论文标题
一线治疗中原发性胆管炎患者的表型分层的潜在类混合模型
Latent class mixed modelling for phenotypic stratification of primary biliary cholangitis patients on first line treatment
论文作者
论文摘要
在原发性胆道炎（PBC）的患者中，在用ursoxyoxycholic Acid治疗期间测量的血清肝脏生物化学（UDCA反应）可以准确预测长期预后。在这项研究中，我们试图使用计算建模方法将肝脏生物化学，尤其是碱性磷酸酶（ALP）作为疾病活性的替代标记，以在PBC中进行表型分层。我们的目的是确定具有不同疾病轨迹的患者的不同疾病亚组。方法：我们使用了1,601名PBC患者的纵向ALP在用UDCA治疗和应用潜在的类混合建模（LCMM）的第一线治疗中，以鉴定独特的表型亚组，每个组具有不同的疾病轨迹，并且终末期肝病（ESLD）的风险。结果：我们确定了我们的PBC队列中的四个良好歧视的表型亚组，每个亚组都有不同的疾病轨迹。
In patients with primary biliary cholangitis (PBC), the serum liver biochemistry measured during treatment with ursodeoxycholic acid (the UDCA response) accurately predicts long-term outcome. In this study we sought to use liver biochemistry, and in particular alkaline phosphatase (ALP), as a surrogate marker of disease activity, for phenotypic stratification in PBC using a computational modelling approach. Our aim here was to identify distinct disease subgroups of patients with distinct disease trajectories. Methods: We used longitudinal ALP results from 1,601 PBC patients on first line treatment with UDCA, and applied latent class mixed modelling (LCMM), to identify distinct phenotypic subgroups, each with distinct disease trajectories, and risks of end stage liver disease (ESLD). Results: We identified four well discriminated phenotypic subgroups within our PBC cohort, each with distinct disease trajectories.
