Development of a three-dimensional scoring model for the assessment of continuous glucose monitoring data in type 1 diabetes
Introduction Despite the improvements in diabetes management by continuous glucose monitoring (CGM) it is difficult to capture the complexity of CGM data in one metric. We aimed to develop a clinically relevant multidimensional scoring model with the capacity to identify the most alarming CGM episod...
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BMJ Publishing Group
2024-09-01
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| Series: | BMJ Open Diabetes Research & Care |
| Online Access: | https://drc.bmj.com/content/12/4/e004350.full |
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| author | Markus Lundgren Daniel Espes Per-Ola Carlsson Henrik Hill José Caballero-Corbalan Jeanie Dawnbringer Lars Cederblad |
| author_facet | Markus Lundgren Daniel Espes Per-Ola Carlsson Henrik Hill José Caballero-Corbalan Jeanie Dawnbringer Lars Cederblad |
| author_sort | Markus Lundgren |
| collection | DOAJ |
| description | Introduction Despite the improvements in diabetes management by continuous glucose monitoring (CGM) it is difficult to capture the complexity of CGM data in one metric. We aimed to develop a clinically relevant multidimensional scoring model with the capacity to identify the most alarming CGM episodes and/or patients from a large cohort.Research design and methods Retrospective CGM data from 2017 to 2020 available in electronic medical records were collected from n=613 individuals with type 1 diabetes (total 82 114 days). A scoring model was developed based on three metrics; glycemic variability percentage, low blood glucose index and high blood glucose index. Values for each dimension were normalized to a numeric score between 0–100. To identify the most representative score for an extended time period, multiple ways to combine the mean score of each dimension were evaluated. Correlations of the scoring model with CGM metrics were computed. The scoring model was compared with interpretations of a clinical expert board (CEB).Results The dimension of hypoglycemia must be weighted to be representative, whereas the other two can be represented by their overall mean. The scoring model correlated well with established CGM metrics. Applying a score of ≥80 as the cut-off for identifying time periods with a ‘true’ target fulfillment (ie, reaching all targets for CGM metrics) resulted in an accuracy of 93.4% and a specificity of 97.1%. The accuracy of the scoring model when compared with the CEB was high for identifying the most alarming CGM curves within each dimension of glucose control (overall 86.5%).Conclusions Our scoring model captures the complexity of CGM data and can identify both the most alarming dimension of glycemia and the individuals in most urgent need of assistance. This could become a valuable tool for population management at diabetes clinics to enable healthcare providers to stratify care to the patients in greatest need of clinical attention. |
| format | Article |
| id | doaj-art-9305687716354d2aae017f1235ed699c |
| institution | Kabale University |
| issn | 2052-4897 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open Diabetes Research & Care |
| spelling | doaj-art-9305687716354d2aae017f1235ed699c2024-12-16T23:15:09ZengBMJ Publishing GroupBMJ Open Diabetes Research & Care2052-48972024-09-0112410.1136/bmjdrc-2024-004350Development of a three-dimensional scoring model for the assessment of continuous glucose monitoring data in type 1 diabetesMarkus Lundgren0Daniel Espes1Per-Ola Carlsson2Henrik Hill3José Caballero-Corbalan4Jeanie Dawnbringer5Lars Cederblad6Department of Clinical Sciences Malmö, Lund University, Malmö, SwedenScience for Life Laboratory, Department of Medical Sciences, Uppsala University, Uppsala, SwedenDepartment of Medical Sciences, Uppsala University, Uppsala, SwedenDepartment of Women’s and Children’s Health, Uppsala University, Uppsala, SwedenDepartment of Medical Sciences, Uppsala University, Uppsala, SwedenOneTwo Analytics AB, Stockholm, SwedenOneTwo Analytics AB, Stockholm, SwedenIntroduction Despite the improvements in diabetes management by continuous glucose monitoring (CGM) it is difficult to capture the complexity of CGM data in one metric. We aimed to develop a clinically relevant multidimensional scoring model with the capacity to identify the most alarming CGM episodes and/or patients from a large cohort.Research design and methods Retrospective CGM data from 2017 to 2020 available in electronic medical records were collected from n=613 individuals with type 1 diabetes (total 82 114 days). A scoring model was developed based on three metrics; glycemic variability percentage, low blood glucose index and high blood glucose index. Values for each dimension were normalized to a numeric score between 0–100. To identify the most representative score for an extended time period, multiple ways to combine the mean score of each dimension were evaluated. Correlations of the scoring model with CGM metrics were computed. The scoring model was compared with interpretations of a clinical expert board (CEB).Results The dimension of hypoglycemia must be weighted to be representative, whereas the other two can be represented by their overall mean. The scoring model correlated well with established CGM metrics. Applying a score of ≥80 as the cut-off for identifying time periods with a ‘true’ target fulfillment (ie, reaching all targets for CGM metrics) resulted in an accuracy of 93.4% and a specificity of 97.1%. The accuracy of the scoring model when compared with the CEB was high for identifying the most alarming CGM curves within each dimension of glucose control (overall 86.5%).Conclusions Our scoring model captures the complexity of CGM data and can identify both the most alarming dimension of glycemia and the individuals in most urgent need of assistance. This could become a valuable tool for population management at diabetes clinics to enable healthcare providers to stratify care to the patients in greatest need of clinical attention.https://drc.bmj.com/content/12/4/e004350.full |
| spellingShingle | Markus Lundgren Daniel Espes Per-Ola Carlsson Henrik Hill José Caballero-Corbalan Jeanie Dawnbringer Lars Cederblad Development of a three-dimensional scoring model for the assessment of continuous glucose monitoring data in type 1 diabetes BMJ Open Diabetes Research & Care |
| title | Development of a three-dimensional scoring model for the assessment of continuous glucose monitoring data in type 1 diabetes |
| title_full | Development of a three-dimensional scoring model for the assessment of continuous glucose monitoring data in type 1 diabetes |
| title_fullStr | Development of a three-dimensional scoring model for the assessment of continuous glucose monitoring data in type 1 diabetes |
| title_full_unstemmed | Development of a three-dimensional scoring model for the assessment of continuous glucose monitoring data in type 1 diabetes |
| title_short | Development of a three-dimensional scoring model for the assessment of continuous glucose monitoring data in type 1 diabetes |
| title_sort | development of a three dimensional scoring model for the assessment of continuous glucose monitoring data in type 1 diabetes |
| url | https://drc.bmj.com/content/12/4/e004350.full |
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