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Ann Plast Surg. 2013 Sep;71(3):261-5. doi: 10.1097/SAP.0b013e3182773915.

Predicting abdominal closure after component separation for complex ventral hernias: maximizing the use of preoperative computed tomography.

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1
Department of Plastic Surgery, Georgetown University Hospital, Washington, DC, USA.

Abstract

BACKGROUND:

Component separation techniques (CSTs) have allowed for midline fascial reapproximation in large midline ventral hernias. In certain cases, however, fascial apposition is not feasible, resulting in a bridged repair that is suboptimal. Previous estimates on myofascial advancement are based on hernia location and do not take into account variability between patients. Examination of preoperative computed tomography (CT) may provide insight into these variabilities and may allow for prediction of abdominal closure with CST.

STUDY DESIGN:

A retrospective review was conducted of patients who underwent abdominal wall reconstruction from 2007 to 2012 with CST. Preoperative CT was obtained, and specific parameters were analyzed using image analysis software. Logistic regression was used to predict ideal operative closure. Multivariate analyses were adjusted for age and sex. An a priori value was set at P < 0.05.

RESULTS:

Fifty-four patients met the criteria and had preoperative CT available for analysis. Forty-eight patients had fascial reapproximation achieved, whereas 6 patients had a bridged repair. Age, sex, weight, and body mass index were similar between groups (P > 0.05). Significant differences were seen between groups in 3 variables: transverse defect size (19.8 vs 10 cm, P < 0.05), defect area (420 vs 184.2 cm, P < 0.05), and percent abdominal wall defect (18.9% vs 10.6%, P < 0.05).

CONCLUSIONS:

Preoperative determination of abdominal wall defect ratios and hernia defect areas may represent a more accurate method to predict abdominal wall closure after CST. Predicting midline approximation after CST is critical because outcomes after bridged repair can result in higher recurrence rates.

PMID:
23945530
DOI:
10.1097/SAP.0b013e3182773915
[Indexed for MEDLINE]

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