Nicholson Brian D, Aveyard Paul, Hamilton Willie, Bankhead Clare R, Koshiaris Constantinos, Stevens Sarah, Hobbs Frederick Dr, Perera Rafael
Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX26GG, UK,
College of Medicine and Health, University of Exeter, Exeter EX1 2LU, UK.
Clin Epidemiol. 2019 Jan 25;11:145-155. doi: 10.2147/CLEP.S189989. eCollection 2019.
To use recorded weight values to internally validate weight status and weight change coding in the primary care Electronic Health Record (EHR).
We included adult patients with weight-related Read codes recorded in the UK's Clinical Practice Research Datalink EHR between 2000 and 2017. Weight status codes were compared to weight values recorded on the same day and positive predictive values (PPVs) were calculated for commonly used codes. Weight change codes were validated using three methods: the percentage (%) difference in kilograms at the time of the code and 1) the previous weight measurement, 2) the weight predicted using linear regression, and 3) the historic mean weight. Weight change codes were validated if estimates were consistent across two out of three methods.
A total of 8,108,481 weight codes were recorded in 1,000,002 patients' EHR. Twice as many were recorded in females (n=5,208,593, 64%). The mean body mass index for "overweight" codes ranged from 31.9 kg/m to 46.9 kg/m and from 17.4 kg/m to 19.2 kg/m for "underweight" codes. PPVs for the most commonly used weight status codes ranged from 81.3% (80%-82.5%) to 99.3% (99.2%-99.4%). Across the estimation methods, and using only validated weight change codes, mean weight loss ranged from - 5.2% (SD 5.8%) to -7.9% (SD 7.3%) and mean weight gain from 4.2 % (SD 5.5%) to 7.9 % (SD 8.2%). The previous and predicted weight methods were most consistent.
We have developed an internationally applicable methodology to internally validate weight-related EHR coding by using available weight measurement data. We demonstrate the UK Read codes that can be confidently used to classify weight status and weight change in the absence of weight values. We provide the first evidence from primary care that a Read code for unexpected weight loss represents a mean loss of ≥ 5 % in a 6-month period, which was broadly consistent across age groups and gender.
利用记录的体重值对基层医疗电子健康记录（EHR）中的体重状况和体重变化编码进行内部验证。
我们纳入了2000年至2017年间在英国临床实践研究数据链EHR中记录有与体重相关的Read编码的成年患者。将体重状况编码与同一天记录的体重值进行比较，并计算常用编码的阳性预测值（PPV）。使用三种方法对体重变化编码进行验证：编码时千克数的百分比（%）差异与1）前一次体重测量值、2）使用线性回归预测的体重以及3）历史平均体重。如果三种方法中的两种方法的估计结果一致，则体重变化编码有效。
在1,000,002名患者的EHR中总共记录了8,108,481个体重编码。女性记录的编码数量是男性的两倍（n = 5,208,593，64%）。“超重”编码的平均体重指数范围为31.9 kg/m至46.9 kg/m，“体重过轻”编码的平均体重指数范围为17.4 kg/m至19.2 kg/m。最常用的体重状况编码的PPV范围为81.3%（80%-82.5%）至99.3%（99.2%-99.4%）。在各种估计方法中，仅使用经过验证的体重变化编码，平均体重减轻范围为-5.2%（标准差5.8%）至-7.9%（标准差7.3%），平均体重增加范围为4.2%（标准差5.5%）至7.9%（标准差8.2%）。前一次体重和预测体重方法最为一致。
我们开发了一种国际适用的方法，通过使用可用的体重测量数据对与体重相关的EHR编码进行内部验证。我们证明了在没有体重值的情况下，可以放心地使用英国Read编码对体重状况和体重变化进行分类。我们提供了基层医疗的首个证据，即意外体重减轻的Read编码表示在6个月内平均体重减轻≥5%，这在各年龄组和性别中大致一致。
