Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2024 Oct 25;8(4):323-330.
doi: 10.23922/jarc.2024-036. eCollection 2024.

Machine Learning Predicts the Need for Surgical Intervention in Adhesive Small Bowel Obstruction

Akihisa Matsuda  1   2 , Sho Kuriyama  1   2 , Fumihiko Ando  1   2 , Tomohiko Yasuda  1 , Satoshi Matsumoto  1 , Nobuyuki Sakurazawa  1   2 , Yoichi Kawano  1   2 , Kumiko Sekiguchi  1 , Takeshi Yamada  2 , Hideyuki Suzuki  1 , Hiroshi Yoshida  2
Affiliations

Machine Learning Predicts the Need for Surgical Intervention in Adhesive Small Bowel Obstruction

Akihisa Matsuda et al. J Anus Rectum Colon. .

Abstract

Objectives: To explore the predictive performance on the need for surgical intervention in patients with adhesive small bowel obstruction (ASBO) using machine-learning (ML) algorithms and investigate the optimal timing for transition to surgery.

Methods: One hundred and six patients with ASBO who initially underwent long transnasal intestinal tube (LT) decompression were enrolled in this retrospective study. Traditional logistic regression analysis and ML algorithms were used to evaluate the risk of need for surgical intervention.

Results: Non-operative management (NOM) by LT decompression failed in 28 patients (26%). Multivariate logistic regression analysis identified a drainage volume ≥665 ml via LT on day 1, interval between ASBO diagnosis and LT intubation, and small bowel dilatation at 48 h after LT intubation to be independent predictors of transition to surgery (odds ratios 7.10, 1.42, and 19.81, respectively; 95% confidence intervals 1.63-30.94, 1.00-2.02, and 3.04-129.10; P-values 0.009, 0.047, and 0.002). The random forest algorithm showed the best predictive performance of five ML algorithms tested, with an area under the curve of 0.889, accuracy of 0.864, and precision of 0.667 in the test set. 97.4% of patients without transition to surgery (n=78) had passes of first flatus until three days.

Conclusions: This is the first study to demonstrate that ML algorithm can predict the need for surgery in patients with ASBO. The guideline recommended period for initial NOM of 72 h seems to be reasonable. These findings can be used to develop a framework for earlier clinical decision-making in these patients.

Keywords: adhesive small bowel obstruction; long transnasal intestinal tube; machine-learning; non-operative management; surgery.

PubMed Disclaimer

Conflict of interest statement

Conflicts of Interest There are no conflicts of interest.

Figures

Figure 1.
Figure 1.
Feature importances of random forest algorithm (A) and gradient boosting (B) for prediction of surgical intervention in ASBO patients. Abbreviations: ASBO, adhesive small bowel obstruction; LT, long transnasal intestinal tube; WSCA, water-soluble contrast agent
Figure 2.
Figure 2.
ROC curves of ML algorithms predicting surgical intervention in ASBO patients in training set. Abbreviations: ROC, receiver operating characteristics; ML, machine-learning; ASBO, adhesive small bowel obstruction
Figure 3.
Figure 3.
ROC curves of ML algorithms predicting surgical intervention in ASBO patients in test set. Abbreviations: ROC, receiver operating characteristics; ML, machine-learning; ASBO, adhesive small bowel obstruction
Figure 4.
Figure 4.
Interval from LT intubation to pass first flatus among patients in the non-operative group. Abbreviations: LT, long transnasal intestinal tube
See this image and copyright information in PMC

Similar articles

See all similar articles

References

    1. Aquina CT, Becerra AZ, Probst CP, et al. Patients With Adhesive Small Bowel Obstruction Should Be Primarily Managed by a Surgical Team. Ann Surg. 2016 Sep; 264(3): 437-47. - PubMed
    1. Matsuda A, Yamada T, Ohta R, et al. Surgical Site Infections in Gastroenterological Surgery. J Nippon Med Sch. 2023 Mar; 90(1): 2-10. - PubMed
    1. Kossi JA, Salminen PT, Laato MK. Surgical workload and cost of postoperative adhesion-related intestinal obstruction: importance of previous surgery. World J Surg. 2004 Jul; 28(7): 666-70. - PubMed
    1. Parikh JA, Ko CY, Maggard MA, et al. What is the rate of small bowel obstruction after colectomy? Am Surg. 2008 Oct; 74(10): 1001-5. - PubMed
    1. Shih SC, Jeng KS, Lin SC, et al. Adhesive small bowel obstruction: how long can patients tolerate conservative treatment? World J Gastroenterol. 2003 Mar; 9(3): 603-5. - PMC - PubMed