The internal validation of weight and weight change coding using weight measurement data within the UK primary care Electronic Health Record
Received 9 October 2018
Accepted for publication 21 December 2018
Published 25 January 2019 Volume 2019:11 Pages 145—155
Checked for plagiarism Yes
Review by Single-blind
Peer reviewers approved by Dr Justinn Cochran
Peer reviewer comments 4
Editor who approved publication: Professor Vera Ehrenstein
Brian D Nicholson,1 Paul Aveyard,1 Willie Hamilton,2 Clare R Bankhead,1 Constantinos Koshiaris,1 Sarah Stevens,1 Frederick DR Hobbs,1 Rafael Perera1
1Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX26GG, UK; 2College of Medicine and Health, University of Exeter, Exeter EX1 2LU, UK
Purpose: To use recorded weight values to internally validate weight status and weight change coding in the primary care Electronic Health Record (EHR).
Patients and methods: 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.
Results: 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/m2 to 46.9 kg/m2 and from 17.4 kg/m2 to 19.2 kg/m2 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.
Conclusion: 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.
Keywords: validation studies, electronic health records, body weight, weight loss, weight gain, primary health care, data quality
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