Department of Industrial Engineering, University of Houston, Houston, Texas.
Center for Health Services Research, Department of Management, Policy, and Community Health, UTHealth School of Public Health, Houston, Texas.
Ophthalmol Retina. 2023 Jun;7(6):532-542. doi: 10.1016/j.oret.2023.01.001. Epub 2023 Jan 5.
Although teleretinal imaging has proved effective in increasing population-level screening for diabetic retinopathy (DR), there is a lack of quantitative understanding of how to incorporate teleretinal imaging into existing screening guidelines. We develop a mathematical model to determine personalized DR screening recommendations that utilize teleretinal imaging and evaluate the cost-effectiveness of the personalized screening policy.
A partially observable Markov decision process is employed to determine personalized screening recommendations based on patient compliance, willingness to pay, and A1C level. Deterministic sensitivity analysis was conducted to evaluate the impact of patient-specific factors on personalized screening policy. The cost-effectiveness of identified screening policies was evaluated via hidden-Markov chain Monte Carlo simulation on a data-based hypothetical cohort.
Screening policies were simulated for a hypothetical cohort of 500 000 patients with parameters based on the literature and electronic medical records of 2457 patients who received teleretinal imaging from 2013 to 2020 from the Harris Health System.
Population-based mathematical modeling study. Interventions included dilated fundus examinations referred to as clinical screening, teleretinal imaging, and wait and watch recommendations.
Personalized screening recommendations based on patient-specific factors. Accumulated quality-adjusted life-years (QALYs) and cost (USD) per patient under different screening policies. Incremental cost-effectiveness ratio to compare different policies.
For the base cohort, on average, teleretinal imaging was recommended 86.7% of the time over each patient's lifetime. The model-based personalized policy dominated other standardized policies, generating more QALY gains and cost savings for at least 57% of the base cohort. Similar outcomes were observed in sensitivity analyses of the base cohort and the Harris Health-specific cohort and rural population scenario analysis.
A mathematical model was developed as a decision support tool to identify a personalized screening policy that incorporates both teleretinal imaging and clinical screening and adapts to patient characteristics. Compared with current standardized policies, the model-based policy significantly reduces costs, whereas it is performing comparably, if not better, in terms of QALY gain. A personalized approach to DR screening has significant potential benefits that warrant further exploration.
FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found after the references.
虽然远程视网膜成像已被证明可有效增加糖尿病视网膜病变（DR）的人群筛查率，但对于如何将远程视网膜成像纳入现有筛查指南，我们尚缺乏定量了解。我们开发了一种数学模型，以确定利用远程视网膜成像的个性化 DR 筛查建议，并评估个性化筛查策略的成本效益。
采用部分可观察马尔可夫决策过程（POMDP），根据患者依从性、支付意愿和糖化血红蛋白（A1C）水平来确定个性化筛查建议。进行确定性敏感性分析，以评估患者特定因素对个性化筛查策略的影响。通过基于数据的假设队列中的隐马尔可夫链蒙特卡罗模拟，评估所确定的筛查策略的成本效益。
根据文献和 2013 年至 2020 年哈里斯健康系统（Harris Health System）接受远程视网膜成像的 2457 名患者的电子病历参数，对一个由 50 万名患者组成的假设队列模拟筛查策略。
基于人群的数学建模研究。干预措施包括称为临床筛查的散瞳眼底检查、远程视网膜成像以及观察等待建议。
基于患者特定因素的个性化筛查建议。不同筛查策略下每位患者的累积质量调整生命年（QALY）和成本（美元）。比较不同策略的增量成本效益比。
对于基础队列，平均而言，在患者的整个生命周期中，远程视网膜成像的推荐率为 86.7%。基于模型的个性化策略优于其他标准化策略，为基础队列至少 57%的患者带来更多的 QALY 获益和成本节约。在基础队列和哈里斯健康特定队列的敏感性分析以及农村人群情景分析中观察到类似的结果。
我们开发了一种数学模型，作为决策支持工具，以确定一种将远程视网膜成像和临床筛查相结合并适应患者特征的个性化筛查策略。与当前的标准化策略相比，基于模型的策略可显著降低成本，同时在 QALY 获益方面表现相当，甚至更好。DR 筛查的个性化方法具有显著的潜在效益，值得进一步探索。
参考文献后可能会发现专有或商业披露。
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