PDF(Pubmed)
Abstract：
BACKGROUND: The diagnosis of inflammatory rheumatic diseases (IRDs) is often delayed due to unspecific symptoms and a shortage of rheumatologists. Digital diagnostic decision support systems (DDSSs) have the potential to expedite diagnosis and help patients navigate the health care system more efficiently.
OBJECTIVE: The aim of this study was to assess the diagnostic accuracy of a mobile artificial intelligence (AI)-based symptom checker (Ada) and a web-based self-referral tool (Rheport) regarding IRDs.
METHODS: A prospective, multicenter, open-label, crossover randomized controlled trial was conducted with patients newly presenting to 3 rheumatology centers. Participants were randomly assigned to complete a symptom assessment using either Ada or Rheport. The primary outcome was the correct identification of IRDs by the DDSSs, defined as the presence of any IRD in the list of suggested diagnoses by Ada or achieving a prespecified threshold score with Rheport. The gold standard was the diagnosis made by rheumatologists.
RESULTS: A total of 600 patients were included, among whom 214 (35.7%) were diagnosed with an IRD. Most frequent IRD was rheumatoid arthritis with 69 (11.5%) patients. Rheport\'s disease suggestion and Ada\'s top 1 (D1) and top 5 (D5) disease suggestions demonstrated overall diagnostic accuracies of 52%, 63%, and 58%, respectively, for IRDs. Rheport showed a sensitivity of 62% and a specificity of 47% for IRDs. Ada\'s D1 and D5 disease suggestions showed a sensitivity of 52% and 66%, respectively, and a specificity of 68% and 54%, respectively, concerning IRDs. Ada\'s diagnostic accuracy regarding individual diagnoses was heterogenous, and Ada performed considerably better in identifying rheumatoid arthritis in comparison to other diagnoses (D1: 42%; D5: 64%). The Cohen κ statistic of Rheport for agreement on any rheumatic disease diagnosis with Ada D1 was 0.15 (95% CI 0.08-0.18) and with Ada D5 was 0.08 (95% CI 0.00-0.16), indicating poor agreement for the presence of any rheumatic disease between the 2 DDSSs.
CONCLUSIONS: To our knowledge, this is the largest comparative DDSS trial with actual use of DDSSs by patients. The diagnostic accuracies of both DDSSs for IRDs were not promising in this high-prevalence patient population. DDSSs may lead to a misuse of scarce health care resources. Our results underscore the need for stringent regulation and drastic improvements to ensure the safety and efficacy of DDSSs.
BACKGROUND: German Register of Clinical Trials DRKS00017642; https://drks.de/search/en/trial/DRKS00017642.
OBJECTIVE: The aim of this study was to assess the diagnostic accuracy of a mobile artificial intelligence (AI)-based symptom checker (Ada) and a web-based self-referral tool (Rheport) regarding IRDs.
METHODS: A prospective, multicenter, open-label, crossover randomized controlled trial was conducted with patients newly presenting to 3 rheumatology centers. Participants were randomly assigned to complete a symptom assessment using either Ada or Rheport. The primary outcome was the correct identification of IRDs by the DDSSs, defined as the presence of any IRD in the list of suggested diagnoses by Ada or achieving a prespecified threshold score with Rheport. The gold standard was the diagnosis made by rheumatologists.
RESULTS: A total of 600 patients were included, among whom 214 (35.7%) were diagnosed with an IRD. Most frequent IRD was rheumatoid arthritis with 69 (11.5%) patients. Rheport\'s disease suggestion and Ada\'s top 1 (D1) and top 5 (D5) disease suggestions demonstrated overall diagnostic accuracies of 52%, 63%, and 58%, respectively, for IRDs. Rheport showed a sensitivity of 62% and a specificity of 47% for IRDs. Ada\'s D1 and D5 disease suggestions showed a sensitivity of 52% and 66%, respectively, and a specificity of 68% and 54%, respectively, concerning IRDs. Ada\'s diagnostic accuracy regarding individual diagnoses was heterogenous, and Ada performed considerably better in identifying rheumatoid arthritis in comparison to other diagnoses (D1: 42%; D5: 64%). The Cohen κ statistic of Rheport for agreement on any rheumatic disease diagnosis with Ada D1 was 0.15 (95% CI 0.08-0.18) and with Ada D5 was 0.08 (95% CI 0.00-0.16), indicating poor agreement for the presence of any rheumatic disease between the 2 DDSSs.
