Social Determinants of Health (SDH) Research Centre, Research Institute for Health, Babol University of Medical Sciences, Babol, Iran.
Department of Epidemiology and Biostatistics, School of public health, Tehran University of Medical Sciences, Tehran, Iran.
BMC Med Res Methodol. 2020 Mar 11;20(1):56. doi: 10.1186/s12874-020-00934-y.
Attention deficit hyperactivity disorder (ADHD) is one of the most common childhood mental health disorders. Stimulant drugs as the most commonly used treatment and first-line therapy for ADHD have side effects. One of the newest approaches to select the best choices and optimize dosages of medications is personalized medicine.
This historical cohort study was carried out on the data taken from the period of 2008 to 2015. Eligible subjects were included in the study randomly. We used mixed-effects logistic regression models to personalize the dosage of Methylphenidate (MPH) in ADHD. The patients' heterogeneity was considered using subject-specific random effects, which are treated as the realizations of a stochastic process. To recommend a personalized dosage for a new patient, a two-step procedure was proposed. In the first step, we obtained estimates for population parameters. In the second step, the dosage of the drug for a new patient was updated at each follow-up.
Of the 221 children enrolled in the study, 169 (76.5%) were male and 52 (23.5%) were females. The overall mean age at the beginning of the study is 82.5 (± 26.5) months. In multivariable mixed logit model, three variables (severity of ADHD, time duration receiving MPH, and dosage of MPH) had a significant relationship with improvement. Based on this model the personalized dosage of MPH was obtained.
To determine the dosage of MPH for a new patient, the more the severity of baseline is, the more of an initial dose is required. To recommend the dose in the next times, first, the estimation of random coefficient should be updated. The optimum dose increased when the severity of ADHD increased. Also, the results show that the optimum dose of MPH as one proceeds through the period of treatment will decreased.
注意缺陷多动障碍（ADHD）是最常见的儿童心理健康障碍之一。兴奋剂药物作为 ADHD 的最常用治疗方法和一线治疗方法，具有副作用。选择最佳选择和优化药物剂量的最新方法之一是个性化医学。
本历史队列研究是在 2008 年至 2015 年期间的数据上进行的。随机纳入符合条件的受试者。我们使用混合效应逻辑回归模型对 ADHD 中哌醋甲酯（MPH）的剂量进行个体化。考虑到患者的异质性，使用个体特定的随机效应，将其视为随机过程的实现。为了为新患者推荐个性化剂量，提出了两步程序。在第一步中，我们获得了群体参数的估计值。在第二步中，在每次随访时更新新患者的药物剂量。
在 221 名入组的儿童中，169 名（76.5%）为男性，52 名（23.5%）为女性。研究开始时的总体平均年龄为 82.5（±26.5）个月。在多变量混合对数模型中，三个变量（ADHD 的严重程度、接受 MPH 的时间长短和 MPH 的剂量）与改善有显著关系。基于该模型获得了 MPH 的个性化剂量。
为确定新患者的 MPH 剂量，基线时的严重程度越高，初始剂量越高。为了在下一次推荐剂量，首先应更新随机系数的估计值。ADHD 严重程度增加时，最佳剂量增加。此外，结果表明，随着治疗期间的进展，MPH 的最佳剂量将减少。
