External validation of the International Risk Prediction Algorithm for the onset of generalized anxiety and/or panic syndromes (The Predict A) in the US general population

Abstract

IntroductionMultivariable risk prediction algorithms are useful for making clinical decisions and health planning. While prediction algorithms for new onset of anxiety disorders in Europe and elsewhere have been developed, the performance of these algorithms in the Americas is not known. The objective of this study was to validate the PredictA algorithm for new onset of anxiety and/or panic disorders in the US general population. MethodsLongitudinal study design was conducted with approximate 2-year follow-up data from a total of 24 626 individuals who participated in Wave 1 and 2 of the US National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) and who did not have generalized anxiety disorder (GAD) and panic disorder in the past year at Wave 1. The PredictA algorithm was directly applied to the selected participants. ResultsAmong the participants, 5.4% developed GAD and/or panic disorder over two years. The PredictA algorithm had a discriminative power (C-statistics = 0.62, 95%CI: 0.61; 0.64), but poor calibration (p < 0.001) with the NESARC data. The observed and the mean predicted risk of GAD and/or panic disorders in the NESARC were 5.3% and 3.6%, respectively. Particularly, the observed and predicted risks of GAD and/or panic disorders in the highest decile of risk score in the NESARC participants were 13.3% and 10.4%, respectively. ConclusionThe PredictA algorithm has acceptable discrimination, but the calibration with the NESARC data was poor. The PredictA algorithm is likely to underestimate the risk of GAD/panic disorders in the US population. Therefore, the use of PredictA in the US general population for predicting individual risk of GAD and/or panic disorders is not encouraged.