Scholarlink

Aim <p>This study aims to predict risk factors for hypoglycemia in patients with type 2 diabetes mellitus (T2DM) using continuous glucose monitoring (CGM) and with time in range (TIR) &gt; 70%.</p> Methods <p>Data from 111 patients with T2DM who underwent CGM with TIR &gt; 70% were analyzed. A hypoglycemia episode was defined as CGM-detected glucose &lt; 3.9mmol/L sustained for at least 5&#xa0;min. Logistic regression analysis was performed to examine the relationship between hypoglycemia and mean blood glucose (MBG), glycemic variability (GV) metrics [including mean amplitude of glucose excursion (MAGE), largest amplitude of glycemic excursion (LAGE), mean of daily difference (MODD), coefficient of variation (CV), standard deviation (SD)], and low blood glucose index (LBGI). A nomogram model was constructed, and its diagnostic performance was assessed. Data were bootstrapped 1000 times for internal validation, and a calibration curve was drawn to evaluate the model’s predictive ability. Decision curve analysis was performed to assess its clinical usefulness.</p> Results <p>Among the 111 included patients, 53 experienced hypoglycemic event during wearing CGM (47.75%). GV metrics were higher in hypoglycemia group, while MBG was lower. The multivariable logistic regression analysis showed that the MBG, GV metrics, LBGI were independently associated with hypoglycemia. The receiver operating characteristics (ROC) analysis indicated that the area under the curve (AUC) for the MBG-SD-LBGI model was 0.93 (95% CI = 0.88–0.97). The calibration curve showed good consistency between the predicted and observed probabilities. Decision curve analysis demonstrated strong clinical applicability.</p> Conclusion <p>This study demonstrates a significant correlation between CGM metrics and hypoglycemia in patients with T2DM who achieved TIR &gt; 70%. These findings suggest that CGM metrics can predict the risk of hypoglycemia in T2DM patients with a TIR &gt; 70%, and the nomogram developed from these metrics holds strong potential for clinical application.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A CGM-Based model for predicting hypoglycemia in type 2 diabetes patients with TIR in target

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月24日

查看全文
  • Jianwen Lu,
  • Danrui Chen,
  • Beisi Lin,
  • Zhigu Liu,
  • Yanling Yang,
  • Ling He,
  • Jinhua Yan,
  • Daizhi Yang,
  • Wen Xu

摘要

Aim

This study aims to predict risk factors for hypoglycemia in patients with type 2 diabetes mellitus (T2DM) using continuous glucose monitoring (CGM) and with time in range (TIR) > 70%.

Methods

Data from 111 patients with T2DM who underwent CGM with TIR > 70% were analyzed. A hypoglycemia episode was defined as CGM-detected glucose < 3.9mmol/L sustained for at least 5 min. Logistic regression analysis was performed to examine the relationship between hypoglycemia and mean blood glucose (MBG), glycemic variability (GV) metrics [including mean amplitude of glucose excursion (MAGE), largest amplitude of glycemic excursion (LAGE), mean of daily difference (MODD), coefficient of variation (CV), standard deviation (SD)], and low blood glucose index (LBGI). A nomogram model was constructed, and its diagnostic performance was assessed. Data were bootstrapped 1000 times for internal validation, and a calibration curve was drawn to evaluate the model’s predictive ability. Decision curve analysis was performed to assess its clinical usefulness.

Results

Among the 111 included patients, 53 experienced hypoglycemic event during wearing CGM (47.75%). GV metrics were higher in hypoglycemia group, while MBG was lower. The multivariable logistic regression analysis showed that the MBG, GV metrics, LBGI were independently associated with hypoglycemia. The receiver operating characteristics (ROC) analysis indicated that the area under the curve (AUC) for the MBG-SD-LBGI model was 0.93 (95% CI = 0.88–0.97). The calibration curve showed good consistency between the predicted and observed probabilities. Decision curve analysis demonstrated strong clinical applicability.

Conclusion

This study demonstrates a significant correlation between CGM metrics and hypoglycemia in patients with T2DM who achieved TIR > 70%. These findings suggest that CGM metrics can predict the risk of hypoglycemia in T2DM patients with a TIR > 70%, and the nomogram developed from these metrics holds strong potential for clinical application.