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. A prospective database was queried for patients with ventral hernias who underwent open AWR by experienced surgeons and had preoperative computed tomography images containing the entire hernia defect. An 8-layer convolutional neural network was generated to analyze image characteristics. Images were batched into training (approximately 80%) or test sets (approximately 20%) to analyze model output. Test sets were blinded from the convolutional neural network until training was completed. For the surgical complexity model, a separate validation set of computed tomography images was evaluated by a blinded panel of 6 expert AWR surgeons and the surgical complexity DLM. Analysis started February 2020. Exposures: Image-based DLM. Main Outcomes and Measures: The primary outcome was model performance as measured by area under the curve in the receiver operating curve (ROC) calculated for each model; accuracy with accompanying sensitivity and specificity were also calculated. Measures were DLM prediction of surgical complexity using need for component separation techniques as a surrogate and prediction of postoperative surgical site infection and pulmonary failure. The DLM for predicting surgical complexity was compared against the prediction of 6 expert AWR surgeons. Results: A total of 369 patients and 9303 computed tomography images were used. The mean (SD) age of patients was 57.9 (12.6) years, 232 (62.9%) were female, and 323 (87.5%) were White. The surgical complexity DLM performed well (ROC = 0.744; P < .001) and, when compared with surgeon prediction on the validation set, performed better with an accuracy of 81.3% compared with 65.0% (P < .001). Surgical site infection was predicted successfully with an ROC of 0.898 (P < .001). However, the DLM for predicting pulmonary failure was less effective with an ROC of 0.545 (P = .03). Conclusions and Relevance: Image-based DLM using routine, preoperative computed tomography images was successful in predicting surgical complexity and more accurate than expert surgeon judgment. An additional DLM accurately predicted the development of surgical site infection.

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 32.5 days before AWR, without complications. Comorbidities included diabetes (31%), history of smoking (27%), and obesity (mean body mass index 30.5 ± 7.7). Hernias were recurrent in 57%, massive (mean defect width 15.3 ± 5.5 cm; hernia sac volume 2154 ± 3251 cm3) and had significant loss of domain (mean 46% visceral volume outside abdominal cavity). Contamination was present in 38% of patients. Fascial closure was achieved in 91%, with 57% requiring component separation techniques (CSTs). Subxiphoidal hernias needed a form of CST in 88% compared with 50% for hernia not extending subxiphoidal (P < 0.001). Mesh augmentation was used in 98%. Postoperative complications occurred in 40%: 19% surgical site occurrences, 12% surgical site infections, and 7% respiratory failure requiring intubation, 2% mesh infection and no fascial dehiscence. Recurrence was identified in seven patients after mean 14 months of follow-up. Patients undergoing AWR with CST had more surgical site occurrences (29 versus 7%, p0.003) and respiratory failures (18 versus 0%, P = 0.002) than patients who did not require CST. CONCLUSIONS: In patients with massive ventral hernias, the use of preoperative BTA injections for AWR is safe and is associated with high fascial closure rates and excellent recurrence rates.