使用匹配的电子牙健康记录数据预测Sjögren病的诊断
Jason Mao1,2,3, Grace Gomez Felix Gomez2,3, Mei Wang2
1Department of Biostatistics and Health Data Science, Indiana University Richard M. Fairbanks School of Public Health, 410 W. 10th Street, Indianapolis, IN, 46202, USA.
BMC medical informatics and decision making
|February 9, 2024
概括
将电子牙科记录与健康记录整合起来,有助于早期预测Sjögren病 (SD). 这种方法提高了诊断的准确性,可能预防疾病并发症.
科学领域:
- 自身免疫性疾病的研究.
- 在医疗保健中的数据集成.
- 医学中的预测建模.
背景情况:
- 斯约格伦病 (SD) 的诊断是具有挑战性的,因为其症状多样化,并与其他自身免疫性疾病重叠.
- 在SD中,早期的口腔表现可能会先于显著的干眼或口腔症状.
- 识别SD的早期诊断预测因素对于及时干预至关重要.
研究的目的:
- 评估使用链接电子牙科记录 (EDR) 和电子健康记录 (EHR) 用于早期SD诊断预测的可行性.
- 从EDR和EHR数据中确定与SD风险相关的关键因素.
- 为了比较使用单独EHR数据与联合EHR和EDR数据的模型的预测性能.
主要方法:
- 一个匹配的病例控制研究,利用来自大量患者群体的链接EDR和EHR数据.
- 条件后勤回归 (CLR) 模型是使用最小绝对收缩和选择运算符 (LASSO) 回归开发的.
- 模型的预测能力是使用特定于CLR的一致性索引来评估的.
主要成果:
- 与SD风险增加相关的因素包括口腔干燥 (OR=6.19),关节疼痛 (OR=2.54),膜不足 (OR=27.04) 和类风湿因子检测 (OR=6.97).
- 手术牙科手术 (OR=2.33) 与较高的SD风险有关,而诊断牙科手术 (OR=0.45) 显示相关性降低.
- 综合EDR-EHR模型的一致性略有改善 (0.834) 与仅使用EHR模型 (0.811) 相比.
结论:
- 将EDR数据与EHR数据集成显示了增强SD预测模型的前景.
- 改进的预测模型可以促进Sjögren病的早期诊断.
- 早期诊断SD对于缓解疾病进展和预防并发症至关重要.
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