Daniël M. C. Janssen, Sander M. J. van Kuijk, Boudewijn d’Aumerie, Paul Willems
April 2019, Volume 28, Issue 4, pp 775 - 782 Original Article Read Full Article 10.1007/s00586-018-05877-z
First Online: 07 January 2019
The aim of this study was to develop and internally validate a multivariable model for accurate prediction of surgical site infection (SSI) after instrumented spine surgery using a large cohort of a Western European academic center.
Data of potential predictor variables were collected in 898 adult patients who underwent instrumented posterior fusion of the thoracolumbar spine. We used logistic regression analysis to develop the prediction model for SSI. The ability to discriminate between those who developed SSI and those who did not was quantified as the area under the receiver operating characteristic curve (AUC). Model calibration was evaluated by visual inspection of the calibration plot and by computing the Hosmer and Lemeshow goodness-of-fit test.
Sixty patients (6.7%) were diagnosed with an SSI. After backward stepwise elimination of predictor variables, we formulated a model in which an individual’s risk of an SSI can be computed. Age, body mass index, ASA score, degenerative or revision surgery and NSAID use appeared to be independent predictor variables for the risk of SSI. The AUC was 0.72 (95% CI 0.65–0.79), indicating reasonable discriminative ability.
We developed and internally validated a prediction model for SSI after instrumented thoracolumbar spine surgery using predictor variables of standard clinical practice that showed reasonable discriminative ability and calibration. Identification of patients at risk for SSI allows for individualized patient risk assessment with better patient-specific counseling and may accelerate the implementation of multi-disciplinary strategies for reduction of SSI.
These slides can be retrieved under Electronic Supplementary Material.[Figure not available: see fulltext.]
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