Predicting postoperative infections: prediction models and their evaluation

BACKGROUND: Pre-operative assessment for postoperative infection risk helps identify patients for personalised decision-making and management. OBJECTIVE: This systematic review evaluates existing prediction models for infection, focusing on their validation and implementation status. DATA SOURCES AND ELIGIBILITY CRITERIA: PubMed, Embase and the Cochrane Library were searched for studies on the development, validation- and implementation of multivariable models utilising pre-operative predictors to estimate the risk of postoperative infections within 30-days of elective, major noncardiac, non-intracranial surgery. RESULTS: Of 151 included studies, 88 reported model development (267 distinct models), 88 assessed model validity (314 validation analyses), and none described implementation. Models predominantly predicted surgical site infections (SSI, n = 88), pneumonia (n = 45) and general (unspecified) infections (n = 57). Age (66%), sex (53%) and ASA score (48%) were the most common predictors. The American College of Surgeons Surgical Risk Calculator (ACS SRC) and SUrgical Risk Pre-operative Assessment System (SURPAS) were most frequently validated, with 225 and 35 external validations respectively. Reported c-statistics of the ACS SRC were median 0.61 [range 0.43 to 0.85], 0.66 [0.44 to 0.95] and 0.64 [0.31 to 0.97] for prediction of SSI, pneumonia, and urinary tract infection (UTI), respectively. For SURPAS, median c-statistics were 0.62 [range 0.52 to 0.78] and 0.59 [0.52 to 0.82] for UTI and general infection. Overall, most studies scored high risk of bias. CONCLUSIONS: Out of 267 prediction models for postoperative infections identified, ACS SRC and SURPAS were most frequently validated. However, the clinical utility of even these models is limited because of poor and highly variable predictive performance and low methodological quality of validation studies.