Outpatient clinic for rare inflammatory systemic diseases, Department of Nephrology, Hannover Medical School, Carl-Neuberg-Straße 1, Hannover, 30625, Germany.
Ada Health GmbH, Adalbertstraße 20, Berlin, 10997, Germany.
Orphanet J Rare Dis. 2019 Mar 21;14(1):69. doi: 10.1186/s13023-019-1040-6.
Rare disease diagnosis is often delayed by years. A primary factor for this delay is a lack of knowledge and awareness regarding rare diseases. Probabilistic diagnostic decision support systems (DDSSs) have the potential to accelerate rare disease diagnosis by suggesting differential diagnoses for physicians based on case input and incorporated medical knowledge. We examine the DDSS prototype Ada DX and assess its potential to provide accurate rare disease suggestions early in the course of rare disease cases.
Ada DX suggested the correct disease earlier than the time of clinical diagnosis among the top five fit disease suggestions in 53.8% of cases (50 of 93), and as the top fit disease suggestion in 37.6% of cases (35 of 93). The median advantage of correct disease suggestions compared to the time of clinical diagnosis was 3 months or 50% for top five fit and 1 month or 21% for top fit. The correct diagnosis was suggested at the first documented patient visit in 33.3% (top 5 fit), and 16.1% of cases (top fit), respectively. Wilcoxon signed-rank test shows a significant difference between the time to clinical diagnosis and the time to correct disease suggestion for both top five fit and top fit (z-score -6.68, respective -5.71, α=0.05, p-value <0.001).
Ada DX provided accurate rare disease suggestions in most rare disease cases. In many cases, Ada DX provided correct rare disease suggestions early in the course of the disease, sometimes at the very beginning of a patient journey. The interpretation of these results indicates that Ada DX has the potential to suggest rare diseases to physicians early in the course of a case. Limitations of this study derive from its retrospective and unblinded design, data input by a single user, and the optimization of the knowledge base during the course of the study. Results pertaining to the system's accuracy should be interpreted cautiously. Whether the use of Ada DX reduces the time to diagnosis in rare diseases in a clinical setting should be validated in prospective studies.
罕见病的诊断通常会延误数年。导致这种延误的一个主要因素是缺乏对罕见病的了解和认识。概率诊断决策支持系统(DDSS)有可能通过根据病例输入和纳入的医学知识为医生提供鉴别诊断,从而加速罕见病的诊断。我们检查了 Ada DX 原型 DDSS,并评估了它在罕见病病例早期提供准确罕见病建议的潜力。
在 93 例病例中,Ada DX 在 53.8%(50/93)的情况下,在前 5 个最符合的疾病建议中,比临床诊断更早地提示了正确的疾病,并且在 37.6%(35/93)的情况下,是最符合的疾病建议。与临床诊断时间相比,正确疾病建议的中位数优势为 3 个月或 50%(前 5 个符合)和 1 个月或 21%(最符合)。正确诊断分别在首次有记录的患者就诊时在 33.3%(前 5 个符合)和 16.1%(最符合)的病例中得到提示。Wilcoxon 符号秩检验显示,对于前 5 个符合和最符合的病例,Ada DX 从临床诊断时间到正确疾病建议时间之间存在显著差异(z 分数-6.68 和-5.71,α=0.05,p 值<0.001)。
Ada DX 为大多数罕见病病例提供了准确的罕见病建议。在许多情况下,Ada DX 在疾病的早期就为医生提供了正确的罕见病建议,有时甚至是在患者就诊的开始阶段。对这些结果的解释表明,Ada DX 有可能在病例的早期向医生提示罕见疾病。本研究的局限性源于其回顾性和非盲设计、单一用户的数据输入以及知识库在研究过程中的优化。应谨慎解释与系统准确性相关的结果。Ada DX 是否能在临床环境中减少罕见病的诊断时间,还需要前瞻性研究来验证。