Institute of Mental Health Research, University of Ottawa, Ottawa, Canada; The Ottawa Hospital Research Institute, Ottawa, Canada.
Institute of Mental Health Research, University of Ottawa, Ottawa, Canada; School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, Ottawa, Canada.
J Anxiety Disord. 2019 May;64:40-44. doi: 10.1016/j.janxdis.2019.03.004. Epub 2019 Apr 3.
Multivariable 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.
Longitudinal 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.
Among 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.
The 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.
多变量风险预测算法可用于做出临床决策和健康规划。虽然已经开发出用于预测欧洲和其他地区新发焦虑障碍的算法，但这些算法在美洲的性能尚不清楚。本研究的目的是验证 PredictA 算法在美国普通人群中新发焦虑和/或恐慌障碍的预测能力。
采用纵向研究设计，对总共 24626 名参加了美国全国酒精相关情况流行病学调查（NESARC）第 1 波和第 2 波、且在第 1 波时过去 1 年中没有广泛性焦虑障碍（GAD）和恐慌障碍的个体进行了为期约 2 年的随访数据。直接将 PredictA 算法应用于所选参与者。
在参与者中，5.4%在两年内患上 GAD 和/或恐慌障碍。PredictA 算法具有判别能力（C 统计量=0.62，95%CI：0.61；0.64），但与 NESARC 数据的校准效果较差（p<0.001）。在 NESARC 中观察到的和平均预测的 GAD 和/或恐慌障碍风险分别为 5.3%和 3.6%。特别是，在 NESARC 参与者中风险评分最高的十分位数中观察到的和预测的 GAD 和/或恐慌障碍风险分别为 13.3%和 10.4%。
PredictA 算法具有可接受的判别能力，但与 NESARC 数据的校准效果较差。PredictA 算法可能低估了美国人群中 GAD/恐慌障碍的风险。因此，不鼓励在美国普通人群中使用 PredictA 来预测个体 GAD 和/或恐慌障碍的风险。
