Joint modeling of longitudinal change in pulse rate and survival time of heart failure patients treated at Arbaminch General Hospital, Southern Ethiopia

Abstract

IntroductionHeart failure is a chronic progressive disease in which the heart muscle is unable to pump enough blood to meet the body’s need. It is a severe health problem around the world with high re-hospitalization and death rates. The main aim of this study was to identify the factors associated with longitudinal change of pulse rate and survival time to death of congestive heart failure patients treated at Arba Minch General Hospital.MethodsA retrospective study design was undertaken on congestive heart failure patients admitted to the Arba Minch General Hospital from January 2017 to December 2020. Data was collected from a total of 199 patients. After evaluating the longitudinal data with a linear mixed model and the survival time to death data with cox proportional model, Bayesian joint model of both sub models was fitted in R software using JMbayes2 package.ResultsFindings from Bayesian joint model revealed that the estimated value for the association parameter was positive and statistically significant. This indicates that there is significant evidence of an association between the mean longitudinal change of pulse rate and the risk of death. Weight of patients at baseline, gender, chronic kidney disease, left ventricular ejection fraction, New York Heart Association classification, diabetes, tuberculosis, pneumonia and family history were statistically significant factors associated with mean evolution of pulse rate of congestive heart failure patients. Left ventricular ejection fraction, etiology of congestive heart failure, type of congestive heart failure, chronic kidney disease, smoking, family history, alcohol and diabetes were found to be statistically significant factors associated with survival time to death.ConclusionTo reduce the risk level, health professionals should give attention to congestive heart failure patients with high pulse rate, co-morbidities of chronic kidney disease, tuberculosis, diabetic, smoking status, family history, and pneumonia in the study area.