Development and Preliminary Validation of a Diagnostic Score for Identifying Patients Affected with Adult-Onset Autoinflammatory Disorders

Abstract

To date, the rate of detection of autoinflammatory gene mutations in patients suspected of having an autoinflammatory disorder is very low. However, most of these data refer to pediatric populations. The relative rarity and lack of information on adult-onset autoinflammatory diseases make it likely that mutations will be found in an even smaller percentage of cases. Our aim was to develop and validate a set of variables for predicting the risk that a given adult patient presenting with recurrent fever episodes carries mutations in the MEFV or TNFRSF1A genes, in order to increase the probability of obtaining positive results on genetic testing. One hundred and ten consecutive patients with a clinical history of periodic fever attacks were screened for mutations in the TNFRSF1A and the MEFV genes. The mean age at disease onset was 27.85 years. Detailed information about each patient's family history, personal history, and clinical manifestations were retrospectively collected. A diagnostic score was constructed based on univariate and multivariate analysis in a randomly-selected dataset (training set; n=40). The score was validated on an independent set of the remaining patients (validation set; n=70). Age at onset (odds ratio [OR] 0.958, P = 0.050), positive family history of recurrent fever episodes (OR 5.738, P = 0.006), thoracic pain (OR 7.390, P = 0.002), abdominal pain (OR 2.853, P = 0.038) and skin involvement (OR 8.241, P = 0.003) were independently correlated with a positive genetic test result. A diagnostic score was calculated using the linear combination of the estimated coefficients of the logistic model (cut off equal to 0.24) revealing high sensitivity (0.94), high specificity (0.94) and high accuracy (0.94). We have identified variables that appear to be strongly related to the probability of detecting gene mutations in MEF and TNFRSF1A in adults, thus improving the evaluation of patients with suspected autoinflammatory disorders.