International journal of chronic obstructive pulmonary disease · 2026
A Nomogram for Predicting 5-Year Risk of New-Onset Type 2 Diabetes in Patients with COPD: A Two-Center Retrospective Cohort Study.
СтатьяМногоцентровое исследованиеПоддержка исследования: вне правительства СШАВалидационное исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Pan Jingjing, Chen Feiju, Liao Weifeng, Ye Weilong, Chen Bainian, Lu Tong, Li Kunning, Liu Fang, Deng Xuyu, Sun Ting, Chen Riken, Yao Weimin
Аннотация
PURPOSE: Type 2 diabetes mellitus (T2DM) often coexists with chronic obstructive pulmonary disease (COPD) and is accompanied by adverse outcomes, including high mortality. We developed and externally validated a nomogram to predict the 5-year new-onset T2DM risk in COPD patients without prior diabetes.
PATIENTS AND METHODS: Patients hospitalized for COPD between May 2018 and December 2019 were enrolled and followed until December 2024. The development cohort was randomly divided into training and internal validation sets at a 7:3 ratio. In the training set, predictors were selected via least absolute shrinkage and selection operator (LASSO) regression and used to construct a nomogram. Model discrimination was evaluated by the receiver operating characteristic (ROC) curve with the area under the curve (AUC). Calibration curves and decision curve analysis (DCA) were used to assess calibration and clinical utility.
RESULTS: A total of 998 patients in our development cohort, 153 (15.3%) developed new-onset T2DM during follow-up. The final nomogram contained four predictors: Triglyceride-glucose index (TyG), hypertension (HTN), cardiovascular disease (CVD), and high-density lipoprotein (HDL). The AUCs in the training and internal validation sets were 0.749 and 0.758. External validation in an independent cohort of 1,018 patients, including 132 incident T2DM cases, produced an AUC of 0.798. DCA plots showed net clinical benefit across clinically relevant thresholds.
CONCLUSION: This nomogram demonstrated good discrimination and calibration, and may facilitate risk stratification for T2DM among COPD patients.
PATIENTS AND METHODS: Patients hospitalized for COPD between May 2018 and December 2019 were enrolled and followed until December 2024. The development cohort was randomly divided into training and internal validation sets at a 7:3 ratio. In the training set, predictors were selected via least absolute shrinkage and selection operator (LASSO) regression and used to construct a nomogram. Model discrimination was evaluated by the receiver operating characteristic (ROC) curve with the area under the curve (AUC). Calibration curves and decision curve analysis (DCA) were used to assess calibration and clinical utility.
RESULTS: A total of 998 patients in our development cohort, 153 (15.3%) developed new-onset T2DM during follow-up. The final nomogram contained four predictors: Triglyceride-glucose index (TyG), hypertension (HTN), cardiovascular disease (CVD), and high-density lipoprotein (HDL). The AUCs in the training and internal validation sets were 0.749 and 0.758. External validation in an independent cohort of 1,018 patients, including 132 incident T2DM cases, produced an AUC of 0.798. DCA plots showed net clinical benefit across clinically relevant thresholds.
CONCLUSION: This nomogram demonstrated good discrimination and calibration, and may facilitate risk stratification for T2DM among COPD patients.