Pre-operative Risk Assessment of Surgical Site Infection After Cardiac Surgery

NCT04762446 | Completed DMC

• 1 other identifier: 294270
• Study Type: observational
• Target: 6,379
• Locations: 1 country
• Sites: 3

Brief Summary

Surgical site infections (SSI) are serious complications accounting for 20% of all the healthcare-associated infections and are considered the second most frequent type of hospital-acquired infection in Europe and the United States. SSI after cardiac surgery is associated with delays to patient's discharge, readmissions and re-operations; and can result in increased hospital costs for staffing, diagnostics and treatment. Risk assessment has been identified as potentially useful intervention in SSI prevention and in identifying at risk populations who may benefit from specific interventions to reduce this possible complication of cardiac surgery. However, there is currently a lack of evidence as to which risk tools are the most valid and reliable to be used in clinical practice. The investigators developed and locally validated the Barts Heart Centre Surgical Infection Risk (B-SIR) tool to include patients with various types of cardiac surgeries and found that the B-SIR tool is a better tool in predicting SSI risk compared with the existing cardiac risk tools in the study population. However, various literatures recognised that the predictive performance of a risk model tends to vary across settings, populations and periods. Hence, the investigators aim to do a multi-centre validation of the newly developed B-SIR tool and apply all the other tools (Australian Cardiac Risk Index and Brompton and Harefield Infection Score) to identify what tool performs best that can potentially be use for the UK population. Further, the outcome of the study will be beneficial to future cardiac surgery patients to assess their risk of developing SSI and help identify those patients who may benefit from specific interventions. Existing patients' data, which will be anonymised, from the participating cardiac centres will be utilised to analyse and compare the performance of each risk tools.

Conditions

Surgical Site Infection; Risk Assessment; Cardiac Surgery

Outcome Measures

Primary Outcomes (1)

• Predictive power of the risk tools

  The primary outcome will be the assessment and comparison of the predictive power of B-SIR, ACRI and BHIS tools. The predictive power of each risk tool will be determined using the area under the curve (AUC) from receiver operating characteristic (ROC) curve. AUC can be between 0.5 - 1; higher score (closer to 1) indicates greater predictive ability.

  January 2018 - December 2019

Secondary Outcomes (1)

• Calibration scores of the risk tools

  January 2018 - December 2019

Study Arms (2)

SSI group

Participants who developed surgical site infection (SSI) based on the definition of Centre for Disease Control and Prevention.

Non-SSI group

Participants who did not develop SSI.

Eligibility Criteria

• Age: 18 Years - 120 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

All consecutive patients who underwent coronary artery bypass graft (CABG), valve surgery, or combined surgery in the UK cardiac centres between January 2018 - December 2019 will be considered for inclusion.

You may qualify if:

• \>/= 18 years old at the time of surgery; and
• had a primary surgery (CABG, valve surgery or both) in the UK cardiac centres.

You may not qualify if:

• patients undergoing grown-up congenital heart disease related surgery;
• patients with concurrent aortovascular surgery;
• patients who had ventricular-assist device (VAD), haemolung, impellar and/or extracorporeal membrane oxygenator (ECMO) before and/or after cardiac surgery;
• patients who had an open-chest immediately after surgery.

Sponsors & Collaborators

• Liverpool Heart and Chest Hospital NHS Foundation Trust: collaborator
• Barts & The London NHS Trust: lead
• University Hospitals, Leicester: collaborator
• Oxford University Hospitals NHS Trust: collaborator
• South Tees Hospitals NHS Foundation Trust: collaborator
• Cardiff and Vale University Health Board: collaborator
• Belfast Health and Social Care Trust: collaborator

Study Sites (3)

Liverpool Heart and Chest Hospital

Liverpool, United Kingdom

Location

James Cook University Hospital

Middlesbrough, United Kingdom

Location

Oxford University Hospital

Oxford, United Kingdom

Location

Related Publications (8)

• Lamagni T CK, Wloch C, Harrington P. The epidemiology of cardiac surgical site infection in Englad, 2018/19. 30th European Congress of Clinical Microbiology and Infectious Diseases. 2020; Paris, France: Clin Microbiol Infect 2020.

  BACKGROUND

• Magboo R, Drey N, Cooper J, Byers H, Shipolini A, Sanders J. Predicting cardiac surgical site infection: development and validation of the Barts Surgical Infection Risk tool. J Clin Epidemiol. 2020 Dec;128:57-65. doi: 10.1016/j.jclinepi.2020.08.015. Epub 2020 Aug 25.

  PMID: 32853763 BACKGROUND

• Debray TP, Vergouwe Y, Koffijberg H, Nieboer D, Steyerberg EW, Moons KG. A new framework to enhance the interpretation of external validation studies of clinical prediction models. J Clin Epidemiol. 2015 Mar;68(3):279-89. doi: 10.1016/j.jclinepi.2014.06.018. Epub 2014 Aug 30.

  PMID: 25179855 BACKGROUND

• Pennells L, Kaptoge S, White IR, Thompson SG, Wood AM; Emerging Risk Factors Collaboration. Assessing risk prediction models using individual participant data from multiple studies. Am J Epidemiol. 2014 Mar 1;179(5):621-32. doi: 10.1093/aje/kwt298. Epub 2013 Dec 22.

  PMID: 24366051 BACKGROUND

• Royston P, Parmar MK, Sylvester R. Construction and validation of a prognostic model across several studies, with an application in superficial bladder cancer. Stat Med. 2004 Mar 30;23(6):907-26. doi: 10.1002/sim.1691.

  PMID: 15027080 BACKGROUND

• Steyerberg EW, Moons KG, van der Windt DA, Hayden JA, Perel P, Schroter S, Riley RD, Hemingway H, Altman DG; PROGRESS Group. Prognosis Research Strategy (PROGRESS) 3: prognostic model research. PLoS Med. 2013;10(2):e1001381. doi: 10.1371/journal.pmed.1001381. Epub 2013 Feb 5.

  PMID: 23393430 BACKGROUND

• Vergouwe Y, Moons KG, Steyerberg EW. External validity of risk models: Use of benchmark values to disentangle a case-mix effect from incorrect coefficients. Am J Epidemiol. 2010 Oct 15;172(8):971-80. doi: 10.1093/aje/kwq223. Epub 2010 Aug 31.

  PMID: 20807737 BACKGROUND

• Bleeker SE, Moll HA, Steyerberg EW, Donders AR, Derksen-Lubsen G, Grobbee DE, Moons KG. External validation is necessary in prediction research: a clinical example. J Clin Epidemiol. 2003 Sep;56(9):826-32. doi: 10.1016/s0895-4356(03)00207-5.

  PMID: 14505766 BACKGROUND

Related Links

• CDC definition
• GIRFT 2019
• NICE SSI Prevention Guidelines

MeSH Terms

Conditions

Surgical Wound Infection

Condition Hierarchy (Ancestors)

Wound Infection; Infections; Postoperative Complications; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: February 18, 2021
• First Posted: February 21, 2021
• Study Start: July 15, 2022
• Primary Completion: November 30, 2023
• Study Completion: November 30, 2023
• Last Updated: April 10, 2024

Record last verified: 2024-04

Locations

GB (3)