Knevel Rachel, Knitza Johannes, Hensvold Aase, Circiumaru Alexandra, Bruce Tor, Evans Sebastian, Maarseveen Tjardo, Maurits Marc, Beaart-van de Voorde Liesbeth, Simon David, Kleyer Arnd, Johannesson Martina, Schett Georg, Huizinga Tom, Svanteson Sofia, Lindfors Alexandra, Klareskog Lars, Catrina Anca
Leiden University Medical Center, Leiden, Netherlands.
Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, United Kingdom.
Front Med (Lausanne). 2022 Apr 25;9:774945. doi: 10.3389/fmed.2022.774945. eCollection 2022.
Digital diagnostic decision support tools promise to accelerate diagnosis and increase health care efficiency in rheumatology. is an online tool developed by specialists in rheumatology and general medicine together with patients and patient organizations. It calculates a risk score for several rheumatic diseases. We ran a pilot study retrospectively testing for its ability to differentiate symptoms from existing or emerging immune-mediated rheumatic diseases from other rheumatic and musculoskeletal complaints and disorders in patients visiting rheumatology clinics.
The performance of was tested using in three university rheumatology centers: (A) patients at Risk for RA (Karolinska Institutet, = 50 individuals with musculoskeletal complaints and anti-citrullinated protein antibody positivity) (B) patients with early joint swelling [dataset B (Erlangen) = 52]. (C) Patients with early arthritis where the clinician considered it likely to be of auto-immune origin [dataset C (Leiden) = 73]. In dataset A we tested whether could predict the development of arthritis. In dataset B and C we tested whether could predict the development of an immune-mediated rheumatic diseases. We examined the discriminative power of the total score with the Wilcoxon rank test and the area-under-the-receiver-operating-characteristic curve (AUC-ROC). Next, we calculated the test characteristics for these patients passing the first or second expert-based scoring threshold.
The total test scores differentiated between: (A) Individuals developing arthritis or not, median 245 vs. 163, < 0.0001, AUC-ROC = 75.3; (B) patients with an immune-mediated arthritic disease or not median 191 vs. 107, < 0.0001, AUC-ROC = 79.0; but less patients with an immune-mediated arthritic disease or not amongst those where the clinician already considered an immune mediated disease most likely (median 262 vs. 212, < 0.0001, AUC-ROC = 53.6). Threshold-1 (advising to visit primary care doctor) was highly specific in dataset A and B (0.72, 0.87, and 0.23, respectively) and sensitive (0.67, 0.61, and 0.67). Threshold-2 (advising to visit rheumatologic care) was very specific in all three centers but not very sensitive: specificity of 1.0, 0.96, and 0.91, sensitivity 0.05, 0.07, 0.14 in dataset A, B, and C, respectively.
is a web-based patient-centered multilingual diagnostic tool capable of differentiating immune-mediated rheumatic conditions from other musculoskeletal problems. The current scoring system needs to be further optimized.
数字诊断决策支持工具有望加快诊断速度并提高风湿病学领域的医疗效率。 是一款由风湿病学和普通医学专家与患者及患者组织共同开发的在线工具。它可以计算几种风湿性疾病的风险评分。我们进行了一项回顾性试点研究，测试 区分就诊于风湿病诊所的患者中现有或新发免疫介导的风湿性疾病与其他风湿性和肌肉骨骼疾病及病症症状的能力。
在三个大学风湿病中心对 的性能进行了测试：（A）类风湿关节炎风险患者（卡罗林斯卡学院， = 50名有肌肉骨骼症状且抗瓜氨酸化蛋白抗体呈阳性的个体）（B）早期关节肿胀患者[数据集B（埃尔朗根） = 52]。（C）临床医生认为很可能是自身免疫性起源的早期关节炎患者[数据集C（莱顿） = 73]。在数据集A中，我们测试了 是否能预测关节炎的发展。在数据集B和C中，我们测试了 是否能预测免疫介导的风湿性疾病的发展。我们使用Wilcoxon秩和检验以及受试者工作特征曲线下面积（AUC - ROC）来检验总分的判别能力。接下来，我们计算了这些通过基于专家的 评分阈值第一或第二个阈值的患者的检验特征。
总测试分数在以下方面存在差异：（A）是否发展为关节炎的个体，中位数分别为245和163， < 0.0001，AUC - ROC = 75.3；（B）是否患有免疫介导的关节炎疾病的患者，中位数分别为191和107， < 0.0001，AUC - ROC = 79.0；但在临床医生已经认为最可能是免疫介导疾病的患者中，是否患有免疫介导的关节炎疾病的患者差异较小（中位数分别为262和212， < 0.0001，AUC - ROC = 53.6）。阈值1（建议就诊于初级保健医生）在数据集A和B中具有高度特异性（分别为0.72、0.87和0.23）且敏感（分别为0.67、0.61和0.67）。阈值2（建议就诊于风湿病护理）在所有三个中心都非常特异但不太敏感：在数据集A、B和C中的特异性分别为1.0、0.96和0.91，敏感性分别为0.05、0.07和0.14。
是一个以患者为中心的基于网络的多语言诊断工具，能够区分免疫介导的风湿性疾病与其他肌肉骨骼问题。当前的评分系统需要进一步优化。
