Differential diagnosis between benign and malignant ascites is of great significance in clinical practice, however, there is no single index with both perfect sensitivity and specificity. Aims: To establish a logistic regression model for differential diagnosis between benign and malignant ascites and to assess the diagnostic value of the model. Methods: A total of 103 consecutive ascitic patients were enrolled in this study, 50 in malignant ascites group and 53 in benign ascites group. Ascitic telomerase, DNA ploidy, adenosine deaminase (ADA), lactate dehydrogenase (LDH), tumor markers CEA, CA125, CA19-9, and CA153, and serum-ascites albumin gradient (SAAG) were measured and analyzed by using univariate and multivariate logistic regression to form a regression model. Then the optimal cut-off point of probability predictive value P and the diagnostic efficiency were obtained by using ROC curve. Results: Ascitic telomerase, CEA, CA153 and SAAG entered the logistic regression model. Area under curve (AUC) of P value of the diagnostic model was 0.986; and the optima) cut-off point, sensitivity and specificity were 0.469, 96.0% and 98.1%, respectively. Both AUC and sensitivity of the diagnostic model were superior to those of any single ascitic index. Conclusions: A logistic regression model based on multiple ascitic indices was established in this study. Compared with any single ascitic indices, the sensitivity and accuracy for differential diagnosis between benign and malignant ascites can be improved by using the diagnostic model.%背景:良、恶性腹水的鉴别诊断具有重要临床意义,但目前尚缺乏兼具敏感性和特异性优势的单项指标.目的:建立腹水性质鉴别诊断的logistic回归模型并探讨其诊断价值.方法:连续纳入103例腹水患者,其中恶性腹水组50例,良性腹水组53例.检测端粒酶、DNA倍体、腺苷脱氨酶(ADA)、乳酸脱氢酶(LDH)、肿瘤标记物CEA、CA125、CA19-9、CA153、血清-腹水白蛋白梯度(SAAG)九项腹水指标,应用单因素、多因素logistic回归分析和ROC曲线建立logistic回归模型并确定概率预测值P的最适诊断界点及其诊断效率.结果:端粒酶、CEA、CA153、SAAG四项腹水指标进入logistic回归模型,诊断模型P值的曲线下面积(AUC)为0.986,最适诊断界点为0.469,敏感性为96.0％,特异性为98.1％,AUC和敏感性均优于各单项腹水指标.结论:与各单项腹水指标相比,应用本研究建立的多项腹水指标logistic回归模型鉴别诊断腹水性质可提高诊断敏感性和准确性.
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