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Post-surgical (bacterial) infections are the most frequent post-surgical complications, including deep or superficial wound infections, urinary tract infections, pneumonia, and even sepsis. Approximately 6.5-25% of all surgical patients will develop any type of bacterial infection. To personalize surgical infection management, (Artificial Intelligence) models are in the making to predict which patients are at high or low risk of developing a post-surgical infection. In order to benchmark these prediction models to the predictive capabilities of surgeons, the investigators aim to investigate the performance of surgeons in predicting the risk of a patient developing (any type) of post-surgical infection within 30 days.
Full description
A prospective non-interventional study is performed to collect surgeons' predictions on the risk of a patient developing a postoperative infection within 30 days of surgery. Surgeons are asked to fill in a short questionnaire asking about the estimated infection risk. The actual outcome (infection < 30 days of surgery) of a patient will be collected retrospectively after completion of the study. This study will have no effect on standard care: surgical interventions and postoperative care will be carried out according to standard clinical practice. Besides a one-time estimate of the surgeon, immediately after the surgical procedure, no other interventions will be performed and surgical specialists will carry out their normal post-surgical care, including screening and treating (if necessary) their patients for postoperative infections.
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Predictions are made for patients with the following inclusion and exclusion criteria:
Inclusion Criteria:
Exclusion Criteria:
594 participants in 1 patient group
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Data sourced from clinicaltrials.gov
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