Matsuda Akihisa, Kuriyama Sho, Ando Fumihiko, Yasuda Tomohiko, Matsumoto Satoshi, Sakurazawa Nobuyuki, Kawano Yoichi, Sekiguchi Kumiko, Yamada Takeshi, Suzuki Hideyuki, Yoshida Hiroshi
Department of Surgery, Nippon Medical School Chiba Hokusoh Hospital, Inzai, Japan.
Department of Gastroenterological Surgery, Nippon Medical School, Tokyo, Japan.
J Anus Rectum Colon. 2024 Oct 25;8(4):323-330. doi: 10.23922/jarc.2024-036. eCollection 2024.
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.
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.
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; -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.
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.
使用机器学习（ML）算法探讨粘连性小肠梗阻（ASBO）患者手术干预需求的预测性能，并研究过渡到手术的最佳时机。
本回顾性研究纳入了106例最初接受长鼻肠管（LT）减压的ASBO患者。采用传统逻辑回归分析和ML算法评估手术干预需求的风险。
28例患者（26%）LT减压非手术治疗（NOM）失败。多因素逻辑回归分析确定，第1天经LT引流量≥665 ml、ASBO诊断与LT插管间隔时间以及LT插管后48小时小肠扩张是过渡到手术的独立预测因素（比值比分别为7.10、1.42和19.81；95%置信区间为1.63 - 30.94、1.00 - 2.02和3.04 - 129.10；P值分别为0.009、0.047和0.002）。在测试的五种ML算法中，随机森林算法显示出最佳预测性能，测试集中曲线下面积为0.889，准确率为0.864，精确率为0.667。97.4%未过渡到手术的患者（n = 78）在三天内首次排气。
这是第一项证明ML算法可预测ASBO患者手术需求的研究。指南推荐的初始NOM 72小时似乎是合理的。这些发现可用于为这些患者制定早期临床决策框架。
