Department of Endocrinology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830017, China.
Sci Rep. 2023 Jan 23;13(1):1298. doi: 10.1038/s41598-023-28059-4.
Type 2 diabetes mellitus (T2DM) with hypertension (DH) is the most common diabetic comorbidity. Patients with DH have significantly higher rates of cardiovascular disease morbidity and mortality. The objective of this study was to develop and validate a nomogram model for the prediction of an individual's risk of developing DH. A total of 706 T2DM patients who met the criteria were selected and divided into a training set (n = 521) and a validation set (n = 185) according to the discharge time of patients. By using multivariate logistic regression analysis and stepwise regression, the DH nomogram prediction model was created. Calibration curves were used to evaluate the model's accuracy, while decision curve analysis (DCA) and receiver operating characteristic (ROC) curves were used to evaluate the model's clinical applicability and discriminatory power. Age, body mass index (BMI), diabetic nephropathy (DN), and diabetic retinopathy (DR) were all independent risk factors for DH (P < 0.05). Based on independent risk factors identified by multivariate logistic regression, the nomogram model was created. The model produces accurate predictions. If the total nomogram score is greater than 120, there is a 90% or higher chance of developing DH. In the training and validation sets, the model's ROC curves are 0.762 (95% CI 0.720-0.803) and 0.700 (95% CI 0.623-0.777), respectively. The calibration curve demonstrates that there is good agreement between the model's predictions and the actual outcomes. The decision curve analysis findings demonstrated that the nomogram model was clinically helpful throughout a broad threshold probability range. The DH risk prediction nomogram model constructed in this study can help clinicians identify individuals at high risk for DH at an early stage, which is a guideline for personalized prevention and treatments.
2 型糖尿病伴高血压（DH）是最常见的糖尿病合并症。DH 患者的心血管疾病发病率和死亡率明显更高。本研究旨在建立和验证一种预测个体发生 DH 风险的列线图模型。共选择了 706 名符合标准的 2 型糖尿病患者，根据患者的出院时间将其分为训练集（n=521）和验证集（n=185）。通过多变量逻辑回归分析和逐步回归，建立了 DH 列线图预测模型。校准曲线用于评估模型的准确性，而决策曲线分析（DCA）和受试者工作特征（ROC）曲线用于评估模型的临床适用性和区分能力。年龄、体重指数（BMI）、糖尿病肾病（DN）和糖尿病视网膜病变（DR）均为 DH 的独立危险因素（P<0.05）。基于多变量逻辑回归确定的独立危险因素，建立了列线图模型。该模型具有准确的预测能力。如果总列线图评分大于 120，则发生 DH 的概率为 90%或更高。在训练集和验证集中，模型的 ROC 曲线分别为 0.762（95%CI 0.720-0.803）和 0.700（95%CI 0.623-0.777）。校准曲线表明，模型的预测结果与实际结果之间具有良好的一致性。决策曲线分析结果表明，该列线图模型在广泛的阈值概率范围内具有临床帮助。本研究构建的 DH 风险预测列线图模型有助于临床医生在早期识别出发生 DH 的高危个体，为个性化预防和治疗提供了指导。
