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一个新的8个预测器签名来预测儿科发病克罗恩病的复杂疾病过程:基于人口的研究
Hélène Sarter1,2, Guillaume Savoye3, Guillemette Marot4,5
1Lille Hospital and University, Public Health, Epidemiology and Economic Health, EPIMAD registry, Regional house of clinical research, F-59000 Lille, France.
早期识别高风险儿科克罗恩病 (CD) 患者至关重要. 一个结合临床,血清学和遗传因素的新模型准确地预测了儿童复杂的疾病过程.
科学领域:
- 儿科胃肠病学 儿科胃肠病学
- 炎症性肠道疾病研究研究
- 自身免疫性疾病的遗传学
背景情况:
- 早期识别高风险患者对于定制克罗恩病 (CD) 治疗至关重要.
- 儿科发病的CD需要特定的预测模型,因为不同的疾病轨迹.
- 致残性疾病在CD的病程需要主动的管理策略.
研究的目的:
- 开发和评估儿童发病CD复杂疾病过程的预测模型.
- 评估结合临床,血清学和遗传标记的实用性.
- 确定与年轻CD患者疾病进展相关的关键因素.
主要方法:
- 基于人口的儿科CD患者队列的回顾性分析 (EPIMAD注册表).
- 定义了复杂的疾病过程,如在5年内严格入/透行为或肠切除.
- 使用拉索逻辑回归,包括临床,血清学 (如ASCA,PANCA) 和遗传 (369个SNP) 数据.
主要成果:
- 包括疾病位置,pANCA和6个SNP在内的PREDICT-EPIMAD模型预测了复杂的CD,其AUC为0.80 (经校正).
- 该模型显示出良好的区分 (灵敏度79%,特异性74%) 和校准.
- 35%的患者在诊断后5年内经历了复杂的疾病过程.
结论:
- 临床,血清学和遗传变量的组合有效预测儿科CD的疾病进展.
- PREDICT-EPIMAD模型显示了临床实用性,用于儿童克罗恩病的风险分层.
- 在临床实施之前需要进一步的独立验证.
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