Dr. Navdeep Tangri, developer of the Kidney Failure Risk Equation (KFRE), which uses laboratory data and demographic information to accurately predict the risk of renal failure in patients with chronic kidney disease (CKD), was recently appointed to the Viewics advisory board. Dr. Tangri is a medical researcher in the Chronic Disease Innovation Center at Seven Oaks General Hospital and an associate professor in the University of Manitoba Faculty of Medicine. His research program focuses on improving clinical decision making for patients with advanced CKD.
I was intrigued by his background and wanted to know more about him, and how he came to develop the KFRE. Here is part one of my interview with him.
Heidi: Give us a brief synopsis of your story. How did you get to where you are today?
Dr. Tangri: I’m from Toronto, Ontario, and I grew up, like most good kids, thinking that medicine was a good career — that it was a noble profession where you earned a good living by helping people. And that was really the reason I thought, “This may be something for me.” And then got really interested in it when I went to the University of Waterloo, where I took some courses in human physiology. I thought the science of it was really fascinating. I was the first person in my family to go into medicine. I always thought that I’d be putting up my shingle somewhere, setting up a clinic and seeing patients like the family doctor I grew up seeing, and that’s what I would do for the rest of my life.
But then you hear in medical school that it’s good to participate in research — that it’s good for your CV. So, I took an opportunity in my second year of medicine to do research at the University of Toronto with David Naimark. He’s an interesting guy. He knew that I’d gone to Waterloo. (Waterloo is, incidentally, sort of like the MIT of Canada. It’s where — several of us still consider this a badge of honor — it’s where the whole Blackberry ecosystem originated. The whole email-on-phone thing came from Waterloo.) I had taken some courses in computer science and database management during my undergrad, which was in biology, because that’s what you did at Waterloo. Everybody took some computer science classes, no matter who you were. It was just part of the culture.
So, David Naimark says to me, “I’ve had this project, wanting to look at machine models and how they predict outcomes in dialysis patients. I’ve never really found the right student to work on it. Do you think you’d be interested?” And I said, “Oh yeah, I think I’d be interested.” So, I took this summer job, essentially, in my second year of medical school, and by the end of the summer I was thoroughly convinced that I was going to do research for the rest of my life. Since then, it’s just sort of been an upward trajectory on constantly trying to innovate, and always asking the question, “Why can’t we do that?” Identifying a problem, realizing there’s a solution that’s needed, and then asking, “Well, why don’t we just do it? What’s stopping us?”
I finished medical school, did my residency at McGill, went to do a PhD in Boston at Tufts with a guy named Andrew Levey, who essentially developed the entire staging system for how we look at kidney disease. After I finished that, I wanted to come back to Canada, so here I am in Winnipeg, Manitoba. I’ve been here for almost five years. We run, I think, one of the most innovative research programs out here. We’re really always trying to push the envelope.
Heidi: That’s a great background. It’s exciting to hear about how you found your passion, and how you said you knew you wanted to do research for the rest of your life. I can hear the passion coming through when you talk about it. That gives me a sense of your mindset of being the one to solve these problems and innovate, so I’m not surprised that you would have been the one to come up with the Kidney Failure Risk Equation. I’m sure there’s a story behind how that came about.
Dr. Tangri: It’s a good story. I was in internal medicine as a resident, and I was thinking I wanted to do nephrology as my specialty. So I would go to these clinics, and the doctors and the residents would all be saying, “Well, this patient has kidney disease, but what does it really mean? Are they going to go on dialysis? I don’t know.” This was the big elephant in the room. This was a big clinical question. And I thought this would really be worth answering. So, I proposed this to my supervisor, Andrew Levey, and he said, “Yup, I think this is a big ticket question. I think it’s worth trying to answer it.”
We’re not the only people who did this; other people who developed equations, too. But what was unique was that I felt that the only way this equation was going to stand the test of time, to be globally applicable and globally used, was if it was lab-based. There’s so much heterogeneity. I review papers all the time from journals that ask me to review other people’s equations. Sometimes they include variables like the stage of heart failure, or whether the patient exhibits certain symptoms. As you can imagine, the reporting of those kinds of things is variable from patient to patient, doctor to doctor, and health system to health system. So, any equations that rely heavily on subjective content, or patient- and physician-reported content, are never going to be globally applicable and integratable.
“The only way this equation was going to stand the test of time, to be globally applicable and globally used, was if it was lab-based.”
I told Andy that the equation has to be lab-based. I told him which values I think need to be there. And we did it. But, we didn’t know whether it would get uptake. There are lots of things that are published that doctors never use. So, I realized that publishing it wasn’t going to be enough. Even though we got JAMA to publish it in 2011, we said that we were not going to publish it without an app, without a spreadsheet, without an online calculator. There was no way that we were just going to publish this equation as a theoretical concept. It had to be ready for use. And that’s really what drove the whole thing. From the moment it was conceived, all the way out, it’s always been focused on something doctors will use in their daily practice.
Heidi: I think you’ve come up with the right solution there. Lab test results, like you said, are more objective.
Dr. Tangri: That’s right. They’re objective, they’re reproducible, and they’re integratable. In regards to reporting, the issue is time. With any lab-based equation, the reporting can be done directly in the lab information system or the EMR. The reporting of the output. Whereas anything that requires you asking the patient a question and then them answering it means you’ve got to go on while the patient is there, pull out your computer or your phone… it’s just, it’s not going to happen in a busy clinical practice.
Read part two of my interview with Dr. Tangri here.
Click here to read the press release.