Abstract:
Peritoneal Dialysis (PD) is a process of replacing kidney function which cleans
waste from the blood and remove extra fluid from the body. In most cases, the
process of PD is slowed down by a peritoneal membrane infection called peritonitis.
Despite recent advancements in treatments and prevention, peritonitis still
remains the leading complication which results in high morbidity and technique
failure among PD patients. Using a prospective peritonitis dataset of 159 kidney
patients who were on PD from 2008 to 2015 in Pietersburg Provincial Hospital,
the aim of this study was to identify potential social, demographic and biological
risk factors that contribute to the first episode of peritonitis. Both semi-parametric
(Cox PH) and parametric (Accelerated Failure Time: Weibull, exponential, loglogistic,
and gamma) survival models were fitted to the peritonitis dataset. Akaike
Information Criterion (AIC) was applied to select models which best fit to the peritonitis
data. Accordingly, log-logistic Accelerated Failure Time (AFT) model was
found to be a working model that best fit to the data. A total of 96 (60.38%) peritonitis
cases were recorded over the follow-up period with majority of peritonitis
infection coming from females (65.4%) and rural dwellers (65.7%) with (62.6%)
of black Africans showing higher risk of developing peritonitis. The multivariate
log-logistic AFT model revealed that availability of water (p-value=0.018), electricity
(p-value=0.018), dwelling (p-value=0.008), haemoglobin status (p-value=0.002)
and duration on PD (p-value=0.001) are significant risk factors for the development
of peritonitis. Therefore, patients with no water and electricity, coming from rural
background with low level of haemoglobin and shorter duration on PD are associii
ated with high risk or hazard of developing peritonitis for the first time.
