Objective preoperative parameters predict difficult pelvic dissections and clinical outcomes

Academic Article

Abstract

  • © 2018 Elsevier Inc. Background: Objective criteria to predict difficult pelvic dissection with prognostic significance are lacking. Previous studies have focused on predicting intraoperative conversion and not evaluated factors specific to pelvic surgery. We aimed to develop an objective, prognostic, preoperative assessment to predict difficult pelvic dissections and clinical outcomes. Such a model is much needed, may facilitate objective comparisons between rectal cancer centers, or may serve as a stratification variable in clinical trials. Materials and methods: Patients who underwent low anterior resection or abdominoperineal resection for rectal cancer within 10 cm of the anal verge (2009-2014) were retrospectively analyzed. Procedures were categorized into “routine” or “difficult” based on predefined criteria. All patients underwent 14 measurements on preoperative imaging. Outcomes were compared between the two groups. Stepwise multivariate logistic regression was used to develop the prediction model, which was validated in an independent data set. Results: Of the 280 patients analyzed, 80 fulfilled the inclusion criteria. Baseline characteristics were similar except for more males having a “difficult” pelvis. “Difficult” patients were significantly more likely to have a narrower pelvis, smaller pelvic volumes, a longer pelvis, more curved sacrum, and more acute anorectal angle. Difficult cases correlated significantly with higher blood loss, hospital costs, longer operative time, and length of stay. A practical model to predict difficult pelvic dissections was created and included male gender, previous radiation, and length from promontory to pelvic floor > 130 mm. Model validation was performed in 40 patients from an independent data set. Conclusions: An objective, validated model that predicts a difficult pelvic dissection and associated worse clinical outcome is possible.
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    Author List

  • Iqbal A; Khan A; George TJ; Tan S; Qiu P; Yang K; Trevino J; Hughes S
  • Start Page

  • 15
  • End Page

  • 25
  • Volume

  • 232