Current CKD management guidelines stage patients according to their glomerular filtration rate (GFR). Typically, stages 1 and 2 are managed by a primary care physician (PCP). These patients are considered to be at very low risk for end-stage renal disease (ESRD) — if they’re even diagnosed at this level. There is very low awareness of the disease in these early stages; many people with stage 1 – 2 CKD are undiagnosed. Stage 3 is actually a mix of 3a and 3b. Many PCPs do not refer these patients to nephrologists.
This is a gray area. There are too many people in stage 3 to refer all of them to nephrologists, but stage 3b patients should probably be referred, according to current guidelines. At this stage, effective treatment and drugs can be useful and impactful. But, progression to ESRD is unpredictable, and many stage 3b patients will never progress to ESRD. Similarly, there are too many stage 4 patients for all of them to be referred to nephrologists. And, significant numbers of patients in stage 4 are actually at low risk of progressing to ESRD. In stage 5, patients and their doctors are discussing dialysis options, looking to create dialysis access, preferably through the placement of an arteriovenous (AV) fistula. Interestingly, 66% of people with ESRD weren’t referred in time for a fistula. This is where the problem really comes into play.
The CKD volume-risk dilemma
You can look at the situation of CKD as a volume-risk dilemma. There’s a small group of younger patients, and then there’s a really massive group that are elderly. Although there are fewer younger patients, they’re at higher risk of progressing to ESRD. And although there’s a large number of elderly, most of them are at low risk of progressing. PCPs generally think that they don’t need to refer their elderly CKD patients to nephrologists, but there is a subset of this group that will progress. It’s figuring out who those are, and how to know which of them to refer, that is widely recognized as a problem.
Need predictive model to identify progressors
The CKD provider community has acknowledged the need for a predictive model to identify progressors for several years. RTI Health Solutions said in 2012, “With current treatment, nearly half of patients progress to unfavorable renal and cardiovascular outcomes….Attention to traditional measures of kidney function (e.g., eGFR) is no longer adequate to optimally manage and care for patients with CKD.” This has been a recognized problem, with calls for better prediction models, within the CKD care provider community for some time now.
“With current treatment, nearly half of patients progress to unfavorable renal and cardiovascular outcomes….Attention to traditional measures of kidney function (e.g., eGFR) is no longer adequate to optimally manage and care for patients with CKD.”
Summing up the challenges of CKD
In stages 1 and 2, there is a problem of poor awareness; typically patients aren’t caught in these early stages. In the middle stages, the condition is highly variable, and staging is not very accurate. Therefore, doctors vary widely in how they’re managing patients. There’s no accurate way to know who’s going to progress to ESRD, causing difficulties in managing CKD patients. And, there are treatments in specialty care that can impact outcomes, but they have to be appropriately targeted because they are costly, and some treatments aren’t clinically appropriate for some patients. So, there’s a problem that needs to be fixed, because healthcare is wasting a lot of money, and patients are suffering needlessly.
A new predictive analytics solution
A new CKD management program employs a globally validated algorithm for accurately predicting which patients will progress to ESRD. This program can enable ACOs, health systems, payers, and dialysis providers to give better care, improve outcomes, and increase patient satisfaction, while realizing substantial cost savings. Learn about the program and hear from the algorithm’s creator, Dr. Navdeep Tangri, in an on-demand webinar: Revolutionizing Renal Care with Predictive Analytics for CKD.