Office of Rare Diseases Research (ORDR), National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Bethesda, MD, 20817, USA.
NCATS NIH, Bethesda, MD, 20817, USA.
Orphanet J Rare Dis. 2021 Oct 22;16(1):429. doi: 10.1186/s13023-021-02061-3.
Rare diseases (RD) are a diverse collection of more than 7-10,000 different disorders, most of which affect a small number of people per disease. Because of their rarity and fragmentation of patients across thousands of different disorders, the medical needs of RD patients are not well recognized or quantified in healthcare systems (HCS).
We performed a pilot IDeaS study, where we attempted to quantify the number of RD patients and the direct medical costs of 14 representative RD within 4 different HCS databases and performed a preliminary analysis of the diagnostic journey for selected RD patients.
The overall findings were notable for: (1) RD patients are difficult to quantify in HCS using ICD coding search criteria, which likely results in under-counting and under-estimation of their true impact to HCS; (2) per patient direct medical costs of RD are high, estimated to be around three-fivefold higher than age-matched controls; and (3) preliminary evidence shows that diagnostic journeys are likely prolonged in many patients, and may result in progressive, irreversible, and costly complications of their disease CONCLUSIONS: The results of this small pilot suggest that RD have high medical burdens to patients and HCS, and collectively represent a major impact to the public health. Machine-learning strategies applied to HCS databases and medical records using sentinel disease and patient characteristics may hold promise for faster and more accurate diagnosis for many RD patients and should be explored to help address the high unmet medical needs of RD patients.
罕见病(RD)是由 7000 多种不同疾病组成的多样疾病集合,其中大多数疾病影响的患者人数较少。由于 RD 患者的疾病种类繁多且分布零散,其医疗需求在医疗保健系统(HCS)中并未得到充分认识或量化。
我们进行了一项 Ideas 研究的试点,试图在 4 个不同的 HCS 数据库中对 14 种代表性 RD 患者的数量和直接医疗费用进行量化,并对选定 RD 患者的诊断过程进行初步分析。
总体研究结果值得注意的是:(1)使用 ICD 编码搜索标准在 HCS 中对 RD 患者进行量化非常困难,这可能导致对其真实影响的低估和低估;(2)RD 患者的每位患者直接医疗费用很高,估计比同龄对照组高出三到五倍;(3)初步证据表明,许多患者的诊断过程可能会延长,并可能导致其疾病的进行性、不可逆转和昂贵的并发症。
这项小型试点研究的结果表明,RD 给患者和 HCS 带来了沉重的医疗负担,它们共同对公共卫生造成了重大影响。应用于 HCS 数据库和医疗记录的机器学习策略,使用标志性疾病和患者特征,可能为许多 RD 患者的更快、更准确诊断带来希望,应该加以探索,以帮助满足 RD 患者的高未满足医疗需求。