Modeling Rate of Change in Renal Function for Individual Patients: A Longitudinal Model Based on Routinely Collected Data
published: Jan. 23, 2012, recorded: December 2011, views: 3831
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Population data suggest that there is a steady decline in estimated glomerular filtration rate (eGFR) as patients get older. However this takes little account of individual patient fluctuation; with many patients exceeding levels of change which should lead to referral to specialist care. In this study, we develop an algorithm to estimate the rate of change in renal function with age and eGFR as covariates. The produced chart enables clinicians to look up the probability that this change was within the normal range. To achieve this, we used a routine clinical database – the Quality Improvement in Chronic Kidney Disease (QICKD) trial database – in which 18,476 eligible patients have been identified. The key innovation here is the use of a regression model to smooth the underlying data, which enforces the dependency of the eGFR measurement on both the patient and time. A significant advantage of our approach is that daily fluctuation in the biological measurement is taken into account. This allows a clinician to reveal trends previously dismissed or ignored. As a possible application, we produced a plot of distribution of the rate of change in eGFR as a function of age and each level of eGFR. These data are also presented as a video online at: http://goo.gl/mtWfV. One the key findings is the presence of considerable variance of the rate of change in eGFR across patients at all ages and levels of renal function. Consequently, collating the data at the population level can adversely impact on the trend at the individual patient level. This points to the need for modeling the rate change at the patient level. Despite this, our time- and patient-enforced estimate is still sensitive enough to detect the minute change at the population level. This represents a significant improvement considering that the conventional approach that bins the patient by age group and eGFR value, as currently practiced, may not be able to detect such a minute change.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !