Sprengers Ralf W, Janssen Kristel J M, Moll Frans L, Verhaar Marianne C, van der Graaf Yolanda
Department of Vascular Surgery, University Medical Center Utrecht, Utrecht, The Netherlands.
J Vasc Surg. 2009 Dec;50(6):1369-76. doi: 10.1016/j.jvs.2009.07.095. Epub 2009 Oct 17.
Patients with peripheral arterial disease (PAD) are at high risk of secondary cardiovascular death and events such as myocardial infarction or stroke. To minimize this elevated risk, cardiovascular risk factors should be treated in all PAD patients. Secondary risk management may benefit from a prediction tool to identify PAD patients at the highest risk who could be referred for an additional extensive workup. Stratifying PAD patients according to their risk of secondary events could aid in achieving optimal therapy compliance. To this end we developed a prediction model for secondary cardiovascular events in PAD patients.
The model was developed using data from 800 PAD patients who participated in the Second Manifestations of ARTerial disease (SMART) cohort study. From the baseline characteristics, 13 candidate predictors were selected for the model development. Missing values were imputed by means of single regression imputation. Continuous predictors were truncated and transformed where necessary, followed by model reduction by means of backward stepwise selection. To correct for over-fitting, a bootstrapping technique was applied. Finally, a score chart was created that divides patients in four risk categories that have been linked to the risk of a cardiovascular event during 1- and 5-year follow-up.
During a mean follow-up of 4.7 years, 120 events occurred (27% nonfatal myocardial infarction, 21% nonfatal stroke, and 52% mortality from vascular causes), corresponding to a 1- and 5-year cumulative incidence of 3.1% and 13.2%, respectively. Important predictors for the secondary risk of a cardiovascular event are age, history of symptomatic cardiovascular disease, systolic blood pressure, high-density lipoprotein cholesterol, smoking behavior, ankle-brachial pressure index, and creatinine level. The risk of a cardiovascular event in a patient as predicted by the model was 0% to 10% and 1% to 28% for the four risk categories at 1- and 5-year follow-up, respectively. The discriminating capacity of the prediction model, indicated by the c statistic, was 0.76 (95% confidence interval, 0.71-0.80).
A prediction model can be used to predict secondary cardiovascular risk in PAD patients. We propose such a prediction model to allow for the identification of PAD patients at the highest risk of a cardiovascular event or cardiovascular death, which may be a viable tool in vascular secondary health care practice.
外周动脉疾病（PAD）患者发生继发性心血管死亡以及心肌梗死或中风等事件的风险很高。为了将这种升高的风险降至最低，所有PAD患者均应治疗心血管危险因素。二级风险管理可能受益于一种预测工具，以识别出风险最高的PAD患者，这些患者可被转诊接受进一步的全面检查。根据PAD患者继发性事件的风险进行分层有助于实现最佳的治疗依从性。为此，我们开发了一种用于预测PAD患者继发性心血管事件的模型。
该模型是使用参与动脉疾病的第二次表现（SMART）队列研究的800例PAD患者的数据开发的。从基线特征中，选择了13个候选预测因子用于模型开发。通过单回归插补法估算缺失值。对连续预测因子进行截断和必要的转换，然后通过向后逐步选择进行模型简化。为了校正过度拟合，应用了自抽样技术。最后，创建了一个评分表，将患者分为四个风险类别，这些类别与1年和5年随访期间的心血管事件风险相关。
在平均4.7年的随访期间，发生了120起事件（27%为非致命性心肌梗死，21%为非致命性中风，52%为血管性病因导致的死亡），1年和5年的累积发病率分别为3.1%和13.2%。心血管事件继发性风险的重要预测因子包括年龄、有症状心血管疾病史、收缩压、高密度脂蛋白胆固醇、吸烟行为、踝臂压力指数和肌酐水平。在1年和5年随访时，模型预测的患者发生心血管事件的风险在四个风险类别中分别为0%至10%和1%至28%。预测模型的辨别能力以c统计量表示，为0.76（95%置信区间，0.71 - 0.80）。
一种预测模型可用于预测PAD患者的继发性心血管风险。我们提出这样一种预测模型，以便识别出发生心血管事件或心血管死亡风险最高的PAD患者，这可能是血管二级医疗保健实践中的一种可行工具。
