预测结直肠息肉切除术后迟发性出血的列线图。

Nomogram for the Prediction of Delayed Colorectal Post-Polypectomy Bleeding.

机构信息

Department of Critical Care Medicine, Jinhua Hospital of Zhejiang University, Jinhua, Zhejiang, People's Republic of China.

Department of Gastroenterology, Jinhua Hospital of Zhejiang University, Jinhua, Zhejiang, People's Republic of China.

出版信息

Turk J Gastroenterol. 2021 Sep;32(9):727-734. doi: 10.5152/tjg.2021.20842.

DOI:10.5152/tjg.2021.20842

PMID:34609301

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8975504/

Abstract

BACKGROUND

Delayed colorectal post-polypectomy bleeding (PPB) is a fairly common complication after polypectomy. The present study aimed to build a novel nomogram-based model of delayed PPB.

METHODS

A cohort of 2494 patients who had undergone colonoscopic polypectomy between January 2016 and April 2020 were consecutively enrolled. The patient demographics, polyp characteristics, laboratory factors, and pathological parameters were collected. The least absolute shrinkage and selection operator (LASSO) regression was applied for selecting potential variables. Multivariate logistic regression was used to develop the nomogram. A bootstrapping method was employed for internal validation. The performance of the nomogram was evaluated on the basis of its calibration, discrimination, and clinical usefulness.

RESULTS

Of 2494 patients undergoing colonoscopic polypectomy, 40 (1.6%) developed delayed PPB. The LASSO regression identified 6 variables (age, gender, polyp location, polyp morphology, antithrombotic medication use, and modality of polypectomy), and a predictive model was subsequently established. The area under the curve (AUC) of the predictive model and the internal validation were 0.838 (95% CI: 0.775-0.900) and 0.824 (95% CI: 0.759-0.889), respectively. The predictive model provided acceptable calibration, and a decision curve analysis (DCA) showed its clinical utility.

CONCLUSION

This predictive model may enable clinicians to predict the risk of delayed PPB and optimize preoperative decision-making, for effective treatment.

摘要

背景

结直肠息肉切除术后迟发性出血（PPB）是息肉切除术后较为常见的并发症。本研究旨在建立一种基于列线图的迟发性 PPB 新预测模型。

方法

连续纳入 2016 年 1 月至 2020 年 4 月期间接受结肠镜下息肉切除术的 2494 例患者。收集患者的人口统计学、息肉特征、实验室因素和病理参数。应用最小绝对收缩和选择算子（LASSO）回归筛选潜在变量。采用多变量逻辑回归建立列线图。采用 bootstrap 方法进行内部验证。基于校准度、判别度和临床实用性评估列线图的性能。

结果

在 2494 例行结肠镜下息肉切除术的患者中，有 40 例（1.6%）发生迟发性 PPB。LASSO 回归筛选出 6 个变量（年龄、性别、息肉位置、息肉形态、抗血栓药物使用和息肉切除术方式），并建立了预测模型。预测模型的曲线下面积（AUC）和内部验证分别为 0.838（95%CI：0.775-0.900）和 0.824（95%CI：0.759-0.889）。预测模型具有较好的校准度，决策曲线分析（DCA）显示其具有临床实用性。