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A Technique for Subcutaneous Abdominal Adipose Tissue Biopsy via a Non-diathermy Method
Published on: September 30, 2017
Thickness of subcutaneous fat is a risk factor for incisional surgical site infection in acute appendicitis surgery:
Bikas Thapa1, Edward Sutanto2, Raju Bhandari3
1Department of General and Gastrointestinal Surgery, Tribhuvan University Teaching Hospital, Institute of Medicine, Tribhuvan University, Maharajgung, Kathmandu, Nepal.
Background:
Incisional surgical site infection (SSI) is a significant source of postoperative morbidity resulting in increased length of stay and cost. In this study, our aim was to evaluate the association between thickness of subcutaneous fat (TSF) and incisional SSI among patients undergoing open appendectomy in low-resource settings.
Methods:
90 patients with acute uncomplicated appendicitis who underwent emergency open appendectomy from December 2017 to August 2018 were included in this prospective study. TSF was measured preoperatively using ultrasound. TSF and other possible predictors of incisional SSI, including body mass index and other clinical characteristics, were assessed by univariate and multivariable logistic regression analysis. Receiver operating characteristic (ROC) curve analysis evaluated the predictive value of TSF and the optimum cut-off value for TSF was determined using the Youden index.
Results:
The prevalence of incisional SSI was 13.3% (12/90). TSF was independently associated with incisional SSI (P < 0.001). Additionally, history of smoking (P = 0.048) was also associated with incisional SSI. A model of incisional SSI using a cut-off of 23.0 mm for TSF was moderately accurate (area under curve 0.83, confidence interval 0.70-0.97; sensitivity 83.3%; specificity 76.9%).
Conclusions:
The study demonstrated that TSF, as evaluated by ultrasound, is a predictor in the development of incisional SSI in patients with acute appendicitis undergoing open appendectomy. These findings suggest that ultrasound is useful both for the evaluation of TSF and the prediction of incisional SSI risk factor in low-resource settings.
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