Cargando…
The internal validation of weight and weight change coding using weight measurement data within the UK primary care Electronic Health Record
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 betwee...
Autores principales: | , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Dove Medical Press
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6354686/ https://www.ncbi.nlm.nih.gov/pubmed/30774449 http://dx.doi.org/10.2147/CLEP.S189989 |
_version_ | 1783391218168758272 |
---|---|
author | Nicholson, Brian D Aveyard, Paul Hamilton, Willie Bankhead, Clare R Koshiaris, Constantinos Stevens, Sarah Hobbs, Frederick DR Perera, Rafael |
author_facet | Nicholson, Brian D Aveyard, Paul Hamilton, Willie Bankhead, Clare R Koshiaris, Constantinos Stevens, Sarah Hobbs, Frederick DR Perera, Rafael |
author_sort | Nicholson, Brian D |
collection | PubMed |
description | 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/m(2) to 46.9 kg/m(2) and from 17.4 kg/m(2) to 19.2 kg/m(2) 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. |
format | Online Article Text |
id | pubmed-6354686 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Dove Medical Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-63546862019-02-15 The internal validation of weight and weight change coding using weight measurement data within the UK primary care Electronic Health Record Nicholson, Brian D Aveyard, Paul Hamilton, Willie Bankhead, Clare R Koshiaris, Constantinos Stevens, Sarah Hobbs, Frederick DR Perera, Rafael Clin Epidemiol Original Research 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/m(2) to 46.9 kg/m(2) and from 17.4 kg/m(2) to 19.2 kg/m(2) 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. Dove Medical Press 2019-01-25 /pmc/articles/PMC6354686/ /pubmed/30774449 http://dx.doi.org/10.2147/CLEP.S189989 Text en © 2019 Nicholson et al. This work is published and licensed by Dove Medical Press Limited The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. |
spellingShingle | Original Research Nicholson, Brian D Aveyard, Paul Hamilton, Willie Bankhead, Clare R Koshiaris, Constantinos Stevens, Sarah Hobbs, Frederick DR Perera, Rafael The internal validation of weight and weight change coding using weight measurement data within the UK primary care Electronic Health Record |
title | The internal validation of weight and weight change coding using weight measurement data within the UK primary care Electronic Health Record |
title_full | The internal validation of weight and weight change coding using weight measurement data within the UK primary care Electronic Health Record |
title_fullStr | The internal validation of weight and weight change coding using weight measurement data within the UK primary care Electronic Health Record |
title_full_unstemmed | The internal validation of weight and weight change coding using weight measurement data within the UK primary care Electronic Health Record |
title_short | The internal validation of weight and weight change coding using weight measurement data within the UK primary care Electronic Health Record |
title_sort | internal validation of weight and weight change coding using weight measurement data within the uk primary care electronic health record |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6354686/ https://www.ncbi.nlm.nih.gov/pubmed/30774449 http://dx.doi.org/10.2147/CLEP.S189989 |
work_keys_str_mv | AT nicholsonbriand theinternalvalidationofweightandweightchangecodingusingweightmeasurementdatawithintheukprimarycareelectronichealthrecord AT aveyardpaul theinternalvalidationofweightandweightchangecodingusingweightmeasurementdatawithintheukprimarycareelectronichealthrecord AT hamiltonwillie theinternalvalidationofweightandweightchangecodingusingweightmeasurementdatawithintheukprimarycareelectronichealthrecord AT bankheadclarer theinternalvalidationofweightandweightchangecodingusingweightmeasurementdatawithintheukprimarycareelectronichealthrecord AT koshiarisconstantinos theinternalvalidationofweightandweightchangecodingusingweightmeasurementdatawithintheukprimarycareelectronichealthrecord AT stevenssarah theinternalvalidationofweightandweightchangecodingusingweightmeasurementdatawithintheukprimarycareelectronichealthrecord AT hobbsfrederickdr theinternalvalidationofweightandweightchangecodingusingweightmeasurementdatawithintheukprimarycareelectronichealthrecord AT pererarafael theinternalvalidationofweightandweightchangecodingusingweightmeasurementdatawithintheukprimarycareelectronichealthrecord AT nicholsonbriand internalvalidationofweightandweightchangecodingusingweightmeasurementdatawithintheukprimarycareelectronichealthrecord AT aveyardpaul internalvalidationofweightandweightchangecodingusingweightmeasurementdatawithintheukprimarycareelectronichealthrecord AT hamiltonwillie internalvalidationofweightandweightchangecodingusingweightmeasurementdatawithintheukprimarycareelectronichealthrecord AT bankheadclarer internalvalidationofweightandweightchangecodingusingweightmeasurementdatawithintheukprimarycareelectronichealthrecord AT koshiarisconstantinos internalvalidationofweightandweightchangecodingusingweightmeasurementdatawithintheukprimarycareelectronichealthrecord AT stevenssarah internalvalidationofweightandweightchangecodingusingweightmeasurementdatawithintheukprimarycareelectronichealthrecord AT hobbsfrederickdr internalvalidationofweightandweightchangecodingusingweightmeasurementdatawithintheukprimarycareelectronichealthrecord AT pererarafael internalvalidationofweightandweightchangecodingusingweightmeasurementdatawithintheukprimarycareelectronichealthrecord |