Diabetes, obesity & metabolism · 2025
Nomogram-based prediction of hypoglycemia risk in elderly community-dwelling patients with type 2 diabetes: Development and validation in a multicentre real-world cohort.
СтатьяМногоцентровое исследованиеВалидационное исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Chen Lingxue, Wang Li, Fu Adan, Yang Jing, Yi Lan, Wang Zhongjing, Wang Juan, Cheng Shuang
Аннотация
AIMS: Hypoglycemia is one of the most common and challenging adverse events in diabetes management. Recurrent episodes increase both the economic burden and all-cause mortality among elderly patients with diabetes living at home. This study aimed to develop and internally validate a nomogram based on real-world data to predict 6-month hypoglycemia risk in elderly community-dwelling type 2 diabetes mellitus (T2DM) patients.
MATERIALS AND METHODS: A multicentre prospective cohort of 815 elderly T2DM patients was recruited from five community health centres between 1 March 2024 and 31 May 2024. Each participant was followed for 6 months after enrolment. The primary endpoint was the first hypoglycemic event during follow-up. Candidate variables identified by univariate analysis were refined via least absolute shrinkage and selection operator (LASSO) regression and incorporated into multivariable logistic regression to construct the model. Internal validation was performed with bootstrap resampling. Model performance was assessed by the area under the receiver operating characteristic curve (AUC), calibration plots, decision curve analysis (DCA), and clinical impact curves (CIC).
RESULTS: The 6-month cumulative incidence of hypoglycemia was 17.5% in the study. The final model included 10 independent predictors: history of hypoglycemia, HbA1c, BMI, self-management ability, living situation, exercise intensity, frequency of blood glucose monitoring, medication use, insulin use, and hypoglycemia fear-behaviour scale score. The model achieved an AUC of 0.80 with good calibration and clinical utility.
CONCLUSIONS: This nomogram accurately quantifies the 6-month risk of hypoglycemia in elderly community-dwelling T2DM patients. It provides evidence-based decision support for hypoglycemia prevention in primary healthcare settings and may assist community physicians in delivering individualised chronic disease management.
MATERIALS AND METHODS: A multicentre prospective cohort of 815 elderly T2DM patients was recruited from five community health centres between 1 March 2024 and 31 May 2024. Each participant was followed for 6 months after enrolment. The primary endpoint was the first hypoglycemic event during follow-up. Candidate variables identified by univariate analysis were refined via least absolute shrinkage and selection operator (LASSO) regression and incorporated into multivariable logistic regression to construct the model. Internal validation was performed with bootstrap resampling. Model performance was assessed by the area under the receiver operating characteristic curve (AUC), calibration plots, decision curve analysis (DCA), and clinical impact curves (CIC).
RESULTS: The 6-month cumulative incidence of hypoglycemia was 17.5% in the study. The final model included 10 independent predictors: history of hypoglycemia, HbA1c, BMI, self-management ability, living situation, exercise intensity, frequency of blood glucose monitoring, medication use, insulin use, and hypoglycemia fear-behaviour scale score. The model achieved an AUC of 0.80 with good calibration and clinical utility.
CONCLUSIONS: This nomogram accurately quantifies the 6-month risk of hypoglycemia in elderly community-dwelling T2DM patients. It provides evidence-based decision support for hypoglycemia prevention in primary healthcare settings and may assist community physicians in delivering individualised chronic disease management.