老年午睡者衰弱风险预测模型的开发和验证。

IF 3.9

Experimental gerontology Pub Date : 2025-03-01 DOI:10.1016/j.exger.2025.112723

Lijing Chen , Jiaxian Wang , Ning Liu , Li Geng , Jiahui Li , Aifang He , Xuemei Shi , Yi Li

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引用次数: 0

摘要

背景：老年人身体虚弱已受到社会的广泛关注，尤其是午睡者。本研究的目的是为午睡者建立一个虚弱的预测模型。方法：数据来源为中国健康与退休纵向研究，对1830名老年午睡者进行队列研究。我们采用最小绝对收缩和选择算子从多因素中筛选最佳预测因子，采用logistic回归分析探索老年睡眠者衰弱的最佳预测因子，并采用nomogram建立预测模型。采用标定曲线评价模型的精度，通过分析特征曲线和决策曲线下的面积来评价模型的预测性能。结果：老年午睡者的虚弱患病率为28.9 %（528/1830）。慢性疾病、体力活动、睡眠质量、疼痛、疲劳、抑郁、午睡时间和夜间睡眠时间是老年午睡者身体虚弱的最佳预测因素。训练集的曲线下面积（AUC）为0.751(95 %置信区间[CI] = 0.724-0.779)，特异性为0.662，敏感性为0.711。验证集的AUC为0.781(95 % CI = 0.749-0.812)，特异性为0.730，敏感性为0.714。Hosmer-Lemeshow检验值均为p > 0.05。模态图模型具有良好的一致性和准确性。结论：我们构建了一个nomogram，可以作为一个有价值和方便的工具来评估老年午睡者的虚弱患病率。

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Development and validation of a risk prediction model for frailty in older nappers

Background

Frailty among older adults has received widespread attention from society, especially among nappers. The objective of this study was to develop a frailty prediction model for nappers.

Methods

The data source was the China Health and Retirement Longitudinal Study, with a cohort of 1830 older nappers. We used the least absolute shrinkage and selection operator to screen the best predictors from multiple factors, logistic regression analysis to explore the best predictors of frailty in older nappers, and nomogram to establish a prediction model. A calibration curve was used to evaluate the precision of the model, and the predictive performance was assessed by analyzing the area under the characteristic and decision curves.

Results

The prevalence of frailty among older nappers was 28.9 % (528/1830). Chronic diseases, physical activity, sleep quality, pain, fatigue, depression, nap duration, and nighttime sleep duration were the best predictive factors for frailty in older nappers. The area under the curve (AUC) in the training set was 0.751 (95 % confidence interval [CI] = 0.724–0.779) with a specificity of 0.662 and sensitivity of 0.711. The AUC in the validation set was 0.781 (95 % CI = 0.749–0.812) with a specificity of 0.730 and sensitivity of 0.714. The Hosmer–Lemeshow test values were both p > 0.05. The nomogram model showed good concordance and accuracy.