title: Prediction of pancreatic anastomotic failure after pancreatoduodenectomy: the use of preoperative, quantitative computed tomography to measure remnant pancreatic volume and body composition.
abstract: OBJECTIVE
  To determine whether remnant pancreatic volume (RPV), subcutaneous/visceral adipose tissue(SAT/VAT) area, and skeletal muscle (SM) area calculated from preoperative computed tomography (CT) can predict the occurrence of pancreatic anastomotic failure (PAF) after pancreatoduodenectomy (PD).
  
  
  BACKGROUND
  Increased body mass index, small main pancreatic duct, and soft pancreatic texture are well-established predictors of PAF after PD. The impact on PAF of anthropomorphic measurements, such as RPV and body composition, is unknown.
  
  
  METHODS
  In 173 patients undergoing PD from 2004 to 2009, cross sections of SAT/VAT/SM area were quantitated volumetrically, respectively, from preoperative CT. RPV was calculated from the CT as the sum of pancreatic tissue area to the left of the presumed pancreatic transection site. The predictive ability for multiple models using combinations of body mass index, RPV, SAT/VAT area, SM area, main pancreatic duct size, and pancreatic gland texture was described using a concordance index (c-index).
  
  
  RESULTS
  Clinically relevant PAF occurred in 22 patients (13%). Multivariate logistic regression analysis identified RPV (P = 0.0012), VAT area (P = 0.0003), and SM area (P = 0.0006) as independent predictors of PAF. Using previously identified risk factors, the best 2-predictor model (body mass index and pancreatic duct size) resulted in a c-index of 0.748. Using anthropomorphic factors, however, the 2-predictor model using VAT and SM areas revealed a superior c-index of 0.959.
  
  
  CONCLUSIONS
  Our 2-predictor model using VAT area and SM area based on volumetric quantification using preoperative CT may offer clinical benefit as an objective prognostic measure to predict clinically relevant PAF after PD.
authors.affiliation: Division of Gastroenterologic and General Surgery, Mayo Clinic, Rochester, MN 55905, USA.
authors.forename: Yujiro
authors.initials: Y
authors.lastname: Kirihara
authors.forename: Naoki
authors.initials: N
authors.lastname: Takahashi
authors.forename: Yasushi
authors.initials: Y
authors.lastname: Hashimoto
authors.forename: Guido M
authors.initials: GM
authors.lastname: Sclabas
authors.forename: Saboor
authors.initials: S
authors.lastname: Khan
authors.forename: Toshiyuki
authors.initials: T
authors.lastname: Moriya
authors.forename: Junichi
authors.initials: J
authors.lastname: Sakagami
authors.forename: Marianne
authors.initials: M
authors.lastname: Huebner
authors.forename: Michael G
authors.initials: MG
authors.lastname: Sarr
authors.forename: Michael B
authors.initials: MB
authors.lastname: Farnell
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001823:Body Composition; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D008297:Male; D008875:Middle Aged; D010179:Pancreas / Q000000981:diagnostic imaging* / Q000601:surgery; D016577:Pancreaticoduodenectomy / Q000009:adverse effects*; D011183:Postoperative Complications / Q000000981:diagnostic imaging / Q000453:epidemiology* / Q000209:etiology; D011237:Predictive Value of Tests; D011300:Preoperative Care / Q000379:methods*; D012189:Retrospective Studies; D018570:Risk Assessment / Q000379:methods*; D014057:Tomography, X-Ray Computed / Q000706:statistics & numerical data*