在线工具帮助医生在社区转诊时甄别SpA患者

在线工具帮助医生在社区转诊时甄别SpA患者

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

背景:目前已开发了多种转诊策略以优化脊柱关节炎(SpA)的早期诊断,最终确诊SpA的比例约为30?40%。我们应该进一步探索以减少延迟诊断并优化转诊至二级医疗机构的转诊条件。英国NHS基金信托医院之一的皇家国立风湿病医院(RNHRD)开发了一种脊柱关节炎诊断评估(SPADE)工具(www.spadetool.co.uk),以帮助医务专业人士针对45岁之前有慢性背痛发作史而且X线摄片无明显异常的患者评估诊断中轴型脊柱关节炎(axSpA)的可能性。“SPADE工具”用图形直观地显示某转诊患者的axSpA诊断概率,并为使用者开展下一步诊疗工作列出明晰的指导意见(图1)。本研究旨在在二级医疗机构层面评估“SPADE工具”的性能。

方法:
RNHRD开设了每周一次的早期背痛(EBP)门诊。对所有患者(axSpA和机械性背痛)采集相关数据。根据所采集的信息,包括相关临床特征、CRP、HLA-B27和MRI检查,
SPADE工具为每一位EBP患者计算出SpA诊断概率,据此可以将患者分为四种类型, (1)不太可能是SpA,
(2)需要其它检查(HLA-B27或MRI,考虑转诊到专科), (3)SpA可能,
(4)明确的SpA。将SPADE工具的推测与专科医生的评判进行比较,相关比较参数包括基于该队列的观察值计算所得敏感性、特异性、阳性预测值(PPV)以及阴性预测值(NPV)。根据一个假设的基于x的二项式模型,其中参数x是SpA患者在SPADE工具预设概率界值时未被转诊的患者例数,推算出NPV的95%可信区间。

结果:共纳入87例转诊患者(男性49例),其中有44例(50.5%)随后被确诊为SpA患者。将87例转诊患者按照四种诊断概率分组:第1类为0/21,第2类为6/21,第3类为7/9,第4类为31/36。由表1可见SPADE工具在每种诊断概率类型的敏感性、特异性、PPV和NPV。

结论:
SPADE工具是一个有用的资源,它协助临床医生对慢性背痛患者评判SpA的诊断概率。该工具的阴性预测值较高,尤其是诊断概率类型为2或3时,提示该工具最大用途是排除SpA。需要注意的是,该样本的SpA患病率高于目标人群(初级医疗),这意味着上述各种诊断概率的NPV可能被低估了。未来需要在初级医疗层面对SPADE工具进行验证。

表1. SPADE工具辅助诊断SpA的敏感性、特异性和阳/阴性预测值

图1. 示例

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原文链接或参见以下信息。

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

Shabina
Habibi¹, Susan Doshi², Raj
Sengupta¹, ¹Rheumatology,
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.