目的: 构建糖尿病患者发生下肢深静脉血栓风险的临床预测模型,为临床提供预警。方法: 选取同济大学附属同济医院2018年11月至2021年11月的住院患者共281例,其中明确诊断糖尿病患者合并下肢深静脉血栓的111例患者作为试验组,明确诊断糖尿病无下肢深静脉血栓的170例患者作为对照组。对该模型进行评价,使用最小绝对收缩和选择算子(lasso)回归模型对糖尿病下肢深静脉血栓风险模型的特征选择进行优化。应用单因素和多元Logistic回归分析,将lasso回归模型中选取的特征相结合,建立预测模型诺模图(nomogram)。使用C指数、校准曲线图和决策曲线分析来评估预测模型的区分性、校准和临床实用性。结果: 单因素分析结果显示两组患者年龄、餐后2 h血糖、糖化血红蛋白、总胆固醇、高密度脂蛋白、纤维蛋白原、凝血酶原时间、D2聚体、血红蛋白、C反应蛋白、总蛋白、中性粒细胞数、淋巴细胞数、碱性磷酸酶、谷草转氨酶存在显著差异(P＜0.05)。多因素分析结果显示年龄、餐后2 h血糖、糖化血红蛋白、D2聚体、C反应蛋白、碱性磷酸酶、淋巴细胞数、谷丙转氨酶为糖尿病患者合并下肢深静脉血栓的独立相关因素。该模型具有良好的区分性,C指数为0.928,具有良好的校准性,受试者曲线显示良好的预测能力。结论: 本研究构建的nomogram结合了年龄、餐后2 h血糖、糖化血红蛋白、D2聚体、血红蛋白、C反应蛋白、碱性磷酸酶、谷草转氨酶,可以方便准确地对糖尿病合并深静脉血栓的风险预测。
Objective: To construct a clinical prediction model for the indication of DVT risk in patients with diabetes, and to provide early warning for clinical practice. Methods: A total of 281 inpatients were selected from November 2018 to November 2021 in Shanghai Tongji Hospital Affiliated to Tongji University, among which 111 patients were diagnosed of diabetes mellitus complicated with lower extremity and deep vein thrombosis and set as the experimental group, and the other 170 patients during the same period diagnosed of diabetes without deep vein thrombosis were selected as the control group. The model was evaluated and optimized using the least absolute contraction and selection operator regression model for the feature selection of the diabetic lower extremity deep vein thrombosis risk model. Univariate and multivariate logistic regression analysis combined with the least absolute shrinkage and features selected in the selection operator regression model were applied to build a predictive model. C-index, calibration curve plots, and decision curve analysis were used to assess the discriminability, calibration, and clinical utility of predictive models. Result: Single factor analysis results showed significant differences(P＜0.05)among factors such as age, 2 hours after meal, glycosylated hemoglobin, total cholesterol, high-density lipoprotein cholesterol, fibrinogen, prothrombin time, D2 polymers, hemoglobin, c-reactive protein, total protein, neutrophil count, lymphocyte count, alkaline phosphatase, and aspertate aminotransferase. Multivariate analysis showed that age, two-hour postprandial blood glucose, glycosylated hemoglobin, D2 polymers, C-reactive protein, alkaline phosphatase, lymphocyte count, and alanine aminotransferase were independent factors associated with lower extremity deep vein thrombosis in patients with diabetes mellitus. The model is discriminative with a C-index of 0.928, which is with well calibrated. The subject curve showed good predictive power. Conclusion: The nomogram plotted with age, two-hour postprandial blood glucose, glycosylated hemoglobin, D2-mer, hemoglobin, C-reactive protein, alkaline phosphatase, and aspartate aminotransferase, can be conveniently used for risk prediction of diabetes complicated with deep vein thrombosis.
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