1. Wellington Regional Rheumatology Unit, Hutt Hospital, Wellington, New Zealand
ClASsification criteria for the diagnosis of Psoriatic ARthritis: The CASPAR Study
Taylor W4, Helliwell P1, Gladman D3, Mease P5, Mielants H6, Marchesoni A2, for the CASPAR investigators .
1Academic Unit of Musculoskeletal Medicine, University of Leeds, UK; 2Istitutio Ortopedico G. Pini, Milan, Italy; 3University of Toronto, Canada; 4University of Otago, Wellington, New Zealand; 5Seattle Rheumatology Associates, USA; 6University of Ghent, Belgium.
Thank you for the opportunity of presenting the main results from the CASPAR study, on behalf of the Steering Committee and the many colleagues around the world who have collected data for this project, whom I would like to acknowledge briefly.
Rehabilitation Teaching & Research Unit, Wellington School of Medicine & Health Sciences, University of Otago

2. The CASPAR investigators
Australia: M Lassere; Belgium: H Mielants, M Van de Berghe, H Zmierczak, K de Vlam; Canada: A Russell, D Gladman; France: B Fournie, M Dougados, E Dernis, L Gossec, D Zerkak; Ireland: D Veale, O Fitzgerald, M O’Rouke; Morocco: N Hajjaj-Hassouni, N Bentalha; New Zealand: W Taylor, P Healy; Italy: A Marchesoni, C Salvarani, P Macchioni, E Lubrano, I Olivieri; South Africa: A Kalla, J Potts, G Mody, N Patel; Spain: J Torre Alonso; Sweden: B Svensson, U Lindqvist, G Holmstrom, E Theander, S Dahlqvist, G Alenius, K Ek; United Kingdom: A Isdale, D McGonagle, J Holdsworth, H Sharlala, A Adebajo, L Kay, N McHugh, J Lewis, P Owen, N Barkham, V Bejarano, P Emery, P Helliwell, G Ibrahim; United States: C Ritchlin, L Espinoza, L Candia, P Mease, L Wang, L Gunter.
I am presenting the results of CASPAR on Friday morning to the main meeting, and it is very useful to have this group to preview the results first since interpretation of the data is not completely clear-cut and there is some important discussion that will inform the presentation on Friday.
Rehabilitation Teaching & Research Unit, Wellington School of Medicine & Health Sciences, University of Otago

3. Background
Classification criteria for distinguishing between groups of PsA and non-PsA
There are 7 proposed classification criteria for the diagnosis of PsA
Only 1 has been derived from observed data
None have been adequately validated
The starting point for this project was the lack of agreed classification criteria for psoriatic arthritis, despite several proposed criteria.
When I am talking of classification criteria I mean criteria that distinguishes the disease from non-disease or from other diseases. In PsA, the terminology is a little confusing since classification may also refer to subgroup classification within the disease itself. It is important to emphasize that this study wished to address the problem of distinguishing PsA from other diseases and not subgroup classification. It is also important to emphasize that such classification criteria are for distinguishing groups of patients from one another, and not for individual diagnosis, even though they may be a useful rule-of-thumb in the clinic or for teaching key features of a disease.
There had been 7 different methods to accomplish this, but most were based on theoretical concepts of the disease and none have been adequately validated.
Rehabilitation Teaching & Research Unit, Wellington School of Medicine & Health Sciences, University of Otago

4. Moll & Wright (1973) Bennett (1979) Vasey & Espinoza (1984)
Gladman (1987)
ESSG (1991)
McGonagle (1999)
Fournie (1999)
I do not have time to run through the details of each of these classification methods, but I would point out that while there is quite a lot of overlap, there are also significant differences between these criteria, especially in relation to the absence of rheumatoid factor or whether family history of psoriais can substitute for evident psoriasis. Some criteria are much more complex than others, so issues of feasibility as well as accuracy are relevant. Some criteria involve radiological features, while other do not; and one involves HLA analysis.
Rehabilitation Teaching & Research Unit, Wellington School of Medicine & Health Sciences, University of Otago

5. Inflammatory arthritis Clinical sacroiliitis Clinical spondylitis
RF negative
Inflammatory arthritis
Clinical sacroiliitis
Clinical spondylitis
Clinical enthesitis
Dactylitis
Xray features
HLA
Evidence of skin disease
Family history
Other features
Moll and Wright ü
Bennett
DIP, nodules, asymmetry, synovial tissue and fluid analysis
Vasey and Espinoza
DIP disease
Gladman
Excluding other defined diseases
ESSG
Asymmetrical lower limb
McGonagle
DIP, rare associated conditions
Fournie
DIP involvement
It is difficult to easily summarise the main features of the different criteria in a single slide, but I have tried anyway. You see that there are various combinations of similar features. Family history are included in 3 criteria sets as a sufficient indicator of psoriais. The Fournie criteria includes HLA B16 or B17.
Rehabilitation Teaching & Research Unit, Wellington School of Medicine & Health Sciences, University of Otago

6. Background
There are 7 proposed classification criteria for the diagnosis of PsA
Only 1 has been derived from observed data
None have been adequately validated
In 2001, CASPAR arose from a collaborative effort determined to address this problem
During 2001, a group of rheumatologists interested in PsA was assembled by Philip Helliwell and a study was designed to address the criteria validation problem. Initially, a European group was formed but during the ILAR meeting in 2001 the scope of the study was considerably broadened to involve 30 rheumatologists in 13 countries.
Rehabilitation Teaching & Research Unit, Wellington School of Medicine & Health Sciences, University of Otago

7. Aim
To compare the test performance characteristics of existing classification criteria
To determine whether new criteria derived from observed data would be more accurate than existing criteria
There were 2 principle aims of the project. The first was to directly compare the performance of existing criteria and the second was to derive a further method and see whether this performs any better than the best criteria presently available. By performance charcteristics I basically mean sensitivity and specificity of the criteria.
Rehabilitation Teaching & Research Unit, Wellington School of Medicine & Health Sciences, University of Otago

8. Design
Prospective, observational study of consecutive clinic patients with PsA and other inflammatory arthritis
Target sample size of 1012 in total
30 clinics in 13 countries
Gold-standard of diagnosis based on physician’s opinion
Data collected between Feb 02 to Mar 04
The design of the study reflected one of the major purposes of classification criteria: which was to identify groups of patients with the disease in question from patients with similar diseases, especially in the context of recruitment for clinical trials. Subjects were therefore consecutive rheumatology clinic patients with inflammatory arthritis. Controls were the next clinic attendee and roughly matched for disease duration (more or less than 12 months). We also stipulated that around half of controls should have rheumatoid arthritis, since this is the pattern observed in general rheumatology clinics.
Subjects were recruited from 30 clinics in 13 countries.
We calculated a required sample size of 1012 (cases plus controls) with power 80%, alpha 0.01 to detect a difference of 2 percentage points between specificity of criteria using McNemar’s test.
The diagnosis was based on the physician diagnosis but we examined the validity of this as I shall show.
Rehabilitation Teaching & Research Unit, Wellington School of Medicine & Health Sciences, University of Otago

9. Methods Data collected included:
Clinical and examination features
Xrays of spine, sacroiliac joints, hands and feet
Rheumatoid factor, [HLA], anti-CCP, stored blood
Xrays were read centrally by 2 readers in tandem, blinded to diagnosis
Clinical gold-standard validated by quality control and Latent Class Analysis (statistical modelling)
New criteria developed using CART and logistic regression
A wide range of clinical data were collected, including joint counts, PASI scores, treatment details and spinal mobility measures. Xrays of the spine and sacroiiliac joints, hand feet and heels were obtained and read in tandem by 2 experienced observers (one a MSK radiologist) blind to diagnosis. Unfortunately, HLA data is not yet available but we anticipate that this shall be available for the majority of subjects.
We validated the clinical gold-standard by examining closely the submitted records for a randomly selected 10% of subjects and also used a statistical clustering technique known as Latent Class Analysis. Without going into the details of this, essentially it used the distribution of agreement between the different criteria sets to model the true class of the subject.
We used 2 different statistical approaches to identify potential new criteria – logistic regression and Classification and Regression Tree methods.
Rehabilitation Teaching & Research Unit, Wellington School of Medicine & Health Sciences, University of Otago

10. Characteristics of the cohort
PsA (n=588)
Controls (n=536)
Disease (%)
RA (70), AS (13), UA (7), CTD (3), other (5)
Age, yrs (mean, SE)
50.3 (0.54)
55.2 (0.62)*
Disease duration, yrs (mean, SE)
12.5 (0.40)
13.3 (0.46)
Male (%)
52.0
37.0*
RF positive (%)
4.6
57.3*
Anti-CCP positive (%)
7.6
54.5*
PASI (median, range)
2.15 (0 to 54)
* p<0.001
Data was collected on 588 cases with PsA and 536 controls with other inflammatory arthritis. 70% of controls had RA.
Controls were a little older and more likely to be female in accordance with the expected demographic of the diseases. Disease duration was around 12 to 13 years; only 4 to 5% of the cohort had disease duration of less than 12 months.
Subjects with psoriatic arthritis had mild psoriais as judged by PASI scores.
Rehabilitation Teaching & Research Unit, Wellington School of Medicine & Health Sciences, University of Otago

11. In comparing the accuracy of existing criteria, the main results are shown in this graph.
For each method, we see the percentage not able to be classified because of missing data items in yellow, the sensitivity in blue, specificity in red and the diagnostic odds ratio which is numbered and is plotted on a log axis. The DOR is the ratio between the odds of a positive test in cases compared to a positive test in controls, and the odds of a negative test in cases compared to a negative test in controls. The higher the DOR, the better – it is a combined index of test performance.
It will be appreciated that Fournie was difficult to use, because we still do not have the results of HLA analysis at the time of analysis: it is expected to be available some time this year.
Secondly, all criteria have very good specificity.
Thirdly, most criteria have good sensitivity, except the ESSG and Bennett criteria.
Finally, the criteria with the best combination of sensitivity and specificity was Vasey & Espinoza, with a DOR of 776.
Rehabilitation Teaching & Research Unit, Wellington School of Medicine & Health Sciences, University of Otago

12. Worse specificity  Better specificity
It was then necessary to see whether there were statistically significant differences between the criteria. We used conditional logistic regression to formally compare each criteria with Vasey & Espinoza. The criteria of Fournie was not included in this analysis since too many subjects could not be classified by that method.
The logistic regression is a convenient means of firstly showing that there is an effect of the different criteria – a single omnibus test of statistical significance that then allows more confidence in the tests of significance for individual criteria.
2 regression analyses were conducted, one upon controls and one upon cases. The first regression analysed the odds of classifying controls as PsA (the false positive rate or 1-specificity). Here higher odds means worse specificity. Bennett was more specific than Vasey, Moll and Wright and Gladman were the same, and McGonagle and ESSG were less specific.
 Better specificity
Rehabilitation Teaching & Research Unit, Wellington School of Medicine & Health Sciences, University of Otago

13. Worse specificity  Better specificity Better sensitivity
The second analysis shows sensitivity (lower panel). This analyses the odds of classifying cases as PsA for each criteria compared to Vasey and Espinoza. Higher OR means that more cases were classified as PsA by the method, hence more sensitive. We found that all criteria were less sensitive than Vasey and Espinoza except McGonagle, which was not significantly different.
Better sensitivity
Worse sensitivity
Rehabilitation Teaching & Research Unit, Wellington School of Medicine & Health Sciences, University of Otago

14. Validation of ‘gold-standard’
Data control committee closely examined the data for 124 randomly selected subjects (only 1 control reclassified as PsA)
Latent Class Anlaysis used to reclassify subjects on the basis of modelling using the agreement pattern between existing criteria: very close agreement with clinical diagnosis (kappa >0.9)
Because of a concern about the robustness of clinical diagnosis for a gold-standard, we attempted to externally validate the case-control status in 2 ways.
There was only 1 of 124 subjects (0.8%) who was reclassified after quality control.
Latent class analysis is a kind of cluster analysis that used the distribution of results from each of 6 criteria to form the clusters. This permitted a reclassification by the statistical model. The latent class analysis showed very high agreement with the clinical diagnosis, with a kappa of The sensitivity and specificity of each method was also very similar when the model defined case-ness was used, in comparison to the clinically defined cases-ness.
We were therefore happy with the clinical diagnoses.
Rehabilitation Teaching & Research Unit, Wellington School of Medicine & Health Sciences, University of Otago

15. Probability of a positive response, given subject is in class-3
Latent class model fitted to the data (n=949)
Criteria
Sensitivity
Specificity
Probability of a positive response, given subject is in class-3
McGonagle
0.99
1.00
0.93
Vasey & Espinoza
0.24
Moll & Wright
0.95
0.003
ESSG
0.76
0.69
Gladman
0.08
Bennett
0.53
0.002
Since it is not clear that a clinical diagnosis is an adequate gold-standard, we looked at 2 ways to validate the clinical diagnosis. Firstly, the data quality committee examined a random selection of 100 subjects and confirmed the correct diagnosis. Secondly, we employed the statistical technique of Latent Class Analysis.
A latent class model was fitted to the data using the LatentGold statistical software package. The principle feature of latent class analysis is that multiple imperfect indications of the true status of a subject are used to construct probabilistic mathematical equations that describe a model. The conditional probabilities, that is the probability of a positive response for each criteria given the class that correspond to psa, indicates the sensitivity of the criteria. The probability of a negative response given the class that corresponds to controls, indicate the specificity of the criteria.
A 2-class model did not fit the model but a 3-class model did. The 3rd class accounted for only 5% of the subjects and appeared to be mainly controls classified as PsA by McGonagle and ESSG, probably because of a family history of PsA. The other 2 classes corresponded strongly to PsA and controls.
The specificity of the criteria were all perfect within 2 decimal places but the sensitivity of each criteria was similar to when the clinical diagnosis was used.
Furthermore when we compared the case-control status according to clinic diagnosis with the case-control status according to the statistical model, there was very high agreement (kappa > 0.9).
So we concluded that the clinical diagnosis was a satisfactory gold-standard.
Rehabilitation Teaching & Research Unit, Wellington School of Medicine & Health Sciences, University of Otago

16. Vasey & Espinoza In: Calin A, editor. Spondyloarthropathies
Vasey & Espinoza In: Calin A, editor. Spondyloarthropathies. Orlando, Florida: Grune & Stratton; p
Psoriatic skin or nail involvement [current psoriasis, history of psoriasis, or nail disease]
PLUS One of these 2
(a) Peripheral pattern (any of):
1/ DIP involvement [finger DIP swollen]
2/ Asymmetry or dactylitis
3/ Symmetry in absence of RF and nodules
4/ Pencil-in-cup deformity, whittling of terminal phalanges, fluffy periostitis and bony ankylosis [radiographic osteolysis, tuft erosion, ankylosis, or juxta-articular new bone formation]
(b) Axial pattern (any of):
1/ Spinal pain and stiffness with the restriction of motion present for over 4 weeks
2/ Grade 2 symmetric sacroiliitis according to the New York criteria
3/ Grade 3 or 4 unilateral sacroiliitis
Thus far, the data appeared to most support the criteria of Vasey & Espinoza, so I am showing this in more detail now. It requires psoriasis plus one peripheral feature or one axial feature.
We next wanted to see whether the data themselves could produce an even better criteria.
Rehabilitation Teaching & Research Unit, Wellington School of Medicine & Health Sciences, University of Otago

17. CART analysis
Iterative partitioning of the dataset using most discriminating variables to produce the most ‘pure’ groups; progressive pruning back of the tree to balance complexity with accuracy
CART selected only 2 variables:
history of psoriasis and current psoriasis
surrogates: nail dystrophy, family history, dactylitis, RF
specificity 96.8%; sensitivity 96.1%
Classifcation and regression tree method have been used in disease classification problems before, for example in RA. The method iteratively splits the dataset into progressively purer groups, selecting the most powerful splitters first. It keeps going until a very large and complex tree is grown, and then prunes back the tree, testing for accuracy of the model, before deciding on the tree with the best balance of complexity and accuracy. You will apreciate that this is a simplistic description of what the method does.
On the CASPAR dataset, only 2 variables were selected; history and current psoriasis – these alone gave a sensitivity of 96.8% and specificity 96.1%. Of additional interest were the surrogate variables chosen: these represent alternate variables that could be used if the primary splitting variables were missing.
This approach, by itself, did not appear to be an advance over the Vasey & Espinoza criteria.
So we also looked at individual data items themselves, using logistic regression analysis to identify which items independently discriminated between PsA and controls.
Rehabilitation Teaching & Research Unit, Wellington School of Medicine & Health Sciences, University of Otago

18. Discrimination of clinical, laboratory and xray items
Univariate analysis
Multivariate analysis
Sensitivity
Specificity
OR (95% CI)
p-value
RF negative
95.1
60.2
27.8 ( )
<0.001
Current dactylitis
History of dactylitis
53.6
94.6
17.9 ( )
6.0 ( )
History of psoriasis
93.6
97.6
102.6 ( )
Family history of psoriasis
46.7
91.3
5.6 ( )
Current psoriasis
88.3
97.8
22.5 ( )
Juxta-articular new bone formation
18.7
95.4
4.6 (
The regression analysis tested 26 clinical and laboratory variables and 18 radiographic variables. 6 items were selected by the analysis as being independently associated with PsA.
We then constructed a model using the items identified by the CART and logistic regression analysis, which in fact overlapped to a considerable degree.
Rehabilitation Teaching & Research Unit, Wellington School of Medicine & Health Sciences, University of Otago

19. History of psoriasis (if current psoriasis not present)
Family history of psoriasis (if neither history nor current psoriasis is present)
Psoriatic nail dystrophy
RF negative
Current dactylitis
History of dactylitis (if current dactylitis not present)
Xray signs of juxta-articular new bone formation
(3 or more items)
The number of items being present was summed and a receiver operating charcteristic curve was constructed. This had excellent discrimination with an area under the curve of The optimal cutpoint was judged to be the presence of 3 or more of these items, which gave a sensitivity of 93% and specificity of 98.7%.
The sensitivity and specificity was also calculated from the average of 10 randomly selected 50% splits of the data, and gave very similar estimates.
This criteria was tested formally against Vasey and Espinoza and the new criteria were confirmed to have better specificity but worse sensitivity. Recalling the Vasy criteria have specificity of 96% and sensitivity 97%.
Rehabilitation Teaching & Research Unit, Wellington School of Medicine & Health Sciences, University of Otago

20. CASPAR criteria
Inflammatory musculoskeletal disease (joint, spine, or entheseal)
With 3 or more of the following:
1. Current psoriasis
Psoriatic skin or scalp disease present today as judged by a rheumatologist
2. Personal history of psoriasis (if current psoriasis not present)
A history of psoriasis that may be obtained from patient, family doctor, dermatologist or rheumatologist
3. Family history of psoriasis (if personal history of psoriasis or current psoriasis is not present)
A history of psoriasis in a first or second degree relative according to patient report
4. Psoriatic nail dystrophy
Typical psoriatic nail dystrophy including onycholysis, pitting and hyperkeratosis observed on current physical examination
5. A negative test for rheumatoid factor
By any method except latex but preferably by ELISA or nephlemetry, according to the local laboratory reference range
6. Current dactylitis
Swelling of an entire digit
7. History of dactylitis (if current dactylitis is not present)
A history of dactylitis recorded by a rheumatologist
8. Radiological evidence of juxta-articular new bone formation
Ill-defined ossification near joint margins (but excluding osteophyte formation) on plain xrays of hand or foot
These then are the CASPAR criteria. They have very high specificity and fairly high sensitivity and are easy to use. High specificity has some advantages for clinical intervention or longitudinal observational studies. On the other hand, they may not be suited for population epidemiological studies, where higher sensitivity may be more desirable. But also note that since the controls in this study had rheumatic diseases, the specificity will likely be different in a general population context, or for that matter other clinical populations.
Rehabilitation Teaching & Research Unit, Wellington School of Medicine & Health Sciences, University of Otago

21. Conclusion
Robust design (large sample, many centres, unselected subjects, validation of gold-standard, large number of items examined, convergence of separate statistical approaches)
Results not applicable to early disease or to non-rheumatic populations (e.g. general population)
CASPAR criteria are simple, highly specific, and derived from observed patient data: a new standard for the case-definition of PsA?
The CASPAR project has strong design characteristics with a large sample able to detect small differences, many centres involved to reduce diagnostic bias, unselected subjects with and without PsA, a statistical and quality-control approach to validated the clinical diagnosis as a gold standard, testing a large number of potential diagnostic features, and using different statistical techniques to derive new criteria.
However, it is important to note that the patient sample had a long duration of disease, with insufficient numbers of early disease to test the validity of criteria in this subset. Further studies are required to test these criteria in early disease. Secondly. The choice of controls in this study limits the applicability to non-rheumatic disease populations, for example dermatology clinics or the general population. It will be necessary to conduct further studies in these groups to determine the specificity of the criteria in these contexts.
The CASPAR criteria have 3 main advantages – they are easy to use, highly specific in the rheumatology clinic setting and have been derived from observed patient data. The main disadvantage is that they are not quite as sensitive as other criteria. We would argue that specificity is more important for many purposes, for instance entry criteria for clinical trials, and therefore propose that these criteria become the new standard for case-definition.
Rehabilitation Teaching & Research Unit, Wellington School of Medicine & Health Sciences, University of Otago

22. Acknowledgements
Funding: EULAR, Barnsley District NHS Trust, Groote Schuur Hospital (Cape Town), Department of Medical Sciences (University Hospital, Uppsala), Krembil Foundation, St. Vincent’s University Hospital Radiology Department (Dublin), Inkosi Albert Luthuli Central Hospital (Durban), El Ayachi Hospital (Morocco), National Psoriasis Foundation (USA), The Foundation for Scientific Research of the Belgian Society of Rhumatology, Arthritis New Zealand.
Data Quality Committee: Dennis McGonagle, Philip Helliwell, Mike Green, Leeds; Deborah Symmons, Manchester, UK
Radiology: Guy Porter, Keighly, UK
CCP analysis: Neil McHugh, Pat Owen, Bath, UK
Statistical analysis: John Horwood, Christchurch, NZ
Rehabilitation Teaching & Research Unit, Wellington School of Medicine & Health Sciences, University of Otago

23. Discussion Is better specificity worth the trade-off with sensitivity?
Construction of CASPAR criteria
Dependent nature of some items
Meaning of the initial mandatory criterion (note that 1.6% of dataset had no involved joints)?
Should some degree of chronicity be required?
Practical application of CASPAR criteria
Should dactylitis be excluded from the initial mandatory criterion (“double-dipping”)?
Hands and feet xrays required
Rehabilitation Teaching & Research Unit, Wellington School of Medicine & Health Sciences, University of Otago

24. Statistical comparison of sensitivity and specificity.
OR (95% CI) for classifying cases as PsA (true positive rate) and classifying controls as PsA (false positive rate) compared to Vasey & Espinoza (red means significantly different)
Rule
True positive rate
(value of more than 1 means better sensitivity)
False positive rate
(value of less than 1 means better specificity)
Gladman
0.36 ( )
0.72 ( )
McGonagle
1.72 ( )
3.65 ( )
ESSG
0.11 ( )
3.14 ( )
Moll & Wright
0.38 ( )
0.36 ( )
Bennett
0.04 ( )
0.04 ( )
We used conditional logistic regression to formally compare each criteria with Vasey & Espinoza. The criteria of Fournie was not included in this analysis since too many subjects could not be classified by that method.
The logistic regression is a convenient means of firstly showing that there is an effect of the different criteria – a single omnibus test of statistical significance that allows more confiednce in the tests of significance for individual criteria.
2 regression analyses were conducted. The first for sensitivity. This analyses the odds of classifying cases as PsA for each citeria compared to Vasey and Espinoza. Higher OR means that more cases were classified as PsA by the method, hence more sensitive. We found that all criteria were less sensitive than Vasey and Espinoza except McGonagle, which was not significantly different.
The second regression analysed the odds of classifying controls as PsA (the false positive rate or 1-specificity). Here higher odds means worse specificity. Bennett was more specific than Vasey, Moll and Wright and Gladman were the same, and McGonagle and ESSG were less specific.
Rehabilitation Teaching & Research Unit, Wellington School of Medicine & Health Sciences, University of Otago

25. Anti-CCP performance
Rehabilitation Teaching & Research Unit, Wellington School of Medicine & Health Sciences, University of Otago

26. Item (frequency, N)
Sens.
Spec.
Normal acute phase reactant (279)
31.8
80.3
RF negative (727)
95.1
60.2
Anti-CCP negative (483)
90.4
56.8
Any tender enthesis (436)
53.2
76.8
Chest wall pain (239)
25.2
83.0
Diffuse enthesis pain (122)
14.0
92.5
Inflammatory heel pain (335)
39.4
80.7
Inflammatory LBP (408)
46.0
74.4
Lumbar stiffness (481)
53.0
51.0
Inflammatory neck pain (407)
38.4
66.2
Neck stiffness (695)
69.0
31.0
Inflammatory thoracic spinal pain (180)
19.6
87.9
Thoracic stiffness (176)
17.0
84.0
Clinical sacroiliitis (263)
30.6
84.4
Absence of subcutaneous nodules (1039)
99.8
15.7
Iritis (62)
4.6
93.4
Dactylitis (338)
53.6
94.6
Nail dystrophy (352)
57.9
97.7
History of psoriasis (538)
93.6
97.6
Item (frequency, N)
Sens.
Spec.
Family history of psoriasis (318)
46.7
91.3
Current psoriasis on exam (532)
88.3
97.8
Less than 4 MCP involved (628)
65.7
55.0
Any DIP involved (202)
28.9
94.0
Any toe DIP involved (168)
23.3
94.2
Symmetry of joint involvement (924)
79.8
15.1
All joints of a single ray involved (90)
11.2
95.5
Interphalangeal bony ankylosis (74)
11.9
97.0
Bilateral sacroiliitis (111)
11.0
87.0
DIP erosive disease (170)
61.9
89.0
Entheseal erosion (76)
6.7
90.0
Entheseal bony proliferation (143)
15.8
85.0
Juxtaarticular new bone formatn (116)
18.7
95.4
Marginal syndesmophytes (56)
4.5
91.4
Non-marginal syndesmophytes (87)
10.0
90.2
Joint osteolysis (102)
12.6
91.5
Ray involvement (37)
6.1
98.6
Tuft osteolysis (22)
4.3
100.0
Unilateral sacroiliitis (34)
5.38
98.4
Rehabilitation Teaching & Research Unit, Wellington School of Medicine & Health Sciences, University of Otago