Abstract
BACKGROUND Congestive heart failure could be defined as a condition where there is a performance or structural impairment of the heart. So this study was targeted on the determinants of the longitudinal mean arterial pressure among heart failure patients treated from January 2016 to December 2019 at Felege Hiwot Referral Hospital in Bahir Dar, Ethiopia. METHODS Hospital-based retrospective data were assembled from the medical chart of 218 randomly selected congestive heart failure patients. The linear mixed effects model corresponding to unstructured_ _covariance structure was employed to spot out the determinants of mean arterial pressure among in patients with congestive heart failure. RESULTS Individual profile plot of mean arterial pressure showed the existence of variability among and between those patients. Moreover, the mean profile plot demonstrated a linearly increasing pattern over the follow-up times. The random intercept and slope model corresponding to unstructured covariance structure was the best fit (AIC: 6001.9(χ^2=80.83), P < 0.0001) as compared to the remaining models. The estimates for age, left ventricle ejection fraction, serum sodium concentration, visit times, serum hemoglobin concentration, residence(rural) and New York Heart Association Classes I, II, and III were given as 0.3758(P-value: <0.0001), 0.2933(P-value: <0.0001), 0.1941(P-value: <0.0001), 0.4471(P-value: 0.0059), 0.5501(P-value: 0.0053), -9.9858(P-value: <0.0001), 18.8943(P-value: 0.0001), 10.8833(P-value: <0.0001) and 2.7318(P-value: 0.0001) respectively, and they are statistically associated with the longitudinal mean arterial pressure of congestive heart failure patients. CONCLUSION The linear mixed effects model corresponding to unstructured covariance structure provides an information on the existence of within and between subjects variations and correlations in addition to identifying the significant factors associated with the longitudinal mean arterial pressure of congestive heart failure patients. So, an application of standard models may ignore such a variation among successive measurements. Thus mixed effects model is recommended for such longitudinal data.