Distinct Patient Characteristics and Risk Profiles in Upper vs. Lower Leg Fractures: Insights from a Comprehensive...

Background and Purpose: Surgical treatment of lower-extremity fractures can result in postoperative complications. Irrespective of specific surgical procedures, postoperative outcomes may be influenced by pre-existing comorbidities, as well as by the severity of the injury. This study investigates whether (I) internal factors and (II) external factors are associated with (III) postoperative complication rates. Material and Methods: A prospective study was conducted at a Level I trauma center between 2014 and 2018. A cohort of 416 patients with surgical treatment after traumatic long bone fractures of the lower extremities was assessed, with comorbidities along with factors such as age, malnutrition, and impaired mobility systematically evaluated. Injury severity was classified using the Abbreviated Injury Scale; fracture type and location using the AO/OTA classification; and postoperative complications using the Clavien-Dindo system. Results: Across the cohort, internal factors such as immobility and malnutrition were associated with an increased complication rate. Age showed a weak correlation. Obesity demonstrated a trend toward a protective effect. Patients with femoral fractures exhibited a higher prevalence of cardiovascular comorbidities, with heart failure representing the most pronounced risk factor. In the subgroup with lower leg fractures, chronic heart disease also remained a significant predictor of complications. Correlation analysis further revealed a significant positive association between injury severity and the occurrence of complications across the overall cohort. Conclusions: This study provides valuable insights into risk factors for complicated postoperative courses. Injury severity appears to be a promising predictor of complication risk in patients with leg fractures. Reduced mobility and malnutrition, likewise, were significantly associated with increased complication rates; these may represent the most readily addressable modifiable risk factors. All data were collected using validated, user-friendly classification systems that may be suitable for predictive modeling.