Cardiovasc Diabetol :2型糖尿病降糖药物和心血管转归的时间分辨轨迹

2022-09-15 从医路漫漫 MedSci原创

SGLT-2抑制剂和GLP-1受体激动剂现在被认为是大多数T2D患者的理想二线GLM,并对那些患有心血管或肾脏疾病的患者有强烈的适应症。

背景:2型糖尿病(T2D)是一种慢性进行性疾病,需要反复调整药物治疗。管理T2D的医疗设备呈指数级增长,新的药物也在快速发布。此外,关于各种降糖药物(GLM)在不同患者群体中的安全性和有效性的证据前所未有的丰富。随着多种治疗组合的出现,流行的观念是T2D的药物治疗应该根据每个患者的特点进行调整。科学学会发布了治疗算法和指南,优先考虑特定亚组患者中的某些GLM,同时降低其他患者的优先级。这种方法必然意味着GLM及其组合的优选顺序。几十年来,二甲双胍代表了T2D无可争议的一线药物治疗,但这一概念可能正在改变。一些类别的GLM,如磺脲类,已被重新定位为后期选择,但在国家和医疗保健系统之间存在相当大的异质性。在大多数情况下,胰岛素的开始被作为一种较晚的策略。因此,当前的算法根据可用的证据说明了患者应该遵循的理想轨迹。随着证据和算法随着时间的推移而改变,许多确诊或长期患有T2D的患者遵循的轨迹根据今天的知识是不合适的。虽然治疗可以调整以适应更现代的方法,但既往用药史的影响仍不清楚。

SGLT-2抑制剂和GLP-1受体激动剂现在被认为是大多数T2D患者的理想二线GLM,并对那些患有心血管或肾脏疾病的患者有强烈的适应症。然而,产生这种证据的随机对照试验(RCT)并没有测试作为一线、二线或更高级治疗方案的药物定位。然而,这些药物的心肾益处似乎在已经使用磺脲类药物或胰岛素的患者中得以保留。因此,还不确定患者在T2D药物治疗方面的详细轨迹是否可以改变疾病的结果。在此,我们希望确定GLMs的时间分辨轨迹在识别T2D患者的主要心血管不良事件(MACE)中的价值。为了做到这一点,我们比较了一个深度学习模型的MACE区分能力,该模型结合了患者GLM使用的整个时间分辨模式,与一个由GLMs的有序序列提供的模型和另一个仅由药物列表提供的模型进行了比较。我们还通过对原始药物清单的人工重新排序来挑战深度学习模型。此外,我们通过深度学习模型中实施的注意机制,探索了个体GLM模式和MACE发生之间关系的主要趋势。我们假设,在患者病史中使用的GLM手段下,相对于仅考虑GLM类型或其非时间特征序列所能达到的结果,有序的时间分辨治疗模式能更好地区分发生MACE的患者和未发生MACE的患者。

目的:治疗算法定义了用于治疗二型糖尿病(T2D)的降糖药物(GLM)的种类,但治疗轨迹是否与主要心血管不良事件(MACE)相关尚不清楚。我们研究了GLM使用的时间分辨率是否能区分经历过4P-梅斯事件(心力衰竭、心肌梗死、中风、各种原因死亡)的患者。

方法:我们使用了一个行政数据库(意大利东北部威尼托地区,2011-2018年),并实施了具有特定结果注意力地图的递归神经网络(RNN)。模型输入包括年龄、性别、糖尿病持续时间和4P-梅斯或审查前的GLM模式矩阵。模型输出是辨别度,报告为受试者特征曲线下面积(AUROC)。注意力分布图显示了药物的时间分辨轨迹对辨别是最重要的。

结果:对147,135名患者进行了训练和模型选择分析,并对10,000名患者进行了验证分析。收集的数据跨越了大约6年的时间。RNN模型有效地区分了以4P-梅斯结束的GLM时间模式和以AUROC为0.911(95% c . I . 0.904–0.919)的无事件审查结束的时间模式。这种优异的性能明显优于没有结合时间分辨GLM轨迹的其他模型:(I)对编码患者曾经服用的所有GLM的词袋进行逻辑回归(AUROC 0.75495%置信区间为0.743–0.765);㈡一个模型,包括没有时间关系的GLM序列(AUROC 0.74995%置信区间为0.737–0.761);(iii)具有相同构造规则的RNN模型,但是包括GLM的时间反转或随机顺序。注意力图谱确定了最常见的一线药物(二甲双胍)、二线药物(磺脲类药物)GLM和胰岛素(甘精胰岛素)的时间分辨模式,作为决定辨别能力的因素。

图1模型的结构。GLM,降血糖药物。RNN,递归神经网络。主要心血管不良事件复合结局的4P-梅斯4个组成部分

表1模型辨别性能

表2与标准模型的比较

图24P-梅斯辨别表演。该图总结了通过表2和表3所示的模型鉴别4P-梅斯的ROC曲线下面积(AUROC)。*相对于具有真实GLM序列的RNN模型,p < 0.05

图3与4P-梅斯成分相关的注意力景观。每幅图显示了与各自结果相关的平均注意力分布图,每三个月标准化一次。X轴表示以月为单位的时间,作为事件或退出时间的负偏移量;Y轴代表输入变量(年龄、性别、糖尿病持续时间或GLM ATCs);Z轴是所有训练对象的标准化平均注意力矩阵。对于每一个结果,具有最多样的注意力景观的变量通过实心多边形突出显示。A10BB09,格列齐特。A10BB12,格列美脲。A10BA02,二甲双胍。A10BD02二甲双胍和磺脲类药物。A10AE04,甘精胰岛素

结论:GLM应用的时间分辨模式比单纯的处方GLM列表或序列更好地识别了随后发生心血管事件的患者。因此,患者的治疗轨迹可以决定疾病的结果。

原文出处:Longato E,  Di Camillo B,  Sparacino G, Time-resolved trajectory of glucose lowering medications and cardiovascular outcomes in type 2 diabetes: a recurrent neural network analysis.Cardiovasc Diabetol 2022 08 22;21(1)

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