Clinical features in primary care electronic records before diagnosis of Ankylosing Spondylitis: a nested case-control study.
Background Ankylosing spondylitis (AS) often has a long period from first symptom presentation to diagnosis. We examined the occurrence of symptoms, prescriptions and diagnostic tests in primary care electronic records over time prior to a diagnosis of AS.
Methods Nested case-control study using anonymised primary care electronic health records from Scotland. Cases were 74 adults with a first diagnosis of AS between 2000 and 2010. Controls were matched for age, sex and GP practice: (a) 296 randomly selected adults (b) 169 adults whose records contained codes indicating spinal conditions or symptoms.
We extracted clinical features (symptoms, AS-related disorders, prescriptions and diagnostic tests). Conditional logistic regression was used to examine the association between clinical features (both individually and in combinations) and diagnosis of AS. We examined the associations between clinical features and diagnosis over time prior to diagnosis.
Results Several new composite pointers were predictive of AS: including distinct episodes of axial pain separated by more than 6 months (OR 12.7, 95% CI 4.7 to 34.6); the occurrence of axial pain with and tendon symptoms within the same year (OR 21.7, 95% CI 2.6 to 181.5); and the co-occurrence (within 30 days) of axial pain and a prescription for nonsteroidal anti-inflammatory drug (OR 10.4, 95%CI 4.9 to 22.1). Coded episodes of axial pain increased steadily over the three years before diagnosis. In contrast, large joint symptoms and enthesopathy showed little or no time trend prior to diagnosis.
Conclusions We identified novel composite pointers to a diagnosis of AS in GP records. These may represent valuable targets for diagnostic support systems.
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Posted 14 Apr, 2020
On 08 Apr, 2020
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Received 28 Jan, 2020
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On 05 Nov, 2019
Clinical features in primary care electronic records before diagnosis of Ankylosing Spondylitis: a nested case-control study.
Posted 14 Apr, 2020
On 08 Apr, 2020
On 07 Apr, 2020
On 07 Apr, 2020
On 04 Apr, 2020
On 31 Mar, 2020
On 30 Mar, 2020
On 30 Mar, 2020
On 19 Feb, 2020
Received 18 Feb, 2020
Received 28 Jan, 2020
On 23 Jan, 2020
On 03 Jan, 2020
Invitations sent on 28 Nov, 2019
On 27 Nov, 2019
On 26 Nov, 2019
On 26 Nov, 2019
On 12 Nov, 2019
On 07 Nov, 2019
On 06 Nov, 2019
On 06 Nov, 2019
On 05 Nov, 2019
Background Ankylosing spondylitis (AS) often has a long period from first symptom presentation to diagnosis. We examined the occurrence of symptoms, prescriptions and diagnostic tests in primary care electronic records over time prior to a diagnosis of AS.
Methods Nested case-control study using anonymised primary care electronic health records from Scotland. Cases were 74 adults with a first diagnosis of AS between 2000 and 2010. Controls were matched for age, sex and GP practice: (a) 296 randomly selected adults (b) 169 adults whose records contained codes indicating spinal conditions or symptoms.
We extracted clinical features (symptoms, AS-related disorders, prescriptions and diagnostic tests). Conditional logistic regression was used to examine the association between clinical features (both individually and in combinations) and diagnosis of AS. We examined the associations between clinical features and diagnosis over time prior to diagnosis.
Results Several new composite pointers were predictive of AS: including distinct episodes of axial pain separated by more than 6 months (OR 12.7, 95% CI 4.7 to 34.6); the occurrence of axial pain with and tendon symptoms within the same year (OR 21.7, 95% CI 2.6 to 181.5); and the co-occurrence (within 30 days) of axial pain and a prescription for nonsteroidal anti-inflammatory drug (OR 10.4, 95%CI 4.9 to 22.1). Coded episodes of axial pain increased steadily over the three years before diagnosis. In contrast, large joint symptoms and enthesopathy showed little or no time trend prior to diagnosis.
Conclusions We identified novel composite pointers to a diagnosis of AS in GP records. These may represent valuable targets for diagnostic support systems.
Figure 1
Figure 2