Generic placeholder image

Current Medical Imaging

Editor-in-Chief

ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

Research Article

Multivariate Prediction of Small-bowel Ischemia and Necrosis using CT in Emergent Patients with Small-bowel Obstruction

Author(s): Bo Li and Zhifeng Wu*

Volume 20, 2024

Published on: 16 August, 2023

Article ID: e010823219331 Pages: 10

DOI: 10.2174/1573405620666230801105613

Price: $65

Abstract

Background: It is difficult to accurately determine whether emergent patients with small-bowel obstruction (SBO) have small-bowel ischemia and necrosis (SBIN). Therefore, in this study, we aimed to assess the ability of abdominal CT scans to predict SBIN and establish a new predictive model.

Methods: From March 2018 to May 2023, a rigorous posthoc analysis was conducted on whether 177 emergent patients with SBO had SBIN. Four clinical indexes and 19 CT signs were analyzed, and a multivariate scoring model for predicting SBIN was established using logistic regression analysis. A receiver operating characteristic (ROC) curve was used to assess the accuracy of this model.

Results: Multivariate analysis showed that mesenteric edema and effusion (OR=23.450), significant thickening and the target sign on the small-bowel wall on plain scans (OR=23.652), significant thinning of the small-bowel wall (OR=30.439), significant decrease in small-bowel wall density (OR=12.885), and significant increase in small-bowel wall density (OR=19.550) were significantly correlated with SBIN (P<0.05). According to their multivariate ORs, an appropriate “predictive score” was assigned to each sign, and the rates of SBIN among those with a total score of 0-4, 5-6, and 7-8 were 2.2%, 86.4%, and 96.9%, respectively. The AUC of this predictive scoring model for SBIN exceeded 0.980.

Conclusion: We have developed a predictive scoring model for SBIN, for which the incidence of SBIN increases with increasing predictive scores. This model can be useful for clinical treatment.

