Aims: The objective of this study is to establish a predictive model using transparent machine learning (ML) to identify any drivers that characterize therapeutic inertia.
Methods: Data in the form of both descriptive and dynamic variables collected from electronic records of 1.5 million patients seen at clinics within the Italian Association of Medical Diabetologists between 2005-2019 were analyzed using logic learning machine (LLM), a "clear box" ML technique. Data were subjected to a first stage of modeling to allow ML to automatically select the most relevant factors related to inertia, and then four further modeling steps individuated key variables that discriminated the presence or absence of inertia.
Results: The LLM model revealed a key role for average glycated hemoglobin (HbA1c) threshold values correlated with the presence or absence of insulin therapeutic inertia with an accuracy of 0.79. The model indicated that a patient's dynamic rather than static glycemic profile has a greater effect on therapeutic inertia. Specifically, the difference in HbA1c between two consecutive visits, what we call the HbA1c gap, plays a crucial role. Namely, insulin therapeutic inertia is correlated with an HbA1c gap of <6.6 mmol/mol (0.6%), but not with an HbA1c gap of >11 mmol/mol (1.0%).
Conclusions: The results reveal, for the first time, the interrelationship between a patient's glycemic trend defined by sequential HbA1c measurements and timely or delayed initiation of insulin therapy. The results further demonstrate that LLM can provide insight in support of evidence-based medicine using real world data.
目的: 本研究的目的是利用透明机器学习(ML)建立预测模型, 以识别表征治疗惯性的任何驱动因素。 材料和方法: 使用逻辑学习机(LLM)(一种“clear box”ML技术)分析了从2005-2019年意大利医学糖尿病学家协会(AMD)诊所的150万名患者的电子记录中收集的描述性和动态变量形式数据。数据经过第一阶段的建模, 以允许ML自动选择与惯性相关的最相关因素, 然后再经过四个建模步骤, 对关键变量进行个性化, 以区分惯性的存在或不存在。 结果: LLM模型揭示了平均HbA1c 阈值与胰岛素治疗惯性的存在或不存在之间的关键作用, 准确率为0.79。该模型表明, 与静态血糖代谢情况相比, 患者动态血糖代谢情况对治疗惯性具有更大的影响。具体而言, 两次连续访问之间的HbA1c 差异(我们称之为HbA1c 间隙)发挥了至关重要的作用。即, 胰岛素治疗惯性与HbA1c 间隙<6.6mmol/L(0.6%)有关, 但与HbA1c 间隙>11mmol/L(1.0%)无关。 结论: 该结果揭示了由连续HbA1c 测量确定的患者血糖趋势与及时或延迟开始胰岛素治疗之间的相互关系。研究结果进一步表明, LLM可以利用真实世界的数据为循证医学(EBM)提供支持。.
Keywords: 2型糖尿病; artificial intelligence; insulin therapy; machine learning; therapeutic inertia; type 2 diabetes; 人工智能; 机器学习; 治疗惯性.
© 2023 Associazione Medici Diabetologi (AMD). Journal of Diabetes published by Ruijin Hospital, Shanghai JiaoTong University School of Medicine and John Wiley & Sons Australia, Ltd.