目的:Delta胆红素(白蛋白共价结合胆红素)可能在识别结合胆红素的肝排泄受损方面提供重要的临床应用,但它不能在临床实验室中实时测量用于诊断目的。
方法:共收集了210个样本,使用高效液相色谱法测量其δ胆红素水平四次。收集的数据包括年龄,性别,诊断代码,δ胆红素,总胆红素,直接胆红素,总蛋白质,白蛋白,球蛋白,天冬氨酸转氨酶,丙氨酸转氨酶,碱性磷酸酶,γ-谷氨酰转移酶,乳酸脱氢酶,血红蛋白,血清溶血值,溶血指数,翼值(IV),黄疸指数(II),血脂值(Lv),和血脂指数。进行特征选择,确定变量的最优组合,线性回归机器学习进行了1000次。
结果:选择的变量是总胆红素,直接胆红素,总蛋白质,白蛋白,血红蛋白,Iv,二,还有Lv.白蛋白-直接胆红素-血红蛋白-Iv-Lv的组合可实现高δ胆红素浓度的最佳预测性能。由这些变量组成的最终方程式如下:δ胆红素=0.35×Iv0.05×Lv-0.23×直接胆红素-0.05×血红蛋白-0.04×白蛋白0.10。
结论:本研究中建立的方程是实用的,可以轻松地在临床实验室中实时应用。
OBJECTIVE: Delta bilirubin (albumin-covalently bound bilirubin) may provide important clinical utility in identifying impaired hepatic excretion of conjugated bilirubin, but it cannot be measured in real-time for diagnostic purposes in clinical laboratories.
METHODS: A total of 210 samples were collected, and their delta bilirubin levels were measured four times using high-performance liquid chromatography. Data collected included age, sex, diagnosis code, delta bilirubin, total bilirubin, direct bilirubin, total protein, albumin, globulin, aspartate aminotransferase, alanine transaminase, alkaline phosphatase, gamma-glutamyl transferase, lactate dehydrogenase, hemoglobin, serum hemolysis value, hemolysis index, icterus value (Iv), icterus index (Ii), lipemia value (Lv), and lipemia index. To conduct feature selection and identify the optimal combination of variables, linear regression machine learning was performed 1,000 times.
RESULTS: The selected variables were total bilirubin, direct bilirubin, total protein, albumin, hemoglobin, Iv, Ii, and Lv. The best predictive performance for high delta bilirubin concentrations was achieved with the combination of albumin-direct bilirubin-hemoglobin-Iv-Lv. The final equation composed of these variables was as follows: delta bilirubin = 0.35 × Iv + 0.05 × Lv - 0.23 × direct bilirubin - 0.05 × hemoglobin - 0.04 × albumin + 0.10.
CONCLUSIONS: The equation established in this study is practical and can be easily applied in real-time in clinical laboratories.