College of Sport Science, Sungkyunkwan University, 2066 Seoburo, 16419, Jangan-gu, Suwon, Republic of Korea.
Sci Rep. 2024 Sep 8;14(1):20909. doi: 10.1038/s41598-024-71904-3.
This study aimed to develop and validate distinct nomogram models for assessing CVD risk in individuals with prediabetes and diabetes. In a cross-sectional study design, we examined data from 2294 prediabetes and 1037 diabetics who participated in the National Health and Nutrition Examination Survey, which was conducted in the United States of America between 2007 and 2018. The dataset was randomly divided into training and validation cohorts at a ratio of 0.75-0.25. The Boruta feature selection method was used in the training cohort to identify optimal predictors for CVD diagnosis. A web-based dynamic nomogram was developed using the selected features, which were validated in the validation cohort. The Hosmer-Lemeshow test was performed to assess the nomogram's stability and performance. Receiver operating characteristics and calibration curves were used to assess the effectiveness of the nomogram. The clinical applicability of the nomogram was evaluated using decision curve analysis and clinical impact curves. In the prediabetes cohort, the CVD risk prediction nomogram included nine risk factors: age, smoking status, platelet/lymphocyte ratio, platelet count, white blood cell count, red cell distribution width, lactate dehydrogenase level, sleep disorder, and hypertension. In the diabetes cohort, the CVD risk prediction nomogram included eleven risk factors: age, material status, smoking status, systemic inflammatory response index, neutrophil-to-lymphocyte ratio, red cell distribution width, lactate dehydrogenase, high-density lipoprotein cholesterol, sleep disorder, hypertension, and physical activity. The nomogram models developed in this study have good predictive and discriminant utility for predicting CVD risk in patients with prediabetes and diabetes.
本研究旨在开发和验证用于评估糖尿病前期和糖尿病患者心血管疾病（CVD）风险的独特列线图模型。在一项横断面研究设计中，我们研究了 2294 名糖尿病前期患者和 1037 名糖尿病患者的数据，这些患者参加了美国 2007 年至 2018 年期间进行的国家健康和营养调查。数据集以 0.75-0.25 的比例随机分为训练和验证队列。在训练队列中，使用 Boruta 特征选择方法确定 CVD 诊断的最佳预测因素。使用选定的特征使用基于网络的动态列线图，在验证队列中对其进行验证。Hosmer-Lemeshow 检验用于评估列线图的稳定性和性能。使用接收者操作特征和校准曲线评估列线图的有效性。使用决策曲线分析和临床影响曲线评估列线图的临床适用性。在糖尿病前期队列中，CVD 风险预测列线图包括九个危险因素：年龄、吸烟状况、血小板/淋巴细胞比值、血小板计数、白细胞计数、红细胞分布宽度、乳酸脱氢酶水平、睡眠障碍和高血压。在糖尿病队列中，CVD 风险预测列线图包括十一个危险因素：年龄、物质状况、吸烟状况、全身炎症反应指数、中性粒细胞/淋巴细胞比值、红细胞分布宽度、乳酸脱氢酶、高密度脂蛋白胆固醇、睡眠障碍、高血压和身体活动。本研究开发的列线图模型在预测糖尿病前期和糖尿病患者的 CVD 风险方面具有良好的预测和判别能力。
