Association of Glycemic Control Trajectory with Short-Term Mortality in Diabetes Patients with High Cardiovascular Risk: a Joint Latent Class Modeling Study
Association of Glycemic Control Trajectory with Short-Term Mortality in Diabetes Patients with High Cardiovascular Risk: a Joint Latent Class Modeling Study
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DOI：
10.1007/s11606-020-05848-5
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发表时间：
2020-04-24
影响因子：
5.7
通讯作者：
Caplan, Liron
中科院分区：
文献类型：
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作者：
Raghavan, Sridharan;Liu, Wenhui G.;Caplan, Liron
背景危险因素或生物标志物轨迹与同期短期临床结果之间的关系尚不清楚。在糖尿病患者中，尚不清楚血红蛋白A1c （HbA1c）轨迹是否与临床结果相关，是否可以在单一HbA1c无法提供信息的情况下为护理提供信息，例如，在诊断为冠状动脉疾病（CAD）后。目的比较HbA1c轨迹和单一HbA1c值与冠心病评估糖尿病患者短期死亡率的关系设计回顾性观察队列研究参与者（n = 7780）伴有和未伴有血管造影诊断的冠心病的糖尿病患者主要测量方法我们使用联合潜类混合模型同时拟合HbA1c轨迹并估计其与心导管术后2年死亡率的关系。调整临床和人口统计学协变量。确定了三种HBA1c轨迹分类：血糖稳定（A类；n = 6934[89%]；平均基线HBA1c 6.9%）， HBA1c下降（B类；n = 364[4.7%]；平均基线HBA1c 11.6%），以及HBA1c升高（C类；n = 482[6.2%]；平均基线HBA1c 8.5%）。HbA1c轨迹分级与校正后2年死亡率相关（A级为3.0% [95% CI 2.8, 3.2]， B级为3.1% [2.1,4.2]，C级为4.2%[3.4,4.9]；总体P = 0.047， A级和C级比较P = 0.03，其他两两比较P = 0.05）。基线HbA1c与2年死亡率无关（P = 0.85；相对于HbA1c < 7%， HbA1c 7-9%和>= 9%时的风险比分别为1.01[0.96,1.06]和1.02[0.95,1.10]）。HbA1c轨迹与死亡率之间的关联在有和没有CAD的患者之间没有差异（相互作用P = 0.1）。在单一HbA1c测量提供有限信息的临床环境中，HbA1c轨迹可能有助于对糖尿病患者并发症风险进行分层。联合潜在类别建模提供了一种通用的方法来检查生物标志物轨迹和临床结果之间的关系，在几乎普遍采用电子健康记录的时代。
Background The relationship between risk factor or biomarker trajectories and contemporaneous short-term clinical outcomes is poorly understood. In diabetes patients, it is unknown whether hemoglobin A1c (HbA1c) trajectories are associated with clinical outcomes and can inform care in scenarios in which a single HbA1c is uninformative, for example, after a diagnosis of coronary artery disease (CAD). Objective To compare associations of HbA1c trajectories and single HbA1c values with short-term mortality in diabetes patients evaluated for CAD Design Retrospective observational cohort study Participants Diabetes patients (n = 7780) with and without angiographically defined CAD Main Measures We used joint latent class mixed models to simultaneously fit HbA1c trajectories and estimate association with 2-year mortality after cardiac catheterization, adjusting for clinical and demographic covariates. Key Results Three HBA1c trajectory classes were identified: individuals with stable glycemia (class A; n = 6934 [89%]; mean baseline HbA1c 6.9%), with declining HbA1c (class B; n = 364 [4.7%]; mean baseline HbA1c 11.6%), and with increasing HbA1c (class C; n = 482 [6.2%]; mean baseline HbA1c 8.5%). HbA1c trajectory class was associated with adjusted 2-year mortality (3.0% [95% CI 2.8, 3.2] for class A, 3.1% [2.1, 4.2] for class B, and 4.2% [3.4, 4.9] for class C; global P = 0.047, P = 0.03 comparing classes A and C, P > 0.05 for other pairwise comparisons). Baseline HbA1c was not associated with 2-year mortality (P = 0.85; hazard ratios 1.01 [0.96, 1.06] and 1.02 [0.95, 1.10] for HbA1c 7-9% and >= 9%, respectively, relative to HbA1c < 7%). The association between HbA1c trajectories and mortality did not differ between those with and without CAD (interaction P = 0.1). Conclusions In clinical settings where single HbA1c measurements provide limited information, HbA1c trajectories may help stratify risk of complications in diabetes patients. Joint latent class modeling provides a generalizable approach to examining relationships between biomarker trajectories and clinical outcomes in the era of near-universal adoption of electronic health records.