PhD Scientific Days 2019

Budapest, April 25–26, 2019

The analysis of quality of life and quality-adjusted life year (QALY) influencing factors in Hungarian dialysis patient population

Kisvarga, Zoltán

Zoltán Kisvarga1,2, Judit Lám1, Éva Belicza1
1 Semmelweis University Health Services Management Training Centre, Budapest
2 Fresenius Medical Care Dialízis Center Kft., Budapest

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Text of the abstract

Introduction: In the recent decades, the assessment of patients’ subjective condition and quality of life (QoL) has received increasingly importance besides medical endpoints.

Aims: In our study, we examine the explanatory effect of different clinical and demographic data on the QoL variables, the relationship between them and the differentiation of the patient groups characterized by different explanatory criterias.

Method: We perform a questionnaire survey (disease specific and generic QoL, clinical and demographic questionnaire) in 13 Hungarian dialysis centers. After post-survey data quality corrections and data validation, descriptive evaluation of the questionnaires is performed using descriptive statistical methods. We use life expectancy analysis and variance analysis to investigate the explanatory power of clinical and demographic variables. The relationship between the corresponding variables of QoL questionnaires is analyzed by regression tests. To specify the characteristics of the distinguishable patient groups, discriminant analysis is performed. In this phase, we highlight some descriptive features of the available patient data.

Results: Based on a descriptive data analysis of the first evaluated dialysis center (n = 28, 46% male), the mean age of patients was 61.62 years (SD 17.28 years). The average value of the EQ-5D-5L VAS scale was 61.5% (SD 22.3%), the median was 70% (P25 50%, P75 80%). After data vizualization, we found the „selfcare” dimension the least (relative distribution of severity ranges from bottom to top are 78.57%, 7.14%, 10.71% and 3.57% respectively), and the „mobility” dimension the most critical variable (28.57%, 21.43%, 25%, 21.43% and 3.57%).

Conclusion: Despite of the limitations caused by the low element number which does not allow any causal analysis yet, our study set showed good feasibilty. By increasing the sample size, we expect that a demostrable relationship structure between QoL variables, demographic and clinical factors will be revealed.

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Mental Health Sciences Doctoral School
Program: Mental Health Sciences
Supervisor: Eva Belicza
E-mail address:
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