Wang Guoqing, Yi Xiang-Long
Department of Ophthalmology, First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China.
Xinjiang Medical University, Urumqi, Xinjiang, China.
Front Med (Lausanne). 2024 Oct 21;11:1452756. doi: 10.3389/fmed.2024.1452756. eCollection 2024.
The aim of this study is to develop and validate a novel multivariable prediction model capable of accurately estimating the probability of cataract development, utilizing parameters such as blood biochemical markers and age.
This population-based cross-sectional study comprised 9,566 participants drawn from the National Health and Nutrition Examination Survey (NHANES) across the 2005-2008 cycles.
Demographic information and laboratory test results from the patients were collected and analyzed using LASSO regression and multivariate logistic regression to accurately capture the influence of biochemical indicators on the outcomes. The SHAP (Shapley Additive Explanations) scale was employed to assess the importance of each clinical feature, excluding age. A multivariate logistic regression model was then developed and visualized as a nomogram. To assess the model's performance, its discrimination, calibration, and clinical utility were evaluated using receiver operating characteristic (ROC) curves, 10-fold cross-validation, Hosmer-Lemeshow calibration curves, and decision curve analysis (DCA), respectively.
Logistic regression analysis identified age, erythrocyte folate (nmol/L), blood glucose (mmol/L), and blood urea nitrogen (mmol/L) as independent risk factors for cataract, and these variables were incorporated into a multivariate logistic regression-based nomogram for cataract risk prediction. The area under the receiver operating characteristic (ROC) curve (AUC) for cataract risk prediction was 0.917 (95% CI: 0.9067-0.9273) in the training cohort, and 0.9148 (95% CI: 0.8979-0.9316) in the validation cohort. The Hosmer-Lemeshow calibration curve demonstrated a good fit, indicating strong model calibration. Ten-fold cross-validation confirmed the logistic regression model's robust predictive performance and stability during internal validation. Decision curve analysis (DCA) demonstrated that the nomogram prediction model provided greater clinical benefit for predicting cataract risk when the patient's threshold probability ranged from 0.10 to 0.90.
This study identified blood urea nitrogen (mmol/L), serum glucose (mmol/L), and erythrocyte folate (mmol/L) as significant risk factors for cataract. A risk prediction model was developed, demonstrating strong predictive accuracy and clinical utility, offering clinicians a reliable tool for early and effective diagnosis. Cataract development may be delayed by reducing levels of blood urea nitrogen, serum glucose, and erythrocyte folate through lifestyle improvements and dietary modifications.
本研究旨在开发并验证一种新型多变量预测模型，该模型能够利用血液生化标志物和年龄等参数准确估计白内障发生的概率。
这项基于人群的横断面研究纳入了2005 - 2008年周期美国国家健康与营养检查调查（NHANES）中的9566名参与者。
收集患者的人口统计学信息和实验室检查结果，并使用套索回归和多变量逻辑回归进行分析，以准确捕捉生化指标对结果的影响。采用SHAP（Shapley加性解释）量表评估每个临床特征（不包括年龄）的重要性。然后建立多变量逻辑回归模型并将其可视化成列线图。为评估该模型的性能，分别使用受试者工作特征（ROC）曲线、十折交叉验证、Hosmer-Lemeshow校准曲线和决策曲线分析（DCA）对其区分度、校准度和临床实用性进行评估。
逻辑回归分析确定年龄、红细胞叶酸（nmol/L）、血糖（mmol/L）和血尿素氮（mmol/L）为白内障的独立危险因素，这些变量被纳入基于多变量逻辑回归的白内障风险预测列线图。在训练队列中，白内障风险预测的受试者工作特征（ROC）曲线下面积（AUC）为0.917（95%CI：0.9067 - 0.9273），在验证队列中为0.9148（95%CI：0.8979 - 0.9316）。Hosmer-Lemeshow校准曲线显示拟合良好，表明模型校准度强。十折交叉验证证实了逻辑回归模型在内部验证期间具有强大的预测性能和稳定性。决策曲线分析（DCA）表明，当患者的阈值概率在0.10至0.90范围内时，列线图预测模型在预测白内障风险方面提供了更大的临床益处。
本研究确定血尿素氮（mmol/L）、血糖（mmol/L）和红细胞叶酸（mmol/L）为白内障的重要危险因素。开发了一种风险预测模型，显示出强大的预测准确性和临床实用性，为临床医生提供了一种早期有效诊断的可靠工具。通过改善生活方式和调整饮食来降低血尿素氮、血糖和红细胞叶酸水平，可能会延缓白内障的发生。
