European journal of gastroenterology & hepatology · 2022
Nomogram model for predicting esophsagogastric varices in hepatocellular carcinoma with cirrhosis.
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Zhang Daoming, Deng Junjian, Guo Xufeng, Zheng Yongfa, Xu Ximing
Аннотация
BACKGROUND/AIMS: The prognosis for hepatocellular carcinoma (HCC) with cirrhosis is poor. The risk of death also increases in patients with esophagogastric varices (EGV). Based on routine clinical features and related noninvasive parameters, a nomogram prediction model was developed in this study to facilitate the early identification of EGV in HCC patients.
METHODS: A retrospective cohort analysis of patients with HCC in the Renmin Hospital of Wuhan University from 2020 to 2021 was performed. Clinical and noninvasive parameters closely related to EGV risk were screened by univariate and multivariate logistic regression analysis and integrated into a nomogram. The nomogram was validated internally and externally by calibration, receiver operating characteristic curve and decision curve analysis (DCA).
RESULTS: A total of 165 patients with HCC-related cirrhosis were recruited. In the raining cohort, multivariate logistic regression analysis identified platelet (PLT) [odds ratio (OR), 0.950; 95% confidence interval (CI), 0.925-0.977; P < 0.001], D-dimer (OR. 3.341; 95% CI, 1.751-6.376, P < 0.001), spleen diameter (SD) (OR, 2.585; 95% CI, 1.547-4.318; P < 0.001) as independent indicators for EGV. The nomogram for predicting EGV risk was well calibrated with a favorable discriminative ability and an area under curve of 0.961. In addition, the nomogram showed better net benefits in the DCA. The results were validated in the validation cohort.
CONCLUSIONS: The proposed nomogram model based on three indicators (PLT, D-dimer and SD) showed an excellent predictive effect, leading to the avoidance of unnecessary esophagogastroduodenoscopy.
METHODS: A retrospective cohort analysis of patients with HCC in the Renmin Hospital of Wuhan University from 2020 to 2021 was performed. Clinical and noninvasive parameters closely related to EGV risk were screened by univariate and multivariate logistic regression analysis and integrated into a nomogram. The nomogram was validated internally and externally by calibration, receiver operating characteristic curve and decision curve analysis (DCA).
RESULTS: A total of 165 patients with HCC-related cirrhosis were recruited. In the raining cohort, multivariate logistic regression analysis identified platelet (PLT) [odds ratio (OR), 0.950; 95% confidence interval (CI), 0.925-0.977; P < 0.001], D-dimer (OR. 3.341; 95% CI, 1.751-6.376, P < 0.001), spleen diameter (SD) (OR, 2.585; 95% CI, 1.547-4.318; P < 0.001) as independent indicators for EGV. The nomogram for predicting EGV risk was well calibrated with a favorable discriminative ability and an area under curve of 0.961. In addition, the nomogram showed better net benefits in the DCA. The results were validated in the validation cohort.
CONCLUSIONS: The proposed nomogram model based on three indicators (PLT, D-dimer and SD) showed an excellent predictive effect, leading to the avoidance of unnecessary esophagogastroduodenoscopy.