Can risk modelling improve treatment decisions in asymptomatic carotid stenosis?
Can risk modelling improve treatment decisions in asymptomatic carotid stenosis?
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DOI：
10.1186/s12883-019-1528-7
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发表时间：
2019-11-22
期刊：
影响因子：
2.6
通讯作者：
Hayward, Rodney A.
中科院分区：
文献类型：
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作者：
Burke, James F.;Morgenstern, Lewis B.;Hayward, Rodney A.
颈动脉内膜切除术（CEA）是无症状颈动脉狭窄的常规治疗方法，但其平均净获益较小。风险分层可以识别明显从治疗中获益的高风险患者。方法采用无症状颈动脉粥样硬化研究（ACAS）的数据进行回顾性队列研究。在向后和向前选择程序中纳入了不良结局的风险因素，以开发估计非围手术期同侧卒中/TIA风险的基线风险模型。所有ACAS参与者的基线风险都进行了估计，并使用社区动脉粥样硬化风险（ARIC）研究的数据进行了外部验证。然后将基线风险纳入治疗风险模型中，该模型探讨了基线风险和治疗状态（CEA与药物管理）对任何卒中或死亡（包括围手术期事件）以患者为中心结局的相互作用。结果三个基线危险因素（BMI、肌酐和对侧狭窄程度）被选入我们的基线危险模型（c-statistic 0.59 [95%CI 0.54-0.65]）。该模型将绝对风险分为最低和最高风险五分位数（5.1%对12.5%）。ARIC的外部验证发现了类似的预测性（c-统计量0.58 [0.49-0.67]），但整个风险谱的校准较差。在治疗风险模型中，CEA在基线风险范围内上级药物管理，治疗效果的大小在最低和最高绝对风险五分位数之间差异很大（3.2% vs. 10.7%）。结论：即使是适度预测的危险分层工具也有可能对无症状颈动脉疾病的临床决策产生有意义的影响。然而，我们的ACAS模型需要在临床应用之前重新校准目标人群。
Background Carotid endarterectomy (CEA) is routinely performed for asymptomatic carotid stenosis, yet its average net benefit is small. Risk stratification may identify high risk patients that would clearly benefit from treatment. Methods Retrospective cohort study using data from the Asymptomatic Carotid Atherosclerosis Study (ACAS). Risk factors for poor outcomes were included in backward and forward selection procedures to develop baseline risk models estimating the risk of non-perioperative ipsilateral stroke/TIA. Baseline risk was estimated for all ACAS participants and externally validated using data from the Atherosclerosis Risk in Communities (ARIC) study. Baseline risk was then included in a treatment risk model that explored the interaction of baseline risk and treatment status (CEA vs. medical management) on the patient-centered outcome of any stroke or death, including peri-operative events. Results Three baseline risk factors (BMI, creatinine and degree of contralateral stenosis) were selected into our baseline risk model (c-statistic 0.59 [95% CI 0.54-0.65]). The model stratified absolute risk between the lowest and highest risk quintiles (5.1% vs. 12.5%). External validation in ARIC found similar predictiveness (c-statistic 0.58 [0.49-0.67]), but poor calibration across the risk spectrum. In the treatment risk model, CEA was superior to medical management across the spectrum of baseline risk and the magnitude of the treatment effect varied widely between the lowest and highest absolute risk quintiles (3.2% vs. 10.7%). Conclusion Even modestly predictive risk stratification tools have the potential to meaningfully influence clinical decision making in asymptomatic carotid disease. However, our ACAS model requires target population recalibration prior to clinical application.