At least ten percent of trauma patients suffer from any sort of complication. The study aims to identify a risk factor profile to early identify trauma patients at risk.

Trauma patients suffer from a high risk of complications that ranges from soft tissue complications, surgical site infection till bone non-union. Comorbidities are often know to be associated with complications. Also certain blood markers could be identified to be an indicator.

However, certain risk factors have only been investigated in small trauma cohorts so far. This study is a prospective multi center cohort trail that aims to collect all clinical data, comorbidities, medication, blood markers in trauma patients after long bone fractures. In a 6 months and one year follow up patients with complications will be identified. Methods of machine learning will help to identify a risk factor profile for certain groups of patients with complications.

The aim of the study is to early identify patients at risk for a complication after trauma according to their blood marker, clinical, comorbidity and medication profile.

Inclusion Criteria:

• acute long bone fracture (humerus, ulna, radius, femur, tibia) with surgical treatment

Exclusion Criteria:

• associated severe injury (ISS > 16)
• associated traumatic brain injury
• associated traumatic spine injury
• dementia

patients that suffer from an acute lone bone fracture, require surgical treatment and belong to the age group of being able to working (18 - 67 years old)

Data of all patients will be pseudonymised. No individual participant data will be identified.