Augstein Petra, Heinke Peter, Vogt Lutz, Vogt Roberto, Rackow Christine, Kohnert Klaus-Dieter, Salzsieder Eckhard
Institute for Diabetes "Gerhardt Katsch" Karlsburg, Greifswalder Str. 11e, 17495, Karlsburg, Germany.
Diabetes Service Center Karlsburg, Greifswalder Str. 11e, 17495, Karlsburg, Germany.
BMC Endocr Disord. 2015 May 1;15:22. doi: 10.1186/s12902-015-0019-0.
Continuous glucose monitoring (CGM) has revolutionised diabetes management. CGM enables complete visualisation of the glucose profile, and the uncovering of metabolic 'weak points'. A standardised procedure to evaluate the complex data acquired by CGM, and to create patient-tailored recommendations has not yet been developed. We aimed to develop a new patient-tailored approach for the routine clinical evaluation of CGM profiles. We developed a metric allowing screening for profiles that require therapeutic action and a method to identify the individual CGM parameters with improvement potential.
Fifteen parameters frequently used to assess CGM profiles were calculated for 1,562 historic CGM profiles from subjects with type 1 or type 2 diabetes. Factor analysis and varimax rotation was performed to identify factors that accounted for the quality of the profiles.
We identified five primary factors that determined CGM profiles (central tendency, hyperglycaemia, hypoglycaemia, intra- and inter-daily variations). One parameter from each factor was selected for constructing the formula for the screening metric, (the 'Q-Score'). To derive Q-Score classifications, three diabetes specialists independently categorised 766 CGM profiles into groups of 'very good', 'good', 'satisfactory', 'fair', and 'poor' metabolic control. The Q-Score was then calculated for all profiles, and limits were defined based on the categorised groups (<4.0, very good; 4.0-5.9, good; 6.0-8.4, satisfactory; 8.5-11.9, fair; and ≥12.0, poor). Q-Scores increased significantly (P <0.01) with increasing antihyperglycaemic therapy complexity. Accordingly, the percentage of fair and poor profiles was higher in insulin-treated compared with diet-treated subjects (58.4% vs. 9.3%). In total, 90% of profiles categorised as fair or poor had at least three parameters that could potentially be optimised. The improvement potential of those parameters can be categorised as 'low', 'moderate' and 'high'.
The Q-Score is a new metric suitable to screen for CGM profiles that require therapeutic action. Moreover, because single components of the Q-Score formula respond to individual weak points in glycaemic control, parameters with improvement potential can be identified and used as targets for optimising patient-tailored therapies.
持续葡萄糖监测（CGM）彻底改变了糖尿病管理方式。CGM能够完整呈现葡萄糖曲线，并发现代谢“薄弱点”。目前尚未开发出一种标准化程序来评估CGM获取的复杂数据并制定针对患者的个性化建议。我们旨在开发一种针对CGM曲线进行常规临床评估的新的个性化方法。我们开发了一种指标，用于筛查需要采取治疗行动的曲线，并确定具有改善潜力的个体CGM参数的方法。
针对1562例1型或2型糖尿病患者的历史CGM曲线，计算了常用于评估CGM曲线的15个参数。进行因子分析和方差最大化旋转以识别解释曲线质量的因素。
我们确定了决定CGM曲线的五个主要因素（中心趋势、高血糖、低血糖、日内和日间变化）。从每个因素中选择一个参数来构建筛查指标公式（“Q值”）。为了得出Q值分类，三位糖尿病专家将766条CGM曲线独立分类为代谢控制“非常好”、“好”、“满意”、“一般”和“差”的组。然后计算所有曲线的Q值，并根据分类组定义界限（<4.0，非常好；4.0 - 5.9，好；6.0 - 8.4，满意；8.5 - 11.9，一般；≥12.0，差）。随着降糖治疗复杂性的增加，Q值显著升高（P <0.01）。因此，与饮食治疗的受试者相比，胰岛素治疗的受试者中一般和差的曲线百分比更高（58.4%对9.3%）。总体而言，分类为一般或差的曲线中有90%至少有三个参数可能可以优化。这些参数的改善潜力可分为“低”、“中”和“高”。
Q值是一种适用于筛查需要采取治疗行动的CGM曲线的新指标。此外，由于Q值公式的单个组成部分对血糖控制中的个体薄弱点有反应，因此可以识别具有改善潜力的参数，并将其用作优化针对患者的个性化治疗的目标。
