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Real‐world practice level data analysis confirms link between variability within Blood Glucose Monitoring Strip (BGMS) and glycosylated haemoglobin (HbA1c) in Type 1 Diabetes
AIMS/HYPOTHESIS: Our aim was to quantify the impact of Blood Glucose Monitoring Strips variability (BGMSV) at GP practice level on the variability of reported glycated haemoglobin (HbA1cV) levels. METHODS: Overall GP Practice BGMSV and HbA1cV were calculated from the quantity of main types of BGMS b...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6766879/ https://www.ncbi.nlm.nih.gov/pubmed/30168887 http://dx.doi.org/10.1111/ijcp.13252 |
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author | Heald, Adrian H. Livingston, Mark Fryer, Anthony Cortes, Gabriela Anderson, Simon G. Gadsby, Roger Laing, Ian Lunt, Mark Young, Robert J. Stedman, Mike |
author_facet | Heald, Adrian H. Livingston, Mark Fryer, Anthony Cortes, Gabriela Anderson, Simon G. Gadsby, Roger Laing, Ian Lunt, Mark Young, Robert J. Stedman, Mike |
author_sort | Heald, Adrian H. |
collection | PubMed |
description | AIMS/HYPOTHESIS: Our aim was to quantify the impact of Blood Glucose Monitoring Strips variability (BGMSV) at GP practice level on the variability of reported glycated haemoglobin (HbA1cV) levels. METHODS: Overall GP Practice BGMSV and HbA1cV were calculated from the quantity of main types of BGMS being prescribed combined with the published accuracy, as % results within ±% bands from reference value for the selected strip type. The regression coefficient between the BGMSV and HbA1cV was calculated. To allow for the aggregation of estimated three tests/day over 13 weeks (ie, 300 samples) of actual Blood Glucose (BG) values up to the HbA1c, we multiplied HbA1cV coefficient by √300 to estimate an empirical value for impact of BGMSV on BGV. RESULTS: Four thousand five hundred and twenty‐four practice years with 159 700 T1DM patient years where accuracy data were available for more than 80% of strips prescribed were included, with overall BGMSV 6.5% and HbA1c mean of 66.9 mmol/mol (8.3%) with variability of 13 mmol/mol equal to 19% of the mean. At a GP practice level, BGMSV and HbA1cV as % of mean HbA1c (in other words, the spread of HbA1c) were closely related with a regression coefficient of 0.176, P < 0.001. Thus, greater variability in the BGMS at a GP practice level resulted in a greater spread of HbA1C readings in T1DM patients. Applying this factor for BGMS to the national ISO accepted standard where 95% results must be ≤±15% from reference, revealed that for BG, 95% results would be ≤±45% from the reference value. Thus, the variation in BG is three times that of the BGMS. For a patient with BG target @10 mmol/L using the worst performing ISO standard strips, on 1/20 occasions (average 1/week) actual blood glucose value could be >±4.5 mmol/L from target, compared with the best performing BGMS with BG >±2.2 mmol/L from reference on 1/20 occasions. CONCLUSION: Use of more variable/less accurate BGMS is associated both theoretically and in practice with a larger variability in measured BG and HbA1c, with implications for patient confidence in their day‐to‐day monitoring experience. |
format | Online Article Text |
id | pubmed-6766879 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-67668792019-10-01 Real‐world practice level data analysis confirms link between variability within Blood Glucose Monitoring Strip (BGMS) and glycosylated haemoglobin (HbA1c) in Type 1 Diabetes Heald, Adrian H. Livingston, Mark Fryer, Anthony Cortes, Gabriela Anderson, Simon G. Gadsby, Roger Laing, Ian Lunt, Mark Young, Robert J. Stedman, Mike Int J Clin Pract Endocrinology AIMS/HYPOTHESIS: Our aim was to quantify the impact of Blood Glucose Monitoring Strips variability (BGMSV) at GP practice level on the variability of reported glycated haemoglobin (HbA1cV) levels. METHODS: