Chen Li, Liu Fengzhen, Luo Yanling, Chen Lili, Li Xia, Wang Xiaolin, Zhao Yu, Guo Liangyun, Zhang Chunquan
Department of Ultrasound, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China.
Jiangxi Provincial Maternal and Child Health Hospital, Nanchang University, Nanchang, China.
Quant Imaging Med Surg. 2025 Mar 3;15(3):2581-2591. doi: 10.21037/qims-24-95. Epub 2024 Nov 21.
Left ventricular longitudinal myocardial systolic dysfunction (LVSD) represents a critical risk factor for diabetes-related cardiovascular events. This study aimed to develop a well-calibrated and convenient risk prediction model to investigate early predictive risk of LVSD in type 2 diabetes mellitus (T2DM) patients with preserved left ventricular ejection fraction (LVEF), and to evaluate its performance.
A total of 310 patients with T2DM from June 2020 to October 2021 at the Second Affiliated Hospital of Nanchang University were prospectively enrolled and randomly assigned to a training set (n=217) and a validation set (n=93) at a 7:3 ratio. Basic characteristics, laboratory tests, echocardiographic parameters, two-dimensional global longitudinal strain (GLS) parameters, and medication use were collected. LVSD in patients with T2DM with preserved LVEF was defined as an absolute value of GLS <18%. The least absolute shrinkage and selection operator (LASSO) regression was applied to optimize the screening variables, followed by multivariate logistic regression to identify independent risk factors for predicting LVSD, and a nomogram was established. The receiver operating characteristic (ROC) curves, area under the curve (AUC) values, calibration plot, and decision curve analysis (DCA) were used to verify and evaluate the nomogram's discrimination, calibration, and clinical validity.
A total of 8 independent risk predictors of LVSD in T2DM were extracted and incorporated into the nomogram, as evaluated using LASSO regression analysis and multivariate logistic regression analysis, including body mass index (BMI), T2DM duration, blood urea nitrogen (BUN), left ventricular (LV) mass index, E/e', diabetic retinopathy, diabetic peripheral neuropathy, and diabetic nephropathy. The nomogram indicated excellent prediction properties with AUC values of 0.922 and 0.918 for the training set and validation set, respectively. Further, the predictive nomogram demonstrated outstanding consistency between the predicted probability and the actual probability in terms of the calibration plots. DCA showed also that the predicted nomogram was clinically beneficial.
This study identified independent risk factors for LVSD in patients with T2DM and developed a predictive nomogram. It allows for clinical decision-making to timely intervene or delay the occurrence of LVSD.
左心室心肌纵向收缩功能障碍（LVSD）是糖尿病相关心血管事件的关键危险因素。本研究旨在建立一个校准良好且便捷的风险预测模型，以研究左心室射血分数（LVEF）保留的2型糖尿病（T2DM）患者LVSD的早期预测风险，并评估其性能。
前瞻性纳入2020年6月至2021年10月在南昌大学第二附属医院就诊的310例T2DM患者，并按7:3的比例随机分为训练集（n = 217）和验证集（n = 93）。收集基本特征、实验室检查、超声心动图参数、二维整体纵向应变（GLS）参数及用药情况。LVEF保留的T2DM患者的LVSD定义为GLS绝对值<18%。应用最小绝对收缩和选择算子（LASSO）回归优化筛选变量，随后进行多因素逻辑回归以确定预测LVSD的独立危险因素，并建立列线图。采用受试者工作特征（ROC）曲线、曲线下面积（AUC）值、校准图和决策曲线分析（DCA）来验证和评估列线图的辨别力、校准度及临床有效性。
通过LASSO回归分析和多因素逻辑回归分析评估，共提取了8个T2DM患者LVSD的独立风险预测因素并纳入列线图，包括体重指数（BMI）、T2DM病程、血尿素氮（BUN）、左心室（LV）质量指数、E/e'、糖尿病视网膜病变、糖尿病周围神经病变和糖尿病肾病。列线图显示出良好的预测性能，训练集和验证集的AUC值分别为0.922和0.918。此外，在校准图方面，预测列线图在预测概率和实际概率之间表现出出色的一致性。DCA还表明预测列线图具有临床益处。
本研究确定了T2DM患者LVSD的独立危险因素并建立了预测列线图。它有助于临床决策，以便及时干预或延缓LVSD的发生。
