Northern Jiangsu People's Hospital, Clinical Teaching Hospital of Medical School, Nanjing University, Yangzhou, China.
General Surgery Institute of Yangzhou, Yangzhou University, Yangzhou, 225001, China.
Surg Endosc. 2024 Jul;38(7):3661-3671. doi: 10.1007/s00464-024-10885-w. Epub 2024 May 22.
Anastomotic stricture significantly impacts patients' quality of life and long-term prognosis. However, current clinical practice lacks accurate tools for predicting anastomotic stricture. This study aimed to develop a nomogram to predict anastomotic stricture in patients with rectal cancer who have undergone anterior resection.
A total of 1542 eligible patients were recruited for the study. Least absolute shrinkage selection operator (Lasso) analysis was used to preliminarily select predictors. A prediction model was constructed using multivariate logistic regression and presented as a nomogram. The performance of the nomogram was evaluated using receiver operating characteristic (ROC) curves, calibration diagrams, and decision curve analysis (DCA). Internal validation was conducted by assessing the model's performance on a validation cohort.
72 (4.7%) patients were diagnosed with anastomotic stricture. Participants were randomly divided into training (n = 1079) and validation (n = 463) sets. Predictors included in this nomogram were radiotherapy, diverting stoma, anastomotic leakage, and anastomotic distance. The area under the ROC curve (AUC) for the training set was 0.889 [95% confidence interval (CI) 0.840-0.937] and for the validation set, it was 0.930 (95%CI 0.879-0.981). The calibration curve demonstrated a strong correlation between predicted and observed outcomes. DCA results showed that the nomogram had clinical value in predicting anastomotic stricture in patients after anterior resection of rectal cancer.
We developed a predictive model for anastomotic stricture following anterior resection of rectal cancer. This nomogram could assist clinicians in predicting the risk of anastomotic stricture, thus improving patients' quality of life and long-term prognosis.
吻合口狭窄显著影响患者的生活质量和长期预后。然而，目前的临床实践缺乏预测吻合口狭窄的准确工具。本研究旨在为接受直肠前切除术的直肠癌患者开发一种预测吻合口狭窄的列线图。
共纳入 1542 名符合条件的患者进行研究。使用最小绝对收缩和选择算子（Lasso）分析初步筛选预测因素。使用多变量逻辑回归构建预测模型，并以列线图呈现。通过接收者操作特征（ROC）曲线、校准图和决策曲线分析（DCA）评估列线图的性能。通过在验证队列中评估模型的性能来进行内部验证。
72 例（4.7%）患者诊断为吻合口狭窄。参与者被随机分为训练集（n=1079）和验证集（n=463）。纳入该列线图的预测因素包括放疗、转流造口术、吻合口漏和吻合口距离。训练集的 ROC 曲线下面积（AUC）为 0.889（95%置信区间 [CI] 0.840-0.937），验证集的 AUC 为 0.930（95%CI 0.879-0.981）。校准曲线显示预测结果与观察结果之间具有很强的相关性。DCA 结果表明，该列线图在预测直肠癌前切除术患者吻合口狭窄方面具有临床价值。
我们开发了一种预测直肠癌前切除术后吻合口狭窄的模型。该列线图可以帮助临床医生预测吻合口狭窄的风险，从而提高患者的生活质量和长期预后。
