BACKGROUND AND AIMS Liver fibrosis is common in children with nonalcoholic fatty liver disease (NAFLD) and an important determinant of outcomes. High performing non-invasive models to assess fibrosis in children are needed. The objectives of this study were to evaluate the performance of existing pediatric and adult fibrosis prediction models and to develop a clinical prediction rule for identifying moderate-to-severe fibrosis in children with NAFLD. APPROACH AND RESULTS We enrolled children with biopsy-proven NAFLD in the Nonalcoholic Steatohepatitis Clinical Research Network (NASH CRN) within 90 days of liver biopsy. We staged liver fibrosis in consensus using the NASH CRN scoring system. We evaluated existing pediatric and adult models for fibrosis and developed a new pediatric model using least absolute shrinkage and selection operator with linear and spline terms for discriminating moderate-to-severe fibrosis from none or mild fibrosis. The model was internally validated with 10-fold cross-validation. We evaluated 1055 children with NAFLD, of whom 26% had moderate-to-severe fibrosis. Existing models performed poorly in classifying fibrosis in children, with area under the receiver operator curves (AUC) ranging from 0.57 to 0.64. In contrast, our new model, Fibrosis in Pediatric NAFLD (Fibro-PeN), was derived from fourteen common clinical variables and had an AUC of 0.79 (95% CI: 0.77-0.81) with 72% sensitivity and 76% specificity for identifying moderate-to-severe fibrosis. CONCLUSION Existing fibrosis prediction models have limited clinical utility in children with NAFLD. Fibro-PeN offers improved performance characteristics for risk stratification by identifying moderate-to-severe fibrosis in children with NAFLD.

中文翻译：

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Fibro-PeN 的开发，一种非酒精性脂肪肝儿童中度至重度纤维化的临床预测模型。

背景和目的 肝纤维化在患有非酒精性脂肪性肝病 (NAFLD) 的儿童中很常见，并且是预后的重要决定因素。需要高性能的非侵入性模型来评估儿童纤维化。本研究的目的是评估现有儿童和成人纤维化预测模型的性能，并制定用于识别 NAFLD 儿童中度至重度纤维化的临床预测规则。方法和结果 我们在肝活检后 90 天内将活检证实的 NAFLD 儿童纳入非酒精性脂肪性肝炎临床研究网络 (NASH CRN)。我们使用 NASH CRN 评分系统一致对肝纤维化进行分期。我们评估了现有的儿科和成人纤维化模型，并开发了一种新的儿科模型，使用最小绝对收缩和选择算子以及线性和样条项来区分中度至重度纤维化与无纤维化或轻度纤维化。该模型通过 10 倍交叉验证进行了内部验证。我们评估了 1055 名患有 NAFLD 的儿童，其中 26% 患有中度至重度纤维化。现有模型在儿童纤维化分类方面表现不佳，接受者操作曲线下面积 (AUC) 范围为 0.57 至 0.64。相比之下，我们的新模型小儿 NAFLD 纤维化 (Fibro-PeN) 源自 14 个常见临床变量，AUC 为 0.79 (95% CI: 0.77-0.81)，识别中度 NAFLD 的敏感性为 72%，特异性为 76%。 - 严重纤维化。结论 现有的纤维化预测模型在 NAFLD 儿童中的临床实用性有限。Fibro-PeN 通过识别 NAFLD 儿童的中度至重度纤维化，为风险分层提供了改进的性能特征。