Prediction and management of strangulated bowel obstruction: a multi-dimensional model analysis

Abstract

Background Distinguishing strangulated bowel obstruction (StBO) from simple bowel obstruction (SiBO) still poses a challenge for emergency surgeons. We aimed to construct a predictive model that could distinctly discriminate StBO from SiBO based on the degree of bowel ischemia. Methods The patients diagnosed with intestinal obstruction were enrolled and divided into SiBO group and StBO group. Binary logistic regression was applied to identify independent risk factors, and then predictive models based on radiological and multi-dimensional models were constructed. Receiver operating characteristic (ROC) curves and the area under the curve (AUC) were calculated to assess the accuracy of the predicted models. Via stratification analysis, we validated the multi-dimensional model in the prediction of transmural necrosis both in the training set and validation set. Results Of the 281 patients with SBO, 45 (16.0%) were found to have StBO, while 236(84.0%) with SiBO. The AUC of the radiological model was 0.706 (95%CI, 0.617–0.795). In the multivariate analysis, seven risk factors including pain duration ≤ 3 days (OR = 3.775), rebound tenderness (OR = 5.201), low-to-absent bowel sounds (OR = 5.006), low levels of potassium (OR = 3.696) and sodium (OR = 3.753), high levels of BUN (OR = 4.349), high radiological score (OR = 11.264) were identified. The AUC of the multi-dimensional model was 0.857(95%CI, 0.793–0.920). In the stratification analysis, the proportion of patients with transmural necrosis was significantly greater in the high-risk group (24%) than in the medium-risk group (3%). No transmural necrosis was found in the low-risk group. The AUC of the validation set was 0.910 (95%CI, 0.843–0.976). None of patients in the low-risk and medium-risk score group suffered with StBO. However, all patients with bowel ischemia (12%) and necrosis (24%) were resorted into high-risk score group. Conclusion The novel multi-dimensional model offers a useful tool for predicting StBO. Clinical management could be performed according to the multivariate score.

Small bowel transmural necrosis secondary to acute mesenteric ischemia and strangulated obstruction: CT findings of 49 patients

“…Although the differences in the CT features of TBN for the 5 etiologies did not reach statistical significance, our study still extends the currently limited knowledge on this topic. Few TBN investigations and a meta-analysis have been performed, and most were conducted from 2015 onward ( Table 4 ) [ 8 , 11 , 13 , [15] , [16] , [17] , [25] , [26] , [27] , [28] , [29] , [30] ]. This increase in research might reflect an advance in the knowledge of the treatment options for patients with bowel ischemia.…”

“…Although nonspecific, mesenteric fat stranding, fluid and ascites were prevalent in TBN of any etiology, with prevalence ranging from 47.6% to 96.2% [ 8 , 11 , 16 , 17 , [25] , [26] , [27] , 29 , 30 ]. These conditions result not only from the elevated mesenteric pressure typical of strangulated SBO but also from transmural infarction or even a reperfusion state [ 8 ].…”

“…These findings are consistent with earlier studies showing a prevalence of ascites ranging from 50% to 96% [ 8 , 11 , 27 ]. In addition, ascites was found to be an independent predictor of strangulated SBO in the study of Xu W et al [ 30 ]. Bowel dilatation is expected in patients with SBO, especially when it is complicated by strangulation.…”

The target sign: a significant CT sign for predicting small-bowel ischemia and necrosis

“…SBN is a serious complication of SBO. Patients with SBN require urgent surgical treatment; otherwise, they may develop secondary systemic infections, sepsis, or even death [ 3 ]. Therefore, accurately distinguishing the type of SBO and whether there is an SBN before surgery is highly important for treatment decision-making.…”

“…Many previous studies have focused on using clinical signs and laboratory tests to predict SBN, while the role of CT findings and signs has been underestimated [ 7 – 9 ]. In other studies, multiple CT signs and clinical laboratory indicators have been used to jointly predict SBN, and predictive models have been developed to predict SBN [ 3 , 10 , 11 ]. However, few studies have focused on exploring the changes in the thickness and density of diseased intestinal wall itself during SBN.…”