在線工具幫助醫生在社區轉診時甄別SpA患者


在線工具幫助醫生在社區轉診時甄別SpA患者

在線工具幫助醫生在社區轉診時甄別SpA患者

Habibi S, et al. Rheumatology 2016. Present ID: 202.

背景:目前已開發了多種轉診策略以優化脊柱關節炎(SpA)的早期診斷,最終確診SpA的比例約為30?40%。我們應該進一步探索以減少延遲診斷並優化轉診至二級醫療機構的轉診條件。英國NHS基金信托醫院之一的皇家國立風濕病醫院(RNHRD)開發了一種脊柱關節炎診斷評估(SPADE)工具(www.spadetool.co.uk),以幫助醫務專業人士針對45歲之前有慢性背痛發作史而且X線攝片無明顯異常的患者評估診斷中軸型脊柱關節炎(axSpA)的可能性。“SPADE工具”用圖形直觀地顯示某轉診患者的axSpA診斷概率,並為使用者開展下一步診療工作列出明晰的指導意見(圖1)。本研究旨在在二級醫療機構層面評估“SPADE工具”的性能。

方法: RNHRD開設了每周一次的早期背痛(EBP)門診。對所有患者(axSpA和機械性背痛)采集相關數據。根據所采集的信息,包括相關臨床特征、CRP、HLA-B27和MRI檢查, SPADE工具為每一位EBP患者計算出SpA診斷概率,據此可以將患者分為四種類型, (1)不太可能是SpA, (2)需要其它檢查(HLA-B27或MRI,考慮轉診到專科), (3)SpA可能, (4)明確的SpA。將SPADE工具的推測與專科醫生的評判進行比較,相關比較參數包括基於該隊列的觀察值計算所得敏感性、特異性、陽性預測值(PPV)以及陰性預測值(NPV)。根據一個假設的基於x的二項式模型,其中參數x是SpA患者在SPADE工具預設概率界值時未被轉診的患者例數,推算出NPV的95%可信區間。

結果:共納入87例轉診患者(男性49例),其中有44例(50.5%)隨后被確診為SpA患者。將87例轉診患者按照四種診斷概率分組:第1類為0/21,第2類為6/21,第3類為7/9,第4類為31/36。由表1可見SPADE工具在每種診斷概率類型的敏感性、特異性、PPV和NPV。

結論: SPADE工具是一個有用的資源,它協助臨床醫生對慢性背痛患者評判SpA的診斷概率。該工具的陰性預測值較高,尤其是診斷概率類型為2或3時,提示該工具最大用途是排除SpA。需要注意的是,該樣本的SpA患病率高於目標人群(初級醫療),這意味着上述各種診斷概率的NPV可能被低估了。未來需要在初級醫療層面對SPADE工具進行驗證。

表1. SPADE工具輔助診斷SpA的敏感性、特異性和陽/陰性預測值

在線工具幫助醫生在社區轉診時甄別SpA患者

圖1. 示例

在線工具幫助醫生在社區轉診時甄別SpA患者


原文鏈接或參見以下信息

PERFORMANCE OF THE SPADE TOOL TO IDENTIFY SPONDYLOARTHRITIS IN PATIENTS REFERRED TO A SPECIALIST

Shabina Habibi1, Susan Doshi2, Raj Sengupta11Rheumatology, Royal National Hospital for Rheumatic Diseases, Bath, UNITED KINGDOM, 2Medical Physics and Bioengineering, Royal United Hospitals, Bath, UNITED KINGDOM.

 

Background: Many referral strategies have been devised to optimize the early diagnosis of spondyloarthritis(SpA). These result in the diagnosis of SpA in 30 to 40% of patients. Strategies to reduce the delay in diagnosis of SpA and optimise the appropriateness of referrals to secondary care should be explored. The Spondyloarthritis Diagnosis Evaluation(SPADE) tool(www.spadetool.co.uk) has been designed to assist healthcare professionals define the probability of axial spondyloarthritis(AxSpA) in patients <45 years of age with chronic back pain and no definite changes on radiographs. The probability of AxSpA derived from the 'SPADE Tool' is displayed on a chart with clear instructions for the user on what action should be taken next. The aim of this study was to assess the performance of the SPADE tool in the secondary care setting.

Methods: The RNHRD runs a weekly Early Back Pain (EBP) clinic. Data on all patients (AxSpA and Mechanical back pain) has been collected. The SPADE tool which consists of questions pertaining to clinical features, CRP, HLA-B27 and MRI findings was applied on all EBP patients with a diagnosis to obtain the probability of SpA in this group of patients as one of the 4 categories: Category 1-improbable, category 2-additional tests needed(HLA-B27 or MRI, consider referral to a specialist), category 3(probable SpA) and category 4(definitive SpA). This was compared with the diagnosis made by the physician Sensitivity, specificity, positive predictive value(PPV) and negative predictive value(NPV) were estimated using observed ratios of patient numbers from this sample. 95% CIs on the NPV were generated by assuming a binomial model for x, where x is the number of patients with SpA who are not referred at a given SPADE threshold.

Results: N=87(49 males); 44(50.5%) had SpA subsequently diagnosed; 0/21 in category 1, 6/21 in category 2, 7/9 in category 3 and 31/36 in category 4. Estimates of PPV, NPV, sensitivity and specificity obtained by using each of the SPADE categories as a threshold for referral are given in the table.

Conclusion: The SPADE tool is valuable resource to assist clinicians define the probability of SpA in patients with chronic backpain. The high NPV, especially with the referral threshold set at 2 or 3, implies that the test is most useful in ruling out SpA. Note that the prevalence in this sample is likely to be higher than in the target population(primary care), meaning that these estimates of NPV are likely to be underestimates. The tool needs to be validated in a primary care setting.

 


注意!

本站转载的文章为个人学习借鉴使用,本站对版权不负任何法律责任。如果侵犯了您的隐私权益,请联系我们删除。



 
粤ICP备14056181号  © 2014-2021 ITdaan.com