■目前,临床数据的不同使用,常规实验室指标或糖尿病自身抗体的检测在糖尿病的诊断和管理中是有限的。因此,这项研究的目的是筛选指标,并建立和验证用于1型糖尿病的无创性差异预测的多因素逻辑回归模型列线图。
■临床数据,常规实验室指标,回顾性分析2018年9月至2022年12月期间收治的糖尿病患者的糖尿病自身抗体谱.采用Logistic回归方法选择独立影响因素,并使用这些独立因素构建了基于多元逻辑回归模型的预测列线图。此外,使用受试者工作特征(ROC)曲线评价列线图的预测准确性和临床应用价值,校正曲线,决策曲线分析(DCA),和临床影响曲线(CIC)。
■本研究共纳入522名糖尿病患者。这些患者以7:3的比例随机分配到训练和验证组中。筛选的预测因素包括年龄,前白蛋白(PA),高密度脂蛋白胆固醇(HDL-C),胰岛细胞自身抗体(ICA),胰岛抗原2自身抗体(IA-2A),谷氨酸脱羧酶抗体(GADA),和C肽水平。基于这些因素,构建了多元模型列线图,训练集和验证集的曲线下面积(AUC)为0.966和0.961,分别。随后,校准曲线证明了图形的高准确性;DCA和CIC结果表明,该图形可用作1型糖尿病鉴别诊断的非侵入性有效预测工具,临床。
■建立的结合患者年龄的预测模型,PA,HDL-C,ICA,IA-2A,GADA,C肽可以辅助1型糖尿病和2型糖尿病的鉴别诊断,为该疾病的临床和治疗管理提供依据。
UNASSIGNED: Currently, distinct use of clinical data, routine laboratory indicators or the detection of diabetic autoantibodies in the diagnosis and management of diabetes mellitus is limited. Hence, this study was aimed to screen the indicators, and to establish and validate a multifactorial logistic regression model nomogram for the non-invasive differential prediction of type 1 diabetes mellitus.
UNASSIGNED: Clinical data, routine laboratory indicators, and diabetes autoantibody profiles of diabetic patients admitted between September 2018 and December 2022 were retrospectively analyzed. Logistic regression was used to select the independent influencing factors, and a prediction nomogram based on the multiple logistic regression model was constructed using these independent factors. Moreover, the predictive accuracy and clinical application value of the nomogram were evaluated using Receiver Operating Characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), and clinical impact curves (CIC).
UNASSIGNED: A total of 522 diabetic patients were included in this study. These patients were randomized into training and validation sets in a 7:3 ratio. The predictors screened included age, prealbumin (PA), high-density lipoprotein cholesterol (HDL-C), islet cells autoantibodies (ICA), islets antigen 2 autoantibodies (IA-2A), glutamic acid decarboxylase antibody (GADA), and C-peptide levels. Based on these factors, a multivariate model nomogram was constructed, which had an Area Under Curve (AUC) of 0.966 and 0.961 for the training set and validation set, respectively. Subsequently, the calibration curves demonstrated a strong accuracy of the graph; the DCA and CIC results indicated that the graph could be used as a non-invasive valid predictive tool for the differential diagnosis of type 1 diabetes mellitus, clinically.
UNASSIGNED: The established prediction model combining patient\'s age, PA, HDL-C, ICA, IA-2A, GADA, and C-peptide can assist in differential diagnosis of type 1 diabetes mellitus and type 2 diabetes mellitus and provides a basis for the clinical as well as therapeutic management of the disease.