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

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

Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Keywords: Computer methods for data anlysis; Electronic health records; General practice; Hypertension; Menopausal women; Metabolic syndrome; Research; Routine data.

Conflict of interest statement

Authors’ information

ŠŠ is a specialist of Family Medicine and Emergency Medicine and a PhD student under the mentorship of LjTM. LJTM is a specialist of family medicine and Assis. Prof. at the Deparment of Internal Medicine and Family Medicine, Faculty of Medicine, University of Osijek, Croatia. Her main fields of interest are: primary care, clinical medicine, ageing diseases, cardiovascular disease, clinical immunology and knowledge discovery in datasets. She is a member of the Holzinger’s HCI-KDD International Network. AV is a Full Prof. in Internal medicine, Head of the Department of Internal medicine and Family medicine and a co-mentor of ŠŠ. FB is Assis. Prof. at the the Department of Cybernetics and Artificial Intelligence, Faculty of Electrical Engineering and Informatics, Technical university of Košice, Slovakia. His research is oriented on data mining and knowledge management. MV is a PhD student supervised by JP at the same department, with the research area in medical data mining. JP is a Full Prof. at the same department and his professional interests are knowledge discovery, knowledge management, scheduling and logistics. AH is head of the Holzinger Group, HCI-KDD, at the Institute of Medical Informatics/Statistics at the Medical University Graz, and Assoc. Prof. of Applied Computer Science at the Institute of Interactive Systems and Data Science at Graz University of Technology. His research interests are in machine learning and knowledge extraction to help to solve problems in health informatics.

Ethics approval and consent to participate

We involved human data in the study. The Ethics Committee of the Faculty of Medicine, JJ Strossmayer University, Osijek, Croatia, approved the study.

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Written informed consent was obtained from all individual participants included in the study.

Consent for publication

Not applicable.

Competing interests

ŠŠ declares that he has no competing interests.

LjTM declares that she has no competing interests.

FB declares that he has no competing interests.

MV declares that he has no competing interests.

JP declares that he has no competing interests.

AV declares that he has no competing interests.

AH is member of the editorial board of BMC MIDM but not in this section and he was neither involved in the editorial nor in the review process.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

Fig. 1
Fig. 1
Study population and the sample
Fig. 2
Fig. 2
Evidence that guided the choice of criteria for the database population
Fig. 3
Fig. 3
The Croatian Primary Health Care (PHC) Information Communication Technology (ICT) System
Fig. 4
Fig. 4
Graphical presentations of differences in distributions of numerical parameters: triglycerides (left), BMI (middle) and waist circumference (right) with respect to the presence or not of the diagnosis of metabolic syndrome
Fig. 5
Fig. 5
Graphical presentations of frequency distributions of women with and without MS according to the categories of parameters: menopause duration (left), diabetes duration (middle) and hypertension duration (right)
Fig. 6
Fig. 6
Decision trees model with all parameters included
Fig. 7
Fig. 7
Decision trees model with excluded parameters closely related to the conventional definition of metabolic syndrome: waist circumference, BMI, triglycerides, HDL-cholesterol and fasting glucose

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