Development and Validation of a Predictive Score for Surgical Site Infections

Plain-language summary

More than 8 millions surgical interventions are carried out each year in France. Postoperative complications, in particular infectious, can occur in 10 to 60% of cases and are the cause of postoperative revision in 30% of cases, an increase in mortality, length of stay, readmissions and lead to significant additional socio-economic costs. Currently, improvements in surgical practices have not reduced the incidence of surgical site complications. In this context, the development of predictive scores for the risk of post-operative complication becomes urgent in order to implement new interventions (pre-habilitation) or to modify surgical decisions (timing, approach) in order to reduce the risk of complications before surgery. Several recent studies highlights the importance of the immune response in postoperative prognosis. In particular, an imbalance between the adaptive and innate response involving MDSCs has been demonstrated in patients with postoperative complications.Thanks to new techniques for analyzing the immune system, in-depth analysis of the immune system before surgery is a very promising approach aimed at identifying predictive biomarkers of postoperative prognosis. Our team has developed and patented a multivariate model integrating mass cytometry data, proteomics and clinical data collected before surgery to accurately predict the occurrence of a surgical site complication (AUC = 0.94, p\<10e-7) in a monocentric cohort of 43 patients to major abdominal surgery (Stanford University). The objective of the present study is to generalize and validate this preoperative predictive score of infectious complications of the surgical site in the 30 days following major digestive surgery on a larger workforce within a multicenter cohort and to validate this score at using a machine learning method.

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