Data-driven identification of long-term glycemia clusters and their individualized predictors in Finnish patients with type 2 diabetes
Abstract 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 c...
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ftunivoulu:oai:oulu.fi:nbnfi-fe2023080994437 2023-09-05T13:20:47+02:00 Data-driven identification of long-term glycemia clusters and their individualized predictors in Finnish patients with type 2 diabetes Lavikainen, P. (Piia) Chandra, G. (Gunjan) Siirtola, P. (Pekka) Tamminen, S. (Satu) Ihalapathirana, A. T. (Anusha T.) Röning, J. (Juha) Laatikainen, T. (Tiina) Martikainen, J. (Janne) 2023 application/pdf http://urn.fi/urn:nbn:fi-fe2023080994437 eng eng Dove Medical Press info:eu-repo/grantAgreement/EC/H2020/825162/EU/Next Generation Health Technology Assessment to support patient-centred, societally oriented, real-time decision-making on access and reimbursement for health technologies throughout Europe/HTx info:eu-repo/semantics/openAccess © 2023 The Author(s). 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. 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://creativecommons.org/licenses/by-nc/3.0/ HbA1c SHAP cluster machine learning type 2 diabetes info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 2023 ftunivoulu 2023-08-23T22:59:35Z Abstract 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. Article in Journal/Newspaper karelia* Jultika - University of Oulu repository |
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Open Polar |
collection |
Jultika - University of Oulu repository |
op_collection_id |
ftunivoulu |
language |
English |
topic |
HbA1c SHAP cluster machine learning type 2 diabetes |
spellingShingle |
HbA1c SHAP cluster machine learning type 2 diabetes Lavikainen, P. (Piia) Chandra, G. (Gunjan) Siirtola, P. (Pekka) Tamminen, S. (Satu) Ihalapathirana, A. T. (Anusha T.) Röning, J. (Juha) Laatikainen, T. (Tiina) Martikainen, J. (Janne) Data-driven identification of long-term glycemia clusters and their individualized predictors in Finnish patients with type 2 diabetes |
topic_facet |
HbA1c SHAP cluster machine learning type 2 diabetes |
description |
Abstract 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 |
Article in Journal/Newspaper |
author |
Lavikainen, P. (Piia) Chandra, G. (Gunjan) Siirtola, P. (Pekka) Tamminen, S. (Satu) Ihalapathirana, A. T. (Anusha T.) Röning, J. (Juha) Laatikainen, T. (Tiina) Martikainen, J. (Janne) |
author_facet |
Lavikainen, P. (Piia) Chandra, G. (Gunjan) Siirtola, P. (Pekka) Tamminen, S. (Satu) Ihalapathirana, A. T. (Anusha T.) Röning, J. (Juha) Laatikainen, T. (Tiina) Martikainen, J. (Janne) |
author_sort |
Lavikainen, P. (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 Medical Press |
publishDate |
2023 |
url |
http://urn.fi/urn:nbn:fi-fe2023080994437 |
genre |
karelia* |
genre_facet |
karelia* |
op_relation |
info:eu-repo/grantAgreement/EC/H2020/825162/EU/Next Generation Health Technology Assessment to support patient-centred, societally oriented, real-time decision-making on access and reimbursement for health technologies throughout Europe/HTx |
op_rights |
info:eu-repo/semantics/openAccess © 2023 The Author(s). 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. 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://creativecommons.org/licenses/by-nc/3.0/ |
_version_ |
1776201426394939392 |