Examining chronic kidney disease screening frequency among diabetics: a POMDP approach

Abstract

Forty percent of diabetics will develop chronic kidney disease (CKD) in their lifetimes. However, as many as 50% of these CKD cases may go undiagnosed. We developed screening recommendations stratified by age and previous test history for individuals with diagnosed diabetes and unknown proteinuria status by race and gender groups. To do this, we used a Partially Observed Markov Decision Process (POMDP) to identify whether a patient should be screened at every three-month interval from ages 30-85. Model inputs were drawn from nationally-representative datasets, the medical literature, and a microsimulation that integrates this information into group-specific disease progression rates. We implement the POMDP solution policy in the microsimulation to understand how this policy may impact health outcomes and generate an easily-implementable, non-belief-based approximate policy for easier clinical interpretability. We found that the status quo policy, which is to screen annually for all ages and races, is suboptimal for maximizing expected discounted future net monetary benefits (NMB). The POMDP policy suggests more frequent screening after age 40 in all race and gender groups, with screenings 2-4 times a year for ages 61-70. Black individuals are recommended for screening more frequently than their White counterparts. This policy would increase NMB from the status quo policy between $1,000 to $8,000 per diabetic patient at a willingness-to-pay of $150,000 per quality-adjusted life year (QALY).

Keywords: Chronic kidney disease; Diabetes; Disease screening; Markov Decision Process; Operations research; POMDP; Proteinuria; Simulation.

Fig. 1

This decision process is repeated each three-month period from ages 30 to 85. The decision tree stops if a patient’s test result shows CKD 3 or above; we assume the patient is then placed on treatment. We estimate the remaining lifetime rewards associated with treatment using a Markov Model that estimates the post-treatment health outcomes and costs starting at that patient’s age. The decision tree terminates at age 85

Fig. 2

Key components of the microsimulation model

Fig. 3

Life expectancy by current age for males and females with eGFR $\ge$ 60