Trajectories of Glycemic Change in a National Cohort of Adults With Previously Controlled Type 2 Diabetes.
Trajectories of Glycemic Change in a National Cohort of Adults With Previously Controlled Type 2 Diabetes.
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DOI：
10.1097/mlr.0000000000000807
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发表时间：
2017-11
期刊：
影响因子：
3
通讯作者：
Shah ND
中科院分区：
文献类型：
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作者：
McCoy RG;Ngufor C;Van Houten HK;Caffo B;Shah ND
个体化糖尿病管理将受益于前瞻性地识别血糖控制良好的患者，这些患者存在血糖控制失控的风险。确定稳定控制的糖尿病患者的血红蛋白A1 c（HbA 1c）变化模式。使用OptumLabs数据仓库的队列研究，2001-2013年。我们开发并应用了一个机器学习框架，该框架使用广义线性混合效应模型的贝叶斯估计来发现血糖轨迹，并使用随机森林特征贡献方法来识别预测其未来血糖轨迹的患者特征。27，005名美国2型糖尿病成人，年龄≥18岁，稳定指标HbA 1c <7.0%。24个月观察期间的HbA 1c值。我们比较了k= 1，2，3，4，5轨迹和基线变量（包括患者年龄、性别、种族/民族、合并症、药物和HbA 1c）的模型。k=3的模型拟合最好，反映了三种不同的血糖变化轨迹：（T1）302名受试者中HbA 1c迅速恶化，（1.1%）（平均年龄55.2岁）基线HbA 1c均值最低的患者，6.05%;（T2）902例患者中HbA 1c逐渐恶化（3.3%）例患者（平均年龄：56.5岁），平均基线HbA 1c最高，6.53%;（T3）25，800例（95.5%）年龄最大（平均58.5岁）且平均基线HbA 1c 6.21%的患者血糖控制稳定。24个月后，HbA 1c在T1和T2中分别上升至8.75%和8.40%，但在T3中保持稳定，为6.56%。控制型2型糖尿病患者遵循三种不同的血糖控制轨迹。这种先进分析方法的新应用可以通过主动识别高风险患者来促进个性化和群体糖尿病护理。
Individualized diabetes management would benefit from prospectively identifying well-controlled patients at risk of losing glycemic control. To identify patterns of hemoglobin A1c (HbA1c) change among patients with stable controlled diabetes. Cohort study using OptumLabs Data Warehouse, 2001–2013. We develop and apply a machine learning framework that uses a Bayesian estimation of the mixture of generalized linear mixed effect models to discover glycemic trajectories, and a random forest feature contribution method to identify patient characteristics predictive of their future glycemic trajectories. 27,005 U.S. adults with type 2 diabetes, age ≥18 years, and stable index HbA1c <7.0%. HbA1c values during 24 months of observation. We compared models with k=1,2,3,4,5 trajectories and baseline variables including patient age, sex, race/ethnicity, comorbidities, medications, and HbA1c. The k=3 model had the best fit, reflecting three distinct trajectories of glycemic change: (T1) rapidly deteriorating HbA1c among 302 (1.1%) youngest (mean, 55.2 years) patients with lowest mean baseline HbA1c, 6.05%; (T2) gradually deteriorating HbA1c among 902 (3.3%) patients (mean, 56.5 years) with highest mean baseline HbA1c, 6.53%; and (T3) stable glycemic control among 25,800 (95.5%) oldest (mean, 58.5 years) patients with mean baseline HbA1c 6.21%. After 24 months, HbA1c rose to 8.75% in T1 and 8.40% in T2, but remained stable at 6.56% in T3. Patients with controlled type 2 diabetes follow three distinct trajectories of glycemic control. This novel application of advanced analytic methods can facilitate individualized and population diabetes care by proactively identifying high risk patients.