Division of Cardiology and Angiology, Department of Internal Medicine II, Medical University of Vienna, Waehringer Guertel 18-20, Vienna 1090, Austria.
Eur Heart J. 2012 Sep;33(18):2282-9. doi: 10.1093/eurheartj/ehs164. Epub 2012 Jun 28.
Previous risk assessment scores for patients with coronary artery disease (CAD) have focused on primary prevention and patients with acute coronary syndrome. However, especially in stable CAD patients improved long-term risk prediction is crucial to efficiently apply measures of secondary prevention. We aimed to create a clinically applicable mortality prediction score for stable CAD patients based on routinely determined laboratory biomarkers and clinical determinants of secondary prevention.
We prospectively included 547 patients with stable CAD and a median follow-up of 11.3 years. Independent risk factors were selected using bootstrapping based on Cox regression analysis. Age, left ventricular function, serum cholinesterase, creatinine, heart rate, and HbA1c were selected as significant mortality predictors for the final multivariable model. The Vienna and Ludwigshafen Coronary Artery Disease (VILCAD) risk score based on the aforementioned variables demonstrated an excellent discriminatory power for 10-year survival with a C-statistic of 0.77 (P < 0.001), which was significantly better than an established risk score based on conventional cardiovascular risk factors (C-statistic = 0.61, P < 0.001). Net reclassification confirmed a significant improvement in individual risk prediction by 34.8% (95% confidence interval: 21.7-48.0%) compared with the conventional risk score (P < 0.001). External validation of the risk score in 1275 participants of the Ludwigshafen Risk and Cardiovascular Health study (median follow-up of 9.8 years) achieved similar results (C-statistic = 0.73, P < 0.001).
The VILCAD score based on a routinely available set of risk factors, measures of cardiac function, and comorbidities outperforms established risk prediction algorithms and might improve the identification of high-risk patients for a more intensive treatment.
以前用于评估冠状动脉疾病（CAD）患者风险的评分主要集中在一级预防和急性冠脉综合征患者上。然而，特别是在稳定型 CAD 患者中，提高长期风险预测能力对于有效实施二级预防措施至关重要。我们旨在创建一种基于常规确定的实验室生物标志物和二级预防临床决定因素的稳定型 CAD 患者的临床适用死亡率预测评分。
我们前瞻性纳入了 547 例稳定型 CAD 患者，中位随访时间为 11.3 年。使用基于 Cox 回归分析的自举法选择独立风险因素。年龄、左心室功能、血清胆碱酯酶、肌酐、心率和糖化血红蛋白被选为最终多变量模型中显著的死亡率预测因素。基于上述变量的维也纳和路德维希港冠状动脉疾病（VILCAD）风险评分在 10 年生存率方面具有出色的区分能力，C 统计量为 0.77（P < 0.001），明显优于基于传统心血管危险因素的风险评分（C 统计量= 0.61，P < 0.001）。净重新分类证实，与传统风险评分相比，该风险评分在个体风险预测方面的改善显著，提高了 34.8%（95%置信区间：21.7-48.0%）（P < 0.001）。在路德维希港风险和心血管健康研究的 1275 名参与者中进行的风险评分外部验证（中位随访时间为 9.8 年）得出了类似的结果（C 统计量= 0.73，P < 0.001）。
基于常规可用的一系列风险因素、心脏功能测量值和合并症的 VILCAD 评分优于已建立的风险预测算法，可能有助于识别高危患者以进行更强化的治疗。
