PURPOSE The American Association of Cardiovascular and Pulmonary Rehabilitation (AACVPR) recommends that patients starting cardiac rehabilitation (CR) undergo stratification to identify risk for exercise-related adverse events (AE), but this tool has not been recently evaluated. METHODS Among patients who enrolled in CR in 2016, we used the AACVPR risk stratification tool to evaluate the risk for AE and clinical events (CE). We defined AE as signs or symptoms that precluded or interrupted exercise during CR, and CE as events requiring an urgent evaluation outside of CR exercise sessions. RESULTS During the study period, 657 patients with cardiovascular diagnoses were included and classified as high (58%), medium (31%), or low risk (11%). Over the course of CR (76 d, 17 sessions), there were 63 AE and 33 CE. Adverse events were mostly minor (no cardiac arrests or deaths) and managed by CR staff members. When compared with the low- or medium-risk groups, the high-risk group was more likely to have AE (HR 3.0 [95% CI, 1.7-5.9], P = .002) and CE (HR 3.7 [95% CI, 1.5-10.8], P = .002) with fair model discrimination (area under the curve: 0.637, P < .001). CONCLUSION The AACVPR risk stratification tool was predictive of both AE and CE with fair discrimination, although event rates were low and mostly minor. Thus, the AACVPR model may require reevaluation to better identify truly at-risk patients for major AE.

中文翻译：

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没有运动测试的美国心血管和肺康复协会运动风险分层分类工具的评估。

目的美国心血管和肺复康协会（AACVPR）建议开始心脏复健（CR）的患者进行分层，以识别运动相关不良事件（AE）的风险，但是该工具尚未得到评估。方法在2016年参加CR的患者中，我们使用AACVPR风险分层工具评估AE和临床事件（CE）的风险。我们将AE定义为CR期间妨碍或中断运动的体征或症状，而CE则定义为需要在CR运动期间进行紧急评估的事件。结果在研究期间，纳入了657例经心血管疾病诊断的患者，分为高（58％），中（31％）或低风险（11％）。在CR的整个过程中（76 d，17节），有63 AE和33 CE。不良事件多为轻微事件（无心脏骤停或死亡），由CR工作人员进行管理。与低风险或中风险组相比，高风险组更容易出现AE（HR 3.0 [95％CI，1.7-5.9]，P = .002）和CE（HR 3.7 [95％CI] ，1.5-10.8]，P = .002），模型判别公平（曲线下面积：0.637，P <.001）。结论AACVPR风险分层工具可预测AE和CE并具有公平的歧视，尽管事件发生率较低，且多数为次要事件。因此，可能需要对AACVPR模型进行重新评估，以更好地识别出真正的主要AE风险患者。002），且模型判别公平（曲线下面积：0.637，P <.001）。结论AACVPR风险分层工具可预测AE和CE并具有公平的歧视，尽管事件发生率较低，且多数为次要事件。因此，可能需要对AACVPR模型进行重新评估，以更好地识别出真正的主要AE风险患者。002），且模型判别公平（曲线下面积：0.637，P <.001）。结论AACVPR风险分层工具可预测AE和CE并具有公平的歧视，尽管事件发生率较低，且多数为次要事件。因此，可能需要对AACVPR模型进行重新评估，以更好地识别出真正的主要AE风险患者。