CONCLUSIONS: To our knowledge, this is the largest comparative DDSS trial with actual use of DDSSs by patients. The diagnostic accuracies of both DDSSs for IRDs were not promising in this high-prevalence patient population. DDSSs may lead to a misuse of scarce health care resources. Our results underscore the need for stringent regulation and drastic improvements to ensure the safety and efficacy of DDSSs.
BACKGROUND: German Register of Clinical Trials DRKS00017642; https://drks.de/search/en/trial/DRKS00017642.
摘要：
背景：炎性风湿性疾病（IRD）的诊断通常由于非特异性症状和风湿病学家的短缺而延迟。数字诊断决策支持系统（DDSS）有可能加快诊断，并帮助患者更有效地导航医疗保健系统。
目的：本研究的目的是评估基于移动人工智能（AI）的症状检查程序（Ada）和基于网络的自我转诊工具（Rheport）对IRD的诊断准确性。
方法：前瞻性，多中心，开放标签，我们对新到3个风湿病中心就诊的患者进行了交叉随机对照试验.参与者被随机分配使用Ada或Rheport完成症状评估。主要结果是DDSS对IRD的正确识别，定义为Ada建议的诊断列表中存在任何IRD或Rheport达到预定阈值评分。金标准是风湿病学家做出的诊断。
结果：共纳入600例患者，其中214人(35.7%)被诊断为IRD。最常见的IRD是类风湿性关节炎，有69例（11.5％）患者。Rheport的疾病建议和Ada的前1（D1）和前5（D5）疾病建议显示，总体诊断准确率为52％，63%,58%,分别,用于IRDs。Rheport对IRD的敏感性为62％，特异性为47％。Ada的D1和D5疾病建议的敏感性分别为52%和66%，分别,特异性为68%和54%，分别,关于IRD。Ada关于个体诊断的诊断准确性是异质性的，与其他诊断相比，Ada在识别类风湿性关节炎方面的表现明显更好（D1：42％；D5：64％）。Rheport对任何风湿性疾病诊断与AdaD1的一致性的Cohenκ统计为0.15（95％CI0.08-0.18），与AdaD5为0.08（95％CI0.00-0.16），表明2个DDSS之间存在任何风湿性疾病的一致性较差。
结论：据我们所知，这是与患者实际使用DDSS的最大比较性DDSS试验.在这种高患病率患者人群中，两种DDSS对IRD的诊断准确性都没有希望。DDSS可能导致滥用稀缺的医疗保健资源。我们的结果强调了需要严格的监管和重大改进，以确保DDSS的安全性和有效性。
背景：德国临床试验注册DRKS00017642;https://drks。de/search/en/trial/DRKS00017642.
目的：本研究的目的是评估基于移动人工智能（AI）的症状检查程序（Ada）和基于网络的自我转诊工具（Rheport）对IRD的诊断准确性。
方法：前瞻性，多中心，开放标签，我们对新到3个风湿病中心就诊的患者进行了交叉随机对照试验.参与者被随机分配使用Ada或Rheport完成症状评估。主要结果是DDSS对IRD的正确识别，定义为Ada建议的诊断列表中存在任何IRD或Rheport达到预定阈值评分。金标准是风湿病学家做出的诊断。
结果：共纳入600例患者，其中214人(35.7%)被诊断为IRD。最常见的IRD是类风湿性关节炎，有69例（11.5％）患者。Rheport的疾病建议和Ada的前1（D1）和前5（D5）疾病建议显示，总体诊断准确率为52％，63%,58%,分别,用于IRDs。Rheport对IRD的敏感性为62％，特异性为47％。Ada的D1和D5疾病建议的敏感性分别为52%和66%，分别,特异性为68%和54%，分别,关于IRD。Ada关于个体诊断的诊断准确性是异质性的，与其他诊断相比，Ada在识别类风湿性关节炎方面的表现明显更好（D1：42％；D5：64％）。Rheport对任何风湿性疾病诊断与AdaD1的一致性的Cohenκ统计为0.15（95％CI0.08-0.18），与AdaD5为0.08（95％CI0.00-0.16），表明2个DDSS之间存在任何风湿性疾病的一致性较差。
结论：据我们所知，这是与患者实际使用DDSS的最大比较性DDSS试验.在这种高患病率患者人群中，两种DDSS对IRD的诊断准确性都没有希望。DDSS可能导致滥用稀缺的医疗保健资源。我们的结果强调了需要严格的监管和重大改进，以确保DDSS的安全性和有效性。
背景：德国临床试验注册DRKS00017642;https://drks。de/search/en/trial/DRKS00017642.
