Division of Pediatric Gastroenterology, Hepatology and Nutrition, Monroe Carell Jr. Children's Hospital at Vanderbilt, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
Am J Gastroenterol. 2022 Feb 1;117(2):272-279. doi: 10.14309/ajg.0000000000001587.
Approximately half of esophageal biopsies from patients with eosinophilic esophagitis (EoE) contain inadequate lamina propria, making it impossible to determine the lamina propria fibrosis (LPF). This study aimed to develop and validate a web-based tool to predict LPF in esophageal biopsies with inadequate lamina propria.
Prospectively collected demographic and clinical data and scores for 7 relevant EoE histology scoring system epithelial features from patients with EoE participating in the Consortium of Eosinophilic Gastrointestinal Disease Researchers observational study were used to build the models. Using the least absolute shrinkage and selection operator method, variables strongly associated with LPF were identified. Logistic regression was used to develop models to predict grade and stage of LPF. The grade model was validated using an independent data set.
Of 284 patients in the discovery data set, median age (quartiles) was 16 (8-31) years, 68.7% were male patients, and 93.4% were White. Age of the patient, basal zone hyperplasia, dyskeratotic epithelial cells, and surface epithelial alteration were associated with presence of LPF. The area under the receiver operating characteristic curve for the grade model was 0.84 (95% confidence interval: 0.80-0.89) and for stage model was 0.79 (95% confidence interval: 0.74-0.84). Our grade model had 82% accuracy in predicting the presence of LPF in an external validation data set.
We developed parsimonious models (grade and stage) to predict presence of LPF in esophageal biopsies with inadequate lamina propria and validated our grade model. Our predictive models can be easily used in the clinical setting to include LPF in clinical decisions and determine its effect on treatment outcomes.
大约一半的嗜酸性食管炎(EoE)患者的食管活检标本固有层不足,无法确定固有层纤维化(LPF)。本研究旨在开发和验证一种基于网络的工具,以预测固有层不足的食管活检标本中的 LPF。
前瞻性收集参加嗜酸性粒细胞性胃肠道疾病研究人员观察性研究联盟的 EoE 患者的人口统计学和临床数据以及 7 种相关 EoE 组织学评分系统上皮特征的评分,用于构建模型。使用最小绝对收缩和选择算子方法,确定与 LPF 强烈相关的变量。使用逻辑回归开发预测 LPF 分级和分期的模型。使用独立数据集验证分级模型。
在发现数据集的 284 名患者中,中位年龄(四分位数)为 16 岁(8-31 岁),68.7%为男性患者,93.4%为白人。患者年龄、基底细胞增生、角化不良的上皮细胞和表面上皮改变与 LPF 的存在相关。分级模型的受试者工作特征曲线下面积为 0.84(95%置信区间:0.80-0.89),分期模型为 0.79(95%置信区间:0.74-0.84)。我们的分级模型在外部验证数据集中预测 LPF 存在的准确率为 82%。
我们开发了简洁的模型(分级和分期)来预测固有层不足的食管活检标本中 LPF 的存在,并验证了我们的分级模型。我们的预测模型可以在临床环境中方便地使用,以将 LPF 纳入临床决策,并确定其对治疗结果的影响。