Pre-operative Risk Assessment of Surgical Site Infection After Cardiac Surgery
Study Details
Study Description
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.
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Detailed Description
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. The incidence of SSI in England at 30-days is 8.6% for coronary artery bypass graft (CABG) and 2.2% for non-CABG operations. 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 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.
Study Design
Arms and Interventions
| Arm | Intervention/Treatment |
|---|---|
| SSI group Participants who developed surgical site infection (SSI) based on the definition of Centre for Disease Control and Prevention. |
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| Non-SSI group Participants who did not develop SSI. |
Outcome Measures
Primary Outcome Measures
- Predictive power of the risk tools [January 2018 - December 2019]
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.
Secondary Outcome Measures
- Calibration scores of the risk tools [January 2018 - December 2019]
The secondary outcome will be the assessment and comparison of the calibration of scores of B-SIR, ACRI and BHIS tools. Hosmer-Lemeshow goodness of fit test will be utilised to determine calibration of scores. This will be done to examine the ability of each model to generate predictions that are on average close to the average observed outcome. For this test, a p-value that is not statistically significant (p > 0.05) will be considered to indicate a reasonable model fit.
Eligibility Criteria
Criteria
Inclusion Criteria:
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/= 18 years old at the time of surgery; and
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had a primary surgery (CABG, valve surgery or both) in the UK cardiac centres.
Exclusion Criteria:
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patients undergoing grown-up congenital heart disease related surgery;
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patients with concurrent aortovascular surgery;
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patients who had ventricular-assist device (VAD), haemolung, impellar and/or extracorporeal membrane oxygenator (ECMO) before and/or after cardiac surgery;
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patients who had an open-chest immediately after surgery.
Contacts and Locations
Locations
No locations specified.Sponsors and Collaborators
- Barts & The London NHS Trust
- Liverpool Heart and Chest Hospital NHS Foundation Trust
- University Hospitals, Leicester
- Oxford University Hospitals NHS Trust
- South Tees Hospitals NHS Foundation Trust
- Cardiff and Vale University Health Board
- Belfast Health and Social Care Trust
Investigators
None specified.Study Documents (Full-Text)
None provided.More Information
Additional Information:
Publications
- 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.
- 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.
- 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.
- 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.
- 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. Review.
- 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.
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