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 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.

Keywords:

• type 2 diabetes
• cluster
• HbA1c
• machine learning
• SHAP

Abbreviations

ATC, Anatomical Therapeutic Chemical; AUC, area under the curve; BIC, Bayesian Information Criteria; BMI, body mass index; CI, confidence interval; CTS, clinical, treatment and socio-economic status; EHR, electronic health records; FPR, Finnish Prescription Register; GMM, growth mixture model; HbA1c, glycated hemoglobin A1c; ICD, International Classification of Diseases; IFCC, International Federation of Clinical Chemistry; LDA, linear discriminant analysis; NN, neural network; OAD, oral antidiabetic drugs or GLP-1 analogues (incl. metformin, sulfonylureas, combinations of oral blood glucose lowering drugs, glitazones, DPP-4 inhibitors, glinides, GLP-1 analogues, and SGLT2 inhibitors); ROC, receiver operating characteristic; SES, socio-economic status; SHAP, Shapley additive explanations; SII, Social Insurance Institution of Finland; SRR, Special Reimbursement Register; T2D, type 2 diabetes; TINIA, turbidimetric inhibition immunoanalytical method.

Data Sharing Statement

Access to data is regulated by the European Union and Finnish laws, and therefore, sharing of sensitive data is not possible and data are not publicly available. An anonymized version of the data is available for researchers who meet the criteria as required by the European Union and Finnish laws for access to confidential data with a data permit of an appropriate authority. Contact information: [email protected] for data requests from the Siun sote – Joint municipal authority for North Karelia social and health services and [email protected] for data requests from the Social Insurance Institute.

Ethics Approval and Informed Consent

Use of the data was approved by the Ethics Committee of the Northern Savonia Hospital District (diary number 81/2012). The study protocol was also approved by the register administrator, the Siun sote. A separate permission to link data on medication purchases and special reimbursements was achieved from the SII (diary number 110/522/2018). Only register-based data were utilized and, in accordance with Finnish legislation, consent from the patients was not needed. The study complies with the Declaration of Helsinki.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Disclosure

JM is a founding partner of ESiOR Oy. This company was not involved in carrying out this research. PL, GC, PS, ST, AI, JR, and TL declare no conflicts of interest.

Additional information

Funding

This study was partly supported by the Finnish Diabetes Association, the Research Committee of the Kuopio University Hospital Catchment Area for the State Research Funding (project QCARE, Joensuu, Finland), the Strategic Research Council at the Academy of Finland (project IMPRO, 312703), and the HTx project, which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 825162. The funders had no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.