Initial assessment of the infant with neonatal cholestasis-Is this biliary atresia?
Initial assessment of the infant with neonatal cholestasis-Is this biliary atresia?
复制标题
DOI：
10.1371/journal.pone.0176275
复制
发表时间：
2017
期刊：
影响因子：
3.7
通讯作者：
Childhood Liver Disease Research Network
中科院分区：
文献类型：
--
作者：
Shneider BL;Moore J;Kerkar N;Magee JC;Ye W;Karpen SJ;Kamath BM;Molleston JP;Bezerra JA;Murray KF;Loomes KM;Whitington PF;Rosenthal P;Squires RH;Guthery SL;Arnon R;Schwarz KB;Turmelle YP;Sherker AH;Sokol RJ;Childhood Liver Disease Research Network
优化胆道闭锁 (BA) 的治疗效果需要及时诊断。胆汁淤积是 BA 以及其他诊断（非 BA）的一个表现特征。识别新生儿胆汁淤积的临床特征将加快做出后续侵入性检测的决定，以正确诊断或排除胆汁淤积症，从而改善预后。分析目标是使用初始演示时可用的数据开发 BA 预测模型。就诊时患有新生儿胆汁淤积（直接胆红素/结合胆红素 >2 mg/dl [34.2 μM]）的婴儿在手术探查之前被纳入一项前瞻性观察性多中心研究 (PROBE-NCT00061828)。分析入组时的临床特征（体检结果、实验室结果、胆囊超声检查）。最初，选择了 19 个特征作为候选预测因子。使用两种方法来构建诊断预测模型：层次分类和回归决策树（CART）和使用逐步选择策略的逻辑回归模型。在 2004 年 4 月至 2014 年 2 月的 PROBE 调查中，401 名婴儿符合 BA 标准，259 名婴儿符合非 BA 标准。单变量分析确定了 BA 和非 BA 之间存在显着差异的 13 个特征。使用 BA 与非 BA 的 CART 预测模型（显着因素：γ-谷氨酰转肽酶、无胆酸粪便、体重），受试者工作特征曲线下面积 (ROC AUC) 为 0.83。 12% 的 BA 婴儿被错误分类为非 BA； 17% 的非 BA 婴儿被错误分类为 BA。逐步逻辑回归确定了预测模型中的七个因素（ROC AUC 0.89）。使用该模型，>0.8 (n = 357) 的预测概率产生 81% 的 BA 真阳性率； <0.2 (n = 120) 产生 11% 的假阴性率。尽管我们优化的预测模型具有相对较好的准确性，但尚未达到区分 BA 与非 BA 所需的高精度。新生儿胆汁淤积婴儿中BA的准确识别需要进一步评估，不应仅根据临床特征排除BA。
Optimizing outcome in biliary atresia (BA) requires timely diagnosis. Cholestasis is a presenting feature of BA, as well as other diagnoses (Non-BA). Identification of clinical features of neonatal cholestasis that would expedite decisions to pursue subsequent invasive testing to correctly diagnose or exclude BA would enhance outcomes. The analytical goal was to develop a predictive model for BA using data available at initial presentation. Infants at presentation with neonatal cholestasis (direct/conjugated bilirubin >2 mg/dl [34.2 μM]) were enrolled prior to surgical exploration in a prospective observational multi-centered study (PROBE–NCT00061828). Clinical features (physical findings, laboratory results, gallbladder sonography) at enrollment were analyzed. Initially, 19 features were selected as candidate predictors. Two approaches were used to build models for diagnosis prediction: a hierarchical classification and regression decision tree (CART) and a logistic regression model using a stepwise selection strategy. In PROBE April 2004-February 2014, 401 infants met criteria for BA and 259 for Non-BA. Univariate analysis identified 13 features that were significantly different between BA and Non-BA. Using a CART predictive model of BA versus Non-BA (significant factors: gamma-glutamyl transpeptidase, acholic stools, weight), the receiver operating characteristic area under the curve (ROC AUC) was 0.83. Twelve percent of BA infants were misclassified as Non-BA; 17% of Non-BA infants were misclassified as BA. Stepwise logistic regression identified seven factors in a predictive model (ROC AUC 0.89). Using this model, a predicted probability of >0.8 (n = 357) yielded an 81% true positive rate for BA; <0.2 (n = 120) yielded an 11% false negative rate. Despite the relatively good accuracy of our optimized prediction models, the high precision required for differentiating BA from Non-BA was not achieved. Accurate identification of BA in infants with neonatal cholestasis requires further evaluation, and BA should not be excluded based only on presenting clinical features.