Sino-German Tongji-Caritas Research Center of Ultrasound in Medicine, Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China.
Department of Geratology, Hubei Provincial Hospital of Integrated Chinese and Western Medicine, Wuhan 430015, Hubei Province, China.
World J Gastroenterol. 2020 Jul 14;26(26):3800-3813. doi: 10.3748/wjg.v26.i26.3800.
The prognosis of acute mesenteric ischemia (AMI) caused by superior mesenteric venous thrombosis (SMVT) remains undetermined and early detection of transmural bowel infarction (TBI) is crucial. The predisposition to develop TBI is of clinical concern, which can lead to fatal sepsis with hemodynamic instability and multi-organ failure. Early resection of necrotic bowel could improve the prognosis of AMI, however, accurate prediction of TBI remains a challenge for clinicians. When determining the eligibility for explorative laparotomy, the underlying risk factors for bowel infarction should be fully evaluated.
To develop and externally validate a nomogram for prediction of TBI in patients with acute SMVT.
Consecutive data from 207 acute SMVT patients at the Wuhan Tongji Hospital and 89 patients at the Guangzhou Nanfang Hospital between July 2005 and December 2018 were included in this study. They were grouped as training and external validation cohort. The 207 cases (training cohort) from Tongji Hospital were divided into TBI and reversible intestinal ischemia groups based on the final therapeutic outcomes. Univariate and multivariate logistic regression analyses were conducted to identify independent risk factors for TBI using the training data, and a nomogram was subsequently developed. The performance of the nomogram was evaluated with respect to discrimination, calibration, and clinical usefulness in the training and external validation cohort.
Univariate and multivariate logistic regression analyses identified the following independent prognostic factors associated with TBI in the training cohort: The decreased bowel wall enhancement (OR = 6.37, < 0.001), rebound tenderness (OR = 7.14, < 0.001), serum lactate levels > 2 mmol/L (OR = 3.14, = 0.009) and previous history of deep venous thrombosis (OR = 6.37, < 0.001). Incorporating these four factors, the nomogram achieved good calibration in the training set [area under the receiver operator characteristic curve (AUC) 0.860; 95%CI: 0.771-0.925] and the external validation set (AUC 0.851; 95%CI: 0.796-0.897). The positive and negative predictive values (95%CIs) of the nomogram were calculated, resulting in positive predictive values of 54.55% (40.07%-68.29%) and 53.85% (43.66%-63.72%) and negative predictive values of 93.33% (82.14%-97.71%) and 92.24% (85.91%-95.86%) for the training and validation cohorts, respectively. Based on the nomogram, patients who had a Nomo-score of more than 90 were considered to have high risk for TBI. Decision curve analysis indicated that the nomogram was clinically useful.
The nomogram achieved an optimal prediction of TBI in patients with AMI. Using the model, the risk for an individual patient inclined to TBI can be assessed, thus providing a rational therapeutic choice.
肠系膜上静脉血栓形成（SMVT）引起的急性肠系膜缺血（AMI）的预后仍不确定，早期发现透壁性肠梗死（TBI）至关重要。发生 TBI 的倾向是临床关注的问题，它可能导致血流动力学不稳定和多器官衰竭的致命性败血症。早期切除坏死的肠可以改善 AMI 的预后，然而，TBI 的准确预测仍然是临床医生面临的挑战。在确定剖腹探查的适应证时，应充分评估导致肠梗死的潜在风险因素。
建立并验证预测急性 SMVT 患者 TBI 的列线图。
纳入 2005 年 7 月至 2018 年 12 月期间武汉同济医院和广州南方医院 207 例急性 SMVT 患者和 89 例患者的连续数据，将其分为训练和外部验证队列。同济医院的 207 例（训练队列）根据最终治疗结果分为 TBI 和可逆性肠缺血组。使用训练数据进行单因素和多因素逻辑回归分析，以确定 TBI 的独立危险因素，并随后建立列线图。在训练和外部验证队列中评估列线图的鉴别能力、校准度和临床实用性。
单因素和多因素逻辑回归分析确定了与训练队列中 TBI 相关的独立预后因素：肠壁增强减弱（OR=6.37，<0.001）、反跳痛（OR=7.14，<0.001）、血清乳酸水平>2mmol/L（OR=3.14，=0.009）和深静脉血栓形成史（OR=6.37，<0.001）。纳入这四个因素后，列线图在训练集（AUC 0.860；95%CI：0.771-0.925）和外部验证集（AUC 0.851；95%CI：0.796-0.897）中均具有良好的校准度。计算列线图的阳性和阴性预测值（95%CI），得到训练队列的阳性预测值为 54.55%（40.07%-68.29%）和 53.85%（43.66%-63.72%），阴性预测值为 93.33%（82.14%-97.71%）和 92.24%（85.91%-95.86%）。根据列线图，Nomo 评分>90 的患者被认为有发生 TBI 的高风险。决策曲线分析表明，该列线图具有临床实用性。
该列线图能够对 AMI 患者的 TBI 进行最佳预测。使用该模型可以评估个体患者发生 TBI 的风险，从而为合理的治疗选择提供依据。
