Objective

Prediction models have become frequent in the medical literature, but most published studies are conducted in a single setting. Heterogeneity between development and validation samples has been posited as a major obstacle for the generalization of models. We aimed to develop a multivariable prognostic model using sociodemographic variables easily obtainable from adolescents at age 15 to predict a depressive disorder diagnosis at age 18 and to evaluate its generalizability in 2 samples from diverse socioeconomic and cultural settings.

Method

Data from the 1993 Pelotas Birth Cohort were used to develop the prediction model, and its generalizability was evaluated in 2 representative cohort studies: the Environmental Risk (E-Risk) Longitudinal Twin Study and the Dunedin Multidisciplinary Health and Development Study.

Results

At age 15, 2,192 adolescents with no evidence of current or previous depression were included (44.6% male). The apparent C-statistic of the models derived in Pelotas ranged from 0.76 to 0.79, and the model obtained from a penalized logistic regression was selected for subsequent external evaluation. Major discrepancies between the samples were identified, impacting the external prognostic performance of the model (Dunedin and E-Risk C-statistics of 0.63 and 0.59, respectively). The implementation of recommended strategies to account for this heterogeneity among samples improved the model’s calibration in both samples.

Conclusion

An adolescent depression risk score comprising easily obtainable predictors was developed with good prognostic performance in a Brazilian sample. Heterogeneity among settings was not trivial, but strategies to deal with sample diversity were identified as pivotal for providing better risk stratification across samples. Future efforts should focus on developing better methodological approaches for incorporating heterogeneity in prognostic research.

中文翻译：

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识别有抑郁风险的青少年：基于 3 个不同大陆的队列中的预测得分表现

客观的

预测模型在医学文献中变得很常见，但大多数已发表的研究都是在单一环境中进行的。开发和验证样本之间的异质性被认为是模型泛化的主要障碍。我们的目标是开发一个多变量预后模型，该模型使用 15 岁青少年容易获得的社会人口统计学变量来预测 18 岁时的抑郁症诊断，并评估其在来自不同社会经济和文化环境的 2 个样本中的普遍性。

方法

来自 1993 年 Pelotas 出生队列的数据用于开发预测模型，并在 2 个具有代表性的队列研究中评估了其普遍性：环境风险 (E-Risk) 纵向双胞胎研究和但尼丁多学科健康与发展研究。

结果

在 15 岁时，包括 2,192 名没有当前或既往抑郁症证据的青少年（44.6% 为男性）。在 Pelotas 中派生的模型的表观 C 统计量在 0.76 到 0.79 之间，选择从惩罚逻辑回归中获得的模型进行后续外部评估。确定了样本之间的主要差异，影响了模型的外部预后性能（但尼丁和 E-Risk C 统计量分别为 0.63 和 0.59）。实施推荐的策略来解释样本之间的这种异质性改善了模型在两个样本中的校准。

结论

在巴西样本中开发了一个包含易于获得的预测因子的青少年抑郁风险评分，具有良好的预后表现。环境之间的异质性并非微不足道，但处理样本多样性的策略被认为是提供更好的跨样本风险分层的关键。未来的努力应侧重于开发更好的方法学方法，将异质性纳入预后研究。