Algorithms to define diabetes type using data from administrative databases: A systematic review of the evidence.

Autor: Sajjadi SF; Baker Heart and Diabetes Institute, Melbourne, Australia; Monash University, School of Public Health and Preventive Medicine, Melbourne, Australia. Electronic address: Forough.Sajjadi@baker.edu.au., Sacre JW; Baker Heart and Diabetes Institute, Melbourne, Australia; Monash University, School of Public Health and Preventive Medicine, Melbourne, Australia., Chen L; Baker Heart and Diabetes Institute, Melbourne, Australia., Wild SH; Usher Institute, University of Edinburgh, Teviot Place, Edinburgh EH8 9AG, Scotland., Shaw JE; Baker Heart and Diabetes Institute, Melbourne, Australia; Monash University, School of Public Health and Preventive Medicine, Melbourne, Australia., Magliano DJ; Baker Heart and Diabetes Institute, Melbourne, Australia; Monash University, School of Public Health and Preventive Medicine, Melbourne, Australia.
Jazyk: angličtina
Zdroj: Diabetes research and clinical practice [Diabetes Res Clin Pract] 2023 Sep; Vol. 203, pp. 110859. Date of Electronic Publication: 2023 Jul 28.
DOI: 10.1016/j.diabres.2023.110859
Abstrakt: Aims: To find the best-performing algorithms to distinguish type 1 and type 2 diabetes in administrative data.
Methods: Embase and MEDLINE databases were searched from January 2000 until January 2023. Papers evaluating the performance of algorithms to define type 1 and type 2 diabetes by reporting diagnostic metrics against a range of reference standards were selected. Study quality was evaluated using the Quality Assessment of Diagnostic Accuracy Studies.
Results: Of the 24 studies meeting the eligibility criteria, 19 demonstrated a low risk of bias and low concerns about the applicability of the study population across all domains. Algorithms considering multiple diabetes diagnostic codes alone were sensitive and specific approaches to classify diabetes type (both metrics >92.1% for type 1 diabetes; >86.9% for type 2 diabetes). Among the top 10-performing algorithms to detect type 1 and type 2 diabetes, 70% and 100% featured multiple criteria, respectively. Information on insulin use was more sensitive and specific for detecting diabetes type than were criteria based on use of oral hypoglycaemic agents.
Conclusions: Algorithms based on multiple diabetes diagnostic codes and insulin use are the most accurate approaches to distinguish type 1 from type 2 diabetes using administrative data. Approaches with more than one criterion may also increase sensitivity in distinguishing diabetes type.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2023 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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