Viewics and Patient Safety & Quality Healthcare recently held a webinar with Dr. Navdeep Tangri, the Canadian nephrologist and medical researcher who developed the Kidney Failure Risk Equation for accurately predicting the risk of progression to renal failure in patients with chronic kidney disease (CKD). Below is an excerpt from his presentation.
Navdeep Tangri, MD, PhD, FRCP(C)
We performed a validation study in cohorts from the CKD Prognosis Consortium. We did individual level meta-analysis, we showed beta coefficients were the same across studies, we showed discrimination was accurate, we showed calibration was improved. These are all model measures, but every single one was accurate just as the original study in this pooled Consortium data. And we’ve pooled all these measures to really show that the model is robust no matter where you apply it.
Cost savings FoR health systems
So, what does that mean in terms of real dollars and real cost savings? Here’s the example. If you remember the case study that I was talking about earlier, there’s about CAD 1,600 spent per patient with stage 4 CKD in Ontario presently. Ontario is a large population. There’s actually 17,000 patients with stage 4 CKD that are registered in Ontario. The ORN, which is the Ontario Renal Network, provides care for another 11,000 patients on dialysis, and their annual budget is CAD 640 million. What Ontario has now done is they’ve changed their model of delivering that expensive interdisciplinary care. They’ve said and recognized that not all patients with stage 4 CKD are the same — that high-intensity care should be for high-risk patients. And they’ve now based this criteria for delivering this care based on the Kidney Failure Risk Equation.
“They’ve recognized that not all patients with stage 4 CKD are the same — that high-intensity care should be for high-risk patients.”
What that’s done is it’s allowed that one-third of patients — there’s about 30 – 40% of patients who are just very low risk for progression to kidney failure — and they’ve allowed them to just be managed by a nephrologist alone. That results in a CAD 1,300 savings annually per patient who’s managed by a nephrologist alone versus the entire team. And that’s resulted in a CAD 7 million approximate annual savings for the ORN. That’s not a small number, because over a five-year period, which is their budget period, that’s CAD 30 – 40 million that can really go toward other important aspects of kidney care. So, patient care is improved, money is saved without patients’ health being compromised — and that’s really the win-win here. It’s really allocating resources according to risk, reducing wait times for really high-risk people, while providing appropriate and conservative, and still monitoring the low-risk people.
“Patient care is improved, money is saved without patients’ health being compromised — and that’s really the win-win here.”
Benefits of predictive analytics for CKD
- Accurately predict the risk of renal failure requiring dialysis in patients with CKD stages 3 – 5 for at least up to five years, if not beyond.
- Risk prediction is highly accurate across multiple countries and subpopulations.
- The equations are simple, and can be easily integrated into your clinic, hospital, or healthcare system, and they can be used for a number of different purposes — from identifying the highest risk individual who needs care now, to identifying the lowest risk individual who doesn’t need as much monitoring.
- Not just in Ontario, but around the world, this can result in substantial cost savings, which can then be allocated to other measures that support patient care.”
See Dr. Tangri’s entire presentation here: Revolutionizing Renal Care with Predictive Analytics for CKD.