Background. The mortality rate among older persons with diabetes has been steadily increasing, resulting in significant health and economic burdens on both society and individuals. The objective of this study is to develop and validate a predictive nomogram for estimating the 5-year all-cause mortality risk in older persons with T2D (T2D). Methods. We obtained data from the National Health and Nutrition Survey (NHANES). A random 7 : 3 split was made between the training and validation sets. By linking the national mortality index up until December 31, 2019, we ensured a minimum of 5 years of follow-up to assess all-cause mortality. A nomogram was developed in the training cohort using a logistic regression model as well as a least absolute shrinkage and selection operator (LASSO) regression model for predicting the 5-year risk of all-cause mortality. Finally, the prediction performance of the nomogram is evaluated using several validation methods. Results. We constructed a comprehensive prediction model based on the results of multivariate analysis and LASSO binomial regression. These models were then validated using data from the validation cohort. The final model includes four independent predictors: age, gender, estimated glomerular filtration rate, and white blood cell count. The C-index values for the training and validation cohorts were 0.748 and 0.762, respectively. The calibration curve demonstrates satisfactory consistency between the two cohorts. Conclusions. The newly developed nomogram proves to be a valuable tool in accurately predicting the 5-year all-cause mortality risk among older persons with diabetes, providing crucial information for tailored interventions.

中文翻译：

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揭开隐藏的负担：估计老年 2 型糖尿病患者的全因死亡风险

背景。老年糖尿病患者的死亡率一直在稳步上升，给社会和个人带来了巨大的健康和经济负担。本研究的目的是开发并验证预测列线图，用于估计老年 T2D (T2D) 患者的 5 年全因死亡风险。方法。我们从国家健康和营养调查 (NHANES) 获得数据。训练集和验证集按照 7:3 的比例随机分配。通过将截至 2019 年 12 月 31 日的全国死亡率指数联系起来，我们确保了至少 5 年的随访时间来评估全因死亡率。使用逻辑回归模型以及最小绝对收缩和选择算子 (LASSO) 回归模型在训练队列中开发列线图，用于预测 5 年全因死亡率风险。最后，使用多种验证方法评估列线图的预测性能。结果。我们根据多变量分析和LASSO二项式回归的结果构建了综合预测模型。然后使用验证队列中的数据对这些模型进行验证。最终模型包括四个独立的预测因子：年龄、性别、估计肾小球滤过率和白细胞计数。训练组和验证组的 C 指数值分别为 0.748 和 0.762。校准曲线显示两个队列之间令人满意的一致性。结论。新开发的列线图被证明是准确预测老年糖尿病患者 5 年全因死亡风险的宝贵工具，为量身定制的干预措施提供了重要信息。