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Metabolic syndrome in hypertensive women in the age of menopause: a case study on data from general practice electronic health records

Overview of attention for article published in BMC Medical Informatics and Decision Making, April 2018
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Title
Metabolic syndrome in hypertensive women in the age of menopause: a case study on data from general practice electronic health records
Published in
BMC Medical Informatics and Decision Making, April 2018
DOI 10.1186/s12911-018-0601-2
Pubmed ID
Authors

Šefket Šabanović, Majnarić Trtica Ljiljana, František Babič, Michal Vadovský, Ján Paralič, Aleksandar Včev, Andreas Holzinger

Abstract

There is potential for medical research on the basis of routine data used from general practice electronic health records (GP eHRs), even in areas where there is no common GP research platform. We present a case study on menopausal women with hypertension and metabolic syndrome (MS). The aims were to explore the appropriateness of the standard definition of MS to apply to this specific, narrowly defined population group and to improve recognition of women at high CV risk. We investigated the possible uses offered by available data from GP eHRs, completed with patients interview, in goal of the study, using a combination of methods. For the sample of 202 hypertensive women, 47-59 years old, a data set was performed, consisted of a total number of 62 parameters, 50 parameters used from GP eHRs. It was analysed by using a mixture of methods: analysis of differences, cutoff values, graphical presentations, logistic regression and decision trees. The age range found to best match the emergency of MS was 51-55 years. Deviations from the definition of MS were identified: a larger cut-off value of the waist circumference measure (89 vs 80 cm) and parameters BMI and total serum cholesterol perform better as components of MS than the standard parameters waist circumference and HDL-cholesterol. The threshold value of BMI at which it is expected that most of hypertensive menopausal women have MS, was found to be 25.5. The other best means for recognision of women with MS include triglycerides above the threshold of 1.7 mmol/L and information on statins use. Prevention of CVD should focus on women with a new onset diabetes and comorbidities of a long-term hypertension with anxiety/depression. The added value of this study goes beyond the current paradigm on MS. Results indicate characteristics of MS in a narrowly defined, specific population group. A comprehensive view has been enabled by using heterogenoeus data and a smart combination of various methods for data analysis. The paper shows the feasibility of this research approach in routine practice, to make use of data which would otherwise not be used for research.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 37 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 7 19%
Other 4 11%
Student > Postgraduate 4 11%
Student > Ph. D. Student 4 11%
Student > Bachelor 3 8%
Other 8 22%
Unknown 7 19%
Readers by discipline Count As %
Medicine and Dentistry 13 35%
Nursing and Health Professions 4 11%
Biochemistry, Genetics and Molecular Biology 2 5%
Pharmacology, Toxicology and Pharmaceutical Science 2 5%
Psychology 2 5%
Other 4 11%
Unknown 10 27%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 08 April 2018.
All research outputs
#10,205,710
of 12,775,568 outputs
Outputs from BMC Medical Informatics and Decision Making
#951
of 1,153 outputs
Outputs of similar age
#206,924
of 274,017 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#3
of 4 outputs
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