Prediction Models for Forecasting Risk of Development of Surgical Site Infection after Lower Limb Revascularization Surgery: A Systematic Review.

ABSTRACT

BACKGROUND: Surgical site infections (SSIs) are a common and potentially preventable complication of lower limb revascularization surgery associated with increased healthcare resource utilization and patient morbidity. We conducted a systematic review to evaluate multivariable prediction models designed to forecast risk of SSI development after these procedures. METHODS: After protocol registration (CRD42022331292), we searched MEDLINE, EMBASE, CENTRAL, and Evidence-Based Medicine Reviews (inception to April 4th, 2023) for studies describing multivariable prediction models designed to forecast risk of SSI in adults after lower limb revascularization surgery. Two investigators independently screened abstracts and full-text articles, extracted data, and assessed risk of bias. A narrative synthesis was performed to summarize predictors included in the models and their calibration and discrimination, validation status, and clinical applicability. RESULTS: Among the 6,671 citations identified, we included 5 studies (n = 23,063 patients). The included studies described 5 unique multivariable prediction models generated through forward selection, backward selection, or Akaike Information Criterion-based methods. Two models were designed to predict any SSI and 3 Szyilagyi grade II (extending into subcutaneous tissue) SSI. Across the 5 models, 18 adjusted predictors (10 of which were preoperative, 3 intraoperative, and 5 postoperative) significantly predicted any SSI and 14 adjusted predictors significantly predict Szilagyi grade II SSI. Female sex, obesity, and chronic obstructive pulmonary disease significantly predicted SSI in more than one model. All models had a "good fit" according to the Hosmer-Lemeshow test (P > 0.05). Model discrimination was quantified using the area under the curve, which ranged from 0.66 to 0.75 across models. Two models were internally validated using non-exhaustive twofold cross-validation and bootstrap resampling. No model was externally validated. Three studies had a high overall risk of bias according to the Prediction model Risk Of Bias ASsessment Tool (PROBAST). CONCLUSIONS: Five multivariable prediction models with moderate discrimination have been developed to forecast risk of SSI development after lower limb revascularization surgery. Given the frequency and consequences of SSI after these procedures, development and external validation of novel prediction models and comparison of these models to the existing models evaluated in this systematic review is warranted.