Application of Multivariate Generalized Linear Mixed Model to Identify Effect of Dialysate Temperature on Physiologic Indicators among Hemodialysis Patients

Omid Hamidi; Seyed Reza Borzu; Saman Maroufizadeh; Payam Amini

doi:10.18502/jbe.v7i3.7298

Omid Hamidi Department of Science, Hamadan University of Technology, Hamadan, Iran.
Seyed Reza Borzu Department of Nursing (Medical-surgical), Hamadan University of Medical Sciences, Hamadan, Iran.
Saman Maroufizadeh School of Nursing and Midwifery, Guilan University of Medical Sciences, Rasht, Iran.
Payam Amini Department of Biostatistics and Epidemiology, School of Health, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.

DOI: https://doi.org/10.18502/jbe.v7i3.7298

Keywords: Dialysis solutions; Multivariate analysis; Renal dialysis; Inpatients; Joint models; Longitudinal

Abstract

Introduction: One of the complications of hemodialysis treatment is hypotension, which can increase morbidity and mortality and compromise dialysis efficacy. Dialysate temperature is an important factor that contributes to hemodynamic stability during hemodialysis. This study investigated the effect of dialysate temperature on the patients' blood pressure and pulse rate. Model-based approaches were used to produce more reliable results compared with traditional methods.

Methods: A total of 30 patients were studied during 9 dialysis sessions. Dialysate temperatures were 37° C, 36° C and 35° C. A joint longitudinal model was used to analyze both responses of blood pressure and pulse rate, simultaneously.

Results: The results showed that low-dialysate temperature was not significantly associated with higher systolic blood pressure (p>0.05) or a higher pulse rate (p>0.05) either during or after dialysis. Pulse rate and blood pressure were higher for women during dialysate (p<0.001). However, increasing age was associated with higher blood pressure and a lower pulse rate (p<0.001).

Conclusion: Using several separate, repeated measure analysis of variances may produce misleading results, when there is more than one response variable measured over time, Multivariate statistical methods (including joint longitudinal models), should be used.