[1]
Maung AA, Johnson DC, Piper GL, et al. Evaluation and management of small-bowel obstruction. J Trauma Acute Care Surg 2012; 73(5): S362-9.
[http://dx.doi.org/10.1097/TA.0b013e31827019de] [PMID: 23114494]
[2]
Santillan CS. Computed tomography of small bowel obstruction. Radiol Clin North Am 2013; 51(1): 17-27.
[http://dx.doi.org/10.1016/j.rcl.2012.09.002] [PMID: 23182505]
[3]
Chang WC, Ko KH, Lin CS, et al. Features on MDCT that predict surgery in patients with adhesive-related small bowel obstruction. PLoS One 2014; 9(2): e89804.
[http://dx.doi.org/10.1371/journal.pone.0089804] [PMID: 24587047]
[4]
Rami RSR, Cappell MS. A systematic review of the clinical presentation, diagnosis, and treatment of small bowel obstruction. Curr Gastroenterol Rep 2017; 19(6): 28.
[http://dx.doi.org/10.1007/s11894-017-0566-9] [PMID: 28439845]
[5]
Paulson EK, Thompson WM. Review of small-bowel obstruction: The diagnosis and when to worry. Radiology 2015; 275(2): 332-42.
[http://dx.doi.org/10.1148/radiol.15131519] [PMID: 25906301]
[6]
Zamary K, Spain DA. Small bowel obstruction: The sun also rises? J Gastrointest Surg 2020; 24(8): 1922-8.
[http://dx.doi.org/10.1007/s11605-019-04351-5] [PMID: 32542559]
[7]
Podda M, Khan M, Di Saverio S. Adhesive small bowel obstruction and the six w’s: Who, how, why, when, what, and where to diagnose and operate? Scand J Surg 2021; 110(2): 159-69.
[http://dx.doi.org/10.1177/1457496920982763] [PMID: 33511902]
[8]
Tyagunov AE, Tyagunov AA, Nechay TV, Vinogradov VN, Kurashinova LS, Sazhin AV. Timing of surgery, intestinal ischemia and other real factors of mortality in acute adhesive small bowel obstruction: A multiple-center study. Khirurgiia 2021; (3): 26-35.
[http://dx.doi.org/10.17116/hirurgia202103126] [PMID: 33710823]
[9]
Kim JH, Ha HK, Kim JK, et al. Usefulness of known computed tomography and clinical criteria for diagnosing strangulation in small-bowel obstruction: Analysis of true and false interpretation groups in computed tomography. World J Surg 2004; 28(1): 63-8.
[http://dx.doi.org/10.1007/s00268-003-6899-6] [PMID: 14648046]
[10]
O’Leary MP, Neville AL, Keeley JA, Kim DY, De Virgilio C, Plurad DS. Predictors of ischemic bowel in patients with small bowel obstruction. Am Surg 2016; 82(10): 992-4.
[http://dx.doi.org/10.1177/000313481608201030] [PMID: 27779991]
[11]
Morris RS, Murphy P, Boyle K, et al. Bowel ischemia score predicts early operation in patients with adhesive small bowel obstruction. Am Surg 2022; 88(2): 205-11.
[http://dx.doi.org/10.1177/0003134820988820] [PMID: 33502222]
[12]
Kim HR, Lee Y, Kim J, et al. Closed loop obstruction of small bowel: CT signs predicting successful non-surgical treatment. Eur J Radiol 2023; 161: 110716.
[http://dx.doi.org/10.1016/j.ejrad.2023.110716] [PMID: 36758277]
[13]
Scaglione M, Galluzzo M, Santucci D, et al. Small bowel obstruction and intestinal ischemia: Emphasizing the role of MDCT in the management decision process. Abdom Radiol 2022; 47(5): 1541-55.
[http://dx.doi.org/10.1007/s00261-020-02800-3] [PMID: 33057806]
[14]
Li Z, Shi L, Zhang J, et al. Imaging signs for determining surgery timing of acute intestinal obstruction. Contrast Media Mol Imaging 2022; 2022: 1-7.
[http://dx.doi.org/10.1155/2022/1980371] [PMID: 35935303]
[15]
Ozawa M, Ishibe A, Suwa Y, et al. A novel discriminant formula for the prompt diagnosis of strangulated bowel obstruction. Surg Today 2021; 51(8): 1261-7.
[http://dx.doi.org/10.1007/s00595-020-02213-1] [PMID: 33420825]
[16]
Calame P, Malakhia A, Turco C, Grillet F, Piton G, Delabrousse E. Transmural bowel necrosis from acute mesenteric ischemia and strangulated small-bowel obstruction: Distinctive CT features. AJR Am J Roentgenol 2020; 214(1): 90-5.
[http://dx.doi.org/10.2214/AJR.19.21693] [PMID: 31553659]
[17]
Diamond M, Lee J, LeBedis CA. Small bowel obstruction and ischemia. Radiol Clin North Am 2019; 57(4): 689-703.
[http://dx.doi.org/10.1016/j.rcl.2019.02.002] [PMID: 31076026]
[18]
Idelevich E, Kashtan H, Mavor E, Brenner B. Small bowel obstruction caused by secondary tumors. Surg Oncol 2006; 15(1): 29-32.
[http://dx.doi.org/10.1016/j.suronc.2006.05.004] [PMID: 16905310]
[19]
Zielinski MD, Eiken PW, Bannon MP, et al. Small bowel obstruction-who needs an operation? A multivariate prediction model. World J Surg 2010; 34(5): 910-9.
