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预测绞窄性肠梗阻风险的列线图模型的开发与验证

Development and validation of a nomogram model to predict the risk of strangulated intestinal obstruction.

作者信息

Zhu Yanjing, Wang Qiangqiang, Cao Lvhao, Zhang Tongyuan, Chang Jiawei, Wang Xingyu

机构信息

Department of Emergency Surgery, the First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, PR China.

出版信息

Sci Rep. 2024 Dec 28;14(1):31049. doi: 10.1038/s41598-024-82131-1.

Abstract

To develop and validate a nomogram model for discriminating simple intestinal obstruction and strangulated intestinal obstruction, thus providing objective evidence for clinical decision-making. Following pre-established inclusion and exclusion criteria, a retrospective analysis was conducted on the clinical data of 560 patients diagnosed with intestinal obstruction who were admitted to the Emergency Surgery Department of the First Affiliated Hospital of Anhui Medical University between January 1, 2020, and December 31, 2022. The data was subsequently split into a training cohort (n = 393) and a validation cohort (n = 167) using a 7:3 ratio. To identify independent risk and protective factors associated with strangulated intestinal obstruction, a multivariate logistic regression analysis was employed. Based on the identified factors, a nomogram prediction model was constructed. The model's discriminatory ability was assessed using the receiver operating characteristic (ROC) curve, the area under the curve (AUC), and the corrected C-index. The Hosmer-Lemeshow test was utilized to evaluate the model's goodness of fit in both the training and validation cohorts. Calibration curves were generated to assess the model's accuracy in predicting the probability of strangulated intestinal obstruction. Finally, decision curve analysis (DCA) was performed to evaluate the model's potential clinical utility. Multivariate logistic regression analysis identified neutrophil percentage, peritoneal irritation sign, and abdominal fluid as independent risk factors for strangulated intestinal obstruction, while albumin emerged as an independent protective factor. These factors were incorporated into the nomogram, demonstrating high discrimination (AUC of 0.842[95%CI: 0.787-0.897] in the training set and 0.839 [95%CI: 0.742-0.937] in the validation set) and good calibration. The corrected C-index further supported the model's performance in the training (0.833) and validation (0.813) cohorts. The Hosmer-Lemeshow test results (p = 0.759 and p = 0.505, respectively) indicated a good model fit in both cohorts. Calibration curves confirmed the close agreement between the nomogram predictions and actual observations. Finally, DCA corroborated the model's net clinical benefit. The comprehensive nomogram developed in this study emerged as a promising and convenient tool for evaluating the risk of strangulated intestinal obstruction, thereby aiding clinicians in screening the high-risk population.

摘要

开发并验证一种用于鉴别单纯性肠梗阻和绞窄性肠梗阻的列线图模型,从而为临床决策提供客观依据。按照预先设定的纳入和排除标准,对2020年1月1日至2022年12月31日期间安徽医科大学第一附属医院急诊外科收治的560例诊断为肠梗阻的患者的临床资料进行回顾性分析。随后,将数据按照7:3的比例分为训练队列(n = 393)和验证队列(n = 167)。为了确定与绞窄性肠梗阻相关的独立危险因素和保护因素,采用多因素logistic回归分析。基于所确定的因素,构建列线图预测模型。使用受试者操作特征(ROC)曲线、曲线下面积(AUC)和校正C指数评估模型的鉴别能力。利用Hosmer-Lemeshow检验评估模型在训练队列和验证队列中的拟合优度。生成校准曲线以评估模型预测绞窄性肠梗阻概率的准确性。最后,进行决策曲线分析(DCA)以评估模型的潜在临床应用价值。多因素logistic回归分析确定中性粒细胞百分比、腹膜刺激征和腹腔积液为绞窄性肠梗阻的独立危险因素,而白蛋白为独立保护因素。将这些因素纳入列线图,显示出高鉴别力(训练集AUC为0.842[95%CI:0.787 - 0.897],验证集AUC为0.839[95%CI:0.742 - 0.937])和良好的校准。校正C指数进一步支持模型在训练队列(0.833)和验证队列(0.813)中的性能。Hosmer-Lemeshow检验结果(分别为p = 0.759和p = 0.505)表明模型在两个队列中拟合良好。校准曲线证实列线图预测与实际观察结果之间高度一致。最后,DCA证实了模型的净临床效益。本研究开发的综合列线图成为评估绞窄性肠梗阻风险的一种有前景且便捷的工具,从而有助于临床医生筛查高危人群。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ec56/11681171/60ff0ceddb87/41598_2024_82131_Fig1_HTML.jpg

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