Background Obesity is a known risk factor for surgical site infection (SSI). Our hypothesis is that morphometric measures of midline subcutaneous fat will be associated with increased risk of SSI, and will predict SSI better than conventional measures of obesity. Study Design We identified 655 patients who underwent midline laparotomy (2006 - 2009) using the Michigan Surgical Quality Collaborative database. Using novel, semi-automated analytic morphometric techniques, the thickness of subcutaneous fat along the linea alba was measured between T12 and L4. To adjust for variations in patient size, subcutaneous fat was normalized to the distance between the vertebrae and anterior skin. Logistic regression analyses were used to identify factors independently associated with the incidence of SSI. Results Overall, SSIs were observed in 12.5% (n = 82) of the population. Logistic regression revealed that patients with increased subcutaneous fat had significantly greater odds of developing a superficial incisional SSI (OR = 1.76 per 10% increase, 95% CI: 1.10 – 2.83, p = 0.019). Smoking, steroid use, ASA classification, and incision-to-close operative time were also significant independent risk factors for superficial incisional SSI. When comparing subcutaneous fat and body mass index (BMI) as the only model variables, subcutaneous fat significantly improved model predictions of superficial incisional SSI (AUC: 0.60, p = 0.023) while BMI did not (AUC = 0.52, p = 0.73). Conclusions Abdominal subcutaneous fat is an independent predictor of superficial incisional SSI following midline laparotomy. Novel morphometric measures may improve risk stratification and help elucidate the pathophysiology of surgical complications.

This study suggests that AA calcification may be related to progression of CV disease and surgical outcomes. A better understanding of the complex interaction of patient physiology with overall ability to recover from major surgery, using novel approaches such as analytic morphomics, has great potential to improve risk stratification and patient selection.

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