Background & aims: It is a challenge to predict how patients with small bowel Crohn's disease (CD) will respond to intensified medical therapy. We aimed to identify factors that predicted surgery within 2 years of hospitalization for CD, to guide medical versus surgical management decisions.
Methods: We performed a retrospective review of adults hospitalized for small bowel CD from 2004 through 2012 at a single academic referral center. Subjects underwent abdominal computed tomography or magnetic resonance imaging within 3 weeks of hospitalization. Imaging characteristics of small bowel dilation, bowel wall thickness, and disease activity were assessed by a single, blinded radiologist. Multivariate analysis by Cox proportional hazards regression techniques was used to generate a prediction model of intestinal resection within 2 years.
Results: A total of 221 subjects met selection criteria, with 32.6% undergoing surgery within 2 years of index admission. Bivariate analysis showed high-dose steroid use (>40 mg), ongoing treatment with anti-tumor necrosis factor agents at admission, platelet count, platelet:albumin ratio, small bowel dilation (≥35 mm), and bowel wall thickness to predict surgery (P ≤ .01). Multivariate modeling demonstrated small bowel dilation >35 mm (hazard ratio, 2.92; 95% confidence interval, 1.73-4.94) and a platelet:albumin ratio ≥125 (hazard ratio, 2.13; 95% confidence interval, 1.15-3.95) to predict surgery. Treatment with anti-tumor necrosis factor agents at admission conferred a nonsignificant increased trend for risk of surgery (hazard ratio, 1.61; 95% confidence interval, 0.994-2.65).
Conclusions: Small bowel dilation >35 mm and high platelet:albumin ratios are independent and synergistic risk factors for future surgery in patients with structuring small bowel CD. Platelet:albumin ratios may capture the relationship between acute inflammation and cumulative damage and serve as markers of intestinal disease that cannot be salvaged with medical therapy.
Keywords: Bowel Obstruction; Outcome; Predictive Model; TNF.
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