1. 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
- Author
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Brenig L. Gwilym, Graeme K. Ambler, Athanasios Saratzis, David C. Bosanquet, Philip Stather, Aminder Singh, Enrico Mancuso, Mohedin Arifi, Mohamed Altabal, Ahmed Elhadi, Abdulmunem Althini, Hazem Ahmed, Huw Davies, Madhu Rangaraju, Maciej Juszczak, Jonathan Nicholls, Nicholas Platt, James Olivier, Emily Kirkham, David Cooper, Iain Roy, Gareth Harrison, James Ackah, Devender Mittapalli, Ian Barry, Toby Richards, Ahmed Elbasty, Hayley Moore, Adnan Bajwa, Andrew Duncan, Andrew Batchelder, Tryfon Vanias, Matthew Brown, Trixie Yap, Lucy Green, George Smith, Katherine Hurst, Daniel U. Rodriguez, Ella Schofield, Hannah Danbury, Tom Wallace, James Forsyth, Amy Stimpson, Luke Hopkins, Kamran Mohiuddin, Sandip Nandhra, Ghazaleh Mohammadi-Zaniani, Konstantinos Tigkiropoulos, Ahmed Shalan, Khalid Bashar, Rachel Sam, Craig Forrest, Samuel Debono, Keith Hussey, Rachel Falconer, Salil Korambayil, Ciaran Brennan, Thomas Wilson, Aled Jones, Tom Hardy, Hannah Burton, Andrew Cowan, Ummul Contractor, Elaine Townsend, Olivia Grant, Michelle Cronin, Michael Rocker, Danielle Lowry, Annie Clothier, Dafydd Locker, Rachael Forsythe, Olivia McBride, Calvin Eng, Russell Jamieson, Nishath Altaf, Fernando Picazo, Kishore Sieunarine, Ruth A. Benson, Alexander Crichton, Nikesh Dattani, Tasleem Akhtar, Helen Suttenwood, Francesca Guest, Bethany Wardle, George Dovell, Natasha Chinai, David Bosanquet, Robert Hinchliffe, Timothy Beckitt, Arsalan Wafi, Ankur Thapar, Paul Moxey, Tristan Lane, Ryan Preece, Kamil Naidoo, Benjamin Patterson, Claire Perrott, Joseph Shalhoub, Thomas Aherne, Ahmed Hassanin, Emily Boyle, Bridget Egan, Sean Tierney, Shaneel Patel, Panagiota Birmpili, Sandhir Kandola, Simon Neequaye, Muhammed Elhadi, Ahmed Msherghi, Ala Khaled, Lewis Meecham, Owain Fisher, Asif Mahmood, David Milgrom, Kerry Burke, Faris Saleh, and Tariq Al-Samarneh
- Subjects
Male ,medicine.medical_specialty ,Multivariate analysis ,Groin ,Risk Assessment ,Risk Factors ,Blood vessel prosthesis ,medicine ,Humans ,Surgical Wound Infection ,Povidone-Iodine ,Statistic ,Aged ,Probability ,business.industry ,Chlorhexidine ,Endovascular Procedures ,Regression analysis ,Middle Aged ,Blood Vessel Prosthesis ,Observational Studies as Topic ,Logistic Models ,medicine.anatomical_structure ,Systematic review ,ROC Curve ,Area Under Curve ,Multivariate Analysis ,Cohort ,Emergency medicine ,Anti-Infective Agents, Local ,Regression Analysis ,Female ,Surgery ,Cardiology and Cardiovascular Medicine ,business ,Cohort study - 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 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.
- Published
- 2021