Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119077, Singapore.
Department of Paediatrics, Khoo Teck Puat-National University Children's Medical Institute, National University Health System, Singapore 119228, Singapore.
Int J Environ Res Public Health. 2022 Jul 27;19(15):9195. doi: 10.3390/ijerph19159195.
We aimed to identify subgroups of young children with differential risks for ADHD, and cross-validate these subgroups with an independent sample of children. All children in Study 1 (N = 120) underwent psychological assessments and were diagnosed with ADHD before age 7. Latent class analysis (LCA) classified children into risk subgroups. Study 2 (N = 168) included an independent sample of children under age 7. A predictive model from Study 1 was applied to Study 2. The latent class analyses in Study 1 indicated preference of a 3-class solution (BIC = 3807.70, p < 0.001). Maternal education, income-to-needs ratio, and family history of psychopathology, defined class membership more strongly than child factors. An almost identical LCA structure from Study 1 was replicated in Study 2 (BIC = 5108.01, p < 0.001). Indices of sensitivity (0.913, 95% C.I. 0.814−0.964) and specificity (0.788, 95% C.I. 0.692−0.861) were high across studies. It is concluded that the classifications represent valid combinations of child, parent, and family characteristics that are predictive of ADHD in young children.
我们旨在确定具有不同 ADHD 风险的幼儿亚组，并使用另一组独立的儿童样本对这些亚组进行交叉验证。研究 1（N=120）中的所有儿童都接受了心理评估，并在 7 岁之前被诊断出患有 ADHD。潜在类别分析（LCA）将儿童分为风险亚组。研究 2（N=168）包括 7 岁以下的一组独立儿童。研究 1 中的预测模型被应用于研究 2。研究 1 中的潜在类别分析表明，倾向于采用 3 类解决方案（BIC=3807.70，p<0.001）。母亲的教育程度、收入与需求比以及家庭精神病史比儿童因素更能定义类别成员。研究 1 中的几乎相同的 LCA 结构在研究 2 中得到了复制（BIC=5108.01，p<0.001）。在两个研究中，敏感性指数（0.913，95%置信区间 0.814−0.964）和特异性指数（0.788，95%置信区间 0.692−0.861）都很高。结论是，这些分类代表了具有预测幼儿 ADHD 能力的儿童、父母和家庭特征的有效组合。
