e17599 Background: Patient frailty is imparative to surgical planning, post-operative morbidity and mortality, and ultimately the ability to undergo adjuvant therapy in cancer treatment. Sarcopenia has been correlated with long term survival in the setting of pancreatic resection for cancer. However, it has not been evaluated in the early post-operative setting. Here, we evaluate the prognostic value of morphometric parameters measured on abdominal CT scans in fifty patients undergoing pancreatic resections and comparing with postoperative complications. Methods: Post-operative complications of fifty patients who underwent pancreatic resection for suspected neoplasm were graded via Clavien Dindo classification and then correlated with standardized morphometric measurements from CT scans. Results: Thirty-two men and 18 women (age 63±13 years) underwent pancreatic resection for cancer. Total psoas muscle area (2555±791 vs 1821±805,p=0.008), L4-alba distance (113±29 vs 119±27,p=0.597), rectus muscle (10.1±2.5 vs 7.8±4.5,p=0.016) and SQ fat thickness (20±11 vs 29±10,p=0.024). Logistic regression modeling including age, gender, and total psoas area predicted complication occurance (pseudo R2=0.350, p=0.008) and their number (pseudo R2=0.191,p=0.002), but not grade 3 and higher complications (pseudo R2=0.68,p=0.451) or pancreatic leak (pseudo R2=0.020,p=0.873). Similar results were obtained when age and gender variables were combined with rectus muscle thickness (pseudo R2=0.422), L4-alba distance (pseudo R2=0.377), and SQ fat thickness (pseudo R2=0.392). In each case, > Grade 3 complications and pancreatic leak was not predicted with morphometric data, age and gender. Conclusions: There are significant age and gender-related differences in morphometric data obtained from abdominal CT scans. Prognostic models provide statistically significant prediction of complication occurrence, but explain only up to 42% of variability in complication occurrence. Moreover, clinically important complications (grade 3 and higher) and pancreatic leak was not predicted with this model based on our limited dataset.
Amylase in drain fluid for the diagnosis of pancreatic leak in post-pancreatic resection