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建议采用柏林公式诊断SpA:来自脊柱关节病早期患者(SPACE)队列结果
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发布时间:2019-06-19

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Proposal for An Adaptation of the Berlin Algorithm for Diagnosing Spa: Results of the SPondyloArthritis Caught Early (SPACE)-Cohort

 

, M. Reijnierse, T.W.J. Huizinga and D.M.F.M. van der Heijde, Leiden University Medical Center, Leiden, Netherlands

 

Presentation Number: 1295

 

Background/Purpose: The recently developed Berlin algorithm is meant to assist clinicians in diagnosing early axial SpA. Inflammatory back pain (IBP) plays a dominant role. However, IBP is not a very specific symptom and might lead to misclassification. The goal is to validate the Berlin algorithm in the SPondyloArthritis Caught Early (SPACE)-cohort.

Method: The SPACE-cohort is set-up in the Leiden University Medical Center (LUMC) to diagnose and treat axial spondyloarthritis (SpA)-patients early. All patients with back pain (>3 months, but <2 years; onset <45 years) were included and underwent a diagnostic work-up; MRI and X-rays of the SI-joints and laboratory assessments. All patients were classified according to the Berlin algorithm and to the ASAS axial SpA classification criteria. The LR-product was calculated based on the present SpA-features; the cut-off ≥80% probability was used. Second, all patients were classified according to a modified algorithm, excluding IBP as entry criterion.

Result: In 6/133 patients no MRI is made, and in 1 patient also no X-ray is performed. These patients are analyzed as MRI- and X-ray-. 4/6 are diagnosed with axial SpA according to the algorithm. 40/53 (75.5%) patients diagnosed as axial SpA according to the algorithm fulfilled the ASAS axial SpA criteria and 50 (94.4%) had ≥80% probability of having SpA. The 3 remaining patients had a probability of 79.5% and 77.7% respectively. 17/80 (21.3%) patients not diagnosed as axial SpA fulfilled the ASAS axial SpA criteria and 9 (11.3%) had ≥80% probability of having axial SpA (figure 1).

The modified algorithm has IBP not as entry criterion but as additional SpA-feature. According to this modified algorithm, 63 patients (47.4%) could be diagnosed as axial SpA, of which 49 (77.8%) fulfilled the ASAS axial SpA criteria and 57 (95.2%) had ≥80% probability of having axial SpA. Again, these 3 additional patients had a probability close to 80%. 9/70 (12.9%) patients not diagnosed as SpA fulfilled the ASAS axial SpA criteria (figure 2).

The modified algorithm could classify 10 additional patients as axial SpA and no single patient with a probability >80% was excluded; the number of false classified patients is decreased. Some HLA-B27- patients are classified false negative by excluding them from the algorithm although they had MRI+ and other present SpA-features. Thus, in HLA-B27- patients with a high suspicion of SpA, we would advice to perform MRI as well.

Conclusion: We propose a slightly modified algorithm excluding IBP as an entry criterion that is better in accordance with the ASAS axial SpA criteria and the LR-product probability ≥80%, especially by reducing false negative classification.

 

References Rudwaleit M, Heijde DM van der, Khan MA, Braun J, Sieper J. Annals Rheum Diseases 2004; 63:535-43.

 

 

R. van den Berg  , et al. ACR 2011. Present No: 1295

背景/目的: 近年发展起来的柏林公式旨在协助临床医生诊断早期SpA。其中,炎症性背痛(IBP)起着主要作用。然而,IBP症状特异性不是非常高,可能导致误诊。本研究目的是为了验证柏林公式在在SPACE队列中的价值。

方法:SPACE队列是荷兰莱顿大学医学中心(LUMC)诊断和治疗的早期中轴脊柱关节炎患者。纳入所有腰背痛患者 (> 3个月,< 2;起病时< 45)并完成诊断工作流程:SI关节的MRIx射线,以及实验室检查。所有患者根据柏林公式和ASAS中轴SpA分类标准进行分类。按照SpA的表现特点计算LR; ≥80%的概率为临界设定值。其次,排除IBP作为纳入标准后,对所有患者根据改良算法进行分类 。

结果:133例患者中有6例未行MRI检查,1例未行x线检查。患者都进行MRIX线分析。6例中有4例根据公式被诊断为中轴SpA40/53 (75.5%)的患者根据该算法诊断为中轴SpA符合ASAS分类标准,50(94.4%)例患者≥80%的概率为SpA。其余3例患者的概率分别为79.5%77.7%17/80(21.3%)的患者未诊断为中轴SpA但符合ASAS标准,其中9(11.3%)≥80%的概率为中轴SpA(1)

改进的公式中IBP作为附加的SpA特征而不是纳入标准。根据这一算法,63例患者(47.4%)诊断为中轴SpA,其中49(77.8%)例符合ASAS中轴SpA标准,57(95.2%)≥80%的概率为SpA。另有3例患者概率接近80%9/70(12.9%)例患者没有被诊断为SpA但符合ASAS标准(2)

改进的算法额外诊断了10例患者为中轴SpA,无一例概率> 80%的患者被排除在外;降低了错误分类的患者数。一些HLA-B27阴性的患者尽管有MRI阳性发现和其他SpA特征,仍然被这一算法错误分类排除。因此,HLA-B27阴性的患者高度怀疑SpA时,我们要建议同时行MRI检查。

结论:我们建议稍作改良的算法,即排除IBP作为入组标准,与ASAS中轴SpA标准一致性更好,其LR值概率≥80%,特别有助于减少假阴性分类。

 

 

转载于:https://www.cnblogs.com/T2T4RD/archive/2011/12/14/5464216.html

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