Dong Milan, Liu Wenjun, Luo Yetao, Li Jing, Huang Bo, Zou Yingbo, Liu Fuyan, Zhang Guoying, Chen Ju, Jiang Jianyu, Duan Ling, Xiong Daoxue, Fu Hongmin, Yu Kai
Department of Critical Care Medicine, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China.
Department of Pediatrics, The People's Hospital of Yubei District of Chongqing City, Chongqing, China.
Front Nutr. 2022 Feb 23;9:757982. doi: 10.3389/fnut.2022.757982. eCollection 2022.
Glucose variability (GV) is a common complication of dysglycemia in critically ill patients. However, there are few studies on the role of GV in the prognosis of pediatric patients, and there is no consensus on the appropriate method for GV measurement. The objective of this study was to determine the "optimal" index of GV in non-diabetic critically ill children in a prospective multicenter cohort observational study. Also, we aimed to confirm the potential association between GV and unfavorable outcomes and whether this association persists after controlling for hypoglycemia or hyperglycemia.
Blood glucose values were recorded for the first 72 h and were used to calculate the GV for each participant. Four different metrics [SD, glycemic lability index (GLI), mean absolute glucose (MAG), and absolute change of percentage (ACACP)] were considered and compared to identify the "best" GV index associated with poor prognosis in non-diabetic critically ill children. Among the four metrics, the SD was most commonly used in previous studies, while GLI- and MAG-integrated temporal information, that is the rate and magnitude of change and the time interval between glucose measurements. The fourth metric, the average consecutive ACACP, was introduced in our study, which can be used in real-time clinical decisions. The primary outcome of this study was the 28-day mortality. The receiver operating characteristic (ROC) curve analysis was conducted to compare the predictive power of different metrics of GV for the primary outcome. The GV index with the largest area under ROC curve (AUC) was chosen for subsequent multivariate analyses. Multivariate Cox regression analysis was performed to identify the potential predictors of the outcome. To compare the contribution in 28-day mortality prognosis between glycemic variability and hyper- or hypoglycemia, performance metrics were calculated, which included AUC, net reclassification improvement (NRI), and integrated discrimination improvement (IDI).
Among 780 participants, 12.4% ( = 97) died within 28 days after admission to the pediatric intensive care unit (PICU). Statistically significant differences were found between survivors and non-survivors in terms of four GV metrics (SD, GLI, MAG, and ACACP), in which MAG (: 0.762, 95% CI: 0.705-0.819, < 0.001) achieved the largest AUC and showed a strong independent association with ICU mortality. Subsequent addition of MAG to the multivariate Cox model for hyperglycemia resulted in further quantitative evolution of the model statistics ( = 0.651-0.681, = 0.001; : 0.017, = 0.044; : 0.224, = 0.186). The impact of hyperglycemia (adjusted hazard ratio []: 1.419, 95% : 0.815-2.471, = 0.216) on outcome was attenuated and no longer statistically relevant after adjustment for MAG (: 2.455, 95% : 1.411-4.270, = 0.001).
GV is strongly associated with poor prognosis independent of mean glucose level, demonstrating more predictive power compared with hypoglycemia and hyperglycemia after adjusting for confounding factors. GV metrics that contain information, such as time and rate of change, are the focus of future research; thus, the MAG may be a good choice. The findings of this study emphasize the crucial role of GVs in children in the PICU. Clinicians should pay more attention to GV for clinical glucose management.
血糖变异性(GV)是危重症患者血糖异常的常见并发症。然而,关于GV在儿科患者预后中的作用的研究较少,并且对于GV测量的合适方法尚无共识。本研究的目的是在前瞻性多中心队列观察性研究中确定非糖尿病危重症儿童GV的“最佳”指标。此外,我们旨在确认GV与不良结局之间的潜在关联,以及在控制低血糖或高血糖后这种关联是否仍然存在。
记录前72小时的血糖值,并用于计算每位参与者的GV。考虑并比较了四种不同的指标[标准差(SD)、血糖波动指数(GLI)、平均绝对血糖(MAG)和绝对变化百分比(ACACP)],以确定与非糖尿病危重症儿童预后不良相关的“最佳”GV指标。在这四种指标中,SD是先前研究中最常用的,而GLI和MAG整合了时间信息,即变化的速率和幅度以及血糖测量之间的时间间隔。本研究引入了第四个指标,即平均连续ACACP,其可用于实时临床决策。本研究的主要结局是28天死亡率。进行受试者工作特征(ROC)曲线分析,以比较不同GV指标对主要结局的预测能力。选择ROC曲线下面积(AUC)最大的GV指标进行后续多变量分析。进行多变量Cox回归分析以确定结局的潜在预测因素。为了比较血糖变异性与高血糖或低血糖在28天死亡率预后中的贡献,计算了性能指标,包括AUC、净重新分类改善(NRI)和综合鉴别改善(IDI)。
在780名参与者中,12.4%(n = 97)在入住儿科重症监护病房(PICU)后28天内死亡。在四种GV指标(SD、GLI、MAG和ACACP)方面,幸存者和非幸存者之间存在统计学显著差异,其中MAG(AUC:0.762,95%CI:0.705 - 0.819,P < 0.001)达到最大AUC,并显示出与ICU死亡率有很强的独立关联。随后将MAG添加到高血糖的多变量Cox模型中导致模型统计量的进一步定量演变(AUC:0.651 - 0.681,P = 0.001;HR:0.017,P = 0.044;χ²:0.224,P = 0.186)。高血糖对结局的影响(调整后风险比[HR]:1.419,95%CI:0.815 - 2.471,P = 0.216)在调整MAG后减弱且不再具有统计学相关性(HR:2.455,9..5%CI:1.411 - 4.270,P = 0.001)。
GV与不良预后密切相关,独立于平均血糖水平,在调整混杂因素后,与低血糖和高血糖相比具有更强预测能力。包含时间和变化速率等信息的GV指标是未来研究的重点;因此,MAG可能是一个不错的选择。本研究结果强调了GV在PICU儿童中的关键作用。临床医生在临床血糖管理中应更加关注GV。