Clinical and investigative medicine. Medecine clinique et experimentale · 2014
Noninvasive prediction of large esophageal varices in liver cirrhosis patients.
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Wang Lijing, Hu JunWei, Dong Shuang, Jian Yi Cheng, Hu Lijuan, Yang Genmei, Wang Jinjun, Xiong Wujun
Аннотация
PURPOSE: Esophageal varices are a dangerous complication of liver cirrhosis. The development of cost effective, noninvasive means for prediction of large esophageal varices could reduce the use of upper gastrointestinal endoscopy in variceal screening and also provide an alternative way to confirm the results of conventional endoscopic diagnosis. Previously proposed predictive models are neither sensitive nor specific.
METHODS: A retrospective study based on a group of 104 liver cirrhosis patients was performed. Multiple statistical approaches were used to evaluate the association of large esophageal varices with 20 individual and six compound clinical laboratory variables. A new predictive model was developed.
RESULTS: Univariate analysis suggested that eight out of 26 variables were significantly associated with large esophageal varices. Further stepwise logistic regression eventually identified three variables (hemoglobin level, portal vein diameter and the ratio of platelet count/spleen diameter) that contributed significantly to the final regression model. Receiver operating characteristic (ROC) curve analysis showed that this new regression model achieved 77.8% and 72% of diagnostic sensitivity and specificity, respectively, for the prediction of large esophageal varices. In our study group, its diagnostic accuracy (AUROC=0.814) was found to be significantly higher than six predictive models previously published.
CONCLUSIONS: No single variable offers self-sufficient predictive function for large esophageal varices. A comprehensive model using multiple variables significantly improves the predictive accuracy in screening the most at risk patients with potential variceal hemorrhage.
METHODS: A retrospective study based on a group of 104 liver cirrhosis patients was performed. Multiple statistical approaches were used to evaluate the association of large esophageal varices with 20 individual and six compound clinical laboratory variables. A new predictive model was developed.
RESULTS: Univariate analysis suggested that eight out of 26 variables were significantly associated with large esophageal varices. Further stepwise logistic regression eventually identified three variables (hemoglobin level, portal vein diameter and the ratio of platelet count/spleen diameter) that contributed significantly to the final regression model. Receiver operating characteristic (ROC) curve analysis showed that this new regression model achieved 77.8% and 72% of diagnostic sensitivity and specificity, respectively, for the prediction of large esophageal varices. In our study group, its diagnostic accuracy (AUROC=0.814) was found to be significantly higher than six predictive models previously published.
CONCLUSIONS: No single variable offers self-sufficient predictive function for large esophageal varices. A comprehensive model using multiple variables significantly improves the predictive accuracy in screening the most at risk patients with potential variceal hemorrhage.