Development of a screening tool using electronic health records for undiagnosed Type 2 diabetes mellitus and impaired fasting glucose detection in the Slovenian population
- PMID: 29460977
- DOI: 10.1111/dme.13605
Development of a screening tool using electronic health records for undiagnosed Type 2 diabetes mellitus and impaired fasting glucose detection in the Slovenian population
Abstract
Aim: To develop and validate a simplified screening test for undiagnosed Type 2 diabetes mellitus and impaired fasting glucose for the Slovenian population (SloRisk) to be used in the general population.
Methods: Data on 11 391 people were collected from the electronic health records of comprehensive medical examinations in five Slovenian healthcare centres. Fasting plasma glucose as well as information related to the Finnish Diabetes Risk Score questionnaire, FINDRISC, were collected for 2073 people to build predictive models. Bootstrapping-based evaluation was used to estimate the area under the receiver-operating characteristic curve performance metric of two proposed logistic regression models as well as the Finnish Diabetes Risk Score model both at recommended and at alternative cut-off values.
Results: The final model contained five questions for undiagnosed Type 2 diabetes prediction and achieved an area under the receiver-operating characteristic curve of 0.851 (95% CI 0.850-0.853). The impaired fasting glucose prediction model included six questions and achieved an area under the receiver-operating characteristic curve of 0.840 (95% CI 0.839-0.840). There were four questions that were included in both models (age, sex, waist circumference and blood sugar history), with physical activity selected only for undiagnosed Type 2 diabetes and questions on family history and hypertension drug use selected only for the impaired fasting glucose prediction model.
Conclusions: This study proposes two simplified models based on FINDRISC questions for screening of undiagnosed Type 2 diabetes and impaired fasting glucose in the Slovenian population. A significant improvement in performance was achieved compared with the original FINDRISC questionnaire. Both models include waist circumference instead of BMI.
© 2018 Diabetes UK.
Similar articles
-
Validation of the Finnish Diabetes Risk Score (FINDRISC) questionnaire for undiagnosed type 2 diabetes screening in the Slovenian working population.Diabetes Res Clin Pract. 2016 Oct;120:194-7. doi: 10.1016/j.diabres.2016.08.010. Epub 2016 Aug 26. Diabetes Res Clin Pract. 2016. PMID: 27592167
-
A more simplified Finnish diabetes risk score for opportunistic screening of undiagnosed type 2 diabetes in a German population with a family history of the metabolic syndrome.Horm Metab Res. 2009 Feb;41(2):98-103. doi: 10.1055/s-0028-1087191. Epub 2008 Oct 29. Horm Metab Res. 2009. PMID: 18975253
-
Predictors of undiagnosed prevalent type 2 diabetes - The Danish General Suburban Population Study.Prim Care Diabetes. 2018 Feb;12(1):13-22. doi: 10.1016/j.pcd.2017.08.005. Epub 2017 Sep 28. Prim Care Diabetes. 2018. PMID: 28964672
-
Screening for type 2 diabetes: a short report for the National Screening Committee.Health Technol Assess. 2013 Aug;17(35):1-90. doi: 10.3310/hta17350. Health Technol Assess. 2013. PMID: 23972041 Free PMC article. Review.
-
Undiagnosed diabetes mellitus and associated factors among adults in Ethiopia: a systematic review and meta-analysis.Sci Rep. 2021 Dec 20;11(1):24231. doi: 10.1038/s41598-021-03669-y. Sci Rep. 2021. PMID: 34931004 Free PMC article.
Cited by
-
Non-Laboratory-Based Risk Prediction Tools for Undiagnosed Pre-Diabetes: A Systematic Review.Diagnostics (Basel). 2023 Mar 29;13(7):1294. doi: 10.3390/diagnostics13071294. Diagnostics (Basel). 2023. PMID: 37046512 Free PMC article. Review.
-
Early detection of type 2 diabetes mellitus using machine learning-based prediction models.Sci Rep. 2020 Jul 20;10(1):11981. doi: 10.1038/s41598-020-68771-z. Sci Rep. 2020. PMID: 32686721 Free PMC article.
-
Prediction Models for Type 2 Diabetes Risk in the General Population: A Systematic Review of Observational Studies.Int J Endocrinol Metab. 2021 Mar 22;19(3):e109206. doi: 10.5812/ijem.109206. eCollection 2021 Jul. Int J Endocrinol Metab. 2021. PMID: 34567135 Free PMC article.
-
Ensemble machine learning reveals key features for diabetes duration from electronic health records.PeerJ Comput Sci. 2024 Feb 26;10:e1896. doi: 10.7717/peerj-cs.1896. eCollection 2024. PeerJ Comput Sci. 2024. PMID: 38435625 Free PMC article.
-
Performance of a prediabetes risk prediction model: A systematic review.Heliyon. 2023 May 6;9(5):e15529. doi: 10.1016/j.heliyon.2023.e15529. eCollection 2023 May. Heliyon. 2023. PMID: 37215820 Free PMC article. Review.
Publication types
MeSH terms
Substances
LinkOut - more resources
Full Text Sources
Other Literature Sources
Medical