Background: Cannabis use disorder (CUD) is a growing public health problem. Early identification of adolescents and young adults at risk of developing CUD in the future may help stem this trend. A logistic regression model fitted using a Bayesian learning approach was developed recently to predict the risk of future CUD based on seven risk factors in adolescence and youth. A nationally representative longitudinal dataset, Add Health was used to train the model (henceforth referred as Add Health model). Methods: We validated the Add Health model on two cohorts, namely, Michigan Longitudinal Study (MLS) and Christchurch Health and Development Study (CHDS) using longitudinal data from participants until they were approximately 30 years old (to be consistent with the training data from Add Health). If a participant was diagnosed with CUD at any age during this period, they were considered a case. We calculated the area under the curve (AUC) and the ratio of expected and observed number of cases (E/O). We also explored re-calibrating the model to account for differences in population prevalence. Results: The cohort sizes used for validation were 424 (53 cases) for MLS and 637 (105 cases) for CHDS. AUCs for the two cohorts were 0.66 (MLS) and 0.73 (CHDS) and the corresponding E/O ratios (after recalibration) were 0.995 and 0.999. Conclusion: The external validation of the Add Health model on two different cohorts lends confidence to the model's ability to identify adolescent or young adult cannabis users at high risk of developing CUD in later life.

翻译：背景：大麻使用障碍（CUD）是一个日益严重的公共卫生问题。早期识别未来有CUD风险的青少年和年轻人可能有助于遏制这一趋势。近期，基于贝叶斯学习方法拟合的逻辑回归模型被开发出来，用于根据青春期和青年期的七个风险因素预测未来CUD风险。模型训练使用了全国代表性的纵向数据集Add Health（以下简称Add Health模型）。方法：我们在两个队列（即密歇根纵向研究（MLS）和基督城健康与发展研究（CHDS））上验证了Add Health模型，使用参与者直至约30岁的纵向数据（与Add Health训练数据一致）。若参与者在此时段内任何年龄被诊断患有CUD，则视为病例。我们计算了曲线下面积（AUC）及期望病例数与观察病例数之比（E/O），并探索了模型重新校准以考虑人群患病率差异。结果：用于验证的队列规模为MLS的424例（含53例病例）和CHDS的637例（含105例病例）。两队列的AUC分别为0.66（MLS）和0.73（CHDS），相应的E/O比值（重新校准后）分别为0.995和0.999。结论：Add Health模型在两个不同队列上的外部验证增强了模型识别具有较高后期CUD风险的青少年或年轻大麻使用者的能力。