eGFR: the problem of false positives

Last week I saw a 58 year old African American woman who was referred to me for an eGFR (i.e. MDRD equation) of 58. Her insurance company notified her primary care doctor about this decreased GFR. I saw the letter that Blue Cross sent and it did not give the physician any guidance on what to do with this information. The insurance company just wanted to make sure the physician was aware that the patient was flagged as having CKD. The primary care doctor sent her to me for further evaluation.

The patient, however, was completely freaked out. She went on the internet and started to learn about kidney disease and to her horror found (correctly) that her lisinopril and simvastatin could cause kidney disease. Since both of these medications had been started in the last few years she suspected (wrongly) that they were the cause of her kidney disease and stoppd both of them.

Her GFRs for three years before referral had been: 65, 63, 65 and 58. When I repeated her GFR it was 62.

This is a classic case of what Dr. Harold Feldman was writing about in the Feburary CJASN (PDF). Here is a patient who stopped the two most important drugs for her future health (statin, ACEi) because of a false positive eGFR.

This article uses a Markov chain Monte Carlo method to simulate use of serum Cr or serum Cr plus eGFR for CKD screening. The model they used is illustrated below:

In the model patients gets screened once a year (a cycle) from age 60 to 78. There are 6 states patients must be assigned to:

1. No kidney disease (CKD stage 0)
   
2. No kidney disease but false positive screening test (my patient)
3. CKD, diagnosed
4. CKD undiagnosed (false negative screening test)
5. ESRD
6. Death

In each cycle every patient must be assigned to a state. Dead patients must remain dead, ESRD patients can remain ESRD or die. Patients must develop CKD (state 3 or 4) at least one cycle prior to progressing to ESRD. Patients in any living state can die. Patients with CKD (state 3 or 4) can not tranition to no CKD (state 1 or 2). And according to the text but not the figure, patients without kidney disease but false positive screening (state 2) would go back to state 1 for the next cycle.

Some assumptions in the calculation:

• Incidence of CKD 0.7% until age 65 then 2.3%
• Mortality without CKD 0.97% from age 60 to 70, then 2.4% after age 70
• Mortality with CKD 0.050% (why this would be half the rate of non-CKD makes no sense)
• Mortality with ESRD age 62-67: 15%; 67-75: 19%; and 75+: 26%
  
• Annual rate of progressing from CKD to ESRD 0.076%
  
• Treatment of CKD had no effect on mortality
• Treatment of CKD reduced the annual rate of progression to ESRD by 21% (from 0.076% to 0.055%)
  

The Baysean test characteristics for eGFR and sCr:

• Sensitivity of eGFR: 0.924
• Specificity of eGFR: 0.835
  
• Sensitivity of serum Cr: 0.559
• Specificity of serum Cr: 0.950

The model used the following evaluation of CKD

• Two clinic visits with a nephrologist
• Limited renal ultrasound
• Renal function panel
• U/A, urine protein, urine creatinine

The costs for the different states are outlined in the table below:


In the initial analysis eGFR was more accurate and more cost-effective than the serum Cr. Use of eGFR kept patients off dialysis (29 patients) and reduced deaths (13 patients) at the expence of an ocean of false positives:


But when you assigned a false positive CKD a slightly lower quality of life than a true negative, 0.98 versus 1.0, the serum creatinine came out more cost effective per quality adjusted life year (QALY).

Summary: the better test (as measured by the area under the curve of a receiver operator characteristics curve) loses to the worse test because of the decreased quality of life that results from a false positive reading. The false positives were so much more prevelant that they overwelmed the benefit from the decrease in death and ESRD found with the more accurate eGFR test.

This study has received tremendous publicity, likely because noone had looked at eGFR in this way before and it had a contrarian view. While the whole nephrology community has been pushing for routine eGFR reporting along with creatinine, Feldman comes and publishes a scathing indictment. Additionally, it makes good copy to say that nephrologists developed a new way to measure renal function that dramatically increases the demand for nephrology services.

My primary concearn with this study is two fold:

1. The eGFR equation is used as a screening test in this study and in real- life. Screening tests need to be as sensitive as possible even at the expense of specificity. The thought is that the increased false positives will be picked up with secondary testing. But a screening test never wants to give patients a clean bill of health when in actuality they have smoldering unrecognized disease. In Feldman's analysis the eGFR works perfectly as a screening test by picking up nearly all of the patients with CKD but the decreased specificity results in numerous false positives. It is interesting that it is not the cost of evaluating the false positives that results in the cost ineffectiveness of eGFR but it is the decreased quality of life that results from the anxiety associated with the initially positive diagnosis of CKD. In my mind this means we need to do a better job educating patients and providers to the nature of the eGFR test.
2. The other problem with the study is it threatens to throw out the baby with the bath water. Even if the study does show that eGFR is not cost effective, part of the problem is that the only utility given to the eGFR is in the early diagnosis of CKD to prevent ESRD. However, I more often use the eGFR to dose adjust medications, to estimate the risk of contrast nephropathy, guide the use of loop versus thiazide diuretics. All of these uses of the eGFR cannot be replaced by a serum creatinine because the serum creatinine does not account for age, gender and race.

False positive diagnosis of CKD by the eGFR are real problems and Dr. Feldman has done the nephrology community a favor by bringing this issue to light. It would be interesting for Feldman to re-run his Monte Carlo simulation with various definitions of CKD, does an eGFR of 50 ml/min reduce the false positives enough to reduce the cost below the benefits? What about 45 ml/min (sometimes called CKD 3b)? It is important for a dialog to be initiated among primary care doctors, nephrologists and payers to come up with better definitions of CKD that don't freak our patients unnecessarily while providing the best care we can.