A risk calculator to predict adult attention-deficit/hyperactivity disorder: generation and external validation in three birth cohorts and one clinical sample - ERRATUM.
A risk calculator to predict adult attention-deficit/hyperactivity disorder: generation and external validation in three birth cohorts and one clinical sample - ERRATUM.
复制标题
一种预测成人注意力缺陷/多动症障碍的风险计算器：三个出生队列和一个临床样本 - 拨言的产生和外部验证。
DOI：
10.1017/s2045796019000337
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发表时间：
2019-07-03
影响因子：
8.1
通讯作者：
Rohde LA
中科院分区：
文献类型：
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作者：
Caye A;Agnew-Blais J;Arseneault L;Gonçalves H;Kieling C;Langley K;Menezes AMB;Moffitt TE;Passos IC;Rocha TB;Sibley MH;Swanson JM;Thapar A;Wehrmeister F;Rohde LA
目的 针对心理健康进行的个性化医学调查很少。我们的目标是生成并验证一个预测成人注意力缺陷/多动障碍 (ADHD) 的风险工具。方法使用逻辑回归模型，我们使用经过内部验证的儿童临床和社会人口统计数据，在代表性人群队列（ALSPAC – 英国，5113 名参与者，从出生到 17 岁）中生成了一个风险工具。预测因素包括性别、社会经济地位、单亲家庭、多动症症状、共病破坏性障碍、儿童期虐待、多动症症状、抑郁症状、母亲抑郁症和智商。结果被定义为年轻成年时 ADHD 的分类诊断，无需发病年龄标准。我们还测试了用于开发风险模型的机器学习方法：随机森林、随机梯度提升和人工神经网络。该风险工具在 E-Risk 队列（英国，2040 名参与者，出生至 18 岁）、1993 Pelotas 出生队列（巴西，3911 名参与者，出生至 18 岁）和 MTA 临床样本（美国，476 名 ADHD 儿童和 241 名对照儿童，从最小 8 岁到最大 26 岁，随访 16 年）中进行了外部验证。 结果 总体患病率成人多动症范围 在基于人群的样本中，这一比例从 8.1% 降至 12%，而在临床样本中，这一比例为 28.6%。该模型在生成样本中的内部性能良好，预测成人 ADHD 的曲线下面积 (AUC) 为 0.82（95% 置信区间 (CI) 0.79–0.83）。校准图显示预测事件频率和观测到的 0 到 60% 概率事件频率之间具有良好的一致性。在英国出生队列测试样本中，AUC 为 0.75（95% CI 0.71-0.78）。在巴西出生队列测试样本中，AUC 显着较低 –0.57 (95% CI 0.54–0.60)。在临床试验测试样本中，AUC为0.76（95% CI 0.73-0.80）。该风险模型不能预测成人焦虑或重度抑郁症。机器学习方法并没有优于逻辑回归模型。生成了一个供临床使用的开源免费风险计算器，可在 https://ufrgs.br/prodah/adhd-calculator/ 在线获取。 结论 基于儿童特征的风险工具专门预测欧洲和北美人群和临床样本中的成人 ADHD，其辨别力与内科常用临床工具相当，并且高于大多数以前的精神和神经学尝试。 失调。然而，它在中等收入环境中的使用需要谨慎。
AimFew personalised medicine investigations have been conducted for mental health. We aimed to generate and validate a risk tool that predicts adult attention-deficit/hyperactivity disorder (ADHD).MethodsUsing logistic regression models, we generated a risk tool in a representative population cohort (ALSPAC – UK, 5113 participants, followed from birth to age 17) using childhood clinical and sociodemographic data with internal validation. Predictors included sex, socioeconomic status, single-parent family, ADHD symptoms, comorbid disruptive disorders, childhood maltreatment, ADHD symptoms, depressive symptoms, mother's depression and intelligence quotient. The outcome was defined as a categorical diagnosis of ADHD in young adulthood without requiring age at onset criteria. We also tested Machine Learning approaches for developing the risk models: Random Forest, Stochastic Gradient Boosting and Artificial Neural Network. The risk tool was externally validated in the E-Risk cohort (UK, 2040 participants, birth to age 18), the 1993 Pelotas Birth Cohort (Brazil, 3911 participants, birth to age 18) and the MTA clinical sample (USA, 476 children with ADHD and 241 controls followed for 16 years from a minimum of 8 and a maximum of 26 years old).ResultsThe overall prevalence of adult ADHD ranged from 8.1 to 12% in the population-based samples, and was 28.6% in the clinical sample. The internal performance of the model in the generating sample was good, with an area under the curve (AUC) for predicting adult ADHD of 0.82 (95% confidence interval (CI) 0.79–0.83). Calibration plots showed good agreement between predicted and observed event frequencies from 0 to 60% probability. In the UK birth cohort test sample, the AUC was 0.75 (95% CI 0.71–0.78). In the Brazilian birth cohort test sample, the AUC was significantly lower –0.57 (95% CI 0.54–0.60). In the clinical trial test sample, the AUC was 0.76 (95% CI 0.73–0.80). The risk model did not predict adult anxiety or major depressive disorder. Machine Learning approaches did not outperform logistic regression models. An open-source and free risk calculator was generated for clinical use and is available online at https://ufrgs.br/prodah/adhd-calculator/.ConclusionsThe risk tool based on childhood characteristics specifically predicts adult ADHD in European and North-American population-based and clinical samples with comparable discrimination to commonly used clinical tools in internal medicine and higher than most previous attempts for mental and neurological disorders. However, its use in middle-income settings requires caution.