Department of Medical Informatics, Hôpital Européen Georges Pompidou, AP-HP, 20 Rue Leblanc, 75015, Paris, France.
INSERM, Centre de Recherche des Cordeliers, UMRS 1138, Université de Paris, Université Sorbonne Paris Cité, Paris, France.
Orphanet J Rare Dis. 2021 Mar 20;16(1):139. doi: 10.1186/s13023-020-01639-7.
Estimating the prevalence of diseases is crucial for the organization of healthcare. The amount of literature on a rare pathology could help differentiate between rare and very rare diseases. The objective of this work was to evaluate to what extent the number of publications can be used to predict the prevalence of a given pathology.
We queried Orphanet for the global prevalence class for all conditions for which it was available. For these pathologies, we cross-referenced the Orphanet, MeSH, and OMIM vocabularies to assess the number of publication available on Pubmed using three different query strategies (one proposed in the literature, and two built specifically for this study). We first studied the association of the number of publications obtained by each of these query strategies with the prevalence class, then their predictive ability.
Class prevalence was available for 3128 conditions, 2970 had a prevalence class < 1/1,000,000, 41 of 1-9/1,000,000, 84 of 1-9/100,000, and 33 of 1-9/10,000. We show a significant association and excellent predictive performance of the number of publication, with an AUC over 94% for the best query strategy.
Our study highlights the link and the excellent predictive performance of the number of publications on the prevalence of rare diseases provided by Orphanet.
评估疾病的患病率对于医疗保健的组织至关重要。关于罕见病理学的文献数量有助于区分罕见病和极罕见病。这项工作的目的是评估文献数量在多大程度上可以用于预测特定病理学的患病率。
我们在 Orphanet 上查询了所有可获得全球患病率类别的疾病。对于这些病理学，我们交叉参考了 Orphanet、MeSH 和 OMIM 词汇表，使用三种不同的查询策略（一种在文献中提出，两种专门为本研究构建）在 Pubmed 上评估可用的出版物数量。我们首先研究了每种查询策略获得的出版物数量与患病率类别的相关性，然后研究了它们的预测能力。
3128 种疾病的患病率类别可获得，2970 种疾病的患病率<1/100 万，41 种疾病的患病率为 1-9/100 万，84 种疾病的患病率为 1-9/100,000，33 种疾病的患病率为 1-9/10,000。我们发现文献数量与患病率之间存在显著关联和极好的预测性能，最佳查询策略的 AUC 超过 94%。
我们的研究强调了 Orphanet 提供的罕见疾病患病率与文献数量之间的联系和极好的预测性能。
