Dun Xiaoyi, Wu Qinfen, Ma Yiming, Hu Wenjiang, Zuo Xiaojing
Department of Hematology, The Fifth Affiliated Hospital of Xinjiang Medical University No. 118, Henan Western Road, Xinshi District, Urumqi 830011, Xinjiang, China.
Department of Neurology, The Second Affiliated Hospital of Xinjiang Medical University No. 38, Nanhu East Road, Shuimogou District, Urumqi 830054, Xinjiang, China.
Am J Transl Res. 2023 Feb 15;15(2):1281-1290. eCollection 2023.
Due to confounders like hyperglycemia, patients with type 2 diabetes mellitus (T2DM) have an increased susceptibility to venous thromboembolism (VTE). However, formal risk assessment models, such as using the Padua score, do not include all T2DM-associated risk factors for VTE. Therefore, this study aims to develop and validate a predictive nomogram for VTE in non-surgical inpatients with T2DM.
We retrospectively analyzed the clinical and biochemical data of 420 non-surgical inpatients with T2DM between 2017 and 2021 from three centers (the PLA 474 hospital, the Second Affiliated Hospital of Xinjiang Medical University and the Fifth Affiliated Hospital of Xinjiang Medical University). A multivariate analysis based on logistic regression model was performed to identify independent risk factors and construct a nomogram. The predictive values were compared by calculating the integrated discrimination improvement (IDI) and net reclassification improvement (NRI), and by decision curve analysis (DCA).
Old age, BMI, D-dimer, hypoproteinemia, acute infection, acute myocardial infarction, cerebral ischemic stroke, reduced mobility, and heart/respiratory failure were independent risk factors for VTE in non-surgical inpatients with T2DM, as indicated by the multivariate analysis. The nomogram demonstrated superior discriminative ability compared to the Padua score (area under the curve: 0.923 vs. 0.849). NRI and IDI were also observed, and the DCA identified the greater net benefit and clinical utilization of the new nomogram.
A predictive nomogram for VTE in non-surgical inpatients with T2DM was developed and validated in this study. The nomogram is highly predictive and easy to operate, but external data verification is required before it can be further used.
由于存在高血糖等混杂因素，2型糖尿病（T2DM）患者发生静脉血栓栓塞症（VTE）的易感性增加。然而，诸如使用帕多瓦评分等正式的风险评估模型并未涵盖所有与T2DM相关的VTE风险因素。因此，本研究旨在开发并验证一种针对非手术住院T2DM患者VTE的预测列线图。
我们回顾性分析了2017年至2021年间来自三个中心（解放军第474医院、新疆医科大学第二附属医院和新疆医科大学第五附属医院）的420例非手术住院T2DM患者的临床和生化数据。基于逻辑回归模型进行多变量分析以识别独立风险因素并构建列线图。通过计算综合判别改善（IDI）和净重新分类改善（NRI）以及决策曲线分析（DCA）来比较预测值。
多变量分析表明，年龄较大、体重指数、D - 二聚体、低蛋白血症、急性感染、急性心肌梗死、脑缺血性卒中、活动能力下降以及心/呼吸衰竭是非手术住院T2DM患者发生VTE的独立风险因素。与帕多瓦评分相比，列线图显示出更好的判别能力（曲线下面积：0.923对0.849）。还观察到了NRI和IDI，并且DCA确定了新列线图具有更大的净效益和临床实用性。
本研究开发并验证了一种针对非手术住院T2DM患者VTE的预测列线图。该列线图具有高度预测性且易于操作，但在可进一步使用之前需要进行外部数据验证。