[http://dx.doi.org/10.1007/s00268-010-0479-3] [PMID: 20217412]
[20]
Balthazar EJ, Birnbaum BA, Megibow AJ, Gordon RB, Whelan CA, Hulnick DH. Closed-loop and strangulating intestinal obstruction: CT signs. Radiology 1992; 185(3): 769-75.
[http://dx.doi.org/10.1148/radiology.185.3.1438761] [PMID: 1438761]
[21]
Hines J, Rosenblat J, Duncan DR, Friedman B, Katz DS. Perforation of the mesenteric small bowel: Etiologies and CT findings. Emerg Radiol 2013; 20(2): 155-61.
[http://dx.doi.org/10.1007/s10140-012-1095-3] [PMID: 23212537]
[22]
Olson MC, Navin PJ, Welle CL, Goenka AH. Small bowel radiology. Curr Opin Gastroenterol 2021; 37(3): 267-74.
[http://dx.doi.org/10.1097/MOG.0000000000000719] [PMID: 33591028]
[23]
Boudiaf M, Soyer P, Terem C, Pelage JP, Maissiat E, Rymer R. Ct evaluation of small bowel obstruction. Radiographics 2001; 21(3): 613-24.
[http://dx.doi.org/10.1148/radiographics.21.3.g01ma03613] [PMID: 11353110]
[24]
Kruk M, Wardziak Ł, Demkow M, et al. Workstation-based calculation of CTA-Based FFR for intermediate stenosis. JACC Cardiovasc Imaging 2016; 9(6): 690-9.
[http://dx.doi.org/10.1016/j.jcmg.2015.09.019] [PMID: 26897667]
[25]
Kim S, McClave SA, Martindale RG, Miller KR, Hurt RT. Hypoalbuminemia and clinical outcomes: What is the mechanism behind the relationship? Am Surg 2017; 83(11): 1220-7.
[http://dx.doi.org/10.1177/000313481708301123] [PMID: 29183523]
[26]
Barberi C, Colaizzi C, Guerrini J, Kurihara H. Whirl sign: A common misinterpreted radiological entity. Intern Emerg Med 2021; 16(6): 1703-5.
[http://dx.doi.org/10.1007/s11739-020-02571-1] [PMID: 33386605]
[27]
Cox VL, Tahvildari AM, Johnson B, Wei W, Jeffrey RB. Bowel obstruction complicated by ischemia: Analysis of CT findings. Abdom Radiol 2018; 43(12): 3227-32.
[http://dx.doi.org/10.1007/s00261-018-1651-8] [PMID: 29858936]
[28]
Huang X, Fang G, Lin J, Xu K, Shi H, Zhuang L. A prediction model for recognizing strangulated small bowel obstruction. Gastroenterol Res Pract 2018; 2018: 1-7.
[http://dx.doi.org/10.1155/2018/7164648] [PMID: 29780412]
[29]
Zielinski MD, Eiken PW, Heller SF, et al. Prospective, observational validation of a multivariate small-bowel obstruction model to predict the need for operative intervention. J Am Coll Surg 2011; 212(6): 1068-76.
[http://dx.doi.org/10.1016/j.jamcollsurg.2011.02.023] [PMID: 21458305]
[30]
Schwenter F, Dominguez S, Meier R, et al. [Acute small bowel obstruction: Conservative or surgical treatment?]. Rev Med Suisse 2011; 7(300): 1341-1344, 1346-1347.
[PMID: 21815533]
[31]
Xu W, Zhong Q, Cai Y, et al. Prediction and management of strangulated bowel obstruction: A multi-dimensional model analysis. BMC Gastroenterol 2022; 22(1): 304.
[http://dx.doi.org/10.1186/s12876-022-02363-1] [PMID: 35733109]
[32]
Kobayashi T, Chiba N, Koganezawa I, et al. Prediction model for irreversible intestinal ischemia in strangulated bowel obstruction. BMC Surg 2022; 22(1): 321.
[http://dx.doi.org/10.1186/s12893-022-01769-8] [PMID: 35996141]
[33]
Goceri N, Goceri E. A neural network based kidney segmentation from MR images. 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA). 09-11 December 2015; Miami, FL, USA. 2015.
[http://dx.doi.org/10.1109/ICMLA.2015.229]
[34]
Göçeri̇ E, Ünlü MZ, Di̇cle O. A comparative performance evaluation of various approaches for liver segmentation from SPIR images. Turk J Electr Eng Comput Sci 2015; 23: 741-68.
[http://dx.doi.org/10.3906/elk-1304-36]
[35]
Goceri E, Unlu M, Guzelis C, et al. An automatic level set based liver segmentation from MRI data sets. 3rd International Conference on Image Processing Theory, Tools and Applications, IPTA. 15 October 2012; Istanbul; Turkey. 2012.
[http://dx.doi.org/10.1109/IPTA.2012.6469551]
[36]
Dura E, Domingo J, Göçeri E, Martí-Bonmatí L. A method for liver segmentation in perfusion MR images using probabilistic atlases and viscous reconstruction. Pattern Anal Appl 2018; 21(4): 1083-95.
[http://dx.doi.org/10.1007/s10044-017-0666-z]
[37]
Goceri E. Automatic Kidney Segmentation Using Gaussian Mixture Model on MRI Sequences. Berlin, Heidelberg: Springer Berlin Heidelberg 2011.
[http://dx.doi.org/10.1007/978-3-642-21747-0_4]

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy