Clinical Studies
Risk Factors and Predictive Models of Giant Cell Arteritis in Polymyalgia Rheumatica

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Abstract

Objective: To identify in polymyalgia rheumatica the best set of predictors for a positive temporal artery biopsy and to define predictive models with either a high or low probability of giant cell arteritis (GCA).

Patients and Methods: Retrospective study of 227 patients, 137 with polymyalgia rheumatica unassociated with arteritis (group A) and 90 with polymyalgia associated with biopsy-proven giant cell arteritis (group B or training set). Data on demographic features, clinical and laboratory abnormalities were collected. Risk factors for arteritis were estimated by nonlinear logistic regressions. Simple predictive models were constructed with those predictors more related to arteritis by multivariable analysis. These models were then tested in group B and in 89 cases of arteritis without polymyalgia rheumatica (group C or test set).

Results: The best predictors of arteritis were a new headache odds ratio (OR) 13.6 (95% confidence interval [CI] 4.7 to 39.3); age at onset < 70 years OR 0.11 (CI 0.04 to 0.35); abnormal temporal arteries OR 4.2 (CI 1.3 to 13.7); raised liver enzymes OR 2.9 (CI 1.1 to 7.8), and jaw claudication OR 4.8 (CI 1.0 to 22.7). Amaurosis was only observed in patients with arteritis. Three subsets had a very high risk of arteritis: (1) Patients with recent headache, abnormal arteries, and ≥70 years at disease onset: sensitivity 44%, positive predictive value (PPV) 93%, likelihood ratio (LR) 20.3; (2) patients with a new headache, jaw claudication, and abnormal arteries: sensitivity 34.4%, PPV 96.9%, LR 47.2; and (3) those, that in addition to the last 3 features, were ≥70 years of age at disease onset: sensitivity 26.7%, PPV 100%. We could also identify a subset with a very low risk of arteritis constituted by patients < 70 years, without headache, and with clinically normal temporal arteries: sensitivity 1.1%, PPV 1.7%, LR 0.03. In group C or the test set, these four predictive models correctly identified 57.3%, 29.2%, 23.6, and 3.4% of patients, respectively.

Conclusions: In polymyalgia rheumatica it is feasible to identify subsets with a very high likelihood of GCA. Although in some of these subsets the diagnosis of arteritis is almost certain, we suggest that even then it should be confirmed by temporal artery biopsy. By contrast, in those patients with polymyalgia < 70 years and without cranial features of giant cell arteritis, the risk of vasculitis is so low that the biopsy could be initially avoided and the patient treated with low-dose corticosteroids.

Section snippets

Patients

We included all patients with polymyalgia rheumatica, either primary or associated with an underlying GCA, treated by the Rheumatology Divisions of these Hospitals during the following periods: January 1975 to January 1993 (Hospital M. Valdecilla), June 1978 to January 1993 (H.N.S. Aranzazu), and June 1981 to January 1993 (H. Xeral). During most of the time, these services had been the only providers of rheumatological care in the three provinces and therefore, this population can be regarded

General Features and Risk Factors for an Abnormal Temporal Artery Biopsy

There was a predominance of women in both groups (92 females/45 males in group A versus 60 females/30 males in group B). As shown in Table 1, patients with GCA were characterized by an older age at disease onset (72.8 ± 5.8 versus 69.7 ± 7.7 years, P < 0.002), higher proportion of cases with cranial symptoms (P < 0.0001 for all of them), and a lower frequency of synovitis (P = 0.006). Moreover, the temporal artery abnormalities were clinically far less evident in the group with polymyalgia

Discussion

The diagnosis of GCA can only be made definitively by demonstrating the characteristic histological abnormalities on temporal artery.[14] Because of the increased awareness of the syndrome, more biopsies are being done, with decreasing positive rates, as low as 11% in recent reports.[15] The goals of our study were to identify the best predictors of a positive temporal artery biopsy and to generate predictive models with either a high or low probability of GCA. There have been several attempts

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