Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Diabetic Medicinearrow_drop_down
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
Diabetic Medicine
Article
Data sources: UnpayWall
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
Diabetic Medicine
Article . 2014 . Peer-reviewed
License: Wiley Online Library User Agreement
Data sources: Crossref
versions View all 2 versions
addClaim

This Research product is the result of merged Research products in OpenAIRE.

You have already added 0 works in your ORCID record related to the merged Research product.

Individualizing HbA1c targets for patients with diabetes: impact of an automated algorithm within a primary care network

Authors: Steven J. Atlas; Richard W. Grant; Deborah J. Wexler; Seth A. Berkowitz;

Individualizing HbA1c targets for patients with diabetes: impact of an automated algorithm within a primary care network

Abstract

AbstractAimsTo develop glycaemic goal individualization algorithms and assess potential impact on a healthcare system and different segments of the population with diabetes.MethodsA cross‐sectional observational study of patients with diabetes in a primary care network age > 18 years with an HbA1c measured between 1 January and 31 December 2011. We applied diabetes guidelines to create targeted algorithms 1 and 2, which assigned HbA1c goals based on age, duration of diabetes (< 15 years or < 10 years), diabetes complications and Charlson co‐morbidity score (< 6 or < 4) [targeted algorithm 2 was designed to assign more patients a goal < 64 mmol/mol (8.0%) than targeted algorithm 1]. Each patient's HbA1c was compared with these targeted goals and to the ‘standard’ goal < 53 mmol/mol (7.0%). Agreement was tested using McNemar's test.ResultsOverall, 55.7% of 12 199 patients would be considered controlled under the ‘standard’ approach, 61.2% under targeted algorithm 1 and 67.5% under targeted algorithm 2. Targeted algorithm 1 reclassified 1213 (23.6%) patients considered uncontrolled under the standard approach to controlled, P < 0.001. Targeted algorithm 2 reclassified 1844 (35.2%) patients, P < 0.001. Compared with those controlled under the standard goal, there was no significant difference in the proportion of those controlled using targeted goals who had Medicaid, had less than a high school diploma or received primary care in a federally qualified health centre.ConclusionsTwo automated targeted algorithms would reclassify one quarter to one third of patients from uncontrolled to controlled within a primary care network without differentially affecting vulnerable patient subgroups.

Related Organizations
Keywords

Blood Glucose, Glycated Hemoglobin, Male, Primary Health Care, Medicaid, Comorbidity, Middle Aged, United States, Cross-Sectional Studies, Diabetes Mellitus, Type 1, Diabetes Mellitus, Type 2, Glycemic Index, Educational Status, Humans, Female, Precision Medicine, Algorithms, Aged

  • BIP!
    Impact byBIP!
    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    13
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
Powered by OpenAIRE graph
Found an issue? Give us feedback
citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
13
Top 10%
Top 10%
Top 10%
bronze