Haaglanden Medical Center Bronovo, Department of Cardiology, The Hague, the Netherlands; Erasmus Medical Center, Department of Cardiology, Rotterdam, the Netherlands.
Erasmus Medical Center, Department of Cardiology, Rotterdam, the Netherlands.
Int J Cardiol. 2021 Dec 1;344:1-7. doi: 10.1016/j.ijcard.2021.09.039. Epub 2021 Sep 21.
Background The performance of current diagnostic algorithms of the American College of Cardiology/American Heart Association (ACC/AHA), National Institute for Health and Care Excellence (NICE) and European Society of Cardiology (ESC) in patients with stable chest pain and coronary artery calcium (CAC) remains a matter of debate. We compared their merits in patients with CAC and investigated the additional value of the CAC score to improve diagnostic accuracy and risk stratification. Methods and results Patient data were obtained from a prospective registry of 642 consecutive patients. Mean age 63 (SD 11) years, 50% male. According to the guidelines, low and intermediate/high pre-test probability groups were constructed. Patients were reclassified based on their CAC score. Obstructive coronary artery disease (CAD) was observed in 14%. All models performed modestly in accurately predicting CAD (c-statistic <0.65). After addition of the CAC score, the c-statistic of the NICE model increased to 0.75 (95% confidence interval (CI) 0.73-0.78) which was just non-significant compared to the ESC model (0.71 95% CI 0.67-0.74) and performed significantly better than ACC/AHA (0.68 (95% CI 0.64-0.72)). After reclassification more than 50% of patients were classified low risk in NICE and ESC, while the prevalence of obstructive CAD (4.8% and 5.2% respectively) did not increase. Conclusions Addition of the CAC score to the studied models improved the ability to safely rule-out obstructive CAD and identified other patients at high risk for future coronary artery events. These results suggest that incorporating CAC score will lead to substantially less downstream testing and lower costs.
背景 当前美国心脏病学会/美国心脏协会 (ACC/AHA)、英国国家卫生与临床优化研究所 (NICE) 和欧洲心脏病学会 (ESC) 的诊断算法在稳定型胸痛和冠状动脉钙化 (CAC) 患者中的表现仍存在争议。我们比较了这些算法在 CAC 患者中的优劣,并研究了 CAC 评分对提高诊断准确性和风险分层的附加价值。
方法和结果 患者数据来自于连续 642 例患者的前瞻性注册研究。平均年龄 63(SD 11)岁,50%为男性。根据指南构建了低和中/高预检测概率组。基于 CAC 评分对患者进行再分类。观察到 14%的患者存在阻塞性冠心病 (CAD)。所有模型在准确预测 CAD 方面表现均欠佳(c 统计量<0.65)。在加入 CAC 评分后,NICE 模型的 c 统计量增加至 0.75(95%置信区间 (CI) 0.73-0.78),与 ESC 模型(0.71,95%CI 0.67-0.74)相比仅略有统计学差异,且明显优于 ACC/AHA(0.68,95%CI 0.64-0.72)。经过重新分类,NICE 和 ESC 模型中超过 50%的患者被归类为低危,而阻塞性 CAD 的患病率(分别为 4.8%和 5.2%)并未增加。
结论 将 CAC 评分加入到研究模型中可以提高安全排除阻塞性 CAD 的能力,并识别出未来发生冠状动脉事件的高危患者。这些结果表明,纳入 CAC 评分将显著减少下游检测和降低成本。