THE OPTIMAL TIME TO PREPARE A FISTULA FOR HEMODIALYSIS PATIENTS

Monday, October 24, 2011
Grand Ballroom AB (Hyatt Regency Chicago)
Poster Board # 1
(MET) Quantitative Methods and Theoretical Developments

Steven M. Shechter, PhD, Nadia Zalunardo, MD, SM, FRCP(C) and M. Reza Skandari, PhD Student, University of British Columbia, Vancouver, BC, Canada

Purpose:

�� The gold standard for hemodialysis delivery is via an arteriovenous (AV) fistula.� Two types of uncertainty make it difficult to know the optimal time to create a fistula: 1) when a CKD patient may need to start dialysis, and 2) when a fistula will mature.� We developed a decision model to evaluate two key timing decisions: 1) when to request laboratory tests to estimate glomerular filtration rate (eGFR), and 2) when to start fistula preparation based on the results.

Methods:

�� Based on observed data, we assume eGFR declines linearly, and that observed values fluctuate around this line according to y(t) = 45 �-.375t + ε, �where ε ~ N(0,42) and t is in months.We assume that patients start dialysis when their eGFR = 15 and that there is a Uniform[3,9] month distribution of time from when fistula preparation commences until it is ready for use.� We assume the clinician obtains noisy eGFR readings upon each test, fits an updated linear regression, estimates when the line crosses 15, and then subtracts a �backtrack time� to factor in the various uncertainties (random observations and time for fistula to mature).� If the resulting time has already passed or is imminent, fistula preparation commences; otherwise one waits until the next scheduled lab test.

�� We simulate the observation process and fistula maturation time via Monte Carlo simulation, and use it to evaluate expected costs associated with various lab testing and fistula start time policies.� We consider three types of cost parameters: 1) cL�cost per day fistula is ready later than ideal dialysis start time, 2)� cE�cost per day fistula is ready early, and 3) cT�cost for each lab test patient undergoes.

Results:

��� Among fixed-spacing testing policies, simulation experiments suggest an optimal (inter-test time, backtrack time) of (3 months, 13 months).� In sensitivity analyses, we changed the slope parameter to -.25 (�slower progressor�) and -.5 (�faster progressor�), and obtained optimal solutions of (4 ,14 ) and (2 , 11), respectively.� Results were not very sensitive to the distribution of fistula maturation time.

Conclusions:

�� Monte Carlo simulation is a useful tool for evaluating the interaction between testing frequencies and fistula initiation policies when establishing guidelines for dialysis preparation.