that will become increasingly important over time. Results from this single-institution study need to be validated at other institutions. These types of surveillance methods do not obviate infection preventionists but can be used to help better focus their valuable time and effort.
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