Risk factors for surgical site infections using a data-driven approach
PLoS ONE , Volume 15 - Issue 10
Objective The objective of this study was to identify risk factors for surgical site infection from digestive, thoracic and orthopaedic system surgeries using clinical and data-driven cut-off values. A second objective was to compare the identified risk factors in this study to risk factors identified in literature. Summary background data Retrospective data of 3 250 surgical procedures performed in large tertiary care hospital in The Netherlands during January 2013 to June 2014 were used. Methods Potential risk factors were identified using a literature scan and univariate analysis. A multivariate forward-step logistic regression model was used to identify risk factors. Standard medical cut-off values were compared with cut-offs determined from the data. Results For digestive, orthopaedic and thoracic system surgical procedures, the risk factors identified were preoperative temperature of �38˚C and antibiotics used at the time of surgery. Creactive protein and the duration of the surgery were identified as a risk factors for digestive surgical procedures. Being an adult (age �18) was identified as a protective effect for thoracic surgical procedures. Data-driven cut-off values were identified for temperature, age and CRP which can explain the SSI outcome up to 19.5% better than generic cut-off values. Conclusions This study identified risk factors for digestive, orthopaedic and thoracic system surgical procedures and illustrated how data-driven cut-offs can add value in the process. Future studies should investigate if data-driven cut-offs can add value to explain the outcome being modelled and not solely rely on standard medical cut-off values to identify risk factors.
|Organisation||Department of Medical Microbiology and Infectious Diseases|
van Niekerk, J.M., Vos, M.C, Stein, A., Braakman-Jansen, L.M.A., in 't Holt, AFV, & Van Gemert-Pijnen, J. (2020). Risk factors for surgical site infections using a data-driven approach. PLoS ONE, 15(10). doi:10.1371/journal.pone.0240995