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Geographically-based screening policies for diabetic retinopathy (DR) can be effective in developing teleretinal imaging (TRI) guidelines while identifying patients with limited geographic access to eye care. This study conducts cost-effectiveness analysis of different screening policies for urban and rural diabetic patients in Western Pennsylvania. A Monte Carlo simulation model was used to evaluate the cost-effectiveness of 2 standardized screening policies (annual clinic-based screening (ACS) and annual TRI-based screening (ATRI)) and a personalized TRI-based screening policy (PTRI) for both urban and rural cohorts. PTRI was generated by a previously developed mathematical model that autonomously makes semi-annual screening recommendations based on each patient’s disease progression and compliance (Dorali et al. IOVS 2022; 63(7)). For each policy, hypothetical urban and rural cohorts of 50,000 patients were simulated and lifetime QALYs and costs were collected for each patient. TRI compliance rates were derived from electronic medical records. Compliance with clinic-based screening was selected from literature-based values (12-45% for rural patients and 50-65% for urban patients). For a base case urban cohort with an A1C level of 7% and entering age of 40, costs per QALY gain (CPQ) for ACS, ATRI, and PTRI were $744.93±1.57, $792.38±1.64, and $714.60±1.56, respectively; PTRI produced more cost saving than ACS with the same QALY gain (See Fig 1). For a base case rural cohort, CPQ for ACS, ATRI, and PTRI were $869.15±1.80, $819.24±1.88, and $761.51±1.42, respectively; both ATRI and PTRI dominated ACS in QALY gains and cost saving (Fig 1). PTRI recommended TRI more to rural patients (94.13±0.01%) than to urban patients (87.20±0.02%). For the rural cohort, the minimum average TRI compliance rate such that ATRI is more cost-effective than ACS was 56% (Fig 2). TRI-based screening was found more beneficial for rural patients. PTRI was found dominant in QALY gain and cost saving for both urban and rural cohorts against standardized policies. These findings suggest that TRI is best utilized when location-specific factors such as geographic access to care or TRI compliance are considered. 

Purpose : Personalized screening guidelines can be an effective strategy to prevent diabetic retinopathy (DR)-related vision loss. However, these strategies typically do not capture behavior-based factors such as a patient’s compliance or cost preferences. This study develops a mathematical model to identify screening policies that capture both DR progression and behavioral factors to provide personalized recommendations. Methods : A partially observable Markov decision process model (POMDP) is developed to provide personalized screening recommendations. For each patient, the model estimates the patient’s probability of having a sight-threatening diabetic eye disorder (STDED) yearly via Bayesian inference based on natural history, screening results, and compliance behavior. The model then determines a personalized, threshold-based recommendation for each patient annually--either no action (NA), teleretinal imaging (TRI), or clinical screening (CS)--based on the patient’s current probability of having STDED as well as patient-specific preference between cost saving ($) and QALY gain. The framework is applied to a hypothetical cohort of 40-year-old African American male patients. Results : For the base population with TRI and CS compliance rates of 65% and 55% and equal preference for cost and QALY, NA is identified as an optimal recommendation when the patient’s probability of having STDED is less than 0.72%, TRI when the probability is [0.72%, 2.09%], and CS when the probability is above 2.09%. Simulated against annual clinical screening, the model-based policy finds an average decrease of 7.07% in cost/QALY (95% CI; 6.93-7.23%) and 15.05% in blindness prevalence over a patient’s lifetime (95% CI; 14.88-15.23%). For patients with equal preference for cost and QALY, the model identifies 6 different types of threshold-based policies (See Fig 1). For patients with strong preference for QALY gain, CS-only policies had an increase in prevalence by a factor of 19.2 (see Fig 2). Conclusions : The POMDP model is highly flexible and responsive in incorporating behavioral factors when providing personalized screening recommendations. As a decision support tool, providers can use this modeling framework to provide unique, catered recommendations.