METHODS: We developed reference charts for adult heights and weights that were stratified on participant sex. Our analysis included 4,076,534 height and 5,207,328 wt measurements from ∼ 150,000 participants. Errors were identified using modified standard deviation scores, differences from their expected values, and significant changes between consecutive measurements. We evaluated our method with chart-reviewed heights (8,092) and weights (9,039) from 250 randomly selected participants and compared it with the current cleaning algorithm in All of Us.
RESULTS: The proposed algorithm classified 1.4 % of height and 1.5 % of weight errors in the full cohort. Sensitivity was 90.4 % (95 % CI: 79.0-96.8 %) for heights and 65.9 % (95 % CI: 56.9-74.1 %) for weights. Precision was 73.4 % (95 % CI: 60.9-83.7 %) for heights and 62.9 (95 % CI: 54.0-71.1 %) for weights. In comparison, the current cleaning algorithm has inferior performance in sensitivity (55.8 %) and precision (16.5 %) for height errors while having higher precision (94.0 %) and lower sensitivity (61.9 %) for weight errors.
CONCLUSIONS: Our proposed algorithm outperformed in detecting height errors compared to weights. It can serve as a valuable addition to the current All of Us cleaning algorithm for identifying erroneous height values.
方法:我们开发了成人身高和体重的参考图表,并根据参与者的性别进行分层。我们的分析包括4,076,534个身高和5,207,328个重量的测量值,来自150,000名参与者。使用修改后的标准偏差分数识别错误,与预期值的差异,以及连续测量之间的显著变化。我们使用来自250名随机选择的参与者的图表审查高度(8,092)和重量(9,039)评估了我们的方法,并将其与“我们所有人”中的当前清洁算法进行了比较。
结果:所提出的算法对整个队列中身高的1.4%和体重的1.5%进行了分类。身高敏感性为90.4%(95%CI:79.0-96.8%),体重敏感性为65.9%(95%CI:56.9-74.1%)。身高的精确度为73.4%(95%CI:60.9-83.7%),体重的精确度为62.9(95%CI:54.0-71.1%)。相比之下,当前的清洁算法在高度误差的灵敏度(55.8%)和精度(16.5%)方面表现较差,而在重量误差方面具有较高的精度(94.0%)和较低的灵敏度(61.9%)。
结论:我们提出的算法在检测身高误差方面优于权重。它可以作为当前AllofUs清洁算法的有价值的补充,用于识别错误的高度值。