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胆结石患者胆囊癌的危险因素分析及预测模型构建

Risk factor analysis and construction of prediction models of gallbladder carcinoma in patients with gallstones.

作者信息

Zhu Zhencheng, Luo Kunlun, Zhang Bo, Wang Gang, Guo Ke, Huang Pin, Liu Qiuhua

机构信息

Department of Hepatobiliary Surgery, Zhangjiagang City First People's Hospital, Suzhou, China.

Department of Hepatobiliary Surgery, The 904th Hospital of Joint Logistic Support Force of PLA, Wuxi, China.

出版信息

Front Oncol. 2023 Feb 27;13:1037194. doi: 10.3389/fonc.2023.1037194. eCollection 2023.

DOI:10.3389/fonc.2023.1037194
PMID:36923422
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10009222/
Abstract

BACKGROUND

Gallbladder carcinoma (GBC) is a biliary tract tumor with a high mortality rate. The objectives of this study were to explore the risk factors of GBC in patients with gallstones and to establish effective screening indicators.

METHODS

A total of 588 patients from medical centers in two different regions of China were included in this study and defined as the internal test samples and the external validation samples, respectively. We retrospectively reviewed the differences in clinicopathologic data of the internal test samples to find the independent risk factors that affect the occurrence of GBC. Then, we constructed three different combined predictive factors (CPFs) through the weighting method, integral system, and nomogram, respectively, and named them CPF-A, CPF-B, and CPF-C sequentially. Furthermore, we evaluated these indicators through calibration and DCA curves. The ROC curve was used to analyze their diagnostic efficiency. Finally, their diagnostic capabilities were validated in the external validation samples.

RESULTS

In the internal test samples, the results showed that five factors, namely, age (RR = 3.077, 95% CI: 1.731-5.496), size of gallstones (RR = 13.732, 95% CI: 5.937-31.762), course of gallstones (RR = 2.438, 95% CI: 1.350-4.403), CEA (RR = 9.464, 95% CI: 3.394-26.392), and CA199 (RR = 9.605, 95% CI: 4.512-20.446), were independent risk factors for GBC in patients with gallstones. Then, we established three predictive indicators: CPF-A, CPF-B, and CPF-C. These models were further validated using bootstrapping with 1,000 repetitions. Calibration and decision curve analysis showed that the three models fit well. Meanwhile, multivariate analysis showed that CPF-B and CPF-C were independent risk factors for GBC in patients with gallstones. In addition, the validation results of the external validation samples are essentially consistent with the internal test samples.

CONCLUSION

Age (≤58.5 . >58.5 years), size of gallstones (≤1.95 . >1.95cm), course of gallstones (≤10 . >10 years), CEA (≤5 . >5 ng/ml), and CA199 (≤37 . >37 U/ml) are independent risk factors for GBC in patients with gallstones. When positive indicators were ≥2 among the five independent risk factors or the score of the nomogram was >82.64, the risk of GBC was high in gallstone patients.

摘要

背景

胆囊癌(GBC)是一种死亡率很高的胆道肿瘤。本研究的目的是探讨胆结石患者发生GBC的危险因素,并建立有效的筛查指标。

方法

本研究纳入了来自中国两个不同地区医疗中心的588例患者,分别定义为内部测试样本和外部验证样本。我们回顾性分析了内部测试样本的临床病理数据差异,以找出影响GBC发生的独立危险因素。然后,我们分别通过加权法、积分系统和列线图构建了三种不同的联合预测因子(CPF),依次命名为CPF-A、CPF-B和CPF-C。此外,我们通过校准和决策曲线分析评估了这些指标。采用ROC曲线分析其诊断效能。最后,在外部验证样本中验证其诊断能力。

结果

在内部测试样本中,结果显示年龄(RR = 3.077,95%CI:1.731 - 5.496)、胆结石大小(RR = 13.732,95%CI:5.937 - 31.762)、胆结石病程(RR = 2.438,95%CI:1.350 - 4.403)、癌胚抗原(CEA)(RR = 9.464,95%CI:3.394 - 26.392)和糖类抗原199(CA199)(RR = 9.605,95%CI:4.512 - 20.446)这五个因素是胆结石患者发生GBC的独立危险因素。然后,我们建立了三个预测指标:CPF-A、CPF-B和CPF-C。这些模型通过1000次重复的自抽样法进一步验证。校准和决策曲线分析表明这三个模型拟合良好。同时,多因素分析表明CPF-B和CPF-C是胆结石患者发生GBC的独立危险因素。此外,外部验证样本的验证结果与内部测试样本基本一致。

结论

年龄(≤58.5岁. >58.5岁)、胆结石大小(≤1.95cm. >1.95cm)、胆结石病程(≤10年. >10年)、CEA(≤5ng/ml. >5ng/ml)和CA199(≤37U/ml. >37U/ml)是胆结石患者发生GBC的独立危险因素。当五个独立危险因素中阳性指标≥2个或列线图得分>82.64时,胆结石患者发生GBC的风险较高。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3381/10009222/cba21e170cf8/fonc-13-1037194-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3381/10009222/131bc79c12a5/fonc-13-1037194-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3381/10009222/84e03b7febbd/fonc-13-1037194-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3381/10009222/b2896b618ae6/fonc-13-1037194-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3381/10009222/8614322c2132/fonc-13-1037194-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3381/10009222/5f1ee0e6b178/fonc-13-1037194-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3381/10009222/102165692fd8/fonc-13-1037194-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3381/10009222/cba21e170cf8/fonc-13-1037194-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3381/10009222/131bc79c12a5/fonc-13-1037194-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3381/10009222/84e03b7febbd/fonc-13-1037194-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3381/10009222/b2896b618ae6/fonc-13-1037194-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3381/10009222/8614322c2132/fonc-13-1037194-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3381/10009222/5f1ee0e6b178/fonc-13-1037194-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3381/10009222/102165692fd8/fonc-13-1037194-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3381/10009222/cba21e170cf8/fonc-13-1037194-g007.jpg

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