Objective  To explore the predictive value of blood glucose variability indices [coefficient of variation (CV) of blood glucose, mean amplitude of glycemic excursions (MAGE), and largest amplitude of glycemic excursions (LAGE)] for 6-month readmission in hospitalized patients with chronic obstructive pulmonary disease (COPD) complicated with respiratory failure, and analyze the dose-effect relationship. Meanwhile, to compare the predictive efficacy of blood glucose variability indices between subgroups with and without type 2 diabetes. Methods  The clinical data of 102 inpatients with COPD complicated with respiratory failure admitted to Zhangjiagang Hospital Affiliated to Soochow University from January 2022 to March 2025 were retrospectively collected. According to whether they were readmitted within 6 months, they were divided into a readmission group (32 cases) and a non-readmission group (70 cases). At the same time, they were divided into a type 2 diabetes subgroup (41 cases) and a non-type 2 diabetes subgroup (61 cases) based on whether they had type 2 diabetes. The independent risk factors for 6-month readmission were analyzed by the multivariate Cox proportional hazards regression model, and the regression models for the total population and different diabetes subgroups were constructed respectively. The predictive efficacy of blood glucose variability indices was evaluated by the receiver operating characteristic (ROC) curve, and the predictive value of blood glucose variability indices in different subgroups was compared. The dose-effect relationship between blood glucose variability index and readmission risk was analyzed by the restricted cubic spline (RCS) model, and the dose-effect characteristics of different diabetes subgroups were explored. Results  The multivariate Cox regression model showed that MAGE, CV, LAGE, and APACHEⅡ score were independent risk factors for 6-month readmission, while albumin was an independent protective factor (P<0.05). In the type 2 diabetes subgroup, MAGE and APACHEⅡ score were independent risk factors for 6-month readmission (P<0.05); in the non-type 2 diabetes subgroup, MAGE, CV, and albumin were independent influencing factors for 6-month readmission (P<0.05). ROC curve analysis showed that in the total population, the areas under curve (AUCs) of CV, MAGE, and LAGE for predicting 6-month readmission were 0.789, 0.809, and 0.753, respectively, all of which were lower than the AUC of the combined detection (0.891). In the type 2 diabetes subgroup, the AUC of MAGE for predicting readmission was 0.825, and in the non-type 2 diabetes subgroup, the AUC of MAGE for predicting readmission was 0.796, both of which were the most effective blood glucose variability indicators in their respective subgroups. The RCS model combined with Cox regression analysis showed that in the total population, MAGE was linearly and positively correlated with the 6-month readmission risk of patients (non-linear test P=0.326), with no obvious threshold effect: when MAGE≤5.83 mmol/L, the trend of readmission risk increase was relatively gentle; when MAGE>5.83 mmol/L, the readmission risk increased at an accelerating rate, and for every 1 mmol/L increase in MAGE, the 6-month readmission risk of patients significantly increased by 18.6%. In the type 2 diabetes subgroup, MAGE was linearly and positively correlated with the readmission risk (non-linear test P=0.289), and for every 1 mmol/L increase in MAGE, the readmission risk increased by 21.5%. In the subgroup without type 2 diabetes, MAGE was also linearly and positively correlated with the risk of readmission (non-linear test P=0.365), and for every 1 mmol/L increase in MAGE, the risk of readmission increased by 16.3%. Conclusions  The glycemic variability indices (especially MAGE) have high predictive value for 6-month readmission of hospitalized patients with COPD and respiratory failure. Moreover. MAGE shows a linear dose-effect relationship with the risk of readmission. This predictive value holds true in both the type 2 diabetes and non-type 2 diabetes subgroups. MAGE is the optimal predictive indicator in both subgroups. Clinically, controlling blood glucose fluctuations can reduce the risk of readmission, and individualized blood glucose fluctuation control targets can be set for patients with different diabetes statuses.

Citation： CAO Xiaojun, ZHOU Ji, CHEN Minjie, CHEN Xiang. Analysis of the predictive value of glycemic variability indices for 6-month readmission of hospitalized patients with COPD and respiratory failure. Chinese Journal of Respiratory and Critical Care Medicine, 2026, 25(7): 461-468. doi: 10.7507/1671-6205.202601016 Copy

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