Abstract：
BACKGROUND: Based on the Khorana score, guidelines recommend anticoagulation for primary prophylaxis (PP) in outpatients with cancer with an intermediate-to-high risk of venous thromboembolism (VTE). ONKOTEV score has been prospectively externally validated as novel risk assessment model (RAM) with good discriminatory performances but no direct comparisons with Khorana Score are available.
METHODS: Using the ONKOTEV validation dataset (n = 425), we applied generalized decision curve analysis (gDCA) which integrates the principles of evidence-based medicine with treatment effects, model accuracy and patient preferences (weighted as the relative value [RV] of avoiding VTE versus major bleeding [MB]). The aim is to select the most optimal treatment strategy among multiple options: \"no treatment\", \"treat all patients with DOAC/LMVH\", or \"use ONKOTEV/KHORANA scores to guide PP with DOAC/LMWH\".
RESULTS: Results showed that ONKOTEV-guided PP (using DOAC or LMWH) remained the most optimal strategy for wide range assumption of treatment efficacy and patient\'s preference. For those patients, who value avoiding VTE more than MB, then offering DOAC to all patients represents the best strategy. When MBs are feared more than the morbidity of VTE, ONKOTEV-guided PP (DOAC) represents the best management strategy. In all cases, ONKOTEV outperformed Khorana for individualized VTE prevention.
CONCLUSIONS: When the two predictive models are integrated within a decision analysis framework, ONKOTEV appears superior to Khorana Score in guiding individualized prevention of cancer-related VTE in outpatients with cancer. The findings herein reported provide cutting edge insights in cancer care and support the spread of ONKOTEV score in the ambulatory cancer setting.
METHODS: Using the ONKOTEV validation dataset (n = 425), we applied generalized decision curve analysis (gDCA) which integrates the principles of evidence-based medicine with treatment effects, model accuracy and patient preferences (weighted as the relative value [RV] of avoiding VTE versus major bleeding [MB]). The aim is to select the most optimal treatment strategy among multiple options: \"no treatment\", \"treat all patients with DOAC/LMVH\", or \"use ONKOTEV/KHORANA scores to guide PP with DOAC/LMWH\".
RESULTS: Results showed that ONKOTEV-guided PP (using DOAC or LMWH) remained the most optimal strategy for wide range assumption of treatment efficacy and patient\'s preference. For those patients, who value avoiding VTE more than MB, then offering DOAC to all patients represents the best strategy. When MBs are feared more than the morbidity of VTE, ONKOTEV-guided PP (DOAC) represents the best management strategy. In all cases, ONKOTEV outperformed Khorana for individualized VTE prevention.
CONCLUSIONS: When the two predictive models are integrated within a decision analysis framework, ONKOTEV appears superior to Khorana Score in guiding individualized prevention of cancer-related VTE in outpatients with cancer. The findings herein reported provide cutting edge insights in cancer care and support the spread of ONKOTEV score in the ambulatory cancer setting.
摘要：
背景：根据Khorana得分，指南推荐对患有中-高风险静脉血栓栓塞(VTE)的门诊患者进行一级预防(PP)的抗凝治疗.ONKOTEV评分已被预期外部验证为具有良好歧视性表现的新型风险评估模型（RAM），但无法与Khorana评分进行直接比较。
方法：使用ONKOTEV验证数据集（n=425），我们应用广义决策曲线分析（gDCA），它将循证医学原理与治疗效果相结合，模型准确性和患者偏好（加权为避免VTE的相对值[RV]与大出血[MB]）。目的是在多种选择中选择最佳的治疗策略：“不治疗”，“用DOAC/LMVH治疗所有患者”，或“使用ONKOTEV/KHORANA评分指导使用DOAC/LMWH的PP”。
结果：结果显示，ONKOTEV指导的PP（使用DOAC或LMWH）仍然是广泛假设治疗效果和患者偏好的最佳策略。对那些病人来说,谁更重视避免VTE超过MB，然后向所有患者提供DOAC代表最佳策略。当MBs比VTE的发病率更令人恐惧时，ONKOTEV指导的PP（DOAC）代表了最佳的管理策略。在所有情况下,ONKOTEV在个体化预防VTE方面优于Khorana。
结论：当将两个预测模型集成到决策分析框架中时，在指导门诊患者癌症相关VTE的个体化预防方面，ONKOTEV似乎优于KhoranaScore。本文报道的发现提供了癌症护理方面的前沿见解，并支持ONKOTEV评分在门诊癌症环境中的传播。
方法：使用ONKOTEV验证数据集（n=425），我们应用广义决策曲线分析（gDCA），它将循证医学原理与治疗效果相结合，模型准确性和患者偏好（加权为避免VTE的相对值[RV]与大出血[MB]）。目的是在多种选择中选择最佳的治疗策略：“不治疗”，“用DOAC/LMVH治疗所有患者”，或“使用ONKOTEV/KHORANA评分指导使用DOAC/LMWH的PP”。
结果：结果显示，ONKOTEV指导的PP（使用DOAC或LMWH）仍然是广泛假设治疗效果和患者偏好的最佳策略。对那些病人来说,谁更重视避免VTE超过MB，然后向所有患者提供DOAC代表最佳策略。当MBs比VTE的发病率更令人恐惧时，ONKOTEV指导的PP（DOAC）代表了最佳的管理策略。在所有情况下,ONKOTEV在个体化预防VTE方面优于Khorana。
结论：当将两个预测模型集成到决策分析框架中时，在指导门诊患者癌症相关VTE的个体化预防方面，ONKOTEV似乎优于KhoranaScore。本文报道的发现提供了癌症护理方面的前沿见解，并支持ONKOTEV评分在门诊癌症环境中的传播。
