Groin Wound Infection after Vascular Exposure (GIVE) Risk Prediction Models: Development, Internal Validation, and Comparison with Existing Risk Prediction Models Identified in a Systematic Literature Review. (August 2021) - Gwilym, Brenig L.; Ambler, Graeme K.; Saratzis, Athanasios; Bosanquet, David C.; ,; Stather, Philip; Singh, Aminder; Mancuso, Enrico; Arifi, Mohedin; Altabal, Mohamed; Elhadi, Ahmed; Althini, Abdulmunem; et al.
Abstract : Objective: This study aimed to develop and internally validate risk prediction models for predicting groin wound surgical site infections (SSIs) following arterial intervention and to evaluate the utility of existing risk prediction models for this outcome. Methods: Data from the Groin wound Infection after Vascular Exposure (GIVE) multicentre cohort study were used. The GIVE study prospectively enrolled 1 039 consecutive patients undergoing an arterial procedure through 1 339 groin incisions. An overall SSI rate of 8.6% per groin incision, and a deep/organ space SSI rate of 3.8%, were reported. Eight independent predictors of all SSIs, and four independent predictors of deep/organ space SSIs were included in the development and internal validation of two risk prediction models. A systematic search of the literature was conducted to identify relevant risk prediction models for their evaluation. Results: The "GIVE SSI risk prediction model" ("GIVE SSI model") and the "GIVE deep/organ space SSI risk prediction model" ("deep SSI model") had adequate discrimination (C statistic 0.735 and 0.720, respectively). Three other groin incision SSI risk prediction models were identified; both GIVE risk prediction models significantly outperformed these other risk models in this cohort (C statistic 0.618 – 0.629; p < .050 for inferior discrimination in all cases). Conclusion: Two models were created and internally validated that performed acceptably in predicting "all" and "deep" groin SSIs, outperforming current existing risk prediction models in this cohort. Future studies should aim to externally validate the GIVE models.