All patients undergoing open and laparoscopic liver resection in a regional HPB network consisting of four centers from 2013 to 2017 were retrieved from a prospectively held database. Logistic regression and LASSO were used to develop a prediction model for SSI after liver resection. Internal and external validation was performed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) reporting guideline for prediction model development and validation were followed.
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The prediction model might be used to identify a group of patients that has little risk and this could be used in efficient application of preventive measures to decrease the number of SSI. Many potential SSI preventive strategies are described, including the use of negative pressure dressings, antimicrobial coated sutures, and physical and nutritional prehabilitation programs. Another possible measure that is described for liver resection is de admission of a single dose of corticosteroids directly pre-operative. The best results in reducing SSI are likely to be achieved using a peri-operative care bundle. However, measures like these burden either hospital resources, the environment, or both. The use of SSI preventing strategies in selected patients using a prediction model might decrease these burdens.
See also the WHO Global guidelines on the prevention of surgical site infection.
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