Department of Internal Medicine, Michigan State University, East Lansing, MI, USA.
Department of Internal Medicine, Sparrow/Michigan State University Internal Medicine Residency Program, Lansing, MI, USA.
Clin Appl Thromb Hemost. 2023 Jan-Dec;29:10760296231206808. doi: 10.1177/10760296231206808.
This study aimed to identify predictors of venous thromboembolism (VTE) in hospitalized cancer patients and develop a predictive model using demographic, clinical, and laboratory data. Our analysis showed that patient groups categorized under a very high risk, and high risk, patients with low hemoglobin levels and renal disease were at a significantly increased risk of developing VTE. We developed a VTE risk-assessment model (RAM) with moderate discriminatory performance, high specificity, and negative predictive value, indicating its potential utility in identifying patients without VTE risk. However, the model's positive predictive value and sensitivity were low due to the low prevalence of VTE within the analyzed population. Future studies are needed to analyze additional predictive factors, and to validate the effectiveness of our VTE RAM to safely rule out VTE, compare it with other VTE RAMs in hospitalized cancer patients, and address any limitations of our study.
本研究旨在确定住院癌症患者静脉血栓栓塞症(VTE)的预测因素,并利用人口统计学、临床和实验室数据建立预测模型。我们的分析表明,血红蛋白水平低和患有肾脏疾病的极高危和高危患者群体发生 VTE 的风险显著增加。我们开发了一种 VTE 风险评估模型(RAM),其具有中等的判别性能、高特异性和阴性预测值,表明其在识别无 VTE 风险的患者方面具有潜在的效用。然而,由于分析人群中 VTE 的低患病率,该模型的阳性预测值和灵敏度较低。未来的研究需要分析其他预测因素,并验证我们的 VTE RAM 排除 VTE 的有效性,将其与住院癌症患者的其他 VTE RAM 进行比较,并解决我们研究的任何局限性。