Laboratory Computer-Based Interventions for Better Adherence to Guidelines in the Diagnosis and Monitoring of Type 2 Diabetes View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-03-28

AUTHORS

Maria Salinas, Maite López-Garrigós, Emilio Flores, Javier Lugo, Carlos Leiva-Salinas, the PRIMary Care-LABoratory (PRIMLAB) Working Group

ABSTRACT

INTRODUCTION: The aim was to present two automated laboratory strategies designed to detect new cases of type 2 diabetes and prediabetes and improve their monitoring. METHODS: To improve diabetes diagnosis, we automatically registered the glycated hemoglobin (HbA1c) levels of every primary care patient between 25 and 46 years old in case of abnormal lipid testing when an HbA1c test had not been requested in the current order or during the previous year and when fasting glucose was > 100 mg/dl. We counted the number of detected cases of diabetes and prediabetes and calculated the cost per identified patient. To improve diabetes monitoring, the levels of HbA1c, total cholesterol, high- and low-density lipoprotein cholesterol and triglycerides and the spot urinary albumin-to-creatinine ratios (ACRs) were automatically registered in patients with diabetes when not ordered according to guidelines. We calculated the total economic costs according to the total number of additional registered tests and reagent cost. RESULTS: Of 103,425 requests, 224 (0.22%) met the inclusion criteria. Seventeen (7.6%) patients were identified as having new cases of diabetes and 149 (66.5%) of prediabetes, at a cost of €15.2 and €2.3, respectively, per case detected. From 13,874 requests in patients with diabetes, 91 HbA1c and 708 lipid tests and 862 ACRs were automatically registered to comply with guidelines, resulting in expenses of €1948.90. CONCLUSIONS: Making use of laboratory technology, it is possible to detect new cases of type 2 diabetes and prediabetes and to improve disease monitoring. More... »

PAGES

1-9

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s13300-019-0600-z

DOI

http://dx.doi.org/10.1007/s13300-019-0600-z

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1113054691

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30924078


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