Zhao Dongying, Gu Shengli, Gong Xiaohui, Li Yahui, Sun Xiaoang, Chen Yan, Deng Zhaohui, Zhang Yongjun
Department of Neonatology, Xinhua Hospital, Shanghai JiaoTong University School of Medicine, Shanghai, China.
Department of Ultrasound, Xinhua Hospital, Shanghai JiaoTong University School of Medicine, Shanghai, China.
Transl Pediatr. 2021 Feb;10(2):225-235. doi: 10.21037/tp-20-170.
Distinguishing biliary atresia from non-biliary atresia in patients with cholestasis is challenging, as these conditions have a similar clinical presentation. We developed and externally validated a screening model for biliary atresia and devised a web-based calculator for use in clinical settings.
A screening model was developed based on data from 227 cholestatic infants (82 and 145 with and without biliary atresia, respectively) and validated in 234 infants (90 and 144 with and without biliary atresia, respectively) admitted to three hospitals. Variables were selected from routine examination results using the least absolute shrinkage and selection operator method and entered into a logistic regression model to construct a biliary-atresia-risk-predicting equation. Cutoff values for risk stratification were estimated using model sensitivity, derived from the receiver-operating characteristic curves.
The final screening model included seven variables (i.e., weight at admission, clay-colored stools, γ-glutamyl transpeptidase and albumin levels at admission, abnormal gallbladder, triangular cord sign, and change in total bilirubin levels). The model generated an area under the curve of 0.94 with a sensitivity of 91.46 and specificity of 86.62 in the derivation cohort. This was confirmed in the validation cohort, as we found an area under the curve of 0.93 with a sensitivity of 93.1 and specificity of 80.15. Patients were stratified into three risk groups (low-, moderate-, and high-risk groups). Biliary atresia was excluded in the low-risk group. The high-risk group showed a higher detection rate of biliary atresia compared to the stool color screening method alone. This model was integrated into a user-friendly web-based system.
The screening tool had a high predictive value and may help in decision-making by physicians at tertiary and community hospitals.
在胆汁淤积患者中区分胆道闭锁与非胆道闭锁具有挑战性，因为这些病症临床表现相似。我们开发并外部验证了一种用于胆道闭锁的筛查模型，并设计了一个基于网络的计算器供临床使用。
基于227例胆汁淤积婴儿（分别有82例和145例患有和未患有胆道闭锁）的数据开发了一种筛查模型，并在三家医院收治的234例婴儿（分别有90例和144例患有和未患有胆道闭锁）中进行了验证。使用最小绝对收缩和选择算子方法从常规检查结果中选择变量，并将其纳入逻辑回归模型以构建胆道闭锁风险预测方程。使用从受试者工作特征曲线得出的模型敏感性估计风险分层的临界值。
最终的筛查模型包括七个变量（即入院时体重、陶土样便、入院时γ-谷氨酰转肽酶和白蛋白水平、胆囊异常、三角索征以及总胆红素水平变化）。该模型在推导队列中的曲线下面积为0.94，敏感性为91.46，特异性为86.62。在验证队列中得到了证实，我们发现曲线下面积为0.93，敏感性为93.1，特异性为80.15。患者被分为三个风险组（低、中、高风险组）。低风险组可排除胆道闭锁。与单独的粪便颜色筛查方法相比，高风险组显示出更高的胆道闭锁检出率。该模型被集成到一个用户友好的基于网络的系统中。
该筛查工具具有较高的预测价值，可能有助于三级医院和社区医院的医生进行决策。
