PDF(Pubmed)
Abstract：
Tracking trajectories of body size in children provides insight into chronic disease risk. One measure of pediatric body size is body mass index (BMI), a function of height and weight. Errors in measuring height or weight may lead to incorrect assessment of BMI. Yet childhood measures of height and weight extracted from electronic medical records often include values which seem biologically implausible in the context of a growth trajectory. Removing biologically implausible values reduces noise in the data, and thus increases the ease of modeling associations between exposures and childhood BMI trajectories, or between childhood BMI trajectories and subsequent health conditions. We developed open-source algorithms (available on github) for detecting and removing biologically implausible values in pediatric trajectories of height and weight. A Monte Carlo simulation experiment compared the sensitivity, specificity and speed of our algorithms to three published algorithms. The comparator algorithms were selected because they used trajectory information, had open-source code, and had published verification studies. Simulation inputs were derived from longitudinal epidemiological cohorts. Our algorithms had higher specificity, with similar sensitivity and speed, when compared to the three published algorithms. The results suggest that our algorithms should be adopted for cleaning longitudinal pediatric growth data.
摘要：
跟踪儿童身体大小的轨迹可以深入了解慢性疾病的风险。衡量小儿体型的一个指标是体重指数(BMI)，身高和体重的函数。测量身高或体重的错误可能导致对BMI的错误评估。然而，从电子医疗记录中提取的儿童身高和体重指标通常包括在成长轨迹背景下生物学上似乎不合理的值。删除生物学上不合理的值减少了数据中的噪声，从而增加了对暴露和儿童BMI轨迹之间的关联进行建模的便利性，或在儿童BMI轨迹和随后的健康状况之间。我们开发了开源算法（可在github上使用），用于检测和删除儿科身高和体重轨迹中的生物学不合理值。蒙特卡罗模拟实验比较了灵敏度，我们的算法对三种已发布算法的特异性和速度。选择比较器算法是因为它们使用了轨迹信息，有开源代码，并发表了验证研究报告。模拟输入来自纵向流行病学队列。我们的算法有更高的特异性，具有相似的灵敏度和速度，与已发布的三种算法相比。结果表明，应采用我们的算法来清理纵向儿科生长数据。
