■糖尿病周围神经病变在患有2型糖尿病的个体中经常发生并严重出现,代表了一个重大的并发症。这项研究的目的是开发DPN的风险列线图,确保其内部有效性并评估其预测条件的能力。
■在此回顾性分析中,宿迁市第一医院2021年1月至2022年6月的队列共397例诊断为T2DM。采用随机数字表法将这些患者分为两组进行训练和验证,按照7:3的比例。通过应用单变量和多变量逻辑回归,对预测因子进行细化以构建列线图.通过ROC面积等指标评估模型的预测精度,HL测试,和校准曲线的分析。DCA进一步评价了该模型的临床适用性。还强调了内部验证,以确认模型的可靠性和一致性。
■在36个评估的临床病理特征中,一组四个,持续时间,TBIL,TG,和DPVD,被确定为构建预测列线图的关键变量。该模型表现出稳健的判别力,训练队列的AUC为0.771(95%CI:0.714-0.828),验证组的AUC为0.754(95%CI:0.663-0.845).校正曲线证实了模型预测与实际结果的一致性。此外,DCA肯定了该模型在预测DPN中的临床价值。
■这项研究引入了一种创新的风险列线图,旨在预测患有2型糖尿病的个体的糖尿病周围神经病变。它为医疗保健专业人员提供了宝贵的资源,以查明那些发生这种并发症的风险较高的人。作为一种功能工具,它是临床上预测DPN的可行选择。
UNASSIGNED: Diabetic peripheral neuropathy frequently occurs and presents severely in individuals suffering from type 2 diabetes mellitus, representing a significant complication. The objective of this research was to develop a risk nomogram for DPN, ensuring its internal validity and evaluating its capacity to predict the condition.
UNASSIGNED: In this retrospective analysis, Suqian First Hospital\'s cohort from January 2021 to June 2022 encompassed 397 individuals diagnosed with T2DM. A random number table method was utilized to allocate these patients into two groups for training and validation, following a 7:3 ratio. By applying univariate and multivariable logistic regression, predictive factors were refined to construct the nomogram. The model\'s prediction accuracy was assessed through metrics like the ROC area, HL test, and an analysis of the calibration curve. DCA further appraised the clinical applicability of the model. Emphasis was also placed on internal validation to confirm the model\'s dependability and consistency.
UNASSIGNED: Out of 36 evaluated clinicopathological characteristics, a set of four, duration, TBIL, TG, and DPVD, were identified as key variables for constructing the predictive nomogram. The model exhibited robust discriminatory power, evidenced by an AUC of 0.771 (95% CI: 0.714-0.828) in the training cohort and an AUC of 0.754 (95% CI: 0.663-0.845) in the validation group. The congruence of the model\'s predictions with actual findings was corroborated by the calibration curve. Furthermore, DCA affirmed the clinical value of the model in predicting DPN.
UNASSIGNED: This research introduces an innovative risk nomogram designed for the prediction of diabetic peripheral neuropathy in individuals suffering from type 2 diabetes mellitus. It offers a valuable resource for healthcare professionals to pinpoint those at elevated risk of developing this complication. As a functional instrument, it stands as a viable option for the prognostication of DPN in clinical settings.