Faedda Gianni L, Ohashi Kyoko, Hernandez Mariely, McGreenery Cynthia E, Grant Marie C, Baroni Argelinda, Polcari Ann, Teicher Martin H
Mood Disorders Center 'Lucio Bini', New York, NY, USA.
Department of Child and Adolescent Psychiatry, NYU Medical Center, New York, NY, USA.
J Child Psychol Psychiatry. 2016 Jun;57(6):706-16. doi: 10.1111/jcpp.12520. Epub 2016 Jan 22.
Distinguishing pediatric bipolar disorder (BD) from attention-deficit hyperactivity disorder (ADHD) can be challenging. Hyperactivity is a core feature of both disorders, but severely disturbed sleep and circadian dysregulation are more characteristic of BD, at least in adults. We tested the hypothesis that objective measures of activity, sleep, and circadian rhythms would help differentiate pediatric subjects with BD from ADHD and typically developing controls.
Unmedicated youths (N = 155, 97 males, age 5-18) were diagnosed using DSM-IV criteria with Kiddie-SADS PL/E. BD youths (n = 48) were compared to typically developing controls (n = 42) and children with ADHD (n = 44) or ADHD plus comorbid depressive disorders (n = 21). Three-to-five days of minute-to-minute belt-worn actigraph data (Ambulatory Monitoring Inc.), collected during the school week, were processed to yield 28 metrics per subject, and assessed for group differences with analysis of covariance. Cross-validated machine learning algorithms were used to determine the predictive accuracy of a four-parameter model, with measures reflecting sleep, hyperactivity, and circadian dysregulation, plus Indic's bipolar vulnerability index (VI).
There were prominent group differences in several activity measures, notably mean 5 lowest hours of activity, skewness of diurnal activity, relative circadian amplitude, and VI. A predictive support vector machine model discriminated bipolar from non-bipolar with mean accuracy of 83.1 ± 5.4%, ROC area of 0.781 ± 0.071, kappa of 0.587 ± 0.136, specificity of 91.7 ± 5.3%, and sensitivity of 64.4 ± 13.6%.
Objective measures of sleep, circadian rhythmicity, and hyperactivity were abnormal in BD. Wearable sensor technology may provide bio-behavioral markers that can help differentiate children with BD from ADHD and healthy controls.
区分儿童双相情感障碍（BD）和注意力缺陷多动障碍（ADHD）具有挑战性。多动是这两种疾病的核心特征，但严重的睡眠障碍和昼夜节律失调在双相情感障碍中更为典型，至少在成年人中如此。我们检验了这样一个假设，即活动、睡眠和昼夜节律的客观测量指标将有助于区分患有双相情感障碍的儿童与患有注意力缺陷多动障碍的儿童以及正常发育的对照儿童。
使用《精神疾病诊断与统计手册》第四版（DSM-IV）标准和儿童版情感障碍及精神分裂症问卷（Kiddie-SADS PL/E）对未用药的青少年（N = 155，97名男性，年龄5 - 18岁）进行诊断。将双相情感障碍青少年（n = 48）与正常发育的对照儿童（n = 42）以及患有注意力缺陷多动障碍的儿童（n = 44）或患有注意力缺陷多动障碍合并共病抑郁障碍的儿童（n = 21）进行比较。对在上学周收集的三到五天的逐分钟佩戴式活动记录仪数据（动态监测公司）进行处理，以得出每个受试者28个指标，并通过协方差分析评估组间差异。使用交叉验证的机器学习算法来确定一个四参数模型的预测准确性，该模型的测量指标反映睡眠、多动和昼夜节律失调，再加上印地语双相情感障碍易感性指数（VI）。
在几项活动测量指标上存在显著的组间差异，特别是平均活动量最低的5个小时、日间活动的偏度、相对昼夜振幅和VI。一个预测性支持向量机模型区分双相情感障碍与非双相情感障碍的平均准确率为83.1 ± 5.4%，ROC面积为0.781 ± 0.071，kappa值为0.587 ± 0.136，特异性为91.7 ± 5.3%，敏感性为64.4 ± 13.6%。
双相情感障碍患者的睡眠、昼夜节律和多动的客观测量指标异常。可穿戴传感器技术可能提供生物行为标志物，有助于区分患有双相情感障碍的儿童与患有注意力缺陷多动障碍的儿童以及健康对照儿童。
