基于频率的罕见诊断作为研究大型数据集中的罕见疾病的新和可访问的方法:一个横截面研究
Thomas S Tröster1, Viktor von Wyl2,3, Patrick E Beeler1,4
1Division of Occupational and Environmental Medicine, Epidemiology, Biostatistics and Prevention Institute, University of Zurich and University Hospital Zurich, Zurich, Switzerland.
BMC medical research methodology
|June 17, 2023
概括
基于频率的罕见诊断 (FB-RDx) 可以识别患有罕见疾病的患者,显示死亡,再入院和更长的住院时间的风险增加. 这种方法有助于在大型医疗数据集中全面识别罕见疾病种群.
科学领域:
- 医疗信息学 医疗信息学
- 流行病学 流行病学
- 罕见疾病 罕见疾病
背景情况:
- 在一般人口中,多达8%的人患有罕见疾病,但许多人缺乏特定的ICD-10代码,这阻碍了在大型医疗数据集中的识别.
- 目前的方法在广泛的患者数据中难以全面识别患有罕见疾病的个体.
- 建议使用基于频率的罕见诊断 (FB-RDx) 的新方法来解决这一识别差距.
研究的目的:
- 探索基于频率的罕见诊断 (FB-RDx) 作为识别罕见疾病的新方法.
- 通过FB-RDx识别的住院患者群体的特征和结果与使用罕见疾病参考清单识别的患者群体的特征和结果进行比较.
- 评估FB-RDx在识别罕见疾病患者及其相关医疗保健结果中的实用性.
主要方法:
- 一项回顾性,横截面,全国性,多中心研究,利用2018年瑞士国家住院患者队列数据集 (830,114名成年住院患者).
- 暴露被定义为FB-RDx (10%的住院患者的诊断频率最低) 与更频繁的诊断相比 (分数 2-10).
- 结果比较了FB-RDx和628名ICD-10编码的罕见疾病患者,分析了医院死亡,30天再入院,ICU入院和停留时间.
主要成果:
- 通过FB-RDx (第1个十进制) 识别的患者显著增加了住院死亡 (OR1.44),30天再入院 (OR1.29),重症监护室入院 (OR1.50) 和更长停留时间的风险.
- 在ICD-10编码的罕见疾病患者中观察到类似的不良结果,包括更高的住院死亡风险 (OR 1.82) 和再入院风险 (OR 1.37).
- FB-RDx显示了与传统的罕见疾病编码相似的与不良结果的相关性.
结论:
- 基于频率的罕见诊断 (FB-RDx) 作为在大型数据集中识别罕见疾病的可行替代品.
- FB-RDx有可能比现有的编码系统更全面地识别罕见病患者.
- 该方法强调了与罕见疾病相关的风险增加,包括死亡率,再入院和长时间住院.
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