Data-Driven Identification of Long-Term Glycemia Clusters and Their Individualized Predictors in Finnish Patients with Type 2 Diabetes
PURPOSE: To gain an understanding of the heterogeneous group of type 2 diabetes (T2D) patients, we aimed to identify patients with the homogenous long-term HbA1c trajectories and to predict the trajectory membership for each patient using explainable machine learning methods and different clinical-,...
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ftpubmed:oai:pubmedcentral.nih.gov:9829833 2023-05-15T17:00:22+02:00 Data-Driven Identification of Long-Term Glycemia Clusters and Their Individualized Predictors in Finnish Patients with Type 2 Diabetes Lavikainen, Piia Chandra, Gunjan Siirtola, Pekka Tamminen, Satu Ihalapathirana, Anusha T Röning, Juha Laatikainen, Tiina Martikainen, Janne 2023-01-05 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9829833/ https://doi.org/10.2147/CLEP.S380828 en eng Dove http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9829833/ http://dx.doi.org/10.2147/CLEP.S380828 © 2023 Lavikainen et al. https://creativecommons.org/licenses/by-nc/3.0/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/ (https://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. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php). CC-BY-NC Clin Epidemiol Original Research Text 2023 ftpubmed https://doi.org/10.2147/CLEP.S380828 2023-01-15T01:58:10Z PURPOSE: To gain an understanding of the heterogeneous group of type 2 diabetes (T2D) patients, we aimed to identify patients with the homogenous long-term HbA1c trajectories and to predict the trajectory membership for each patient using explainable machine learning methods and different clinical-, treatment-, and socio-economic-related predictors. PATIENTS AND METHODS: Electronic health records data covering primary and specialized healthcare on 9631 patients having T2D diagnosis were extracted from the North Karelia region, Finland. Six-year HbA1c trajectories were examined with growth mixture models. Linear discriminant analysis and neural networks were applied to predict the trajectory membership individually. RESULTS: Three HbA1c trajectories were distinguished over six years: “stable, adequate” (86.5%), “improving, but inadequate” (7.3%), and “fluctuating, inadequate” (6.2%) glycemic control. Prior glucose levels, duration of T2D, use of insulin only, use of insulin together with some oral antidiabetic medications, and use of only metformin were the most important predictors for the long-term treatment balance. The prediction model had a balanced accuracy of 85% and a receiving operating characteristic area under the curve of 91%, indicating high performance. Moreover, the results based on SHAP (Shapley additive explanations) values show that it is possible to explain the outcomes of machine learning methods at the population and individual levels. CONCLUSION: Heterogeneity in long-term glycemic control can be predicted with confidence by utilizing information from previous HbA1c levels, fasting plasma glucose, duration of T2D, and use of antidiabetic medications. In future, the expected development of HbA1c could be predicted based on the patient’s unique risk factors offering a practical tool for clinicians to support treatment planning. Text karelia* PubMed Central (PMC) Clinical Epidemiology Volume 15 13 29 |
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Original Research Lavikainen, Piia Chandra, Gunjan Siirtola, Pekka Tamminen, Satu Ihalapathirana, Anusha T Röning, Juha Laatikainen, Tiina Martikainen, Janne Data-Driven Identification of Long-Term Glycemia Clusters and Their Individualized Predictors in Finnish Patients with Type 2 Diabetes |
topic_facet |
Original Research |
description |
PURPOSE: To gain an understanding of the heterogeneous group of type 2 diabetes (T2D) patients, we aimed to identify patients with the homogenous long-term HbA1c trajectories and to predict the trajectory membership for each patient using explainable machine learning methods and different clinical-, treatment-, and socio-economic-related predictors. PATIENTS AND METHODS: Electronic health records data covering primary and specialized healthcare on 9631 patients having T2D diagnosis were extracted from the North Karelia region, Finland. Six-year HbA1c trajectories were examined with growth mixture models. Linear discriminant analysis and neural networks were applied to predict the trajectory membership individually. RESULTS: Three HbA1c trajectories were distinguished over six years: “stable, adequate” (86.5%), “improving, but inadequate” (7.3%), and “fluctuating, inadequate” (6.2%) glycemic control. Prior glucose levels, duration of T2D, use of insulin only, use of insulin together with some oral antidiabetic medications, and use of only metformin were the most important predictors for the long-term treatment balance. The prediction model had a balanced accuracy of 85% and a receiving operating characteristic area under the curve of 91%, indicating high performance. Moreover, the results based on SHAP (Shapley additive explanations) values show that it is possible to explain the outcomes of machine learning methods at the population and individual levels. CONCLUSION: Heterogeneity in long-term glycemic control can be predicted with confidence by utilizing information from previous HbA1c levels, fasting plasma glucose, duration of T2D, and use of antidiabetic medications. In future, the expected development of HbA1c could be predicted based on the patient’s unique risk factors offering a practical tool for clinicians to support treatment planning. |
format |
Text |
author |
Lavikainen, Piia Chandra, Gunjan Siirtola, Pekka Tamminen, Satu Ihalapathirana, Anusha T Röning, Juha Laatikainen, Tiina Martikainen, Janne |
author_facet |
Lavikainen, Piia Chandra, Gunjan Siirtola, Pekka Tamminen, Satu Ihalapathirana, Anusha T Röning, Juha Laatikainen, Tiina Martikainen, Janne |
author_sort |
Lavikainen, Piia |
title |
Data-Driven Identification of Long-Term Glycemia Clusters and Their Individualized Predictors in Finnish Patients with Type 2 Diabetes |
title_short |
Data-Driven Identification of Long-Term Glycemia Clusters and Their Individualized Predictors in Finnish Patients with Type 2 Diabetes |
title_full |
Data-Driven Identification of Long-Term Glycemia Clusters and Their Individualized Predictors in Finnish Patients with Type 2 Diabetes |
title_fullStr |
Data-Driven Identification of Long-Term Glycemia Clusters and Their Individualized Predictors in Finnish Patients with Type 2 Diabetes |
title_full_unstemmed |
Data-Driven Identification of Long-Term Glycemia Clusters and Their Individualized Predictors in Finnish Patients with Type 2 Diabetes |
title_sort |
data-driven identification of long-term glycemia clusters and their individualized predictors in finnish patients with type 2 diabetes |
publisher |
Dove |
publishDate |
2023 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9829833/ https://doi.org/10.2147/CLEP.S380828 |
genre |
karelia* |
genre_facet |
karelia* |
op_source |
Clin Epidemiol |
op_relation |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9829833/ http://dx.doi.org/10.2147/CLEP.S380828 |
op_rights |
© 2023 Lavikainen et al. https://creativecommons.org/licenses/by-nc/3.0/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/ (https://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. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php). |
op_rightsnorm |
CC-BY-NC |
op_doi |
https://doi.org/10.2147/CLEP.S380828 |
container_title |
Clinical Epidemiology |
container_volume |
Volume 15 |
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13 |
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29 |
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1766053021191503872 |