Identifying venous thromboembolism (VTE) is challenging for patients with cardiovascular diseases due to similar clinical presentation. Most hospital-acquired VTE events are preventable, whereas the implementation of VTE prophylaxis in clinical practice is far from sufficient. There is a lack of hospital-acquired VTE prediction models tailored specifically designed for patients with cardiovascular diseases. We aimed to develop a nomogram predicting hospital-acquired VTE specifically for patients with cardiovascular diseases. Consecutive patients with cardiovascular diseases admitted to internal medicine of Fuwai hospital between September 2020 and August 2021 were included. Univariable and multivariable logistic regression were applied to identify risk factors of hospital-acquired VTE. A nomogram was constructed according to multivariable logistic regression, and internally validated by bootstrapping. A total of 27,235 patients were included. During a median hospitalization of four days, 154 (0.57%) patients developed hospital-acquired VTE. Multivariable logistic regression identified that female sex, age, infection, pulmonary hypertension, obstructive sleep apnea, acute coronary syndrome, cardiomyopathy, heart failure, immobility, central venous catheter, intra-aortic balloon pump and anticoagulation were independently associated with hospital-acquired VTE. The nomogram was constructed with high accuracy in both the training set and validation (concordance index 0.865 in the training set, and 0.864 in validation), which was further confirmed in calibration. Compared to Padua model, the Fuwai model demonstrated significantly better discrimination ability (area under curve 0.865 vs. 0.786, net reclassification index 0.052, 95% confidence interval 0.012–0.091, P = 0.009; integrated discrimination index 0.020, 95% confidence interval 0.001–0.039, P = 0.051). The incidence of hospital-acquired VTE in patients with cardiovascular diseases is relatively low. The nomogram exhibits high accuracy in predicting hospital-acquired VTE in patients with cardiovascular diseases.

中文翻译：

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心血管疾病患者医院获得性静脉血栓栓塞的列线图

由于相似的临床表现，对于心血管疾病患者来说，识别静脉血栓栓塞 (VTE) 具有挑战性。大多数医院获得性 VTE 事件是可以预防的，但在临床实践中实施 VTE 预防还远远不够。目前缺乏专门针对心血管疾病患者设计的院内获得性 VTE 预测模型。我们的目标是开发一种列线图，专门针对心血管疾病患者预测医院获得性 VTE。纳入2020年9月至2021年8月阜外医院内科连续收治的心血管疾病患者。应用单变量和多变量逻辑回归来识别医院获得性 VTE 的危险因素。根据多变量逻辑回归构建列线图，并通过引导进行内部验证。总共纳入了 27,235 名患者。在平均住院四天期间，154 名患者 (0.57%) 出现了医院获得性 VTE。多变量逻辑回归发现，女性、年龄、感染、肺动脉高压、阻塞性睡眠呼吸暂停、急性冠脉综合征、心肌病、心力衰竭、不活动、中心静脉导管、主动脉内球囊反搏和抗凝治疗与医院获得性 VTE 独立相关。列线图在训练集和验证中均以高精度构建（训练集的一致性指数为 0.865，验证集的一致性指数为 0.864），这在校准中得到了进一步证实。与Padua模型相比，Fuwai模型表现出明显更好的判别能力（曲线下面积0.865 vs. 0.786，净重分类指数0.052，95%置信区间0.012-0.091，P = 0.009；综合判别指数0.020，95%置信区间0.001- 0.039，P = 0.051）。心血管疾病患者院内获得性VTE发生率相对较低。该列线图在预测心血管疾病患者医院获得性 VTE 方面表现出很高的准确性。