To evaluate and quantify surgical skill by grading surgical performance of the pancreaticojejunostomy from robotic pancreaticoduodenectomies (RPDs). We hypothesized that video grading of surgical performance would contribute to estimating risk of postoperative pancreatic fistula (POPF) after pancreaticoduodenectomy.
POPF majorly contributes to pancreaticoduodenectomy morbidity. Risk scores [Fistula Risk Score (FRS) and Braga] derived from patient variables are validated for predicting POPF. Birkmeyer et al showed assessment of surgical proficiency is an important component of outcomes.
POPF was diagnosed using International Study Group definition. Technical performance of robotic pancreaticojejunostomy video was graded by 2 blinded surgeons using: (1) pancreaticojejunostomy step-by-step variables [PJ-specific variables (PJVs); max = 115]; and (2) the Objective Structured Assessment of Technical Skills (OSATS) score.
One hundred thirty-three pancreaticojejunostomies were analyzed. POPF was 18%. Higher FRS (P = 0.011) and Braga (P = 0.041) scores predicted POPF. Graders’ subjective prediction did not correlate with FRS/Braga scores. Grader 1 scores (P = 0.043), but not grader 2 (P = 0.44), predicted POPF. PJV scores >105 were predictive of POPF (P = 0.039). Scoring only PJV duct-to-mucosa stitches (max = 50) was highly predictive of POPF (P = 0.0053). Higher OSATS scores were associated with a decreased rate of POPF (P = 0.022). On multivariate analysis, adding technical scoring to statistically significant patient variables (ie, gland texture) improves the model and can independently predict POPF. The strongest predictive model for POPF consisted of soft gland (odds ratio = 18.28, 95% confidence interval = 2.19–152.57) and low OSATS (odds ratio = 0.82, 95% confidence interval = 0.70–0.96). OSATS, modeled with FRS or Braga scores, independently predicted POPF.
This is the first study to demonstrate that technical scoring of a surgeon's performance independently predicts patient outcomes in pancreatic surgery. Future studies should consider how to validate and incorporate technical metrics.
*Division of Surgical Oncology, Department of Surgery, University of Pittsburgh, Pittsburgh, PA
†Division of Biostatistics, Department of Surgery, University of Pittsburgh, Pittsburgh, PA
‡Chongqing Medical University Affiliated First Hospital, Chongqing, China
§Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China.
Reprints: Dr Melissa E. Hogg, MD, MS, 3550 Terrace Street, Scaife Hall, Suite 497, A-415, Pittsburgh, PA 15213. E-mail: email@example.com.
This article was presented at the 136th Annual Proceedings of the American Surgical Association, Chicago, IL, April 16, 2016.
MEH receives funding through the Veteran's Administration in the way of salary support.
Conflicts of interest/disclosures: Hogg had an industry sponsored education grant in 2014.