Literature DB >> 29460977

Development of a screening tool using electronic health records for undiagnosed Type 2 diabetes mellitus and impaired fasting glucose detection in the Slovenian population.

G Štiglic1,2, P Kocbek1, L Cilar1, N Fijačko1, A Stožer3, J Zaletel4, A Sheikh5,6, P Povalej Bržan1,2.   

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.

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Year:  2018        PMID: 29460977     DOI: 10.1111/dme.13605

Source DB:  PubMed          Journal:  Diabet Med        ISSN: 0742-3071            Impact factor:   4.359


  2 in total

1.  Early detection of type 2 diabetes mellitus using machine learning-based prediction models.

Authors:  Leon Kopitar; Primoz Kocbek; Leona Cilar; Aziz Sheikh; Gregor Stiglic
Journal:  Sci Rep       Date:  2020-07-20       Impact factor: 4.379

2.  Prediction Models for Type 2 Diabetes Risk in the General Population: A Systematic Review of Observational Studies.

Authors:  Samaneh Asgari; Davood Khalili; Farhad Hosseinpanah; Farzad Hadaegh
Journal:  Int J Endocrinol Metab       Date:  2021-03-22
  2 in total

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