Development and Validation of Image-Based Deep Learning Models to Predict Surgical Complexity and Complications in Abdominal Wall Reconstruction.

Importance: Image-based deep learning models (DLMs) have been used in other disciplines, but this method has yet to be used to predict surgical outcomes.

Objective: To apply image-based deep learning to predict complexity, defined as need for component separation, and pulmonary and wound complications after abdominal wall reconstruction (AWR).

Design, Setting, And Participants: This quality improvement study was performed at an 874-bed hospital and tertiary hernia referral center from September 2019 to January 2020.

Preoperative botulinum toxin A injection in complex abdominal wall reconstruction- a propensity-scored matched study.

Introduction: Fascial closure during complex abdominal wall reconstruction (AWR) improves recurrence and wound infection rates. To facilitate fascial closure in massive ventral hernias preoperative Botulinum Toxin A (BTA) injection can be used.

Methods: 2:1 propensity-scored matching of patients undergoing AWR with and without BTA was performed based on BMI, defect width, and loss of domain using CT-volumetric analysis.

The Effects of Preoperative Botulinum Toxin A Injection on Abdominal Wall Reconstruction.

Background: Fascial closure significantly reduces postoperative complications and hernia recurrence after abdominal wall reconstruction (AWR), but can be challenging in massive ventral hernias.

Methods: A prospective single-institution cohort study was performed to examine the effects of preoperative injection of botulinum toxin A (BTA) in patients undergoing AWR for midline or flank hernias.

Results: A total of 108 patients underwent BTA injection with average 243 units, mean 32.