Comparison of four clinical risk prediction models for the diagnosis of giant cell arteritis: results from the ongoing PREDICT-GCA study

Abstract

Abstract Background The potential role of structured clinical pretest probability assessment in the diagnostic workup of giant cell arteritis (GCA) is not well defined. Patients and methods We applied four clinical prediction rules to patients enrolled in the ongoing prospective PREDICT GCA study. All patients underwent temporal artery biopsy in addition to the detailed clinical and sonographic workup. The final diagnosis was based on the 6 months follow-up and ambiguous cases were judged by an independent expert panel. The diagnostic accuracy of the four models was determined using 2×2 contingency tables and receiver operating characteristic analysis. Patients with and without discordant results in different scoring systems were compared using univariate significance tests. Results Seventy patients were analysed. The highest diagnostic accuracy (area under the curve (AUC) 0.84) and the highest negative predictive value (NPV, 86.7%) were provided by the Ing et al model. The other scores performed worse, with AUC/NPV of 0.74/78.6% (Southend GCA Probability Score), 0.75/73.7% (Bhavsar-Khalidi Score) and 0.70/77.8% (PREDICT score). In 37.5% of patients, clinically significant discordances in categorisation (classification as both low and non-low risk by different scoring systems) were evident. These patients less frequently presented with clinical symptoms and signs of cranial GCA (all p<0.01), but the rate of visual impairment did not differ from that in patients with consistent risk estimation results. Conclusion Clinical prediction models for the diagnosis of GCA differ in their diagnostic accuracy. Clinically relevant discordances of individual risk prediction are common. Trial registration number DRKS00031293.