The use of clinical characteristics to predict the results of temporal artery biopsy among patients with suspected giant cell arteritis.
OBJECTIVE
To develop a mathematical model which predicts temporal artery biopsy results.


METHODS
We collected clinical and laboratory data as well as biopsy results among a consecutive cohort of all individuals who underwent temporal artery biopsy at Mayo Medical Center between January 1, 1988 and December 31, 1991. All biopsies were independently reviewed by one pathologist. Logistic regression was used to identify a set of variables which best predicted the biopsy results. This model was then used to identify patients who were highly likely (> or = 95% predictive value) to have either a negative or a positive biopsy. A receiver operating characteristic (ROC) curve was generated using the best fit model.


RESULTS
Of the 525 people in the study, there were 187 men and 338 women. The logistic regression model and the ROC curve generated from this model were of modest value in predicting biopsy results from prebiopsy clinical characteristics. However, this model identified 60 (11%) individuals who had a > or = 95% probability of having a negative biopsy. None of these individuals had any symptoms of claudication, only 5 of 60 (8%) had temporal artery abnormalities on examination, 45 (75%) had synovitis (suggesting an alternate diagnosis), and their median erythrocyte sedimentation rate was only 31 mm/h (Westergren).


CONCLUSIONS
In individuals with these findings, we recommend a careful search for other diagnoses before temporal artery biopsy.
Gabriel
S E
SE
Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota 55905.
O'Fallon
W M
WM
Achkar
A A
AA
Lie
J T
JT
Hunder
G G
GG
D000368:Aged; D001706:Biopsy; D015331:Cohort Studies; D005260:Female; D013700:Giant Cell Arteritis / Q000150:complications / Q000473:pathology*; D006801:Humans; D007383:Intermittent Claudication / Q000150:complications; D008297:Male; D008954:Models, Biological; D010358:Patient Participation; D011237:Predictive Value of Tests; D012372:ROC Curve; D012044:Regression Analysis; D012680:Sensitivity and Specificity; D013699:Temporal Arteries / Q000473:pathology*