Development of Risk Scoring Tool to Predict Surgical Site Infections

Sampalis, John S.

doi:10.15226/2376-4570/2/2/00116

Cited by 2 publications

(1 citation statement)

References 32 publications

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“…While there are many published procedure-specific SSI risk calculators, those intended to be used across surgical procedures include the Surgical Site Infection Risk Score (SSIRS) [ 4 ], JSS-SSI Risk Scoring Tool [ 5 ], and the American College of Surgeons - National Surgical Quality Improvement Program (ACS-NSQIP) risk score [ 6 ]. The SSIRS is composed of 13 items and has a reported area under the curve (AUC) statistic of 0.8, the JSS-SSI tool is composed of five items and has a reported AUC of 0.66, and the ACS-NSQIP is composed of 21 items with a reported AUC for SSI ranging from 0.73-0.82 [ 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

A Practical Approach to Predicting Surgical Site Infection Risk Among Patients Before Leaving the Operating Room

Woods,

Ekstrom,

Darer

et al. 2023

Cureus

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A surgical site infection (SSI) prediction model that identifies at-risk patients before leaving the operating room can support efforts to improve patient safety. In this study, eight pre-operative and five perioperative patient- and procedure-specific characteristics were tested with two scoring algorithms: 1) count of positive factors (manual), and 2) logistic regression model (automated). Models were developed and validated using data from 3,440 general and oncologic surgical patients. In the automated algorithm, two pre-operative (procedure urgency, odds ratio [OR]: 1.7; and antibiotic administration >2 hours before incision, OR: 1.6) and three intraoperative risk factors (open surgery [OR: 3.7], high-risk procedure [OR: 3.5], and operative time OR: [2.6]) were associated with SSI risk. The manual score achieved an area under the curve (AUC) of 0.831 and the automated algorithm achieved AUC of 0.868. Open surgery had the greatest impact on prediction, followed by procedure risk, operative time, and procedure urgency. At 80% sensitivity, the manual and automated scores achieved a positive predictive value of 16.3% and 22.0%, respectively. Both the manual and automated SSI risk prediction algorithms accurately identified at-risk populations. Use of either model before the patient leaves the operating room can provide the clinical team with evidence-based guidance to consider proactive intervention to prevent SSIs.

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