Abstract

Uncontrolled glycated hemoglobin (HbA1c) levels are associated with adverse events among complex diabetic patients. These adverse events present serious health risks to affected patients and are associated with significant financial costs. Thus, a high-quality predictive model that could identify high-risk patients so as to inform preventative treatment has the potential to improve patient outcomes while reducing healthcare costs. Because the biomarker information needed to predict risk is costly and burdensome, it is desirable that such a model collect only as much information as is needed on each patient so as to render an accurate prediction. We propose a sequential predictive model that uses accumulating patient longitudinal data to classify patients as: high-risk, low-risk, or uncertain. Patients classified as high-risk are then recommended to receive preventative treatment and those classified as low-risk are recommended to standard care. Patients classified as uncertain are monitored until a high-risk or low-risk determination is made. We construct the model using claims and enrollment files from Medicare, linked with patient electronic health records (EHR) data. The proposed model uses functional principal components to accommodate noisy longitudinal data and weighting to deal with missingness and sampling bias. The proposed method demonstrates higher predictive accuracy and lower cost than competing methods in a series of simulation experiments and application to data on complex patients with diabetes. Supplementary materials for this article are available online.

摘要

不受控制的糖化血红蛋白 (HbA1c) 水平与复杂糖尿病患者的不良事件相关。这些不良事件给受影响的患者带来严重的健康风险，并带来巨大的财务成本。因此，高质量的预测模型可以识别高风险患者，从而为预防性治疗提供信息，有可能改善患者的治疗结果，同时降低医疗成本。由于预测风险所需的生物标志物信息成本高昂且繁琐，因此希望这样的模型仅收集每个患者所需的信息，以便提供准确的预测。我们提出了一种序贯预测模型，该模型使用累积的患者纵向数据将患者分类为：高风险、低风险或不确定。然后建议分类为高风险的患者接受预防性治疗，建议分类为低风险的患者接受标准护理。对被分类为不确定的患者进行监测，直到做出高风险或低风险的决定。我们使用 Medicare 的索赔和登记文件构建模型，并与患者电子健康记录 (EHR) 数据相关联。所提出的模型使用函数主成分来适应噪声纵向数据，并使用加权来处理缺失和采样偏差。在一系列模拟实验和对复杂糖尿病患者数据的应用中，所提出的方法表现出比竞争方法更高的预测准确性和更低的成本。本文的补充材料可在线获取。