2型糖尿病合并勃起功能障碍患者器质性勃起功能障碍的相关危险因素分析。

Mingming Lu, Dawei Ni, Wei Wu, Chi Xu, Yanbin Zhang
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引用次数: 0

摘要

背景:糖尿病是一种常见的代谢性疾病,糖尿病勃起功能障碍(DMED)是其常见并发症之一。区分勃起功能障碍(ED)的类型是治疗的基础,但在临床实践中缺乏简单有效的工具。在这项研究中,我们试图利用糖尿病患者常见的临床数据来预测ED类型,旨在建立和评估2型糖尿病患者器质性勃起功能障碍的风险预测模型。方法:采用回顾性分析。数据来自医院的内部医疗记录系统。我们选取并分析了250例2型糖尿病患者的临床资料。采用Lasso回归进行风险因素选择,将选择的变量纳入多因素logistic回归分析,建立风险预测模型。采用自举法进行内部验证,并采用c指数、校准曲线、决策曲线分析(DCA)和受试者工作特征(ROC)曲线评价模型的鉴别性、校准性和临床有效性。结果:250例患者中有168例(67.2%)诊断为器质性ED。logistic回归分析的危险因素包括糖尿病病程、低密度脂蛋白胆固醇(LDL-C)、红细胞分布宽度(RDW)、颈动脉内膜-中膜厚度、糖尿病视网膜病变、糖尿病肾病、周围神经病变。c指数为0.827(95%可信区间(CI) = 0.772 ~ 0.882)。预测值的分布曲线与模型的标定曲线拟合良好。决策曲线分析(DCA)表明,当阈值概率在28% ~ 100%之间时,使用该模型在临床上是有益的。结论:结合糖尿病病程、颈动脉内膜-中膜厚度、糖尿病视网膜病变、糖尿病肾病、周围神经病变、RDW、LDL-C,初步建立了2型糖尿病患者有机ED的风险预测模型。该模型具有良好的预测性能。
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Analysis of Risk Factors Associated with Organic Erectile Dysfunction in Patients with Type 2 Diabetes Mellitus and Erectile Dysfunction.

Background: Diabetes mellitus is a common metabolic disorder, and diabetic erectile dysfunction (DMED) is one of its common complications. The differentiation of the types of erectile dysfunction (ED) is fundamental to treatment, yet there is a lack of simple and efficacious tools for this purpose in clinical practice. In this study, we endeavor to predict ED types using commonly available clinical data from diabetic patients, aiming to develop and assess a risk prediction model for organic erectile dysfunction in individuals with type 2 diabetes.

Methods: The study was a retrospective analysis. Data were obtained from the hospital's internal medical record system. We selected and analyzed the clinical data of 250 patients with type 2 diabetes. Lasso regression was used for risk factor selection, and the selected variables were included in a multivariate logistic regression analysis to establish the risk prediction model. Internal validation was performed using the bootstrap method, and the discrimination, calibration, and clinical effectiveness of the model were evaluated using the C-index, calibration curve, decision curve analysis (DCA), and receiver operating characteristic (ROC) curve.

Results: Among the 250 patients, 168 (67.2%) were diagnosed with organic ED. The risk factors included in the logistic regression analysis were the duration of diabetes, low-density lipoprotein cholesterol (LDL-C), red blood cell distribution width (RDW), intima-media thickness of the carotid artery, diabetic retinopathy, diabetic nephropathy, and peripheral neuropathy. The C-index was 0.827 (95% confidence interval (CI) = 0.772-0.882). The distribution curve of the predicted values and the calibration curve of the model were well fitted. The decision curve analysis (DCA) suggested that using the model could be clinically beneficial when the threshold probability was between 28% and 100%.

Conclusion: By combining the duration of diabetes, carotid artery intima-media thickness, diabetic retinopathy, diabetic nephropathy, peripheral neuropathy, RDW, and LDL-C, this study preliminarily establishes a risk prediction model for organic ED in patients with type 2 diabetes mellitus. The model demonstrates good predictive performance.

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