Overall GP Practice BGMSV and HbA1cV were calculated from the quantity of main types of BGMS being prescribed combined with the published accuracy, as % results within ±% bands from reference value for the selected strip type. The regression coefficient between the BGMSV and HbA1cV was calculated. To allow for the aggregation of estimated three tests/day over 13 weeks (ie, 300 samples) of actual Blood Glucose (BG) values up to the HbA1c, we multiplied HbA1cV coefficient by √300 to estimate an empirical value for impact of BGMSV on BGV. RESULTS: Four thousand five hundred and twenty‐four practice years with 159 700 T1DM patient years where accuracy data were available for more than 80% of strips prescribed were included, with overall BGMSV 6.5% and HbA1c mean of 66.9 mmol/mol (8.3%) with variability of 13 mmol/mol equal to 19% of the mean. At a GP practice level, BGMSV and HbA1cV as % of mean HbA1c (in other words, the spread of HbA1c) were closely related with a regression coefficient of 0.176, P < 0.001. Thus, greater variability in the BGMS at a GP practice level resulted in a greater spread of HbA1C readings in T1DM patients. Applying this factor for BGMS to the national ISO accepted standard where 95% results must be ≤±15% from reference, revealed that for BG, 95% results would be ≤±45% from the reference value. Thus, the variation in BG is three times that of the BGMS. For a patient with BG target @10 mmol/L using the worst performing ISO standard strips, on 1/20 occasions (average 1/week) actual blood glucose value could be >±4.5 mmol/L from target, compared with the best performing BGMS with BG >±2.2 mmol/L from reference on 1/20 occasions. CONCLUSION: Use of more variable/less accurate BGMS is associated both theoretically and in practice with a larger variability in measured BG and HbA1c, with implications for patient confidence in their day‐to‐day monitoring experience. John Wiley and Sons Inc. 2018-08-31 2018-12 /pmc/articles/PMC6766879/ /pubmed/30168887 http://dx.doi.org/10.1111/ijcp.13252 Text en © 2018 The Authors. International Journal of Clinical Practice published by John Wiley & Sons Ltd. This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. |
spellingShingle | Endocrinology Heald, Adrian H. Livingston, Mark Fryer, Anthony Cortes, Gabriela Anderson, Simon G. Gadsby, Roger Laing, Ian Lunt, Mark Young, Robert J. Stedman, Mike Real‐world practice level data analysis confirms link between variability within Blood Glucose Monitoring Strip (BGMS) and glycosylated haemoglobin (HbA1c) in Type 1 Diabetes |
title | Real‐world practice level data analysis confirms link between variability within Blood Glucose Monitoring Strip (BGMS) and glycosylated haemoglobin (HbA1c) in Type 1 Diabetes |
title_full | Real‐world practice level data analysis confirms link between variability within Blood Glucose Monitoring Strip (BGMS) and glycosylated haemoglobin (HbA1c) in Type 1 Diabetes |
title_fullStr | Real‐world practice level data analysis confirms link between variability within Blood Glucose Monitoring Strip (BGMS) and glycosylated haemoglobin (HbA1c) in Type 1 Diabetes |
title_full_unstemmed | Real‐world practice level data analysis confirms link between variability within Blood Glucose Monitoring Strip (BGMS) and glycosylated haemoglobin (HbA1c) in Type 1 Diabetes |
title_short | Real‐world practice level data analysis confirms link between variability within Blood Glucose Monitoring Strip (BGMS) and glycosylated haemoglobin (HbA1c) in Type 1 Diabetes |
title_sort | real‐world practice level data analysis confirms link between variability within blood glucose monitoring strip (bgms) and glycosylated haemoglobin (hba1c) in type 1 diabetes |
topic | Endocrinology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6766879/ https://www.ncbi.nlm.nih.gov/pubmed/30168887 http://dx.doi.org/10.1111/ijcp.13252 |
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