Pre-operative Risk Assessment of Surgical Site Infection After Cardiac Surgery
NCT ID: NCT04762446
Last Updated: 2024-04-10
Study Results
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Basic Information
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COMPLETED
6379 participants
OBSERVATIONAL
2022-07-15
2023-11-30
Brief Summary
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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.
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Detailed Description
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Risk assessment has been identified as potentially useful intervention in SSI prevention and in identifying at risk populations who may benefit from targeted 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 has a greater predictive power of 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. Verification of the robustness and generalisability of a developed model is highly recommended in one or more external validation studies. 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.
This study is a secondary data analysis that will utilise prospectively collected data that were locally collected in 6 UK cardiac centres for the National Institute for Cardiovascular Outcome Research (NICOR) and Public Health of England (PHE) Surgical Site Infection Surveillance. Data on various patients' risk factors will be collected and analysed to compare the ability of each risk assessment tool in predicting SSI after cardiac surgery. The outcome of this 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 targeted interventions.
Conditions
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Study Design
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COHORT
RETROSPECTIVE
Study Groups
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SSI group
Participants who developed surgical site infection (SSI) based on the definition of Centre for Disease Control and Prevention.
No interventions assigned to this group
Non-SSI group
Participants who did not develop SSI.
No interventions assigned to this group
Eligibility Criteria
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Inclusion Criteria
2. had a primary surgery (CABG, valve surgery or both) in the UK cardiac centres.
Exclusion Criteria
2. patients with concurrent aortovascular surgery;
3. patients who had ventricular-assist device (VAD), haemolung, impellar and/or extracorporeal membrane oxygenator (ECMO) before and/or after cardiac surgery;
4. patients who had an open-chest immediately after surgery.
18 Years
120 Years
ALL
No
Sponsors
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Liverpool Heart and Chest Hospital NHS Foundation Trust
OTHER
University Hospitals, Leicester
OTHER
Oxford University Hospitals NHS Trust
OTHER
South Tees Hospitals NHS Foundation Trust
OTHER
Cardiff and Vale University Health Board
OTHER_GOV
Belfast Health and Social Care Trust
OTHER
Barts & The London NHS Trust
OTHER
Responsible Party
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Locations
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Liverpool Heart and Chest Hospital
Liverpool, , United Kingdom
James Cook University Hospital
Middlesbrough, , United Kingdom
Oxford University Hospital
Oxford, , United Kingdom
Countries
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References
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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.
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.
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.
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.
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.
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.
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.
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.
Related Links
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GIRFT 2019
NICE SSI Prevention Guidelines
Other Identifiers
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294270
Identifier Type: -
Identifier Source: org_study_id
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